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Title: Titan: a scheduler for foundation model fine-tuning workloads Abstract: ABSTRACTThe recent breakthrough of foundation model (FM) research raises a new trend to acquire efficient DL models by fine-tuning FMs with low-resource datasets. Current GPU clusters are mainly established to develop DL models by training from scratch. How to tailor a GPU cluster scheduler for FM fine-tuning workloads is still not explored. We propose Titan, a scheduler to improve the efficiency of FM fine-tuning workloads based on their three distinct features. (1) It takes full advantage of the fixed model structure to estimate the job duration accurately and configure the fine-tuning workload efficiently. (2) The multi-task adaptivity of FMs enables multiple fine-tuning workloads to share the same model parameters, which can significantly reduce the GPU resource consumption. (3) It considers the pipeline parallelism of FM fine-tuning workloads and concurrently executes the parameter transmission and gradient computation to hide the overhead of context switch. Preliminary simulation result validates the efficiency of Titan.

Title: Minimizing packet retransmission for real-time video analytics Abstract: ABSTRACTIn smart-city and video analytics (VA) applications, high-quality data streams (video frames) must be accurately analyzed with a low delay. Since maintaining high accuracy requires compute-intensive deep neural nets (DNNs), these applications often stream massive video data to remote, more powerful cloud servers, giving rise to a strong need for low streaming delay between video sensors and cloud servers while still delivering enough data for accurate DNN inference. In response, many recent efforts have proposed distributed VA systems that aggressively compress/prune video frames deemed less important to DNN inference, with the underlying assumptions being that (1) without increasing available bandwidth, reducing delays means sending fewer bits, and (2) the most important frames can be precisely determined before streaming. This short paper challenges both views. First, in high-bandwidth networks, the delay of real-time videos is primarily bounded by packet losses and delay jitters, so reducing bitrate is not always as effective as reducing packet retransmissions. Second, for many DNNs, the impact of missing a video frame depends not only on itself but also on which other frames have been received or lost. We argue that some changes must be made in the transport layer, to determine whether to resend a packet based on the packet's impact on DNN's inference dependent on which packets have been received. While much research is needed toward an optimal design of DNN-driven transport layer, we believe that we have taken the first step in reducing streaming delay while maintaining a high inference accuracy.

Title: CoSpot: a cooperative VM allocation framework for increased revenue from spot instances Abstract: ABSTRACTMost large cloud operators offer a lower-priced, lower-priority alternative to regular (on-demand or reserved) virtual machines, commonly referred to as spot instances. Spot instances are opportunistically allocated to servers in order to utilize any residual cloud capacity, but are evicted whenever regular virtual machines need to use that capacity. This paper proposes CoSpot, a lightweight framework for cooperative allocation of regular virtual machines and spot instances, which allows for easy integration of arbitrary virtual machine and spot allocators. In our experiments, employing the framework achieves up to 245% improvement (average 34% improvement) in spot revenue, with no loss in virtual machine revenue, compared to the baseline VM and spot allocation without using our framework. We also derive and release a reusable workload with both virtual machines and spot instances, based on data previously shared by Microsoft Azure.

Title: A case for using cache line deltas for high frequency VM snapshotting Abstract: ABSTRACTActive-standby schemes for Virtual Machine (VM) high availability require periodic synchronization of memory and CPU state. The most common approach to synchronization is to use page tables and software to identify "dirty" memory pages at the source and in turn copy them to the target via a network or interconnect. However, this approach results in significanct page table traversal and data copying overhead, resulting in considerable VM downtime. A principal contributor to this overhead is that many applications using this approach incur data copy-amplification as a result of copying more data than is necessary; this arises because of the processor's virtual memory system design in which memory pages are 4KiB or larger. With the emergence of CXL-enabled memory devices, it is now possible to track memory changes at a finer-granularity (e.g., 64-byte cache lines instead of 4KiB pages). Moreover, CXL will enable new functions to be pushed down into custom memory controllers that can directly intercept and manipulate memory transactions. This paper is a use case analysis that examines the potential advantages of moving to cache line-based memory change detection and transfer. We focus on exploring continuous synchronization of VM guest memory spaces for the purpose of achieving high availability. For this use case, the maximum outage-time, resulting from snapshot and synchronization latency, must be kept to a minimum. Our analysis examines memory access patterns from 30 different benchmarks and derives a quantitative understanding of potential gains CXL-based technology can offer. The results show that more than 35% of the benchmarks exhibit an amplification factor of greater than 10 and therefore would significantly benefit from the finer granularity proposed. Furthermore, to reduce the data transfer between machines, we combine fine-grained tracking with compression to reduce data copy volume. Our results show that an additional reduction of 1.8x can be achieved with XOR-RLE compression.

Title: Want more unikernels?: inflate them! Abstract: ABSTRACTUnikernels are on the rise in the cloud. These lightweight virtual machines (VMs) specialized to a single application offer the same level of isolation as full-blown VMs, while providing performance superior to standard Linux-based VMs or even to containers. However, their inherent specialization renders memory deduplication ineffective, causing unikernels, in practice, to consume more memory than their small memory footprint would suggest. This makes them less advantageous when thousands of SaaS and/or FaaS unikernels instances have to run on the same server. In this paper we introduce a novel approach to build the next generation of networked services and lambda functions by improving unikernel's memory layout so that it is more likely to share identical pages with other unikernels deployed on the system. Our approach supports SaaS and FaaS architectures and can be used with ASLR. Our experiments show that our approach can reduce the amount of physical memory used by a set of unikernels running on the same server by as much as 3x, with next to no overhead on applications performance.

Title: Quadrant: a cloud-deployable NF virtualization platform Abstract: ABSTRACTNetwork Functions (NFs) now process a significant fraction of Internet traffic. Software-based NF Virtualization (NFV) promised to enable rapid development of new NFs by vendors and leverage the power and economics of commodity computing infrastructure for NF deployment. To date, no cloud NFV systems achieve NF chaining, isolation, SLO-adherence, and scaling together with existing cloud computing infrastructure and abstractions, all while achieving generality, speed, and ease of deployment. These properties are taken for granted in other cloud contexts but unavailable for NF processing. We present Quadrant, an efficient and secure cloud-deployable NFV system, and show that Quadrant's approach of adapting existing cloud infrastructure to support packet processing can achieve NF chaining, isolation, generality, and performance in NFV. Quadrant reuses common cloud infrastructure such as Kubernetes, serverless, the Linux kernel, NIC hardware, and switches. It enables easy NFV deployment while delivering up to double the performance per core compared to the state of the art.

Title: Behind the Mix-Zones Scenes: On the Evaluation of the Anonymization Quality Abstract: ABSTRACTIn the flowering of ubiquitous computing, networks like the Internet of Things and the Internet of Vehicles have contributed to connecting objects and sharing location services in broad environments like smart cities bringing many benefits to citizens. However, these services yield massive and unrestricted mobility data of citizens that pose privacy concerns, among them recovering the identity of citizens with linking attacks. Although several privacy mechanisms have been proposed to solve anonymization problems, there are few studies about their behavior and analysis of the data quality anonymized by these techniques. In this paper, we introduce the anonymization quality. For this, we propose analyzing the mix-zones metrics Amount of Cars on Mix-zone (ACM), Interval of Arrival Time between Cars on Mix-zones (ITM), and Activation Time of the Mix-zone (ATM) for characterizing and evaluating the impacts of anonymization quality over time and space in mobility data. The results showed that mix-zone metrics reflect anonymization behavior and can measure the anonymization quality over time. This insight can contribute significantly to building privacy proposes based on anonymization more effectively than based only on traffic flow. To our knowledge, this is the first work that uses mix-zones metrics analysis to observe the anonymization behavior in quality terms.

Title: Making ALOHA Intelligent Taking Advantage of Working Memory Capacity Abstract: ABSTRACTALOHA with priority acknowledgments (ACK) is transformed into a collision-free channel access method by increasing the amount of working memory capacity with which communicating nodes remember each node that has requested to join the channel successfully. This results in ALOHA-NUI, for neighborhood understood index. The throughput of ALOHA-NUI is compared with the throughput of TDMA with a fixed transmission schedule, ALOHA with priority ACK's, and CSMA with priority ACK's analytically and by simulation. ALOHA-NUI is shown to attain the high throughput of collision-free transmission scheduling methods that usually require clock synchronization while maintaining most of the simplicity of ALOHA with priority ACK's.

Title: Performance Evaluation and Testbed for Delivering SRT Live Content using DASH Low Latency Streaming Systems Abstract: ABSTRACTThe work presented in this paper focuses on the implementation of a testbed for the evaluation of content distribution systems using LL-DASH (Low Latency DASH -Dynamic Adaptive Streaming over HTTP-) and devices that provide real-time sources or servers using real-time protocols, such as RTSP (Real Time Streaming Protocol) or SRT (Secure Reliable Transport). These protocols are widely used by IP (Internet Protocol) cameras or by production and transmission systems, such as OBS Studio or vMix. The objective is to show the necessary processes in detail (so they can be reproduced in future works related to low latency services) and to minimize the end-to-end delay, obtaining values in the order of 2 seconds or less. The implementation has been done using FFmpeg software, players like Dash.js or Shaka-Player and implementing a Python web server with LL-DASH support to optimize the transmission delay.

Title: LoRaWAN-based Smart Parking Service: Deployment and Performance Evaluation Abstract: ABSTRACTThe dramatic increase of the urban population has put a lot of pressure in modern urban transportation systems. This not only implies noteworthy air pollution, and waste in time and energy, but also has led to the critical issue of the parking spots scarcity. Finding a parking spot has become one of the most common problems mentioned by drivers due to the day-by-day increase in number of vehicles. To overcome this problem, smart parking services based on sensors to detect parking spot occupancy status and inform drivers about their availability have been proposed. With the successful deployment of this kind of solutions, costs associated with traffic jams or wasted gas can be considerably reduced. However, one of the main challenges to be addressed when planning and deploying this kind of services is the communication technology employed by parking sensors, which are typically buried under the asphalt, and has to be able to cover large areas of the city in the most cost-efficient way. In this paper, we analyze the behavior and performance of a LoRaWAN network employed for supporting a Smart Parking service in the city of Santander. The sensors and network deployment are described in the paper and the thorough experimental assessment and evaluation of the network behavior is presented. The goal of this evaluation is to provide a better understanding of the key factors affecting the communication of outdoor parking spots. Through the continuous monitoring of two parking sensor deployments in the city, we derive some conclusions and lessons learnt that could benefit the planning and deployment of city-scale IoT infrastructures employing LoRaWAN networks as their wireless access technology.

