Miracle-dz/UniFAR-FTUs huggingface dataset:
Utilizing CSI and RSSI to Achieve High-Precision Outdoor Positioning: A Deep Learning Approach

Location-Based Service (LBS) has been widely deployed. One of the key components of LBS is the positioning algorithm. For outdoor environments, the Global Positioning System (GPS) has been used as the default positioning scheme. However, GPS requires the line of sight to the satellites. When the line of sight is blocked, GPS simply stops working. To tackle the problem with GPS, varied WiFi-based positioning schemes have been proposed. However, the positioning precision of the existing methods is not satisfactory. In this paper, we present a high-precision positioning scheme named Deep Learning based Positioning (DLP). Technically, DLP utilizes both Received Signal Strength Indicator (RSSI) and Channel State Information (CSI) to improve the positioning precision. In detail, a deep neural network is used to model the received RSSI and CSI measurements, which leads to satisfactory positioning accuracy. Our experimental results acquired from a large-scale testbed indicate that DLP outperforms the existing positioning schemes in terms of positioning precision.
Gaussian-Middleton Classification of Cyclostationary Correlated Noise in Hybrid MIMO-OFDM WiNPLC

An effective approach to enhance the data rate in narrowband power line communication (NBPLC) system is multicarrier modulation based on orthogonal frequency-division multiplexing (OFDM) and multiple-input multiple-output (MIMO) transmission over multiple power line phases. A key challenge for achieving reliable communication over MIMO-OFDM NBPLC is to mitigate the effects of the correlated non-stationary additive noise. In fact, substantial components of the noise in NBPLC systems exhibit a cyclostationary behavior with a period of half the AC cycle. Moreover, when MIMO transmission is adopted, an important issue that must be considered is the cross-correlation between the different phases. In this work, we propose to classify the cyclostationary noise into three classes, based on the evaluation of second order statistics. In addition, we derive estimates of the probability density functions for each of the three classes and show that while two of them exhibit a Gaussian behavior, the third one has an impulsive behaviour similar to the Middleton class-A noise. Simulation results show that the bit error rate (BER) of MIMO-OFDM NBPLC significantly changes between different classes of noise. Hence, we develop an algorithm for switching data delivery between MIMO-OFDM NBPLC and MIMO-OFDM wireless transmission in unlicensed frequency band that takes into account knowledge of the periodicity of the three classes of noises. The result is a hybrid MIMO-OFDM wireless/NBPLC system, which we refer to as, hybrid MIMO-OFDM WiNPLC. Our simulation results demonstrate BER improvement of the proposed hybrid system over individual MIMO-OFDM NBPLC or MIMO-OFDM wireless systems.
Estimating and Compensating Residual Carrier Frequency Offset for Commodity WiFi

The various offsets existed on the commodity WiFi devices greatly limit the use of ubiquitous WiFi signals for indoor applications. In this paper, we focus on the estimation and compensation of the residual carrier frequency offset (CFO) for the commodity WiFi devices. Specifically, we introduce a distorted channel state information (CSI) model by taking into consideration various CSI errors such as packet detection delay (PDD) and CFO. We propose a multiscale sparse recovery algorithm to get rid of the effect of PDD and extract the carrier frequency component out of CSI. Then, we formulate the residual CFO estimation as a spectrum estimation problem and propose to utilize the MUSIC algorithm to estimate the residual CFO. Real experiments are conducted to evaluate the performance of the proposed method. The experimental results show that the residual CFO is time-varying, and compared with existing methods, the proposed method can better estimate and compensate the residual CFO, and thus achieve better results.
MP-WFRFT and constellation scrambling based physical layer security system

In this paper, a multiple parameters weighted fractional Fourier transform (MP-WFRFT) and constellation scrambling (CS) method based physical layer (PHY) security system is proposed. The proposed scheme is executed by two steps. In the first step, MP-WFRFT, implemented as the constellation beguiling (CB) method, is applied to change the signal's identity. In the second step the additional pseudo random phase information, regarded as the encryption key, is attached to the original signal to enhance the security. Typically, the pseudo random phase information can be removed effectively by the legitimate receiver. In contrast to the cryptography based encryption algorithms and the conventional PHY secrecy techniques, the main contribution of the proposed scheme is concentrated on the variation in signal's characteristics. Simulation results show that the proposed scheme can prevent the exchanging signal from eavesdroppers' classification or inception. Moreover, the proposed scheme can guarantee the BER performance at a tolerate increasing in computational complexity for the legitimate receivers.
CRP: Optimized SDN Routing Protocol in Server-Only CamCube Data-Center Networks

