Wi-Fi sensing has been extensively explored for various applications, including vital sign monitoring, human activity recognition, indoor localization, and tracking. However, practical implementation in real-world scenarios is hindered by unstable sensing performance and limited knowledge of wireless sensing coverage. While previous works have aimed to address these challenges, they have overlooked the impact of walls on dynamic sensing capabilities in indoor environments. To fill this gap, we present a theoretical model that accounts for the effect of wall-device distance on sensing coverage. By incorporating both the wall-reflected path and the line-of-sight (LoS) path for dynamic signals, we develop a comprehensive sensing coverage model tailored for indoor environments. This model demonstrates that strategically deploying the transmitter and receiver in proximity to the wall within a specific range can significantly expand sensing coverage. We assess the performance of our model through experiments in respiratory monitoring and stationary crowd counting applications, showcasing a notable 11.2% improvement in counting accuracy. These findings pave the way for optimized deployment strategies in Wi-Fi sensing, facilitating more effective and accurate sensing solutions across various applications.

In urban environments, road closures due to construction, maintenance, accidents, or emergency situations pose significant challenges to traffic flow and safety. The cooperative lane-changing (CLC) is a promising solution. Traditional rule-based CLC models often fall short in addressing the complexities introduced by sudden lane reductions and diversions. Therefore, this paper proposes a vehicle-to-everything (V2X)-aided multi-agent CLC (MA-CLC) model, which is tailor-made for road closure scenarios. By leveraging V2X, the connected and autonomous vehicle (CAV) and human driven vehicle (HDV) agents can share information about speeds, distances, and trajectories, which will be used as part of the state space for training. To make the HDV more humanlike, we customize a human-imitating reward function for HDV agents and implement CLC experiments with human expert drivers (HEDs). The results show that the safety and efficiency performance of the proposed MA-CLC model is respectively 15% and 26% higher than other benchmarks on average.

Autonomous driving systems must make precise and reliable decisions to ensure safety and prevent collisions. In this paper, we address the challenge of effectively training autonomous vehicles to handle rarely encountered extreme scenarios in a virtual environment. To achieve this, we propose a hybrid approach that integrates reinforcement learning (RL) for autonomous driving within a federated learning (FL) framework. This approach enables individual vehicles to collaboratively develop a global model capable of handling diverse extreme tasks at intersections. Additionally, it allows local vehicles to train models in conjunction with roadside units (RSUs) without compromising sensitive data, such as perception information. Simulation results demonstrate that the proposed FL framework not only boosts the convergence speed during the training phase by up to 114.26%, but also improves autonomous driving performance, as evidenced by higher reward values, lower collision rates, and reduced travel time, compared to benchmark RL schemes.

Passenger counting is crucial for public transport vehicle scheduling and traffic capacity evaluation. However, most existing methods are either costly or with low counting accuracy, leading to the recent use of Wi-Fi signals for this purpose. In this paper, we develop an efficient edge computing-based passenger counting system consists of multiple Wi-Fi receivers and an edge server. It leverages channel state information (CSI) and received signal strength indicator (RSSI) to facilitate the collaboration among multiple receivers. Specifically, we design a novel CSI feature fusion module called Adaptive RSSI-weighted CSI Feature Concatenation, which integrates locally extracted CSI and RSSI features from multiple receivers for information fusion at the edge server. Performance of our proposed system is evaluated using a real-world dataset collected from a double-decker bus in Hong Kong, with up to 20 passengers. The experimental results reveal that our system achieves an average accuracy and F1-score of over 94 %, surpassing other cooperative sensing baselines by at least 2.27 % in accuracy and 2.34 % in F1-score.