Title: Privacy-Aware Vehicle Emissions Control System for Traffic Light Intersections Abstract: ABSTRACTThis paper proposes a privacy-aware reinforcement learning (RL) framework to reduce carbon emissions of vehicles approaching light traffic intersections. Taking advantage of vehicular communications, traffic lights disseminate their state (i.e., traffic light cycle) among vehicles in their proximity. Then, the RL model is trained using public traffic lights data while preserving private car information locally (i.e., at the vehicle premises). Vehicles act as the agents of the model, and traffic infrastructure serves as the environment where the agent lives. Each time, the RL model decides if the vehicle should accelerate or decelerate (i.e., the model action) based on received traffic light observations. The optimal RL model strategy, dictating vehicles' driving speed, is learned following the proximal policy optimization algorithm. Results show that by moderating vehicles' speed when approximating traffic light intersections, gas emissions are reduced by 25% CO2 and 38% NOx emissions. The same happens for EVs that reduce energy consumption by 20W/h compared to not using the model. at intersections. The final impact of using the model refers to a negligible increment of 20s in the trip duration.

Title: Realistic Assessment of Transport Protocols Performance over LEO-based Communications Abstract: ABSTRACTWe study the performance exhibited by transport protocols, TCP and QUIC, over realistic satellite networks. We propose a novel methodology, which combines real implementation (exploiting virtualization techniques) and simulation, to carry out systematic and repetitive experiments. We modify the default operation of the ns-3 framework and we integrate the dynamism that characterizes links in satellite communications, particularly links to LEO satellites. We carry out a thorough assessment over different setups, changing the operating band and the buffer length. In addition, we ascertain the impact of using the multi-streaming feature that QUIC includes. The results show that QUIC yields lower delays than TCP, although in particular setups it might suffer from higher jitter. In addition, using multiple streams in QUIC does not yield a relevant gain. In any case, we can conclude that the behavior of transport protocols over non-terrestrial-networks might not be always optimum and that QUIC can bring benefits when compared to TCP.

Title: Cost-effective Measurements of 5G Radio Resources Allocation for Telecom Market Regulator's Monitoring Abstract: ABSTRACTThe Regulatory Authority monitors and regulates the telecom market at a national-wide range. One of its main tasks is to put spectrum at disposal of the Mobile Network Operators (MNO) for serving the increase number of users and services. For this scope, the Regulator needs to have an independent estimation of the usage of the radio resources by the operators, so that it may anticipate future needs and to initiate actions (e.g., liberation of frequency bands) directed to fulfill the demands of the market. This paper presents a methodology for Regulator-triggered monitoring of 5G radio resources allocation. The requirements of the methodology are: (1) monitoring measurements must be external to the network, (2) information from the MNO is limited to the data of the radio infrastructure, such as localization and technical data of the antennas, and (3) any measurements need to be cost-effective since it is supposed that the Regulator will make extended test campaigns through the whole national territory. Our results of 5G measurements validate the methodology and show the limitations of the estimation of radio resource allocation.

Title: RPL+: An Improved Parent Selection Strategy for RPL in Wireless Smart Grid Networks Abstract: ABSTRACTRouting protocols play an important role in a Wireless Smart Grid Network (WSGN). The implementation of efficient routing strategies becomes paramount to guarantee the necessary interaction between utilities smart devices like smart meters, and the control centers. This research is focused on improving one of the well known routing protocols for WSGN, the Routing Protocol for Low-Power and Lossy Networks (RPL). More specifically, this paper presents a new parent selection strategy designed to choose the best parent when two or more candidates have the same ranking. The goal is to make better forwarding decisions on the best next-hop node to transmit packets to the destination. The proposed method takes into account the most important routing metrics in this type of network determined by applying Machine Learning (ML) feature importance analysis using Random Forest. The performance evaluation of the proposed strategy shows a significant improvement in terms of Packet Delivery Ratio when comparing to RPL standard implementations.

Title: How does the Selection of Wireless Technology Impact the Performance of the Smart Grid? A Simulation Approach Abstract: ABSTRACTElectric power is a widely used resource around the world and has relied heavily on fossil energy sources that are expensive and polluting. Therefore, there is an urgent need to move to a low-carbon economy, avoid unpredictable fuel costs and replace aging infrastructure. However, this requires a radical transformation of the electricity system, and communication technologies can play this important role. In this work, we present an evaluation by means of network simulations of the leading technologies used in Wireless Smart Grid Neighborhood Area Networks. This sub-network provides communication between home users and utility control centers. In this regard, we have configured and adapted the OMNeT++ and ns-3 simulators to implement IEEE 802.15.4g, LoRa (Longe Range), IEEE 802.11s, and 4G/LTE (Long Term Evolution) wireless technologies in the context of Smart Grid communications. To the best of our knowledge, there are no equivalent works in the literature that evaluate and compare these technologies using realistic network simulations. Simulation results show how wireless technology selection can significantly influence packet delivery ratio, network transit time, and compliant throughput metrics.

Title: Coverage Path Planning for Internet of Drones Abstract: ABSTRACTDrones have been used in several applications, such as monitoring, search and rescue, urban sensing, traffic management, and delivery of goods. Soon, all these applications must share the same airspace forming the Internet of Drones (IoD). The IoD will allow the controlled access of drones to the airspace through the airways, guaranteeing the environment safety, management, and fair use. One of the most prominent challenges of IoD is the mobility of drones that might happen freely in space or along predefined airways, as expected in urban environments. Scanning applications such as monitoring, search and rescue, and sensing may require drones to cover an entire metropolitan area. In IoD, path planning for a drone, in the case of airways, must consider factors such as the number of drones available in the application and the airways. In this work, we propose a method of coverage path planning for IoD (CPP-IoD). Specifically, we introduce a technique that considers the IoD environment. Our results show better path planning coverage regarding traveled distance and uniformity between drone paths compared to the baseline algorithm.

Title: Machine Learning Based Prediction with Parameters Tuning of Multi-Label Real Road Vehicles Characteristics Abstract: ABSTRACTThe real-time traffic characteristics on the road network highly affect the safety conditions and the driving behaviors there. Early detection of crowded areas or hazardous conditions on the road network should affect the drivers' decisions and behavior to guarantee smooth and comfortable trips. Machine learning mechanisms have been mainly used for general prediction after extensive training processes. Over the road networks, trained machines could be really helpful to obtain instant predictions that assist drivers and autonomous vehicles there. However, the quality and efficiency of these machines are affected by several criteria including the quality of the used dataset and the tuning of the parameters of the regression algorithm. In this work, we investigate the performance of the most popular regression algorithms in terms of temporal prediction of the traffic characteristics in a real road scenario. Moreover, we optimize the regression algorithm by tuning the parameters using the grid search technique. From the experimental results, we can clearly notice the enhancements in predicting the traffic characteristics for different periods of time. We have observed that the number of neighbors, the distance, and the metric parameters' values are best tuned with the values of 4, 'Manhattan', and 'Distance', respectively, for the K-Nearest Neighbor (KNN) regression algorithm.

Title: Using Decentralized Aggregation for Federated Learning with Differential Privacy Abstract: ABSTRACTNowadays, the ubiquitous usage of mobile devices and networks have raised concerns about the loss of control over personal data and research advance towards the trade-off between privacy and utility in scenarios that combine exchange communications, big databases and distributed and collaborative (P2P) Machine Learning techniques. On the other hand, although Federated Learning (FL) provides some level of privacy by retaining the data at the local node, which executes a local training to enrich a global model, this scenario is still susceptible to privacy breaches as membership inference attacks. To provide a stronger level of privacy, this research deploys an experimental environment for FL with Differential Privacy (DP) using benchmark datasets. The obtained results show that the election of parameters and techniques of DP is central in the aforementioned trade-off between privacy and utility by means of a classification example.

Title: Detecting Malicious Use of DoH Tunnels Using Statistical Traffic Analysis Abstract: ABSTRACTDNS plays a fundamental role in the operation of ubiquitous networks. All devices connected to these networks need DNS to work, both for traditional domain name to IP address translation, and for more advanced services such as resource discovery. At first, the DNS communication protocol presented certain security problems: integrity, authenticity and confidentiality. DNSSEC provides security but still does not guarantee confidentiality. To solve this problem, DNS over TLS (DoT) and DNS over HTTPS (DoH) were defined. In recent years, DNS tunneling, a covert form of encapsulating data transmission, has been used to encapsulate malicious traffic in a DNS connection. DoT and DoH versions complicate the detection of these tunnels because the encrypted data prevents performing an analysis of the content of the DNS traffic. Previous work has used machine learning techniques to identify DoH tunnels, but these have limitations. In this study, we identify the most significant features that singularize malicious traffic from benign traffic by statistical analysis. Based on the selected features, we obtain satisfactory results in the classification between benign and malicious DoH traffic. The study reveals that it is possible to differentiate traffic based on certain statistical parameters.

Title: Performance Evaluation of Wi-Fi HaLow, NB-IoT and LoRa for Smart City Applications Abstract: ABSTRACTLong-range wireless technologies are at the core of Internet of Things (IoT) and smart city applications. While they offer many advantages in terms of ease of deployment, flexibility, mobility, and ubiquity, to name but a few, they are not equally suitable for smart city applications. Since 'one size fits all' does not hold in this context, finding out which long-range wireless technology is the best requires a thorough performance evaluation of these technologies in specific context and scenarios. In this paper, we focus on performance evaluation of three prominent long-range wireless communications, namely LoRa and Wi-Fi HaLow in the ISM band and NB-IoT in the licensed band, to better understand their benefits and limitations in the context of four smart city application scenarios. These scenarios cover both under and above ground applications in areas with different propagation properties.

Title: Prototype of deployment of Federated Learning with IoT devices Abstract: ABSTRACTIn the age of technology, data is an increasingly important resource. This importance is growing in the field of Artificial Intelligence (AI), where sub fields such as Machine Learning (ML) need more and more data to achieve better results. Internet of Things (IoT) is the connection of sensors and smart objects to collect and exchange data, in addition to achieving many other tasks. A huge amount of the resource desired, data, is stored in mobile devices, sensors and other Internet of Things (IoT) devices, but remains there due to data protection restrictions. At the same time these devices do not have enough data or computational capacity to train good models. Moreover, transmitting, storing and processing all this data on a centralised server is problematic. Federated Learning (FL) provides an innovative solution that allows devices to learn in a collaborative way. More importantly, it accomplishes this without violating data protection laws. FL is currently growing, and there are several solutions that implement it. This article presents a prototype of a FL solution where the IoT devices used were raspberry pi boards. The results compare the performance of a solution of this type with those obtained in traditional approaches. In addition, the FL solution performance was tested in a hostile environment. A convolutional neural network (CNN) and a image data set were used. The results show the feasibility and usability of these techniques, although in many cases they do not reach the performance of traditional approaches.