Facing the exponential growth of the intra-datacenter traffic, the traditional Data-Center Network (DCN) architectures are not capable to stay ahead of the demand in terms of scalability and lowering costs. In this paper, we address the routing problem of the intra-datacenter traffic inside CamCube-based server-only DCNs. The latter are composed of servers only and no additional network equipments are employed. Following the SDN paradigm, we firstly propose a new architecture in which the control plane is hosted in an SDN controller. Next, we propose to emulate the whole DCN using the versatile “Mininet” platform. Then, we formulate the path computation problem, considering the Quality-of-Service (QoS) in terms of the requested bandwidth, as multi-objective combinatorial optimization problem. Next, we propose a thoughtful reformulation for the problem which can be solved using the Branch-and-Cut algorithm. Our proposal, named CamCube Routing Protocol (CRP), yields the optimal routing paths for the considered traffic flows that keep a balanced load on the DCN. Based on extensive emulations using “Mininet” and the “ONOS” SDN controller, the obtained results are very good, compared with the shortest-path approach, in terms of packet error rate and latency.
Feature Engineering for Deep Reinforcement Learning Based Routing

Recent advances in Deep Reinforcement Learning (DRL) techniques are providing a dramatic improvement in decision-making and automated control problems. As a result, we are witnessing a growing number of research works that are proposing ways of applying DRL techniques to network-related problems such as routing. However, such proposals failed to achieve good results, often under-performing traditional routing techniques. We argue that successfully applying DRL-based techniques to networking requires finding good representations of the network parameters: feature engineering. DRL agents need to represent both the state (e.g., link utilization) and the action space (e.g., changes to the routing policy). In this paper, we show that existing approaches use straightforward representations that lead to poor performance. We propose a novel representation of the state and action that outperforms existing ones and that is flexible enough to be applied to many networking use-cases. We test our representation in two different scenarios: (i) routing in optical transport networks and (ii) QoS-aware routing in IP networks. Our results show that the DRL agent achieves significantly better performance compared to existing state/action representations.
On the Delay Advantages of a Network Coded Transport Layer in IoT Nanosatellite Constellations

Given the increasing demand of small satellite-based services for the Internet of Things (IoT), we study the potential benefits of employing Random Linear Network Coding (RLNC) as a transport layer scheme in nanosatellite constellations for IoT. We present a timing analysis of the end-to-end transmission process for the Reliable Datagram Protocol (RDP) and RLNC at the transport layer to compare their performances. It includes the effect of these protocols in terms of delay for different IoT scenarios such as event detection/localization, asset tracking and sensor data aggregation. We verify with numerical simulations, that for our schemes ideal bit rates that minimize the total delay are possible. We observe 50–65% delay reductions for RLNC under the same operational conditions as RDP.
Optimized Real-Time Flight Data Streaming via Air-to-Air Links for Civil Aviation

Flight recorders (FRs) are required to be installed in commercial aircrafts to record operating data of planes while in flight for the purpose of facilitating the investigation of aviation accidents. Although being carefully protected, FRs may still become damaged or lost under the extreme conditions. We therefore propose a software-defined wireless networking framework to fully exploit the inter-aircraft air-to-air radio links to stream flight data in real time from the aircraft to a ground control center. With the knowledge of the physical position and predictable trajectory of each commercial aircraft, we formulate a time-expanded connectivity graph for the network controller to maintain a holistic view of the time-varying network status and propose a branch and price algorithm to optimize the flight data flows transmitted through air-to-air links. We also consider the optimality gap introduced by unpredictable network changes and delayed flow reconfigurations, and propose a traffic reroute policy to keep the gap small while minimizing the flow reconfiguration cost. We conduct extensive experiments with real commercial aircraft trajectories in North Atlantic oceanic airspace and demonstrate the feasibility and efficiency of our scheme to support flight data streaming.
Multicell Massive MIMO Multicasting with Finite-Alphabet Inputs and Statistical CSI

We investigate the multicast precoding design in multicell massive multiple-input multiple-output (MIMO) systems with finite-alphabet inputs. Focusing on the multicast transmission with only statistical channel state information at the base station, we derive a lower bound on the achievable ergodic rate for finite-alphabet inputs, from which we utilize the concave-convex procedure (CCCP) to devise a CCCP-based algorithm maximizing the minimum weighted achievable ergodic rate lower bound. The CCCP-based algorithm is proven to converge to a local optimum. Furthermore, exploiting the channel characteristic in massive MIMO systems, we prove that the optimal precoding vectors should be linear combination of columns of eigenmatrix of transmit correlation matrices in order to maximize the minimum weighted rate lower bound with lower computational complexity. Then, a relation-based algorithm is developed to obtain the optimal solution of the weighted max-min fairness (MMF) problem by using the duality between the MMF and quality of service problem. Numerical results demonstrate the tightness of the achievable ergodic rate lower bound and the significant performance of the proposed algorithms.
Privacy-Preserving and Trustworthy Mobile Sensing with Fair Incentives