Urban vehicular channel estimation (UVCE) has long been a difficult task due to the inter-carrier interference (ICI) and path loss caused by high-speed vehicle motion and urban geographical features (e.g., buildings, vehicles, and trees). Conventional estimators based on pilot symbols and channel statistics generally assume static signal propagation models, such as Free-space and Rayleigh fading. These models are inadequate for addressing path losses caused by geographical features, leading to limited performance. In contrast, centralized learning (CL)-based estimators can provide higher estimation performance by collecting channel data from a specific geographical area for training. However, when the UVCE is scaled to city size, CL estimators cannot precisely recognize the channel characteristics of each location in the city, resulting in decreased estimation accuracy. To further improve the scalability and accuracy of UVCE, this paper proposes a federated learning (FL) and convolutional neural network (CNN)-based channel estimator, referred to as FL-CNN. FL is used to aggregate multiple local channel models (LCM), which are clustered by the K-Dimension (KD)-tree technique. For each LCM, we employ the ray-tracing model to calculate the path loss caused by geographical features and use CNN to estimate the channel. Our results show that at the low signal-to-noise-ratio (SNR) regime (e.g., 10 dB), the estimation and data recovery performance of the FL-CNN estimator are respectively 61% and 65% higher than those of benchmark estimators on average.

Recent studies in Wi-Fi sensing have demonstrated the potential of channel state information (CSI) for indoor crowd counting. However, their practical application remains limited by constraints in sensing range and robustness. While multi-transceiver setups have shown promise in enhancing performance, the impact of transceiver placement strategies on sensing effectiveness remains underexplored. In this work, we systematically investigate how sensor placement affects sensing performance in stationary crowd counting. We extend two foundational models, the Fresnel zone and sensing-signal-to-noise ratio (SSNR) formulations, originally designed for single-target, single-link scenarios, and generalize them to characterize spatial sensing quality in multi-target, multi-link environments. Based on this theoretical foundation, we propose a deployment evaluation model that quantifies sensing performance using a normalized metric termed the Regional Sensing Quality (RSQ), and enables direct comparison among different transceiver topologies. Experimental results across diverse environments show that optimizing deployment can improve crowd counting accuracy by up to 20.48%, with our system achieving 98.14% accuracy for up to 20 individuals. This work provides the first framework that integrates theoretical modeling and practical validation to guide transceiver deployment for robust and scalable CSI-based stationary crowd sensing.

Computing offloading optimization for energy saving is becoming increasingly important in low-Earth orbit (LEO) satellite-terrestrial integrated networks (STINs) since battery techniques have not kept up with the demand of ground terminal devices. In this paper, we design a delay-based deep reinforcement learning (DRL) framework specifically for computation offloading decisions, which can effectively reduce the energy consumption. Additionally, we develop a multi-level feedback queue for computing allocation (RAMLFQ), which can effectively enhance the CPU’s efficiency in task scheduling. We initially formulate the computation offloading problem with the system delay as Delay Markov Decision Processes (DMDPs), and then transform them into the equivalent standard Markov Decision Processes (MDPs). To solve the optimization problem effectively, we employ a double deep Q-network (DDQN) method, enhancing it with an augmented state space to better handle the unique challenges posed by system delays. Simulation results demonstrate that the proposed learning-based computing offloading algorithm achieves high levels of performance efficiency and attains a lower total cost compared to other existing offloading methods.

Contactless respiratory rate monitoring methods have shown significant potential for patient monitoring and home healthcare in recent years because they could supersede traditional wearable equipment, enabling non-contact monitoring. However, existing experiments are constrained to specific devices that are difficult to access in daily life and have strict limitations on the version of the system. Therefore, it would be highly desirable if the latest easily accessible low-cost IoT devices, such as Raspberry Pi, could be used for respiratory rate detection tasks. In this paper, we tackle this limitation by applying Raspberry Pi for respiratory rate detection and introducing the envelop-based preprocessing method. The envelop-based method enables human respiratory pattern extraction from both the amplitude and phase of WiFi channel state information(CSI). The combination of autocorrelation function of selected quality subcarrier then estimates the respiratory rate. Our experiment result indicates that the estimation accuracy from amplitude and phase reach 98.94% and 98.54%, respectively. Compared with the traditional preprocessing method based on the Savitzky-Golay filter, the enveloped-based method reaches 5.98% and 5.78% improvement in the accuracy of exploiting amplitude and phase information respectively, demonstrating the superiority and potential for further applications.