Title: XuILVQ: A River Implementation of the Incremental Learning Vector Quantization for IoT Abstract: ABSTRACTIn machine learning, incremental learning algorithms provide a solution for models that need to dynamically adapt and react to their context by analyzing samples from data streams. These algorithms are especially suitable for Internet of Things (IoT) solutions where devices (like sensors/actuators) have low memory and computation capability. Within this context, we propose an implementation of an Incremental Learning Vector Quantization (ILVQ) algorithm compliant with the well-known River library standards. Our implementation was assessed according to its predicting capacity and also according to its memory and execution time consumption, showing outstanding results.

Title: Comparison of User Presence Information from Mobile Phone and Sensor Data Abstract: ABSTRACTData collected from mobile phones or from motion detection sensors are regularly used as a proxy for user presence in networking studies. However, little attention was paid to the actual accuracy of these data sources, which present certain biases, in capturing actual human presence in a given geographical area. In this work, we conduct the first comparison between mobile phone data collected by an operator and human presence data collected by motion detection sensors in the same geographical area. Through a detailed spatio-temporal analysis, we show that a significant correlation exists between the two datasets, which can be seen as a cross validation of the two data sources. However, we also detect some significant differences at certain times and places, raising questions regarding the data used in certain studies in the literature. For example, we notice that the most important daily mobility peaks detected in mobile phone data are not actually detected by on ground sensors, or that the end of the work-day activities in the considered area is not synchronised between the two data sources. Our results allow to distinguish the metrics and the scenarios where user presence information is confirmed by both mobile phone and sensor data.

Title: Anonymized Counting of Nonstationary Wi-Fi Devices When Monitoring Crowds Abstract: ABSTRACTPedestrian dynamics are nowadays commonly analyzed by leveraging Wi-Fi signals sent by devices that people carry with them and captured by an infrastructure of Wi-Fi scanners. Emitting such signals is not a feature for devices of only passersby, but also for printers, smart TVs, and other devices that exhibit a stationary behavior over time, which eventually end up affecting pedestrian crowd measurements. In this paper we propose a system that accurately counts nonstationary devices sensed by scanners, separately from stationary devices, using no information other than the Wi-Fi signals captured by each scanner in isolation. As counting involves dealing with privacy-sensitive detections of people's devices, the system discards any data in the clear immediately after sensing, later working on encrypted data that it cannot decrypt in the process. The only information made available in the clear is the intended output, i.e. statistical counts of Wi-Fi devices. Our approach relies on an object, which we call comb, that maintains, under encryption, a representation of the frequency of occurrence of devices over time. Applying this comb on the detections made by a scanner enables the calculation of the separate counts. We implement the system and feed it with data from a large open-air festival, showing that accurate anonymized counting of nonstationary Wi-Fi devices is possible when dealing with real-world detections.

Title: Characterizing Wi-Fi Probing Behavior for Privacy-Preserving Crowdsensing Abstract: ABSTRACTSmartphones and the signaling messages they emit allow third parties to learn about the owners' mobility. While Wi-Fi and Bluetooth signaling messages have been (mis)used for tracking individuals, there are also privacy-respecting uses: crowd sensing for estimating the number of people in an area and their dynamics, is one such example. However, the very useful countermeasures against individual tracking, most prominently MAC address randomization, also complicate crowd size estimation. In this paper, we present an online estimation algorithm that operates only on ephemeral MAC addresses and, if desired, signal strength information to distinguish relevant signals from background noise. We use measurements and simulations to calibrate our counting algorithm and collect numerous data sets which we use to explore the algorithm's performance in different scenarios.

Title: Interference Aware Heuristics to Optimize Power Beacons for Battery-less WSNs Abstract: ABSTRACTTo achieve an infinite lifetime of sensing infrastructure in Internet-of-Things, battery-less wireless powered sensor networks (WPSNs) are an important step. The nodes in battery-less WPSNs harvest and store energy in super-capacitors from RF signal which are periodically transmitted by power beacons (PBs) or chargers. However, using multiple power chargers requires a focus on a crucial problem of interference. The sensor nodes which are covered by more than one power beacons become unreliable because of the overlapping signals from chargers since the overlap can be constructive or destructive. In this paper, we propose an algorithm to optimize the number and placement of power beacons such that interference can be reduced. The result shows that our proposed optimal power beacon location (OPBL) algorithm reduces interference in 60% of cases and also reduces data transmission time (DTT) by 30% in 24% of cases in comparison to the state-of-the-art.

Title: A Frequency-Based Intelligent Slicing in LoRaWAN with Admission Control Aspects Abstract: ABSTRACTThe significant deployment of LoRaWAN networks is increasingly questioning its ability to handle massive numbers of IoT devices and its ability to support service differentiation. The few existing attempts to implement service differentiation suffer from a lack of scalability and do not meet the qualitative criteria of the services, since without admission control there is no way to restrain the devices from transmitting. In this paper, we present a scalable probabilistic approach that not only enables an efficient sharing of LoRaWan access networks between different services/slices, but more importantly allows achieving the objectives of the supported services through the integration of an admission control. Since the derivation of devices' repartition probabilities is a very complex problem, we propose an evolutionary algorithm to derive them efficiently. The obtained results clearly show the ability of the proposed solution to efficiently utilize the scarce radio resources, while achieving the qualitative objectives of the prioritized services.

Title: HiPR+: A Protocol for Centimeter 3D Localization based on UWB Abstract: ABSTRACTHiPR+ is an approach for centimeter-accurate indoor localization. It combines distance estimation between ultra-wideband (UWB) transceivers and location estimation using an extended Kalman filter (EKF). The performance is tested with experiments on hardware platforms from Decawave. The distance estimation of HiPR+ achieves an order of magnitude better precision and a multiple improvement in accuracy compared to the company's native solution while it only takes only a fraction the time needed for range computation. We evaluate the 3D localization capabilities with two least-squares approaches and an EKF. A median accuracy below one centimeter can be attained using the proposed ranging error compensations in combination with the EKF-based~positioning.

Title: Optimizing Cell Sizes for Ultra-Reliable Low-Latency Communications in 5G Wireless Networks Abstract: ABSTRACTThe millimeter-wave (mmWave) band with large antenna arrays and dense base station deployments has become the prime candidate for 5G mobile systems and key enabler for ultra-reliable low-latency communications (URLLC). In this paper, we propose an approach to estimating the optimal cell sizes of 5G networks that support URLLC services by combining both physical and data link layers, leveraging concepts from stochastic geometry and queuing theory. Furthermore, the impacts of the densification of base stations on the average blocking probability, which are of practical interest, are investigated with numerical results. The results show that the signal-to-noise-and-interference ratio (SINR) coverage probability and the average blocking probability achieve optimal values at different cell sizes. Moreover, the differences between the two types of optimal values become more significant with higher SINR thresholds. Our results suggest that traditional SINR-based approach for cell sizing will cause over-provisioning of base stations and significantly higher costs. Specifically, we share the insight that the interactions between SINR at physical layer and retransmission at link layer contribute to varying cost saving.

Title: Universal Beamforming: A Deep RFML Approach Abstract: ABSTRACTWe introduce, design, and evaluate a set of universal receiver beamforming techniques. Our approach and system DEFORM, a Deep Learning (DL)-based RX beamforming achieves significant gain for multi-antenna RF receivers while being agnostic to the transmitted signal features (e.g., modulation or bandwidth). It is well known that combining coherent RF signals from multiple antennas results in a beamforming gain proportional to the number of receiving elements. However in practice, this approach heavily relies on explicit channel estimation techniques, which are link specific and require significant communication overhead to be transmitted to the receiver. DEFORM addresses this challenge by leveraging Convolutional Neural Network to estimate the channel characteristics in particular the relative phase to antenna elements. It is specifically designed to address the unique features of wireless signals complex samples, such as the ambiguous 2π phase discontinuity and the high sensitivity of the link Bit Error Rate. The channel prediction is subsequently used in the Maximum Ratio Combining algorithm to achieve an optimal combination of the received signals. While being trained on a fixed, basic RF settings, we show that DEFORM's DL model is universal, achieving up to 3 dB of SNR gain for a two-antenna receiver in extensive evaluation demonstrating various settings of modulations and bandwidths.

Title: Average Age Upon Decisions of Wireless Networks with Truncated HARQ in the Finite Blocklength Regime Abstract: ABSTRACTWe consider an update-and-decide IoT-based wireless network, where information packets generated from dual sources are co-stored in the transmitter's buffer, while decisions are made at the destination. Two practical assumptions about the communications between the transmitter and destination are taken into account: the communications are operating with finite blocklength (FBL) codes, and truncated hybrid automatic repeat request (HARQ) schemes are exploited to improve the FBL reliability, i.e., the number of allowed rounds of (re)transmissions is finite. For the first time, this paper characterizes the timeliness of status updates, namely age upon decisions (AuD) (which highlights the timeliness of the information at decisions in comparison to the concept of age of information), for such truncated HARQ-assisted wireless network. First, we characterize the inter-arrival time between two adjacent successfully transmitted packets, while taking into consideration the preemption policy and the randomness of the number of preempted packets from the same source. In particular, the probability density function, statistical performance of such inter-arrival time are derived. Following these characterizations, we propose a new approach to determine the average AuD and obtain a closed-form expression accordingly. Via simulations, we evaluate the performance and conclude a set of guidelines for designs on the considered network.

Title: A non-Hidden Markovian Modeling of the Reliability Scheme of the Constrained Application Protocol in Lossy Wireless Networks Abstract: ABSTRACTThe Constrained Application Protocol (CoAP) is a lightweight communication protocol designed by the Internet Engineering Task Force (IETF) for wireless sensor networks and Internet-of-Things (IoT) devices. The reliability mechanism in CoAP is based on retransmissions after timeout expiration and on an exponential backoff procedure which is designed to be simple and adapted to constrained devices. In this research work, we propose a new exact analytical model to analyze the performance of CoAP in lossy wireless networks modeled by the well-known Gilbert-Elliott two-state Markov process. We also show how to compute several performance metrics using closed form expressions such as the observed loss ratio, goodput, and the delay before success with a time complexity no more than O(r) with r is the maximum re-transmission limit. This study provides insights about improving CoAP recovery mechanism and highlights the properties -- including the limitations -- of CoAP. Also, it presents guidelines to tune CoAP parameters dynamically in order to adapt to network losses caused by interference and mobility. The model is validated using the realistic environment Cooja/Contiki OS where theoretical and experimental results match very well.

Title: SIREN: Eliminating Multiple Access Interference in Transmission Schedules Established in Multi-Hop Networks Abstract: ABSTRACTThe Scheduling with Interference Removal Established Network-Wide (SIREN) protocol is introduced to enable the scheduling of transmissions in a way that multiple access interference (MAI) is eliminated in multi-hop networks. Unlike all prior medium access control (MAC) methods, SIREN combats MAI as a network-wide problem rather than as a problem confined within a single broadcast link. SIREN ensures that the receivers of a primary transmitter assigned a transmission turn have no MAI, and allow one or multiple concurrent secondary transmitters to transmit during the same transmission turn, as long as no MAI is created. SIREN is implemented on top of the IEEE 802.11b physical layer to show that it is a viable approach using commercial off-the-shelf hardware. SIREN is proven to ensure interference-free transmission schedules in mesh networks, and simulation experiments in ns-3 are used to illustrate the advantages of SIREN over IEEE 802.11b in terms of goodput, fairness and delays.