Pervasive mobile devices and their advances in sensing and networking have led to an emerging mobile sensing paradigm. The diversity of mobile users and the openness of sensing systems raise several crucial concerns for users' privacy, data quantity, and quality. Although different aspects of these issues were addressed separately in existing researches, there is still a need to provide a holistic solution for secure and privacy-aware mobile sensing. In this paper, we propose a privacy-aware and trustworthy mobile sensing scheme with fair incentives. Leveraging group signature, (partial) blind signature, and limited number of pseudonyms technologies, our scheme enables well-behaved users to contribute their data anonymously, and prevents both greedy and malicious users from abusing the privacy protection. Moreover, we design a fair incentive scheme to stimulate users to contribute high-quality data, based on the data quality and the reputation feedback level. Security analysis demonstrates that our proposed scheme achieves the security goals. Extensive evaluation results are presented which demonstrate the effectiveness and efficiency of our scheme.
Context-Aware Adaptive Authentication and Authorization in Internet of Things

The rapid technological advancements in wireless communications, ubiquitous sensing and mobile networking have paved the way for the emergence of the Internet of Things (IoT) era, where “anything” can be connected “anywhere” at “anytime”. However, the flourish of IoT still faces various security and privacy preserving challenges that need to be addressed. In such pervasive and heterogeneous environment where the context conditions dynamically and frequently change, efficient and context-aware mechanisms are required to meet the users' changing needs. Therefore, it seems crucial to design an adaptive access control scheme in order to remotely control smart things while considering the dynamic context changes. In this paper, we propose a Context-Aware Attribute-Based Access Control (CAABAC) approach that incorporates the contextual information with the Ciphertext-Policy Attribute-based Encryption (CP-ABE) to ensure data security and provide an adaptive contextual privacy. From a security perspective, the proposed scheme satisfies the security requirements such as confidentiality, context-aware privacy, and resilience against key escrow problem. Performance analysis proves the efficiency and the effectiveness of the proposed scheme compared to benchmark schemes in terms of storage, communication and computational cost.
EPOCH: Error Bound Analysis Towards Indoor WLAN Positioning Under Colored Gaussian Noisy Channel

Applications of Location-based Services (LBSs) have driven the increasing demand for indoor positioning technology. Motivated by the wide deployment of WLAN infrastructure, the WLAN signal strength based positioning has become one of the superior positioning techniques used in indoor environment, and meanwhile the corresponding error bound analysis has attracted much attention due to its significant guidance meaning in practice. In this paper, we propose a new error bound analysis approach for indoor WLAN positioning based on the information-theoretic view, namely Error Bound Analysis towards Indoor WLAN Positioning under Colored Gaussian Noisy Channel (EPOCH). In concrete terms, first of all, the process of indoor WLAN positioning is analogized into the information propagation process in communication system. Then, the motion behavior of the pedestrian in indoor environment is considered to formulate the relations between the information entropy of belonging regions of the pedestrian and the corresponding region dimension. Finally, the positioning error bound is derived out based on the channel capacity constraint with respect to the information entropy of belonging regions. In addition, the experimental results demonstrate that compared with the widely-known Cramer-Rao Lower Bound (CRLB), the proposed EPOCH is able to achieve better error bound estimation performance under different Access Point (AP) deployment and Reference Point (RP) calibration.
Computing Machinery, Intelligence and Undecidability

In 1950, Alan Turing proposed a decision criterion for intelligence validation in a computer. Most simply, if a human judge was incapable of deciding from two witnesses which was the computer and which was the human, the machine would have acquired artificial intelligence. Here I will argue that the Turing test has a fundamental problem, making it impossible to provide human intelligence validation. In fact, the test is undecidable and thus cannot be considered a valid methodology to test for artificial intelligence. This does not mean that human intelligence simulation in a machine is unattainable. It means that we need a general theory offering common characteristics of intelligent agents and specific metrics to test for it. A theory able to predict intelligence emergence independently of our own subjective appreciation about how a system socially interacts with us. If such a theory is attainable or within our reach, in the coming years, remains an open problem.
Concatenated MMSE Estimation for Quantized OFDM Systems

A novel channel estimation framework is presented for orthogonal frequency division multiplexing (OFDM) system that operates with low-precision analog-to-digital converters (ADCs). The framework is based on concatenated minimum mean square error (MMSE) estimation, which consists of an inner and an outer MMSE estimation blocks. The outer MMSE estimation finds the compound signal, i.e., the unknown channel multiplied by a pilot signal, from the nonlinear distortion of the received signal by the quantization and its mean square error (MSE). Using the estimated compound signal and its MSE, the inner MMSE estimation estimates the desired signal, i.e., the unknown channel value, assuming the resulting estimation error of the outer MMSE block follows the Gaussian distribution. One major finding is that the proposed framework is analytically tractable for quantifying the effective quantization error, unlike the widely-used Bussgang-based approach. From simulations, it is shown that the proposed channel estimation framework provides a significant gain over conventional Bussgang-based methods using the approximated covariance matrix of the quantization error.
Voltage Based Authentication for Controller Area Networks with Reinforcement Learning