Wi-Fi sensing has enabled many applications due to the increasing number of commercial Wi-Fi devices and channel state information (CSI) extraction tools. In this paper, we study the application of CSI-based stationary crowd counting using multiple pairs of transceivers. Specifically, we provide an exact count of the number of passengers on the upper deck of a double-decker bus. Most of the previous solutions count the number of immobile people with a pair of transceivers, which leads to limited sensing scales with the maximum countable number achieved by the state-of-the-art solutions being 15. Indeed, few of these solutions consider the impact of the placement of transceivers on the sensing performance. The major innovation of our work is to identify the optimal topology for multiple receivers based on the Fresnel Zone model to improve the quality of data collection and reduce the overall training effort. We consider the impact of the First Fresnel Zone (FFZ) and the distance between transmitters and receivers when deploying multiple pairs of transceivers. This technique can also be applied to other applications, such as localization, tracking of multiple people, multi-person respiration rate monitoring, etc. The proposed topology was compared with a baseline of a pair of transceivers. Our results show that by properly placing transceivers, the accuracy of counting the exact number of passengers can be improved by more than 11.49%, and the sensing scalability can be extended from 11 to 20 passengers, with an average accuracy of 90.83% with conventional machine learning methods only.

Emergency vehicle preemption (EVP) aims to provide the right-of-way to emergency vehicles (EVs) so that they can travel to the incident location efficiently. The travel time of EVs is the most important indicator of EVP efficiency, which should be minimized by distinct methods or algorithms. However, conventional EVP methods using strobe emitters, light emitters or sirens performs poorly in high-density vehicular traffic. Vehicle-to-everything (V2X) communication plays a pivotal role in intelligent transportation systems (ITS), which can assist EVs to travel safely and efficiently in connected autonomous transport systems (CATS). Enabled by V2X, this paper proposes a deep reinforcement learning-aided mobility-aware lane change algorithm (DRL-MLC) to enhance the efficiency of EVP. In the first stage, the EV learns to change lane based on a policy-based deep reinforcement learning (DRL) algorithm to find the shortest trajectory. In the second stage, autonomous vehicles (AVs) perform mobility-aware lane changing (MLC) to make way for the EV based on the emergency messages (EM) they received. Note that the performance of DRL-MLC strongly relies on the quality of service (QoS) of V2X, and improper network parameters of the on-board units (OBUs) that do not match with the vehicular density will significantly degrade the QoS. Therefore, in the third stage, the proposed algorithm fine-tunes specific parameters including communication range, carrier sensing range, packet rate, and contention window according to the real-time vehicular density based on a curve-fitting optimization method. Our results indicate that at medium-to-high density (e.g., 0.15 veh/m), the average speed of DRL-MLC has more than 49% average improvement than traditional lane changing models, and the ten-minute target travel time for EVs can be achieved by 95% with the proposed algorithm.

Recently, wireless sensing is gaining immense attention in the Internet of Things (IoT) for crowd counting and occupancy detection. As wireless signals propagate, they tend to scatter and reflect in various directions depending on the number of people in the indoor environment. The combined effect of these variations on wireless signals is characterized by the channel state information (CSI), which can be further exploited to identify the presence of people. State-of-the-art CSI-based supervised crowd counting systems are vulnerable to temporal and environmental dynamics in practical scenarios as their performance degrades with fluctuations in the indoor environments due to multipath fading. Inspired by the breakthroughs of transfer learning and advancement in edge computing, we have leveraged in this work the concept of transfer learning to minimize this problem via exploiting the trained model from the source environment for other indoor environments to perform device-free crowd counting (CrossCount) at the target rooms. Our results show that this technique can combat the dynamics of the environment and achieves 4.7% better accuracy with 40% reduction in training time as compared to conventional convolutional neural networks. In essence, our results imply the future possibility of harnessing crowdsourced CSI data collected at different indoor environments to boost the accuracy and efficiency of local crowd counting systems.