Title: Fair Iterative Water-Filling Game for Multiple Access Channels Abstract: ABSTRACTThe water-filling algorithm is well known for providing optimal data rates in time varying wireless communication networks. In this paper, a perfectly coordinated water-filling game is considered, in which each user transmits only on the assigned carrier. Contrary to conventional algorithms, the main goal of the proposed algorithm (FEAT) is to achieve near optimal performance, while satisfying fairness constraints among different users. The key idea within FEAT is to minimize the ratio between the utilities of the best and the worst users. To achieve this goal, we devise an algorithm such that, at each iteration (channel assignment), a channel is assigned to a user, while ensuring that it does not lose much more than other users in the system. In this paper, we show that FEAT outperforms most of the existing related algorithms in many aspects, especially in interference-limited systems. Indeed, with FEAT, we can ensure a low complexity near-optimal, and fair solution. It is shown that the balance between being nearly globally optimal and good from an individual point of view seems hard to sustain with a significant number of users, hence adding robustness to the proposed algorithm.

Title: An Adaptable Module for Designing Jamming Attacks in WiFi Networks for ns-3 Abstract: ABSTRACTThe inherent openness of wireless communication techniques has made them vulnerable to jamming attacks. However, the effectiveness of a jamming attack depends on numerous parameters and varies according to the state of the environment. At the same time, attacks are becoming increasingly sophisticated and attempt to evade basic detection methods. Consequently, there is a real need to evaluate this new type of attack to improve the robustness of the detection and mitigation methods. A simulating tool to assess the impact of jamming attacks on wireless networks has become essential to gain effectiveness against attackers. This paper proposes a module of jamming attack for the discrete network simulator 3 (ns-3). This module, adaptable to any type of jamming attack strategy, provides a set of essential metrics allowing their evaluation. We evaluate the module by comparing the impacts of different types of jamming attacks already carried out in a real environment.

Title: A Flow-Level Wi-Fi Model for Large Scale Network Simulation Abstract: ABSTRACTWi-Fi networks are extensively used to provide Internet access to end-users and to deploy applications at the edge. By playing a major role in modern networking, Wi-Fi networks are getting bigger and denser. However, studying their performance at large-scale and in a reproducible manner remains a challenging task. Current solutions include real experiments and simulations. While the size of experiments is limited by their financial cost and potential disturbance of commercial networks, the simulations also lack scalability due to their models' granularity and computational runtime. In this paper, we introduce a new Wi-Fi model for large-scale simulations. This model, based on flow-level simulation, requires fewer computations than state-of-the-art models to estimate bandwidth sharing over a wireless medium, leading to better scalability. Comparing our model to the already existing Wi-Fi implementation of ns-3, we show that our approach yields to close performance evaluations while improving the runtime of simulations by several orders of magnitude. Using this kind of model could allow researchers to obtain reproducible results for networks composed of thousands of nodes much faster than previously.

Title: Blender: Toward Practical Simulation Framework for BLE Neighbor Discovery Abstract: ABSTRACTFor the widely used Bluetooth Low-Energy (BLE) neighbor discovery, the parameter configuration of neighbor discovery directly decides the results of the trade-off between discovery latency and power consumption. Therefore, it requires evaluating whether any given parameter configuration meets the demands. The existing solutions, however, are far from satisfactory due to unsolved issues. In this paper, we propose Blender, a simulation framework that produces a determined and full probabilistic distribution of discovery latency for a given parameter configuration. To capture the key features in practice, Blender provides adaption to the stochastic factors such as the channel collision and the random behavior of the advertiser. Evaluation results show that, compared with the state-of-art simulators, Blender converges closer to the traces from the Android-based realistic estimations. Blender can be used to guide parameter configuration for BLE neighbor discovery systems where the trade-off between discovery latency and power consumption is of critical importance.

Title: A Novel Mixed Method of Machine Learning Based Models in Vehicular Traffic Flow Prediction Abstract: ABSTRACTHow to effectively improve the efficiency of vehicle traffic in the road system will play an essential role in improving the operational efficiency of the traffic system while eliminating the energy consumption and environmental pollution problems caused in particular, and this is also a key concern in the field of intelligent transportation systems. Timely and accurate traffic flow prediction is regarded as the key to solve the above problems because it can effectively improve the efficiency of traffic flow management. Many prediction methods have been proposed and among them, Machine Learning (ML)-based forecasting methods have gradually become mainstream in recent years because of their inherent ability to learn and predict nonlinear features in traffic information. However, we notice that most of the existing ML-based traffic prediction methods were designed relying fully on historical data while ignoring the structure and the impacts of the whole road network. Therefore, in this paper, we proposed a mixed method to take both historical data and road networks into consideration. Based on the real-world dataset, we conducted simulation experiments. The corresponding test results demonstrate a substantial improvement in the prediction accuracy of our method compared to conventional ML-based methods.

Title: A Spatial Model for Using the Age of Information in Cooperative Driving Applications Abstract: ABSTRACTThe age of information (AoI) has been proposed as a metric for evaluating freshness of information; recently also within the context of intelligent transportation systems (ITS). The most frequently used definition of AoI, however, does only account for the generation time of the data but not for application-specific aspects. In ITS, for example, the distance of vehicles is not considered and nodes farther away may experience an increased AoI due to effects of the wireless communication channel. We propose a new way of interpreting the AoI in such a context, also considering the location of the transmitting vehicle as a metric of importance to the information. In particular, we introduce a weighting coefficient used in combination with the peak age of information (PAoI) metric to describe the AoI requirement, emphasizing on packets from more important neighbors. As an example, we characterize such importance using the orientation and the distance of the involved vehicles. We use the derived model to focus on timely updates of relevant vehicles for meeting a given AoI requirement, which can save resources on the wireless channel while keeping the AoI minimal.

Title: 5G New Radio Sidelink Link-Level Simulator and Performance Analysis Abstract: ABSTRACTSince the Third Generation Partnership Project (3GPP) specified 5G New Radio (NR) sidelink in Release 16, researchers have been expressing increasing interest in sidelink in various research areas, such as Proximity Services (ProSe) and Vehicle-to-Everything (V2X). It is essential to provide researchers with a comprehensive simulation platform that allows for extensive NR sidelink link-level evaluations. In this paper, we introduce the first publicly accessible 5G NR link-level simulator that supports sidelink. Our MATLAB-based simulator complies with the 3GPP 5G NR sidelink standards, and offers flexible control over various Physical Layer (PHY) configurations. It will facilitate researcher's exploration in NR sidelink with a friendly access to the key network parameters and great potential of customized simulations on algorithm developments and performance evaluations. This paper also provides several initial link-level simulation results on sidelink using the developed simulator.

Title: Uncovering 5G Performance on Public Transit Systems with an App-based Measurement Study Abstract: ABSTRACTFifth-generation (5G) networks are now entering a stable phase in terms of commercial release. 5G design is flexible to support a diverse range of radio bands (i.e., low-, mid-, and high-band) and application requirements. Since its initial roll-out in 2019, extensive measurements studies have revealed key aspects of commercial 5G deployments (e.g., coverage, signal strength, throughput, latency, handover, and power consumption among the others) for several scenarios (e.g., pedestrian and car mobility, mid-, and high-bands, etc.). In this paper, we take a different angle than previous studies and carry out an in-depth measurement study of 5G in a large public bus transit system in a major European city. For several mobile network operators, we identify how flexible the network deployment is by analyzing Radio Resource Control (RRC) messages, mobility management, and application performance.

Title: Data Visualization Design Strategies for Promoting Exercise Motivation in Self-Tracking Applications Abstract: ABSTRACTIn self-tracking applications, data visualizations play a fundamental role in delivering efficient information and creating personalized user experiences. Literature consistently indicates that data visualization is a powerful tool to make data persuasive and improve motivation. However, how to leverage different data visualizations to boost motivation remains largely unknown. In this study, the researcher explores the effects of different data visualizations on user motivation within self-tracking mobile applications. Through design space analysis and semi-structured interviews, the researcher defines a set of design factors that impact users’ exercise motivation at different levels of exercise adoption. Based on these factors, the researcher delivers a set of practical design suggestions for design practitioners and people who create visualizations for large data sets.

Title: Performance Analysis of General P4 Forwarding Devices with Controller Feedback Abstract: ABSTRACTSoftware-Defined Networking (SDN) lays the foundation for the operation of future networking applications. The separation of the control plane from the programmable data plane increases the flexibility in network operation. One of the most used languages for describing the packet behavior in the data plane is P4. It allows protocol and hardware independent programming. With the expanding deployment of P4 programmable devices, it is of utmost importance to understand their performance behavior and limitations in order to design a network and provide Quality of Service (QoS) guarantees. One of the most important performance metrics is the packet mean sojourn time in a P4 device. While previous works already modeled the sojourn time in P4 devices with controller feedback, those models were rather simplified and could not capture the system behavior for general cases, resulting in a potential highly inaccurate performance prediction. To bridge this gap, in this paper, we consider the system behavior of P4 devices for the general case, i.e., under general assumptions. To that end, we model the behavior with a queueing network with feedback. As it is impossible to provide closed-form solutions, we consider different approximations for the mean sojourn time. We validate our results against extensive realistic simulations, capturing different behaviors in the data and control planes. Results show that the most accurate approximation in almost all cases is the one in which the queues are decoupled and considered as independent despite the fact that there are dependencies. The level of discrepancy in the worst case does not exceed 18.2% for service times distributions with a coefficient of variation not greater than 1.

Title: Real-Time-Shift: Pseudo-Real-Time Event Scheduling for the Split-Protocol-Stack Radio-in-the-Loop Emulation Abstract: ABSTRACTThe incorporation of real radio hardware and physical emulated radio links into higher layer network and protocol simulation studies has been a largely untouched area of research so far. The Split-Protocol-Stack Radio-in-the-Loop emulation combines pure discrete-event protocol simulation with a hardware-based radio link emulation. Since the basic techniques involve contrary time concepts, event communication between the two domains requires a rethink of scheduling and synchronization. With the Real-Time-Shift conservative synchronization and time compensation scheme, the simulator is decoupled from real-time constraints and limitations by introducing predetermined pause times for event execution. In this paper, we present the core synchronization and event scheduling approach allowing for scalable pseudo-real-time simulations with radio hardware in the loop. This enables discrete-event simulations for wireless host systems and networks with link-level emulation accuracy, accompanied by an overall high modeling flexibility.

