Miracle-dz/UniFAR-FTUs huggingface dataset:
Matching Theory Assisted Resource Allocation in Millimeter Wave Ultra Dense Small Cell Networks

This paper investigates the resource allocation in millimeter wave ultra dense networks, in which the beam assignment and sub-band allocation are jointly considered. Motivating to maximize the sum rate of the network conceived, the optimization problem is formulated as a mixed integer non-linear programing (MINLP) problem, which involves allocating the novel three-dimensional resource blocks (RBs) defined in beam (B), time (T), and frequency (F) dimension, respectively. To tackle the formulated MINLP problem, we propose the low-complexity resource allocation scheme, including the so-called best option first (BOF) beam assignment algorithm, and the many-to-one matching with externalities (M2O-ME) sub-band allocation algorithm. In particular, the BOF beam assignment algorithm is first carried out to coordinate the RBs in terms of T- and B-dimension. Then, with the aid of the mechanism of many-to-one with externalities, the M2O-ME sub-band algorithm is implemented to find the optimal sub-band allocation (i.e. RB allocation in F-dimension) solution. Finally, our simulation results show that the proposed resource allocation scheme can significantly outperform the existing schemes in terms of sum rate of the networks. Therefore, we can conclude that the proposed resource allocation scheme can be considered as a promising candidate for practical ultra dense small-cell networks with mmWave capability.
Achieving Practical OAM Based Wireless Communications with Misaligned Transceiver

Orbital angular momentum (OAM) has attracted much attention for radio vortex wireless communications due to the orthogonality among different OAM-modes. To maintain the orthogonality among different OAM modes at the receiver, the strict alignment between transmit and receive antennas is highly demanded. However, it is not practical to guarantee the transceiver alignment in wireless communications. The phase turbulence, resulting from the misaligned transceivers, leads to serious intermode interference among different OAM modes and therefore fail for signals detection of multiple OAM modes at the receiver. To achieve practical OAM based wireless communications, in this paper we investigate the radio vortex wireless communications with misaligned transmit and receive antennas. We propose a joint Beamforming and Pre-detection (BePre) scheme, which uses two unitary matrices to convert the channel matrix into the equivalent circulant matrix for keeping the orthogonality among OAM-modes at the receiver. Then, the OAM signals can be detected with the mode-decomposition scheme at the misaligned receiver. Extensive simulations obtained validate and evaluate that our developed joint BePre scheme can efficiently detect the signals of multiple OAM-modes for the misaligned transceiver and can significantly increase the spectrum efficiency.
Peer to Peer Packet Dispatching in DC Power Packetized Microgrids

The DC power packet transmission contributes to a reliable integration of distributed energy resources (DERs) in power grids and reduces the load fluctuation. In this paper, we consider a DC packetized-power microgrid for the integration of DERs and propose a power packet dispatching protocol to regulate the peer to peer power interchange within the microgrid. We formulate the joint subscriber matching and energy allocation problem to optimize the subscribers' benefits, which is proved to be NP-hard. Based on the matching theory, we associate the problem equivalent to a many-to-many matching problem and design a two-sided matching algorithm to solve it. We then design a graph coloring based algorithm to schedule the energy transmissions of the matched energy subscribers. Simulation results validate the effectiveness of the proposed protocol in achieving a steady and efficient microgrid power dispatching.
Simulating LoRaWAN: On Importance of Inter Spreading Factor Interference and Collision Effect

The paper introduces a LoRaWAN simulation model implemented for the Riverbed Modeler (former OPNET modeler suite). First, the key components of the developed simulator solution are detailed. Then, the results of the simulator's validation for several test scenarios are presented. The developed simulator is used to investigate the effect of collision models on the results of LoRaWAN performance simulation. Specifically, the three models are studied: a baseline – implying loss off all colliding packets, an intra spreading factor (SF) with capture effect, and intra/inter SF with capture effect. The simulations verify that the results of the baseline model are in line with that for pure Aloha, while the two other demonstrate up to two-three fold higher delivery ratio. The obtained results illustrate the substantial impact of the collision model on the accuracy of simulations and motivate the need for further practical studies for the collision and interference mechanisms within a LoRaWAN network. Based on the results obtained through simulations, several drawbacks related to the use of strictly periodic traffic in LoRaWAN networks are noted.
Network Controlled D2D Communications: Licensed or Unlicensed Spectrum?