Deteriorating water quality leads to the freshwater biodiversity crisis. The interrelationships among water quality parameters and the relationships between these parameters and taxa groups are complicated in affecting biodiversity. Nevertheless, due to the limited types of Internet-of-Things (IoT) sensors available on the market, a large number of chemical and biological parameters still rely on laboratory tests. With the latest advancement in Artificial intelligence and the IoT (AIoT), this technique can be applied to real-time monitoring of water quality, and further conserving biodiversity. In this article, we conducted a comprehensive literature review on water quality parameters that impact the biodiversity of freshwater and identified the top-10 crucial water quality parameters. Among these parameters, the interrelationships between the IoT measurable parameters and IoT unmeasurable parameters are estimated using a general regression neural network (GRNN) model and a multivariate polynomial regression (MPR) model based on historical water quality monitoring data. Conventional field water sampling and in-lab experiments, together with the developed IoT-based water quality monitoring system were jointly used to validate the estimation results along an urban river in Hong Kong. The GRNN can successfully distinguish the abnormal increase of parameters against normal situations. For the MPR model of degree 8, the coefficients of determination results are 0.89, 0.78, 0.87, and 0.81 for NO3-N, BOD5, PO4, and NH3-N, respectively. The effectiveness and efficiency of the proposed systems and models were validated against laboratory results and the overall performance is acceptable with most of the prediction errors smaller than 0.2 mg/L, which provides insights into how AIoT techniques can be applied to pollutant discharge monitoring and other water quality regulatory applications for freshwater biodiversity conservation.

Sharing up-to-date environment information collected by intelligent connected vehicles is critical in achieving travel comfort, convenience, and safety in vehicular networks. Individually collected information should be made available to other vehicular nodes, adjacent or distant, to achieve an informed and well-managed vehicular traffic. The coverage reach of sharing these road data can be maximized by allocating roadside units in strategic positions. In this work, we propose an Enhanced Information SHAring via Roadside Unit Allocation (EISHA–RSU) scheme that strategically determines where RSUs must be deployed from all spatial candidate locations. The urban area is irregularly partitioned into effective regions of movement (ERM) according to vehicular capacity with priority. For each ERM, EISHA–RSU greedily allocates the initial RSU to an effective position and optimally assigns the remaining RSUs to spatial locations that capture the maximum I2V/V2I information sharing based on the area’s average road speed. In effect, the proposed deployment scheme addresses both the issues of coverage and connectivity among vehicles and the infrastructure. We evaluate the proposed RSU allocation scheme by employing three urban empirical mobility datasets and compare its network starvation fairness, effectiveness, and efficiency performance measures with three other deployment benchmarks. Overall, EISHA–RSU reduces the number of required RSUs to cover a certain area, exhibits higher connectivity, and achieves maximum I2V/V2I information sharing among the evaluated schemes.

Accurate localization is a critically important issue for autonomous vehicles as it is closely related to the safety and efficiency of autonomous driving. However, current technologies for autonomous vehicle localization face many challenges. To provide accurate and robust localization services to autonomous vehicles, we propose a novel solution by employing a newly designed pavement marking. This marking operates on color contrast, temperature contrast, and binary code with some special features. We also trained and customized an object detector based on a deep learning model: YOLOv5, and integrated it with the decoding algorithm. The localization system is capable of running at a steady frame rate of more than 50 FPS. Road trials up to 80 km/h were conducted, and satisfactory results confirmed the feasibility and robustness of the localization system. Specifically, with a common onboard camera, more than four continuous frames can be detected and decoded correctly when the speed is slower than 30 km/h. At least one frame can be detected and decoded correctly at a higher speed (i.e., 30– 50 km/h). With a high-speed camera, more than 18 frames can be detected and decoded even at 80 km/h. The findings suggest that the specially designed road marking and associated algorithms can provide a viable and economical option for accurate localization of autonomous vehicles. The performance of the system has potentials for further improvement by using better hardware such as faster CPUs, GPUs, and thermal imaging techniques.