Title: Energy-Constrained D2D Assisted Federated Learning in Edge Computing Abstract: ABSTRACTThe surging of deep learning brings new vigor and vitality to shape the prospect of intelligent Internet of Things (IoT), and edge intelligence arises to provision real-time deep neural network (DNN) inference services for mobile users. To perform efficient and effective DNN model training in edge environments while preserving training data security and privacy of IoT devices, federated learning has been envisioned as an ideal learning paradigm for this purpose. In this paper we study energy-aware DNN model training in an edge environment. We first formulate a novel energy-aware, device-to-device (D2D) assisted federated learning problem with the aim to minimize the global loss of a training DNN model, subject to bandwidth capacity on an edge server and the energy capacity on each IoT device. We then devise an efficient heuristic algorithm for the problem. The crux of the proposed algorithm is to explore the energy usage of neighboring devices of each device for its local model uploading, by reducing the problem to a series of maximum weight matching problems in corresponding auxiliary graphs. We finally evaluate the performance of the proposed algorithm through experimental simulations. Experimental results show that the proposed algorithm is promising.

Title: Multi-step Prediction of Worker Resource Usage at the Extreme Edge Abstract: ABSTRACTDemocratizing the edge by leveraging the prolific yet underutilized computational resources of end devices, referred to as Extreme Edge Devices (EEDs), can open a new edge computing tech market that is people-owned, democratically managed, and accessible/lucrative to all. Parallel computing at EEDs can also move the computing service much closer to end-users, which can help satisfy the stringent Quality-of-Service (QoS) requirements of delay-critical and/or data-intensive IoT applications. However, EEDs are heterogeneous user-owned devices, and are thus subject to a highly dynamic user access behavior (i.e., dynamic resource usage). This makes the process of determining the computational capability of EEDs increasingly challenging. Estimating the dynamic resource usage of EEDs (i.e., workers) has been mostly overlooked. The complexity of Machine Learning (ML)-based models renders them impractical for deployment at the edge for the purpose of such estimations. In this paper, we propose the Resource Usage Multi-step Prediction (RUMP) scheme to estimate the dynamic resource usage of workers over multiple steps ahead in a computationally efficient way while providing a relatively high prediction accuracy. Towards that end, RUMP exploits the use of the Hierarchical Dirichlet Process-Hidden Semi-Markov Model (HDP-HSMM) to estimate the dynamic resource usage of workers in EED-based computing paradigms. Extensive evaluations on a real testbed of heterogeneous workers for multi-step sizes show an 87.5% prediction accuracy for the starting point of 2-steps and coming to as little as a 16% average difference in prediction error compared to a representative of state-of-the-art ML-based schemes.

Title: Constructing Online Spaces Amid a Pandemic: Advocating for Students Through User-Centered Design Abstract: ABSTRACTWith an increase in online learning environments, especially after the onset of the COVID-19 pandemic, this proposed research paper focuses on the need for student-centered design in online spaces as a form of advocacy. The four authors explore the following question that revolves around online spaces: What are the practices through which user-centered design, especially participatory design through cultural, feminist, enviro-materialist, and disability lenses, might be implemented in online, higher education, or other adult-learning courses? This research article addresses the question of how we as educators, students, and technical communicators can advocate for students and their environments while amid a global pandemic.

Title: Mapping Storytelling on Etsy.com as Hyper-differentiation Strategy, and Considerations of Scope Abstract: ABSTRACTThis paper presents findings from a qualitative and quantitative analysis of 100 Etsy shop descriptions. Etsy.com is situated as a site of sellers documenting technical expertise and also marketing handmade goods through storytelling and narratives. Shop “About” sections were analyzed for their narrative structure in an attempt to map common narrative features on the site, as narrative and storytelling function as a hyper-differentiation strategy in post-industrial markets where customization and bespoke products are commonplace. The study found that “About” sections consistently followed the narrative structures suggested by the Etsy seller handbook. It suggests that technical communication researchers must be particularly aware of the scope of analysis, sampling methods, and rhetorical complexity of variables when analyzing hyper-differentiation strategies in order for relatively subtle differences to be identifiable.

Title: A Latency-levelling Load Balancing Algorithm for Fog and Edge Computing Abstract: ABSTRACTWhen deploying a distributed application in the Fog or Edge computing environments, the average service latency among all the involved nodes can be an indicator of how much a node is loaded with respect to the other. Indeed, only considering the average CPU time, or the RAM utilisation, for example, does not give a clear depiction of the load situation because these parameters are application- and hardware-agnostic. They do not give any information about how the application is performing from the user perspective and they cannot be used for a QoS-oriented load balancing of the system. Moreover, due to the displacement of the nodes and the heterogeneity of the computing devices the necessity of a load balancing algorithm is clear. In this paper, we propose a load balancing approach that is focused on the service latency with the objective to level it across all the nodes in a fully decentralized manner, in this way no user will experience a worse QoS than the other. By providing a differential model of the system and an adaptive heuristic to find the solution to the problem, we show both in simulation and in a real-world deployment that our approach is able to level the service latency among a set of heterogeneous nodes organized in different topologies.

Title: The Interplay Between Intelligent Networks and Enabling Technologies for Future Wireless Networks Abstract: ABSTRACTCognitive radio (CR) technology can leverage intelligence enabled by the integration of machine learning (ML) to successfully deliver pervasive connectivity for next-generation wireless networks. In this, a comprehensive overview of the uses of intelligent cognitive radio in a wide range of existing and emerging wireless technologies, including energy harvesting, physical-layer security, Internet of Things (IoT), mobile communications (vehicular and railway), and unmanned aerial vehicle (UAV) communications, will be presented. The interplay between intelligent CR and current and future technologies will be discussed. Emphasis will be on how the aforementioned techniques can benefit from intelligent CR and vice versa. For each technology, the key motivation for using intelligent networks will be highlighted CR with existing state-of-the-art ML approaches. The problems and prospective research avenues, and a futuristic road map exploring different possibilities for overcoming challenges through trending concepts will also be discussed.

Title: Wikipedia Editing as Connective Intelligence: Analyzing the Vandal Fighter Role in the “2022 Russian Invasion of Ukraine” Wikipedia Article Abstract: ABSTRACTThe recent invasion of Ukraine by Russia has seen the need for immediate communication—one that brings with it new civic considerations for structuring social media platforms. On February 23, the “2022 Russian invasion of Ukraine” Wikipedia page was created as its own article, formerly part of the “2021-2022 Russo-Ukrainian crisis.” Starting with seven sections and 112 references, the page expanded 14 sections and nearly 600 references in the span of two weeks. With this emerging situation, previously codified editing roles, such as that of the vandal fighter, adapted to meet the demands of a rapidly evolving situation with fast-paced contributions from a globally distributed network of Wikipedia editors. In this paper, we argue that the vandal fighter role evoked self-expression, or individual personal decisions, expertise, and actions, as well as underlying norms of Wikipedia to coordinate action during this event. Further, we propose that this coordination is a form of connective intelligence, whereby editors connect with others toward a common goal and selectively share self-expressions when salient to the actions of the group.

Title: Technical Communication and Geoengineering Standards: The Case of ISO 14082 Abstract: ABSTRACTA leaked in-progress proposed standard, ISO 14082, details reporting and measuring procedures for radiative forcing (RF), also known as geoengineering. While ISO denies that the standard should be applied to geoengineering approaches, the material detailed in the standard encourages the private development of either solar radiation management or carbon dioxide removal technologies. Understood from a framework of technical communication and geosocial theory, several concerns surface from ISO 14082. The proposed standard advocates for transparency, though does not address all locations for collusion. Likewise, the standard assumes that governments will define risk, though does not acknowledge that RF risks remain unclear. Further, the ISO 14082 intends to promote RF within a carbon credit marketplace, yet misses the opportunity to acknowledge broader climate justice goals and include a diverse array of stakeholders into RF projects. Technical communication scholars, while well-versed in the critical analysis of technical standards, could further adopt a geosocial critique for additional insight into environmental standards development.

Title: Learners as Users: Understanding Learner Experiences With Privacy Policy Pages: Understanding learner experiences with privacy policy pages Abstract: ABSTRACTLearners often find themselves in precarious roles when interacting with texts that communicate privacy policies. This paper argues for more and expanded user research approaches focused on the unique needs and situations of learners, particularly for better understanding of experiences of privacy and surveillance when using educational technologies. Drawing on a humanistic approach to technical communication, we report from a focus group exploring learner experiences with privacy policies. Learners discussed constructs that affected their perceived agency in interacting with policies, as well as practices they used to navigate precarious privacy situations and design strategies that benefitted them. Building off these results, the paper ends with takeaways for learning and communication designers.

Title: Climate Change Games as Boundary Objects: Moving Toward Dialogic Communication in Stakeholder Engagement Abstract: ABSTRACTIn order to enhance resilience in coastal communities, stakeholder engagement events are often used to invite dialogue while encouraging cross-jurisdictional collaboration and comprehensive problem-solving among practitioners (such as floodplain managers and city planners). Inviting such dialogue and collaboration can be challenging, and calls for more inclusive communication models exist in a number of fields, inspiring practitioners in the resilience community to look to climate change (CC) games as a solution. I pose the following research question: in what ways do CC games operate as boundary objects? To answer this question, I observed two game-based resilience-related stakeholder engagement workshops in coastal Virginia: one featuring the Multi-hazard Tournament (MHT) and the other, the Game of Floods. I conducted semi-structured observational field notes and survey research, including interview and questionnaire. Findings suggest that CC games are complex and idiosyncratic; while no one disciplinary tradition can adequately explain their work, the notion of boundary objects can. I merge multidisciplinary scholarship with data from survey research to generate a rhetorical boundary work heuristic that articulates the goals of these games. The rhetorical boundary work heuristic I offer will help practitioners, as well as those researching environmental and risk communication, assess existing games for their ability to fulfill their goals for their intended audience, within their unique contexts, while attending to important strategies in game design. It can be used as a rubric for determining whether a given game operates according to these standards, or it can drive the development of questions for post-test questionnaires if practitioners are assessing the efficacy of their games in use. It will also help in further development of current or future games for stakeholder engagement, as designers can use these features as standards to work toward.

Title: Toward an Access-Oriented Field: Reciprocity as a Guiding Principle for Capacity-Building in Technical Communication Abstract: ABSTRACTTechnical communicators are uniquely positioned to promote accessibility as teachers, researchers, administrators, and practitioners. Participatory design is often positioned as the exemplary advocacy practice on ethical and epistemic grounds. We argue that the participatory/non-participatory dichotomy is enriched by an understanding of how a variety of practices work together as parts of a career-long learning process. For that purpose, we propose the principle of reciprocity as a useful addition to TC's disability and accessibility lexicon. We present a three-part framework for evaluating different kinds of access-oriented practices.