In this paper, we consider a device-to-device (D2D) communications underlaying cellular network where Long Term Evolution (LTE) and D2D users are allowed to communicate over both licensed and unlicensed bands for spectrum efficiency improvement. LTE users utilize spectrum orthogonally and share it with D2D users. To maximize the total throughput of this D2D system, we leverage stochastic geometry to derive the throughput for each kind of users by modeling the deployment of users as Poisson point processes (PPPs), and investigate the mode selection problem for D2D users. Since the problem is NP-hard, we propose a sequential quadratic programming (SQP) based algorithm to obtain the corresponding suboptimal solutions. Theoretically, we evaluate the system performance by analyzing the throughput regions. Simulation results validate the accuracy of the geometric analysis and verify the effectiveness of the proposed algorithm.
Distributed BATS-Based Schemes for Uplink of Industrial Internet of Things

In Industrial Internet of Things (IIoTs), data generated during manufacturing are collected by sensors and need be processed timely. The direction of data transmissions from sensors to processing centers (fog nodes) is often called uplink transmission. In this paper, the cases with single and multiple distributed fog nodes, which are also referred to as centralized and distributed models, are studied. Two distributed schemes based on batched sparse (BATS) codes are proposed separately for the uplink of these two models. The expected rank and the recovery probability of the information from sensors at fog node(s) are derived. Comparison results show that by using the proposed BATS-based schemes, improved transmission reliability can be achieved compared to the XOR-based network coding (NC) scheme.
User Grouping for Sum-Rate Maximization in Multiuser Multibeam Satellite Communications

Aiming at maximizing the sum-rate of multiuser multibeam satellite communications, user grouping algorithms are studied. Different users are divided into several groups, where the users in the same group are simultaneously served by the satellite via space division multiple access (SDMA) and different groups of users are served in different time slots via time division multiple access (TDMA). A sum-rate maximization user grouping (SMUG) algorithm is proposed. Given the number of total time slots, the SMUG algorithm sequentially selects users one by one from the candidate users to maximize the current sum-rate and to guarantee the increase of sum-rate in each time slot. Simulation results verify the effectiveness of our work and show that the proposed SMUG algorithm outperforms the existing algorithms.
Controllable vs. Random: Renewable Generation Competition in a Local Energy Market

Renewable energy resources are playing an increasingly important role in serving consumers at the distribution level of power systems. This paper studies a duopoly two-settlement local renewable energy market, in which one energy supplier has controllable generations (with the help of energy storage) while the other supplier has random generations. In the day-ahead energy market, suppliers determine the bidding prices and quantities, and then consumers decide the energy quantity to purchase from each supplier. In the real-time energy market, a supplier gets penalized if he cannot deliver the amount of energy as committed in the day-ahead market. We formulate the interactions between suppliers and consumers in the day-ahead market as a two-stage problem. The two-dimensional bidding strategies (price and quantity) in the day-ahead market together with the penalty in the real-time market increase the complexity of the equilibrium analysis. To address such a challenge, we first derive weakly dominant bidding quantity strategies for both suppliers, and then characterize the corresponding pure and mixed price equilibrium. We demonstrate that the supplier with controllable generations can earn a much higher payoff than the supplier with random generations. In some cases, however, we show the perhaps counterintuitive result that a higher penalty or a higher variance of random generations may increase both suppliers' payoffs.
A Provably-Efficient Online Algorithm for Re-Utilizing Unused VM Resources for Edge Providers

In recent years, as a result of the rapidly growing volume of generated data, computation has been increasingly migrating from megascale data centers to Internet edges (a.k.a edge computing), for avoiding high latencies and overwhelmed bandwidths. Unlike centralized clouds, edge computing processes workloads generated by users nearby. Thus, due to the lack of statistical multiplexing from a large group of users, the resource demand at an edge data center exhibits more fluctuations, resulting in time-varying unused computation resources. In this paper, we propose our UNusEd spAred VM Re-uTilizing mecHanism, UNEARTH, to utilize different types of unused resources, such as storage, CPU, GPU, and so on, offered by an edge computing provider. Notably, the exact amount of unused VM resources is unknown before selling them. We evaluate the performance of our algorithms under realistic settings, showing that our proposed VM bundle allocation algorithm can achieve (1+ Ω/Ω −1 ε (e)M<sup>1/Ω−1</sup> −1)-approximation in the worst case compared with optimums; and overall, our algorithms outperform the existing and heuristic algorithms.
Impact of Multiple Action Potentials on Communication Properties of Plants

This paper focuses on different properties of an electrochemical signal based model for inter-cellular communication in plants. The input signal in this communication system can be either composed of (a) a number of fast moving charged molecules or (b) randomly diffusing molecules, which are driven by an action potential (AP) signal. APs are usually generated by an external stimulus such as change in temperature or light. We use a model of AP generation in this paper from previous works. The three main contributions of this paper are: firstly we consider the impact of multiple AP signals on the mutual information of the system for multiple (varying) numbers of receiver cells in different configurations, second, we compare the impact of different propagation mechanisms (i.e. fast active movement and random diffusion of molecules) on the output molecules and mutual information of the system, and finally we study the impact of multiple APs on the information propagation speed of the system and compare it for the different receiver cell configurations.
Linear Complexity Noncoherent Miller Sequence Detection for Batteryless RFID/IoT