In this paper, we conduct a complex-network analysis of the Bitcoin network. In particular, we design a new sampling method namely random walk with flying-back (RWFB) to conduct effective data sampling. We then conduct a comprehensive analysis of the Bitcoin network in terms of the degree distribution, clustering coefficient, the shortest path length, the assortativity, and the rich-club coefficient. There are several important observations from the Bitcoin network, such as small- world phenomenon and non-rich-club effect. This work brings up an in-depth understanding of the current Bitcoin blockchain network and offers implications for future directions in malicious activity and fraud detection in cryptocurrency blockchain networks.

Abstract This paper investigates the relay selection problem and proposes a three‐stage relay selection strategy with power allocation (TRSPA) for a spectrum‐sensing‐based full‐duplex (FD) user relaying cooperative non‐orthogonal multiple access (CNOMA) scheme. Uniformly‐distributed strong user relays in the investigated scheme help a weak user communicates with the base station in an efficient and reliable way. The proposed TRSPA strategy maximizes the transmission data rate of the selected relay while ensuring successful transmissions for the weak user by precisely narrowing down relay candidates step‐by‐step and dynamically allocating optimal power coefficients. Exact and asymptotic outage probabilities and ergodic rates are worked out. Accordingly, diversity orders and spatial multiplexing gains are derived. We further exploit the impact of self‐interference (SI) on TRSPA for FD‐CNOMA and then compare its performance with TRSPA applied in other relaying modes, that is half‐duplex and orthogonal multiple access. Finally, simulation results reveal that: (i) theoretical derivation results are correct; (ii) TRSPA always outperforms other relay selection strategies in terms of outage probability and ergodic rate; and (iii) TRSPA for FD‐CNOMA in a real‐world scenario achieves better performance than other relaying modes in spite of the adverse effect of SI in FD mode.

It is not uncommon to experience highly dynamic channels in indoor environments due to time-varying signals as well as moving reflectors and scatterers. This greatly affects the performance of wireless sensing systems that use received signal strength indicator (RSSI) and channel state information (CSI) fingerprints for indoor positioning and event detection. Solutions to this dynamic channel problem often involve laborintensive database maintenance and customized hardware. With this, we present Gaussian models that can withstand temporal and environmental dynamics in practical indoor environments using off-the-shelf devices in this paper. Although systems employing Gaussian models have been previously proposed in the literature, most systems use RSSI instead of CSI to represent the wireless channel. By using a Gaussian distribution to model CSI fingerprints, which offer more abundant information regarding the channel dynamics than RSSI, we can exploit the variance inherent in the wireless channels. Our experiments demonstrate that the Gaussian classifier incurs minimal delay of less than 4 seconds and achieves high classification accuracy compared to other techniques. In particular, it achieves up to 50% and 150% performance improvement over the time-reversal resonating strength (TRRS) and the support vector machines (SVM) methods, respectively.

This paper studies Physical-layer Network Coding (PNC) in a two-way relay channel (TWRC) operated based on OFDM and QPSK modulation but with the presence of carrier frequency offset (CFO). CFO, induced by node motion and/or oscillator mismatch, causes inter-carrier interference (ICI) that impairs received signals in PNC. Our ultimate goal is to empower the relay in TWRC to decode network-coded information of the end users at a low bit error rate (BER) under CFO, as it is impossible to eliminate the CFO of both end users. For that, we first put forth two signal detection and channel decoding schemes at the relay in PNC. For signal detection, both schemes exploit the signal structure introduced by ICI, but they aim for different output, thus differing in the subsequent channel decoding. We then consider CFO compensation that adjusts the CFO values of the end nodes simultaneously and find that an optimal choice is to yield opposite CFO values in PNC. Particularly, we reveal that pilot insertion could play an important role against the CFO effect, indicating that we may trade more pilots for not just a better channel estimation but also a lower BER at the relay in PNC. With our proposed measures, we conduct simulation using repeat-accumulate (RA) codes and QPSK modulation to show that PNC can achieve a BER at the relay comparable to that of point-to-point transmissions for low to medium CFO levels.