Title: What Content Strategists Do and Earn: Findings from an Exploratory Survey of Content Strategy Professionals Abstract: ABSTRACTIn this paper we analyze the results of a survey completed by more than 250 content strategy practitioners who work in various industries across the globe. We find that a bachelor's degree in a language- or communication-related discipline, among others, adequately prepares most students for well-paying careers in content strategy. The scope of the work performed by content strategists poses curricular challenges for institutions that offer programs in content strategy or technical and professional communication.

Title: Experts, Knowledge Making, and Disaster Across Twitter Abstract: ABSTRACTDuring times of disaster, experts can use social media platforms to locate data, validate information, and share community knowledge. Situating our research within a current global crisis, the Russia-Ukraine War, we examine the symbolic-analytic work of two subject matter experts. Findings focus on the ways in which experts can support each other as knowledge workers, share information with their transnational communities, and distribute knowledge, information, and advocate for national and international audiences. Recommendations are made with regards to how platforms can support this work and future areas of research.

Title: Diversifying Knowledge: Presenting and Applying a Framework for Inclusive Graduate Program Websites Abstract: ABSTRACTThis paper reports our experience improving the inclusivity of Utah State University's (USU) Technical Communication and Rhetoric (TCR) graduate program's online materials. We report outcomes and implications of a 20-hour project in which Stevens applies aspects of an analytical framework developed by Alexander. In addition to the nine-tactic framework introduced in this report, readers may find value in four takeaways we share, based on our experience applying the framework.

Title: Stories from the Circle: Extended Reality (XR), Posthumanism, and Decolonizing the Design of Communication Abstract: ABSTRACTThis experience report describes our work to design and develop an Extended Reality (XR) application and supporting materials which allow people to experience, in situ, the spoken narratives of a diverse group of individuals affected by the ongoing efforts to remove the large and storied public monument to Christopher Columbus at Heritage Park in Syracuse, New York. Building on recent studies in posthumanist and intercultural rhetorics, this report describes how XR technologies and intercultural research methods can help to bring about the decolonization of the design of communication.

Title: Coalitional Curricular Design: Industry-Academic Co-creation of Technical Communication Modules Abstract: ABSTRACTThis experience report presents an example of an industry-academic partnership between seventy-five industry partners and transdisciplinary faculty at a large polytechnic university. We examine how program stakeholders determined instructional needs, designed one-credit technical communication modules responsive to those needs, and collaborated with industry partners, faculty from multiple disciplines, and program administrators to embed these modules within the program's studio and capstone coursework. The experience report closes with implications for developing new coursework in technical communication.

Title: Social Justice and the Technical and Professional Communication Internship Abstract: ABSTRACTThis experience report argues that Technical and Professional Communication internships are strong sites for students to engage in social justice-oriented work. Yet, we must address embedded inequities that exist within internship programs themselves. This report details an Internship Fellowship Program that offers both financial and social support for TPC students pursuing such internships. Successes and challenges are discussed, as well as recommendations for implementing similar initiatives at other institutions.

Title: Data as “Brickolage”: Teaching Material Data Visualization Design with LEGO Abstract: ABSTRACTData visualization is a reliable tool for professional communication practitioners for synthesizing and presenting data to a variety of audiences and attention should be given to preparing and teaching our students how to design effective visualizations. In this experience report, the author presents finding from teaching a data visualization activity using LEGO blocks and Indigenous maker practices. Students can then transfer such skills to making and critiquing their own data visualizations following this foray into material practices involving manipulating and playing with data through the activity. The use of hands-on making, alongside the study of good data visualization design, helps to instill maker practices of community and creativity in students as evidenced in the full visualization projects produced by students.

Title: Supporting Community Resilience to Environmental Hazards through User-Centered Design Abstract: ABSTRACTThis experience report discusses how researchers supported resilience to environmental hazards by leveraging user-centered design (UCD) processes to influence the development of the “HazardAware” website. This project serves as an example of how applied communication design can yield social impact as this paper details the risk communication work for multiple phases of website development. We provide examples of how UCD work helped guide development of environmental hazard information that relates to housing to people living within the project's geographical area. In addition, we describe how this information supports users’ ability to build resilience to these risks at both individual and community levels. We conclude by summarizing how UCD helped inform connections between technical communicators and the project team, as well as how we addressed the complexities of environmental justice through research and community outreach.

Title: Utilizing technical communication research and design strategies for community building in an online Master's program Abstract: ABSTRACTThis paper describes an intradepartmental collaboration between one technical communication and two English education faculty to improve an online Master's in English Education program, with a particular emphasis on improving the student experience of community building. We set out to develop improvements that would give our remote students better access to their learning community to better serve our state's rural schools. In doing so, we applied Simmons and Zoetewey's (2012) concept of “productive usability” to the program re-design process to improve the program's community building capability and to increase the accessibility of the program to more rural English teachers interested in an advanced degree. The results help us communicate with our institution's administrators what adjustments to the bureaucratic processes need to be made to better serve our rural communities and thus better fulfill our land-grant mission.

Title: Zines as Empowerment Abstract: ABSTRACTZines are typically self-published magazines designed for small circulation and often DIY'd (Do It Yourself'd). They are often linked to empowerment and personal expression, and that potential for voice-finding can be powerful when explored in the classroom setting. This experience report reflects on a “zine-ing” experience from both an instructor's and student's viewpoint. Our hope is that other technical communication teachers might consider zines and zine-ing as a way to introduce students to self-publishing, design, and radicalized expression. As zines can be as simple or complex as the designer wants, and can take anything as their general subject while offering a clearly-defined deliverable, they work well as a central component for any number of projects.

Title: Medium-Weight Euro Crunch: Technical Communication in the Hobbyist Board Game Distilled Abstract: ABSTRACTThis paper details how design thinking and user experience testing were used to produce documentation for a crowd-funded hobbyist board game. It describes the challenges and changing trends in technical communication for board game rulebooks, discusses the particular case and context for the board game Distilled, poses four research questions the design team faced during documentation, identifies the usability testing methods employed to playtest a new player guide, presents the results of the testing, and remarks on the pedagogical value the project had for technical and professional communication undergraduate coursework.

Title: Teaching Design Systems: Towards a flexible and scalable model for the UX classroom Abstract: ABSTRACTOne of the most important emerging trends in the field of user experience (UX) is the creation and use of design systems, which are a collection of documented elements that embody an organization's design rules and principles. While design systems are becoming ubiquitous among organizations, especially those with mature design practices, few academic programs teach students how to use or create them. In this experience report, we share details on how we incorporated design systems into assignments and courses in three different academic programs. In this experience report, we provide a definition of design systems and introduce a scalable and flexible model for teaching them. We reflect on our motivations, insights, and lessons learned from implementing this model.

Title: Channeling Experience: Reflections on Developing a Technical Communication YouTube Channel Abstract: ABSTRACTThis experience report shares reflections and observations from launching a YouTube channel dedicated to broadcasting research in technical and professional communication to a wide public audience. Authors note the importance and potential for such work to amplify scholars from marginalized communities, to publicly showcase the value of TPC scholarship, and to reach new non-academic audiences with TPC research. The authors also note the challenges of such work, describing the difficulty of developing a content strategy for public academic work on YouTube beyond the role already fulfilled by podcasts.

Title: Miro, Miro: Student perceptions of a visual discussion board Abstract: ABSTRACTThe COVID-19 pandemic forced those in education and industry to rethink how collaboration can take place remotely. This experience report describes the way the author reimagined the typical online discussion using a visual discussion board, Miro, in an asynchronous online graduate course, Media Aesthetics, in Fall of 2020. This paper includes student perceptions of benefits and limitations from the use of this platform along with lessons learned and notes for improving the use of this and other digital tools in future courses. Overall, most students reported enjoying the change of pace from the standard learning management system's discussion board and enjoyed the ability to share images, videos, and links within the platform. Students also indicated that they found Miro to be easy to learn and conducive to collaboration and discussion. Perceived limitations included lack of structure, difficulty locating specific posts at times, and a feeling of isolation from peers due to the asynchronous course structure.

Title: Cultivating Ethics in the Peer Review Process Abstract: ABSTRACTAfter describing the content and implementation of an anti-racist scholarly review training informed by recent scholarship in technical communication (TC), the authors reflect on an unanticipated outcome of that training: a participant using language from the training in an attempt to silence an author they were reviewing. We analyze this experience through a framework of modern virtue ethics scholarship and explore ways to cultivate more ethical peer review practices. Drawing upon elements of ethical self-cultivation articulated by Vallor, we use concepts of moral habituation, relational understanding, and reflective self-examination to understand how to cultivate more ethical, reflexive peer review processes.

Title: Translating the Knowledge Gap Between Researchers and Communication Designers for Improved mHealth Research Abstract: ABSTRACTOur industry insight focuses on the challenges for health researchers collaborating with communication designers during the development of an App for improving maternal mental health and parenting stress. We discuss the challenges around explicating and communicating tacit and domain knowledge across disciplinary boundaries. We believe this report can widen communication design's traditional focus on users in mHealth research to consider partnerships with academic researchers. The lessons learned from our experience developing a mHealth program can be used to reduce challenges in future mHealth research, especially for collaborations between health researchers and communications designers. Considering the growth of interest in mHealth, this is extremely relevant for future team satisfaction, the optimal use of research funds and industry time, and faster development of effective mHealth tools.

Title: Maintaining Momentum in your Technical Communication Career During a Leave of Absence Abstract: ABSTRACTCurrently, there is a lack of guidance for how individuals in the field of technical communication can confidently take a leave of absence without concern for how time away can impact career progression. Leaves of absence are on the rise in the wake of the COVID-19 global pandemic as employees struggle to find work-life balance and provide care for young children and aging parents. During the COVID-19 global pandemic, I gave birth to my son and took a 20-week parental leave of absence from my company. During this time, I continued to professionally develop while also taking time to recover and care for my family. I gained valuable insight into how to maintain the momentum of my career development and want to share the lessons I learned. This Industry Insight Report provides recommendations for planning for a leave of absence, setting goals and boundaries for the time away from work, maintaining career momentum while on leave, gracefully returning to work, and reassessing your work upon your return to ensure that your daily career responsibilities align with both your passion and team goals. This period of discernment allows you to reflect on what work is most important to you and what work helps you to evolve as a technical communicator. With reflection, reassessment, and planned enrichment, you can grow your career in a meaningful way even while you are away from your keyboard.

Title: Developing Workplace-Ready Engineers: New Engineers' Experiences Learning Workplace Communication Competencies Abstract: ABSTRACTThe transition from student to engineer requires individuals to adapt to new workplace communication practices. Using interview data from a focus group of early-career engineers, we examine entry-level employees’ narrative experiences with workplace communication to describe what new engineers know about effective communication in their workplace and how they come to know it. These engineers define effective communication as a reflection of personal identity and as a way to mitigate company liabilities. They cite observational experiences on-site as moments of instruction about how to communicate effectively at work.