Line coding in batteryless, RF energy harvesting RFID/IoT tags is critical, as it balances two conflicting operations: RF energy absorption (for batteryless powering) and RF energy reflection for backscattering (communication). This work studies Miller line coding, one of the two line codes utilized in commercial RFID, deriving rules for zero-centered or not Miller flat fading signal models. A novel, linear complexity in the sequence length algorithm is proposed for noncoherent Miller sequence detection that can operate within a quarter of a dB from the optimal coherent. Simulation results are corroborated by experimentation in a Gen2 RFID SDR-based testbed. This work provides evidence in favor of more lightweight protocols, free of preamble or pilot bits for future RFID/IoT tags, potentially offering a 10% reading speed gain over the contemporary Gen2 protocol or future short-packet IoT protocols.
Secrecy Throughput Maximization for Full-Duplex Wireless Powered Communication Networks

In this paper, we investigate the secrecy throughput for a full-duplex wireless powered communication network. A multi-antenna base station (BS) transmits energy towards nodes all the time and each node harvests energy prior to its transmission time slot. Nodes sequentially transmit their confidential information to the BS in presence of other nodes which are considered as potential eavesdroppers. We derive the secrecy rate and formulate the sum secrecy throughput optimization of all nodes. The optimization variables are the time slot duration and the BS beamforming during different transmission phases. The problem is non-convex and non-trivial. We propose a suboptimal approach in which the BS focuses its beamforming to blind the potential eavesdroppers (other nodes) during the information transmission phase, with which we then obtain the optimum beamforming in each time slot and its duration. We compare our algorithm with uniform time slot and uniform beamforming in different settings and demonstrate its superior performance.
WRT: Constructing Users' Web Request Trees from HTTP Header Logs

As web traffic has already dominated Internet, massive web logs are being generated ceaselessly. It is essential and meaningful for operators to mine valuable information and knowledge from the log data. However, the state-less feature of HTTP and increasing dynamics and complexities of web services bring a challenge to web mining in web logs. To solve the problem, in this paper, we introduce Web Request Tree (WRT) to reconstruct users' web request behaviors from web logs with HTTP header information, which can be applied in many fields. Compared to previous related work, our method pays special attentions to modern technologies to handle cases caused by these technologies such as PJAX. We evaluate the feasibility of our method with a measurement study on referrer policies of Alexa top websites and results show that our method can achieve a high accuracy for most websites. We also conduct experiments on a real-world dataset collected from official websites of a top university in China. We find that a lot of abnormal requests exist in log data by analyzing WRT and WRT is rich in information that is valuable for operators to analyze user behaviors, detect anomalies, optimize performance and so on.
DART: Detecting Unseen Malware Variants using Adaptation Regularization Transfer Learning

Network traffic analysis has been widely used for detecting malware at a large-scale network. Nevertheless, the emerging malware variants and zero-day exploits keep posing significant challenges to malware detection systems. In this paper, we propose DART, a framework for detecting malicious network traffic based on Adaptation Regularization Transfer Learning (ARTL), which effectively copes with the unseen malware variants problem. Specifically, DART trains the adaptive classifier by simultaneously optimizing three factors: (i) the structural risk functions; (ii) the joint distribution between the known malware and unseen malware variants domains; and (iii) the manifold consistency underlying marginal distribution. In addition, DART also works with encrypted network traffic since it does not leverage information related to the packet content. We assess the effectiveness and efficiency of our proposal with a thorough set of experiments. DART achieves over 90% F-measure and 91% recall, outperforming conventional traffic classification methods and other state-of-the-art intrusion detection systems.
Low-Complexity MRC for ESPAR Based on the Polynomial Expansion of the Admittance Matrix

The electronically steerable parasitic array radiator (ESPAR) has been acknowledged as an inexpensive multiple antenna architecture. Nonetheless, due to the non-linear behavior on the loads used in its control, its related computational complexity can be prohibitive for practical implementations. The current paper deals with such an issue by proposing an approximation of its model via a truncated polynomial expansion of the inverse impedance matrix. The proposed approximation allows to reframe the problem of reactance optimization for beamforming, referred to as analog maximum ratio combining (A-MRC) in previous work. The method proposed in this paper provides a means to obtain a computationally efficient solution to the problem of reactance optimization and it avoids the need of an iterative-based approach. Therefore, resulting in a major computational complexity reduction with respect to related work in the literature.
Learning Health State Transition Probabilities via Wireless Body Area Networks