Techniques for indoor positioning systems (IPSs) can be categorised as range‐based or range‐free. Range‐based methods rely on geometric mappings to approximate a location given the calculated distances or angles from multiple reference points. In contrast, range‐free strategies utilise fingerprinting, wherein an acquired fingerprint data is compared to a pre‐collected dataset to identify the best position estimate. Among these, fingerprinting of channel state information (CSI) is preferred over other information such as received signal strength indicator as the former can exploit the effect of multipath propagation and is robust against non‐line‐of‐sight channels. CSI has the potential to achieve cm‐level positioning accuracy with a single reference point only. In this study, the authors survey CSI fingerprinting‐based indoor positioning and mobility tracking systems. The process of fingerprinting that includes site surveying and signal preprocessing is discussed in detail. They determine the potential challenges of such systems and propose remedies to improve positioning accuracy. In general, spatial diversity, such as multiple‐input multiple‐output antennas and wireless sensor networks, or frequency diversity (e.g. high subcarrier count, frequency hopping mechanism) are exploited to achieve high positioning resolution. Such IPS can also be enhanced via additional sensors or spatial graphs for motion detection and tracking.

The integration of machine learning and inter- vehicle communications enables various active safety measures in internet-of-vehicles. Specifically, the environmental perception is processed by the deep learning module from vehicular sensor data, and the extended perception range is achieved by exchanging traffic-related information through inter-vehicle communications. Under such condition, the intelligent vehicles can not only percept the surrounding environment from self-collected sensor data, but also expand their perception range through the information sharing mechanism of Vehicular Ad-hoc Network (VANET). However, the dynamic urban environment in VANET leads to a number of issues, such as the effect of packet loss on the real-time perception accuracy of the received sensor data. In this work, we propose a point cloud object detection module via an end-to-end deep learning system and enable wireless communications between vehicles to enhance driving safety and facilitate real-time 3D mapping construction. Besides, we build a semi- realistic traffic scenario based on the Mong Kok district in Hong Kong to analyze the network performance of data dissemination under the dynamic environment. Finally, we evaluate the impact of data loss on the deep-learning-based object detection performance. Our results indicate that data loss beyond 50% (which is a common scene based on our simulation) can lead to a rapid decline of the object detection accuracy.

In this paper, a novel full-duplex cooperative non-orthogonal multiple access (FD CNOMA) system is proposed, where users intend to exchange messages with the assistance of a decode-and-forward relay. To characterize the potential performance gain brought by the proposed FD CNOMA scheme, the outage probability and ergodic rate are analyzed. Specifically, the closed-form expressions for the outage probabilities, diversity orders, ergodic rates, and system throughputs in delay-limited and delay-tolerant transmission modes are derived under the realistic assumption of imperfect self-interference cancellation. Furthermore, to present the comprehensive performance evaluation, both perfect and imperfect successive interference cancellations (SICs) are taken into consideration. Simulations are performed to validate the accuracy of the derivation results and to illustrate the outstanding performance of the proposed scheme in low signal-to-noise ratio region compared with half-duplex CNOMA system and cooperative orthogonal multiple access system. Our results show that under the conditions of both perfect and imperfect SICs, outage probability floors and ergodic rate ceilings exist for the proposed FD CNOMA scheme due to the inter-user interference among superimposed NOMA signals and the residual self-interference caused by the imperfect self-interference cancellation.

In this paper, we propose a novel method called supernode graph structure representation to model the public transport network structure of the London city. Supernode is a set of geographically closely associated nodes. Using the supernode graph structure, the bus transport and the metro transport network structures are analyzed by treating them as independent mono-layer or multi-layer network structures. A method of spatial amalgamation is proposed to integrate the two transport layers. A set of most influential nodes in the network is identified by assigning node weight to each node with respect to both mono-layer and multi-layer analysis. The behavior of these influential nodes is better characterized by categorizing them as either emitter, absorber or neutral zones.