Title: Cool and Useful: Tabletop Roleplaying Games as Aesthetic Technical Communication Abstract: ABSTRACTTabletop roleplaying games (TTRPGs) blend aesthetic and mechanical information to tell users not only how to play the game but also what kind of an experience they will have while playing the game. Mörk Borg, a recent TTRPG, has a design that brings such often-ignored technical information to the forefront. This invites us as document designers and those who teach document design to consider the ways that our technical design can be both cool and useful. At the same time, Mörk Borg has some accessibility issues that must be considered when making cool and useful designs.

Title: A Content Analysis of Existing mHealth Applications for Potential Older Adult Use Abstract: ABSTRACTThis poster presents initial findings from a content analysis of ten existing mHealth applications from the Apple App Store to determine the usefulness of features for potential older adult users. The study utilizes a qualitative approach to describe features from each selected mobile application, then a proposed framework is suggested for rating based on existing literature on designing for older adults. The intent of this study is to provide designers with a tool to support universal design principles when designing digital products for older users.

Title: Reassessing content design guidelines for visual accessibility, usability, and storytelling: a case study of historical photography archives: Extended Abstract Abstract: ABSTRACTThis research explores how to better support the accessibility of content design without sacrificing informational value presented through visual storytelling. In revisiting Web Content Accessibility Guidelines (WCAG) 2.2 and looking ahead to version 3.0, we reassess current guidelines toward more accessible content design of multimedia information as part of an ongoing reevaluation of inclusionary practice in communication design.

Title: COVID-19 Infographics: A medium for more “inclusive” communication (?) Abstract: ABSTRACTInfographics have been an important genre for communicating important health and risk information during the COVID-19 pandemic. Infographics are great tools for communication because they not only disseminate information but do so in a visually engaging format. This poster covers the value of infographics as a genre of technical communication, specifically during the COVID-19 pandemic, and provides certain considerations that should be kept in mind as communication designers approach the concept of inclusivity.

Title: Convergent Design: Assessing B2C Video Content in Response to the COVID-19 Pandemic Abstract: ABSTRACTThis study reviews 528 B2C social media videos published by The Home Depot and Lowe's Home Improvement to Facebook and Instagram, posted between March 2020 and December 2021. We coded the videos based on primary purpose: brand awareness, presale marketing, convergent, and instructional. Convergent videos integrate both presale and instructional elements in ways that market a product and provide procedural information about using it. Convergence has recently been highlighted in industry publications. This study considers its use during the pandemic. This initial study considers how big brands have merged genre conventions to address users on social media platforms and looks ahead to future research based on apparent trends.

Title: Accessible Communication App for Teaching Technical Writing: Teaching Technical Writing with Accessibly Designed App for Hybrid and Hyflex Classrooms Abstract: ABSTRACTThis project addresses the need for accessible classroom learning technologies that replicate the tools students have used in online learning during the COVID-19 pandemic. My research offers a concept for a communication app designed for use in in-person, hybrid, and hyflex classrooms. This app focuses on user experience (UX) research and universal design for learning (UDL) and is grounded in disability studies and principles of accessibility in higher education.

Title: Including Citizen's Voices in Economic Growth: An Exploration into the Rhetoric of Socio-economic Change in Ukraine Abstract: ABSTRACTTechnical and professional communicators can effect social change through the rhetoric used in their documents and involving the document's end-users. A qualitative study on the E-40 Inland Waterway project in Ukraine (conducted before the current war) exemplifies the need for end-user-focused rhetoric and a human-centered design process when engaging in socio-economic change.

Title: How to Become a Disability-Inclusive Communication Designer: Knowledge and Practices for Professional Development Abstract: ABSTRACTThis study draws on an analysis of a range of texts to trace the learning ecologies which support disability-inclusive teaching, research, administration, and industry practice. As a means to increase our disciplinary and institutional capacity to foster inclusion, I seek to understand the processes through which disability-inclusive designers, environments, and institutions come to be.

Title: Argumentation Mining: Design Thinking from A Feminist Perspective Abstract: ABSTRACTThis paper examines distribution and use of arguments related to women in the IBM Debater Claims and Evidence database to showcase an importance of feminist thinking in designing technology like Wearable Reasoner (WR)—a proof-of-concept wearable system for augmenting human reasoning— which used data from said source. Design thinking from a feminist perspective has an empirical and a pedagogical value as it allows designers to think critically regarding whose voice, agency and positionality are represented while designing certain technology.

Title: Designing for Cultural Inclusivity: A Study of International Patrons’ User Experience with University Library Services Abstract: ABSTRACTThis paper presents the results of a pilot study on international patrons’ experience interacting with a U.S. university library website. It identifies the unique needs, pain points, and emotional and cognitive state of international students as they use the online library services, and makes recommendations on designing usable and culturally-inclusive experiences for global users.

Title: Surveying Technical Communicators in the Video Games Industry: Understanding the Backgrounds, Skills, Content, and Roles of TCs in the Video Games Industry Abstract: ABSTRACTAs a technical communicator working at Epic Games, I conducted a workplace study surveying technical communicators and what I dub “learning-based content developers” to answer the following research question: How do the background, skills, and content that TCs engage with affect their perceptions of their role and the work they do? The findings of this small survey can help technical communication professionals and educators understand the kinds of work TCs are doing in this industry, where they are in their careers, and what their focus is in their work. Overall, TCs employ and develop similar skills and content to TCs in other industries, are passionate about what they do and the products they work on, and value sharing that knowledge to support others.

Title: Intersectional Feminism as a Theoretical Framework for Navigating Bias in the Technical Communication Workplace Abstract: ABSTRACTThis research project describes how I performed a targeted literature review of intersectional feminism research to develop a theoretical framework that technical and professional communication (TPC) scholars and practitioners can use to mitigate workplace bias. As a result, this study seeks to extend the application of feminist theory and expand TPC discussions centered on ethical implications and inclusive communication design.

Title: Performance Evaluation of Edge Computing Models for Internet of Things Abstract: ABSTRACTMulti-access edge computing (MEC) enables the deployment of computation, communication, and storage resources at the network edge, in the proximity where they are demanded. This innovative computing paradigm is essential to enable computation-intensive mobile and Internet of things (IoT) applications (e.g., mobile augmented reality), where demanding tasks are offloaded to be processed at edge servers. Many studies designed task offloading algorithms considering distinct scenarios of mobile and resource-constrained devices, networking communication infrastructure, and edge servers capabilities. However, the frequently considered different offloading and networking models pose unique challenges, in terms of latency and energy consumption, for the application. In this paper, we evaluate the computation and communication models popularly considered in MEC-aided applications. We study and discuss how the offloading ratio and traffic load impacts the energy cost and delay for different edge computing models, and provide insight and guidelines for the further design of task offloading algorithms for MEC-aided IoT applications. Extensive numerical evaluations show the trade-off between task generation and task offloading rate in terms of low latency and low energy consumption.

Title: End-to-end Multipath Optimization for Reliable Aerial Connectivity Abstract: ABSTRACTNext-generation Aerial Vehicles (AVs) demand multipath Air-to-Ground (A2G) connectivity to maintain reliable communications for safety-critical services. While utilizing redundant links improves the achievable Quality of Service (QoS), the link usage must be optimized to minimize the overall cost of connectivity. In this paper, we provide an optimization framework that minimizes the communication cost while ensuring to meet the QoS demands of the aerial application. Given the available access links and their QoS capabilities, the model selects a set of links that can jointly meet the required flow capacity, reliability and latency constraints with redundant transmission. Studying the Remote Piloting (RP) use case, we realistically implement the scenario in a flight and channel simulator, and feed the optimizer with the measured link quality from the simulation. As the optimization model is non-convex, we also linearize it, and benchmark them against conventional heuristic and brute-force algorithms. The results show that while the linearized model is favorable for online optimization scenarios due to fast computation times, the nonlinear model with Taylor approximations achieves more accurate latency behavior, and can be suitable for latency-critical applications in offline scenarios. Although dual cellular connectivity can suffice the QoS demands most of the time, excessive number of link switches imply a demand to orchestrate the access networks in a unified way to minimize connectivity disruptions.

Title: Analyzing the UAVs Traffic Flow to Enhance the Drone's Anonymization on the Internet of Drones Abstract: ABSTRACTThe Internet of Drones (IoD) is a novel mobile network paradigm with particular aspects, demanding security and privacy requirements. Mix Zones (MZ) is a location privacy protection mechanism in traditional mobile networks focusing on anonymizing a node's identity. In this regard, Mix Zone Placement (MZP) is a current challenge related to the design of MZ in IoD. As occur in terrestrial mobile environments, analyzing the vehicles' traffic flow can lead to enhanced strategies to solve MZP. This study conducts the first technical analysis of the MZP in this paradigm. We investigate three MZP strategies: an empirical MZP, a strategy based on visitation frequency, and an Ant-Colony Optimization (ACO) method. Our results reveal a deep relation between the drone's traffic flow and the MZP. Moreover, the ACO method is suitable for deployment in a location privacy protection mechanism, overcoming other strategies regarding location privacy, diversity, and efficiency.

Title: How Accurate is LoRa Positioning in Realistic Conditions? Abstract: ABSTRACTThe LoRa communication technology has been developed for data transmission, but recent research papers showed that it could also be used to estimate the transmitting devices' location. The location can be found with reasonable accuracy, with median error as low as tens of meters; however, in many cases, it is evaluated in very optimistic transmission conditions, e.g. using line-of-sight communication and on a small data set. We estimate how LoRa localization performs in a realistic scenario, using an extensive data set of a telemetric network of a few thousand devices to calculate the location using the trilateration and estimate the error of the calculation. Within the analysed dataset the true-range multilateration provides highly inaccurate location estimation, with a median error of 588.64 m and an average error of 808.57 m. We argue that the LoRa localization in real-life conditions is ineffective, as the estimation of the distance between the node and the gateway changes heavily depending on the location of the node and radio channel attenuation.

Title: Enabling Edge Computing over LoRaWAN: A Device-Gateway Coordination Protocol Abstract: ABSTRACTThe freedom of the LoRaWAN license-free ad-hoc deployment model can significantly reduce the complexity of network deployment, however, it introduces certain problems that hinder network scalability and performance. First, Network Gateways simply forward frames to the centralised Network Server without the possibility of becoming Edge computing processing elements. Second, Network Gateways may have overlapping areas of network coverage (a) resulting in an increase of network traffic at the network backbone as frames are relayed to the Network Server multiple times, (b) may cause unexpected frame collisions and duty-cycle exhaustion. In this paper, a novel decentralised algorithm is presented that assigns each IoT Device to a single Network Gateway so that (a) duplicate message deliveries are completely avoided, (b) Network Gateways can become an intermediate operations layer between the IoT devices and the Network Server providing computational and storage resources. The proposed protocol is implemented in the OMNeT++ simulator and evaluated based on datasets collected from long-term real-world deployments. To improve the accuracy of the experiments in large-scale and dense urban deployments the OMNeT++ LoRa interference model is extended by considering the non-perfect orthogonality of the LoRa Spreading Factors and the channel adjacency. The performance evaluation highlights the benefits of the distributed approach proposed here and provides valuable indications on the overall performance of the network.