Controller area networks (CANs) are vulnerable to spoofing attacks such as frame falsifying attacks, as electronic control units (ECUs) send and receive messages without any authentication and encryption. In this paper, we propose a physical authentication scheme that exploits the voltage features of the ECU signals on the CAN bus and applies reinforcement learning to choose the authentication mode such as the protection level and test threshold. This scheme enables a monitor node to optimize the authentication mode via trial-and-error without knowing the CAN bus signal model and spoofing model. Experimental results show that the proposed authentication scheme can significantly improve the authentication accuracy and response compared with a benchmark scheme.
Device-Free Localization Based on Spatial Sparsity with Basis Error Self-Calibration

With the recent advances in the theory of compressive sensing (CS), spatial sparsity has been applied for device-free localization (DFL) to reduce the number of measurements required by DFL systems while maintaining the high localization accuracy. However, few works considered the problem of basis mismatch in CS-based DFL. In this paper, a new CS-based DFL approach with basis self-calibration (called DFL-SC) is proposed to overcome the problem incurred by basis errors. The novel feature of this method is to simultaneously estimate the perturbations of the basis matrix and the sparse solution based on a joint optimization framework under sparsity constraints. Meanwhile, we add prior information on the support region obtained from last target's location estimation into the sparse reconstruction process for enhancing reconstruction performance. Experimental results verify the effectiveness of the proposed method on the location accuracy.
Ambient Backscatter in Reality: Does Illuminator Signal Structure Matter?

Motivated by the extensive use of simplified models in the recent ambient backscatter literature, this work will demonstrate the importance of utilizing realistic models when deriving appropriate detectors. For this purpose, two recent works on ambient backscatter will be evaluated under realistic channel models, accounting for all communication parameters. It is shown that assuming a complex normal illuminator (i.e., ignoring illuminator's signal structure) leads to significant performance losses compared to explicitly considering illuminator's modulation (e.g., FM). Based on FM illumination, the importance of the latter is further highlighted by deriving a high performance, fully noncoherent sequence detector. In most cases, switching techniques (e.g., SBPSK) are shown to outperform conventional techniques (OOK), at the expense of increased complexity.
Efficient Content Delivery via Interest Queueing

Content sharing is an approach to relieve the congestion of cellular networks with alternative communication technologies such as the Wi-Fi and bluetooth. Through a Content Delivery Network (CDN), only a small portion of users need to download the data directly. Other users obtain packets from these users through short-range communications. However, the uncertainty of movement of mobile users challenges the effectiveness of CDNs. Unlike previous CDN solutions, in this paper, we present a novel scheme that studies the probabilistic meeting of users. When the accessibility to the cellular network is limited, we apply the queueing theory to guide the downloading or waiting strategies of users. In this system, the users who hold the content become seeds in the CDN and benefit their neighbors. Therefore we also consider the seed growing performance in the strategy. The purpose of our scheme is to let every user efficiently obtain their target content with restricted cellular data. The evaluation results show that our scheme gains significant satisfaction throughput improvements compared to the performance of basic downloading strategies.
Nucleolus-Based Profit Sharing for Wireless Small Cells in Content Centric Networks

In this paper, we focus on the resource management in a heterogeneous small cell network implemented in a Content Centric Network (CCN), where a hierarchical innetwork caching framework is formed with small cell base stations (SBSs) and macro base station (MBS) taking the role of the CCN router. In the hierarchical network structure, SBSs in the same hot spot cooperate and serve as the first hop routers, handling the Interest packets issued by the users. While the MBS works as the next hop router when a cache miss occurs at the small cell layer. Taken into consideration the feature of content request and content retrieving in CCN, we establish the link between SBSs and users on the request level rather than adopt the fixed connection regime. This means different SBSs may come to serve the same user in terms the content request it generates. In this context, we model a cooperative game among SBSs with transferable utility, in which the SBSs in a hot spot collaborate to maximize their overall revenue by selecting the best one to respond to each content request. In the meantime, a certain amount of spectrum resource is bought from the operator to satisfy the user's QoS requirement. Finally, the payoff is divided among SBSs via a bankruptcy game, and the solution is found exploiting the Nucleolus fairness concept. A decentralized Nucleolus calculation algorithm is adopted here so that the network can work in a distributed manner.