We consider the use of a wireless body area network (WBAN) for remote health monitoring applications. A partially observable Markov decision process is used to describe the information flow and behavior of the WBAN. We then discuss a sensor activation policy, used for optimizing the tradeoff between power consumption and probability of patient health state misclassification. In order to determine the underlying health state transition probabilities, by which a patient's health state evolves, we develop a learning algorithm which uses the data collected from a group of patients, each being monitored by a WBAN. Finally, a numerical examination demonstrates the applicability of such a system, which applies the learning process and sensor activation policy simultaneously.
Online Learning for Context-Aware Multi-User Package Delivery System with Unmanned Vehicles

With the development of e-commerce and smart cities, utilizing unmanned vehicles to deliver packages has emerged as one of the most important methods to make customers receive packages efficiently and effectively. Hence, how to reasonably utilize multiple unmanned vehicles at the same time is a problem. Another main challenging issue is how to satisfy customers' personalized need. In this paper, we propose a novel context-aware multi-armed bandit-based online learning algorithm with active partition method for context space. To solve the massive injecting data flow problem, we utilize a tree-based structure expanding from top to bottom to choose different vehicles, which supports ever-increasing big metering datasets with historical and contextual information. We prove that our proposed context-aware online learning algorithm achieves sublinear regret performance. Experiment results show our proposal can enhance customers' satisfaction and reduce space cost tremendously.
Joint Optimization of Caching and Routing Strategies in Content Delivery Networks: A Big Data Case

Content delivery networks (CDNs) have been proposed to improve the performance of large-scale content delivery in communication networks, in which content files are dynamically cached in CDN nodes that are close to end-users so as to decrease the transmission latency and traffic redundancy. We investigate a real CDN that is currently utilized by a large social network company in China. We note that there are two important issues that are not well considered in the existing literature. The first is that end-users can usually access multiple CDN nodes with high quality of experience. The second is that the data service price may vary greatly for different regions, which can highly influence the cost of CDNs for large-scale applications. We reconsider the optimal caching and routing problem in CDNs by jointly considering these two practical issues, and propose a joint caching and routing strategy by using the alternating optimization technique. Simulation results show that the proposed algorithm outperforms the current CDN strategy, in which most popular files are cached and end-users are directed to the nearest CDN nodes, by 30% and 12% in terms of latency and data service cost, respectively.
QoS Guaranteed Load Balancing in Broadband Multi-Beam Satellite Networks

Aiming at the load balancing problem for uneven traffic demands among different beams in a broadband multi-beam satellite network (BMSN), we present a mathematical analysis and propose a load balancing algorithm to improve the efficiency of system resource allocation. In contrast to existing studies, our research focuses on the heterogeneous real-time services, including both VoIP and MPEG traffic flows. Subject to the delay constraint of real-time traffic, we introduce a QoS guaranteed resource scheduling framework for each beam. Based on this framework, we simulate the load imbalance scenario through the Poisson random point process. Then, we present joint adjacent beams and hopping beams load balancing algorithm to maintain the better match between the system resource and uneven traffic demands for BMSN over the satellite lifetime. The key point of our algorithm is intended to reduce the packet loss ratio (PLR) for each user and improve the BMSN's throughput. Simulation results show that the proposed load balancing algorithm can effectively reduce the average packet loss ratio of all beams by 71.25% and improve the average throughput of all beams by 13.97%.
Hunting for Invisible SmartCam: Characterizing and Detecting Smart Camera Based on Netflow Analysis

Nowadays, the rapid growth of cloud computing and IoT enabled services among multiple organizations brings both promising prospects and security & privacy challenges. IP cameras have become a top target for hackers because of their relatively high computing power and throughput. To understand the risks of these threats requires learning about IP cameras–where are they, how many are there? Active scanning is considered to be an effective way, like SHODAN. However, deployment of smart cameras in the network address translation (NAT) environments with dynamic locations is usually desired. To find these Invisible Cameras, CamHunter: (i) introduces three statements of smart cameras when they are online, (ii) concludes the most popular smart cameras in China have very similar communication patterns, (iii) proposes a model to detect smart cameras in a passive way constructed by nineteen feature sets, and (iv) raises alarms for IoT manufacturers. Our real-world experiments demonstrate the effectiveness of CamHunter in finding smart cameras even if they are behind NATs and using encrypted connections like SSL/TLS or private protocols. We argue that CamHunter represents an important view of IoT security and privacy, and it can guide the effort of designing and protecting smart cameras.