Title: Demo: A Combined Multipath Connectivity and Flight Simulation Framework Abstract: ABSTRACTFuture aerial vehicles require reliable communications with ground stations to enable, for example, safe operations in Urban Air Mobility (UAM). While multipath connectivity via multiple wireless links is regarded as a key concept to achieve reliability, it needs further research, for which we developed a simulation framework that combines multipath connectivity and flight characteristics. We demonstrate the capabilities of our framework by showing an UAM scenario meeting a high probability that a message is dispatched towards a ground station within a given time.

Title: Ultra-Reliable Computation Offloading for Aerial-Ground Cooperative Vehicular Networks with Joint Mobility Optimization and Transmission Scheduling Abstract: ABSTRACTThe reliability of computation offloading poses a challenge to aerial ground cooperative vehicular networks (AGCVNs) due to the highly dynamic and stochastic nature of ground-to-air (G2A) channels and channel interferences. In this paper, we develop a joint optimization model to maximize the reliability of computation offloading in unmanned aerial vehicle (UAV)-assisted and mobile edge computing (MEC)-enabled AGCVNs. Specifically, we jointly optimize the mobility of a UAV-mounted cloudlet and the data transmission scheduling of a ground vehicle that would like to offload computation tasks to the UAV under a data integrity constraint and a time horizon constraint. We theoretically characterize the distribution of the signal to interference plus noise ratio (SINR) of the G2A channel regarding stochastic interferences and derive the expected offloading capacity and an upper bound of the variance of the offloaded data volume in the channel. We also derive a lower bound of the offloading reliability that the vehicle can successfully offload all the task data to the UAV via the G2A channel within a given time period. The lower bound can provide a mathematically tractable formulation of the reliability optimization objective for the network, which leads to a joint mobility optimization and data transmission scheduling method. We validate the derived theoretical models via simulations. The simulation results also show that the proposed joint optimization method achieves the offloading reliability of 99.4826% on average, outperforming the conventional computation offloading scheme by enabling the adaptive response of computation offloading to the network mobility.

Title: Implementation of a Decentralized Traffic Congestion Avoidance Mechanism for VANETs Abstract: ABSTRACTSimulation remains the preferred option for testing new approaches in relation to Vehicular Ad-hoc Network (VANET) research. This is no different when it comes to road traffic congestion avoidance mechanisms that utilize VANETs. There are several examples of VANET-based approaches to road traffic congestion avoidance that are tested using simulations in the literature. Notwithstanding, the replicability and extensibility of many approaches are limited due to the fact that their implementations are not publicly available. In this work, based on prior work, a decentralized mechanism is designed and implemented to perform road traffic congestion avoidance in VANETs. Evaluation results show that due to vehicles having longer trip times, vehicular density in the road network can be up to 2.5 times higher in scenarios where the road traffic congestion avoidance mechanism is not in use. Unlike prior work, we make the implementation publicly-available to enhance its replicability and extensibility.

Title: Managing the Edge: Challenges, Open Issues, and Case Studies Abstract: ABSTRACTRecent technological trends such as Industry 4.0 introduced new challenges that push the limit of current computer and networking architectures. It demands the connection of thousands, if not millions, of sensors and mobile devices coupled with optimized operations to automate various operations inside factories. This led to the new era of Internet of Things (IoTs) and its many applications, such as, the Internet of Vehicles (IoV) where lightweight (mobile) devices are envisaged to send vital information to cloud data centers (mobile and fixed infrastructure) for further processing and decision making [1]. Current cloud computing systems, however, are not able to efficiently digest and process collected information from IoT devices with strict response requests for two main reasons: (1) the round-trip delay between IoT devices to the processing engines of cloud could exceed an application's threshold, and (2) network links to cloud resources could be clogged when IoT devices flush data in an uncoordinated fashion. Fog and Edge Computing are two solutions to address both previous problems. Though designed to alleviate the same problem, they have fundamental differences that make adopting one more applicable than the other. This talk will overview the practical concerns of exploiting Edge Computing to realize today's IoT implementations through tackling the most important obstacles that hinder their adoption. First, production of applicable network (fixed and mobile) latency models to capture all elements of IoT platforms. Second, building a holistic Edge ecosystem to orchestrate various inter-related layers of IoT platforms, including connectivity, big-data analytics, and workload optimization. Third, proposing viable solutions that can be implemented in IoT-based applications, such as, vehicular networks [2].

Title: Towards Cross-domain Mobility Management in the Edge: New Elements of Cloud-native Mobile IPv6 Full-scale Implementation Abstract: ABSTRACTControl and User Plane Separation (CUPS) allows placing the control plane separately from the actual user plane executor. This allows deploying user plane instances closer to the end-user: traffic serving can be more effortless, and end-to-end latency can be decreased. Mobile IPv6 can also be divided into control and user plane parts. This article will show what this separation presumably will look like in connection with Mobile IPv6. In a dynamic cloud environment, standard Mobile IPv6 must be part of the overall orchestration flow. This is where Cloud-native Mobile IPv6 comes into the picture. This paper presents how ordinary and cloud-native Mobile IPv6 architectures should be refactored with corresponding signaling flows to fit into the CUPS concept. We also show by measurements the latency budget we can gain using localized user-plane instances to connect to a distributed application.

Title: Comparative QoE Study of VoD and Passive Gaming Videos using Cross-Correlated QoS Metrics Abstract: ABSTRACTIn this paper, we compare the end-user Quality of Experience (QoE) of two of the most popular streaming applications: Video on Demand (VoD) applications and Passive Video Game (PVG) streaming applications. Previous studies have shown that in real-life traffic, the Quality of Service (QoS) metrics such as packet delay and packet loss ratio (PLR) have cross-correlation between them. Very few studies have been done on how the correlated QoS metrics affect the end user QoE. In previous work, we have shown that correlated QoS metrics map objective QoE differently than non-correlated QoS metrics. Following the findings, we carried out a subjective QoE analysis to observe if it shows a similar outcome as objective QoE. We used a bespoke version of the NetEm emulator, which allowed us to implement cross-correlated QoS inputs and carried out a subjective QoE survey using 42 human subjects for both VoD and PVG streaming applications. The result showed for low PLR conditions, QoE in both correlated and non-correlated PLR showed similar ratings. But as PLR increased, the average QoE of correlated QoS was considerably higher (sometimes about 50%) than its non-correlated counterparts. It was observed for both VoD and PVG applications. This was more pronounced in the case of PVG when the experience of the subject was taken into consideration. We also found that age and gender do not show significant differences in the subjective QoE. Finally, we compared the existing state-of-the-art objective quality assessment with the subjective QoE of PVG application. We found that QoE with correlated PLR showed a higher correlation coefficient with the existing objective QoE metrics than the non-correlated counterparts. This finding solidifies the importance of studying QoE using correlated QoS metrics.

Title: Model Driven Approach to Design an Automotive CPS with SysReo Language Abstract: ABSTRACTModeling Cyber-Physical Systems (CPS) remains a challenge due to the complex behaviors of their interconnected components that operate in a physical environment. In this paper, we introduce a new modeling approach based on "SysReo" models, that relies on SysML/MARTE, Reo, and OCL to capture the different aspects of CPS with time constraints, including requirements, behavior, architecture, and interaction protocols. The novelty of our approach relies in the combination of SysML and Reo to handle the complexity of CPS architecture and protocols, in the design step by proceeding incrementally. Then we used SysML/MARTE to model the behavior of CPS with timed constraints. Furthermore, we define OCL constraints to specify rules to be respected to model consistently CPS.

Title: Elderly Fall Detection: A Lightweight Kinect Based Deep Learning Approach Abstract: ABSTRACTFall detection is one of the main issues for the elder's health care systems because of its economic and social impact. Whereas the primary metric of such a system remains its accuracy in terms of good detection of falls and avoiding either false detection or missing detection, its deployment raises many issues in terms of the number of devices, their nature (scalar, multimedia, Lidar, etc.) and the technique used. Generally, techniques based on multimedia processing provide better results but at the expense of a high CPU processing and consequently need appropriate devices. This paper explores an approach that uses less-powerful affordable devices (i.e., Raspberry Pi like) with multimedia sensors (i.e., Kinect) and a Deep Learning-based processing mechanism. More precisely, we applied LSTM (Long Short-Term Memory) on features extracted from the time series data acquired from the Kinect. Experimental results we obtained from our lightweight LSTM model on the Raspberry pi show that geometric features are more relevant for fall event detection. Our model achieves advanced performance with metrics that are usually considered (accuracy, precision, sensitivity, and specificity). Furthermore, our lightweight model is very promising for deployment on machines considered "low-cost."

Title: Is the Remote ID a Threat to the Drone's Location Privacy on the Internet of Drones? Abstract: ABSTRACTThe Remote ID is a rule proposed by the Federal Aviation Administration (FAA) of the United States to provide a safer environment for drones. It consists of broadcasting standard messages containing sensitive drone information, for instance, its identity and location. However, this rule can be a threat to the drones' privacy since the information is broadcast as cleartext, and any device with a Remote ID module can store the data. In this study, we technically demonstrate that Remote ID is a risk to the drone's location privacy, mainly in the envisioned Internet of Drones (IoD) scenario. We design a location-based attack to track the drone's trajectory based on the Remote ID message. We highlight that the attack can correctly identify more than 90% of a drone's trajectory through extensive simulations. Furthermore, we discuss that existent IoD protection mechanisms meet the Remote ID specification and can be applied as an enhanced version of this protocol. Indeed, our results also reveal that these mechanisms can mitigate the attack's success, providing a proper level of location privacy.

Title: Deep Q-learning-enabled Deployment of Aerial Base Stations in the Presence of Mobile Users Abstract: ABSTRACTUncrewed aerial vehicle-mounted base stations (UAV-BS) have recently attracted significant attention in order to assist ground base stations (BSs) and provide Internet access to users. UAV-BSs benefit from their mobility nature in the air and are able to constantly move towards the locations where the demand is higher. However, finding the optimal location of UAV-BSs and maintaining it is an NP-hard problem that has no deterministic solution in polynomial time. In this paper, we exploit reinforcement learning (RL) in order to solve the optimization problem of UAV-BSs and find their optimal location in the presence of mobile User Equipment (UEs). We consider UAV-BS as the agent of RL and deploy two algorithms, i.e. Q-learning and deep Q-learning in order to solve the location optimization problem of UAV-BSs. Through simulations, we show that the proposed DQL model with a continuous state space including the mobility information of users can effectively adapt to the environmental changes and improve the user data rate by 46%, packet loss ratio by 70%, and transmission delay by 60%.