Google defines the concept of autonomous driving as one of the applications of big data. Specifically, with the input sensor data, the autonomous vehicles can be provided with the semantic-level driving characteristics for an accurate and safe driving control. However, both the enumeration of handcrafted driving features with expert knowledge and the feature classification with machine learning for characterizing driving behaviors is lack of practicability under a complex scale. Therefore, this study focuses on detecting the sematic-level driving behaviors from large-scale GPS sensor data. Specifically, we classified different driving maneuvers from a huge amount of dataset through a layer-by-layer statistical analysis method. The identified maneuver information with the corresponding driver ID is useful for the supervised learning of high-level feature abstraction with neural network. With the aim of analyzing the sensory data with deep learning in a consumable form, we propose a joint histogram feature map to regularize the shallow features in this paper. Besides, extensive simulation is conducted to evaluate different machine learning and deep learning methodologies for optimal driving behavior characterization. Overall, our results indicate that Deep Neural Network (DNN) is suitable for the driving maneuver classification task with more than 94% accuracy, while Long Short-term Memory (LSTM) neural network performs well with a 92% accuracy in identifying a specific driver. However, LSTM shows degraded accuracy when the scale of the identification task becomes larger. In this case, a hierarchical deep learning model is proposed, and simulation results show that the combination of DNN and LSTM in this hierarchical model can well maintain the prediction accuracy even when the scale of the recognition task is four times larger.

Applying physical-layer network coding (PNC) to vehicular ad-hoc networks (VANETs) can theoretically boost the network throughput by 100%, thus partially addressing the intermittent node connectivity and short contact time issues caused by high speed vehicle motions. However, the application of OFDM modulated PNC in VANETs faces detrimental effects caused by carrier frequency offsets (CFOs) and time-frequency-selective channels. CFOs may destroy the orthogonality of OFDM subcarriers, resulting in inter-carrier interference (ICI). The CFOs of two transmitters may also be different, and cannot be removed by CFO tracking and equalization at the receiver as in conventional single-user communication even if the CFOs are known. In addition, time-frequency- selective channels due to delay and Doppler spreads are difficult to estimate and non-accurate channel estimations will increase the detection bit error rate (BER). To address the two challenges, this paper proposes an ICI-aware approach that jointly exploits pilot and data for channel estimation and data detection. Specifically, our approach jointly uses the belief propagation (BP) algorithm to mitigate the CFO/ICI effect for data detection, and the expectation maximization (EM) algorithm to accurately estimate the channels. A linear interpolation method and an ICI compensation method are simulated as benchmarks. Simulation results indicate that our approach improves the BER performance compared to the two benchmarks (more than 2 dB SNR gain in most cases), especially in the high SNR regime.

Indoor Positioning Systems should be able to locate an object or person in a dynamic environment. In this research work, we present experimental results when performing indoor positioning in a dynamic environment when using channel frequency response as the indoor location's fingerprint. Dynamic environments introduce varying channel frequency responses for a given indoor location, which makes it difficult to locate desired indoor objects. According to our experiments, we find that indoor fingerprints become uncorrelated within a 15-min window. To bridge the gap between daily fingerprints, we propose three quick remedies in updating the Channel Frequency Response (CFR) database. These updates allow indoor localization even in the presence of dynamic environmental changes (e.g., people movement, interference of other wireless signals, etc.) and are able to 100% localize indoor locations separated by at least five (5) centimeters. We highlight that in the experiments, there is only one anchor node used to estimate the desired indoor location.

Within the smart city framework, information dissemination in vehicular ad-hoc networks (VANET) is attracting considerable interest in both the research community and industry. Efficient data dissemination has long been a problem in ad-hoc networks. In VANET, the problem is even more challenging given the high mobility of vehicles, high density of buildings, and intermittent network connectivity. Realistic modelling of the mobility patterns of vehicles (instead of random models like Random Waypoint or Manhattan Models in previous works) is important for accurate performance evaluation. In this paper, the transmission of on-road feature detection data (images or videos) with fountain code in VANET is studied. Specifically, we propose a robust license plate detection module and applied fountain coding in the application layer to largely reduce the average transmission delay of multi-media data. The proposed system is rigorously evaluated under a semi-realistic simulation of an inter-bus communication network in the Mong Kok urban district in Hong Kong with the consideration of real-world traffic parameters, such as different traffic density at different hours of a day and building obstacles. Specifically, we find that fountain coded data dissemination shows better performance boost in more realistic signal propagation model in urban areas. In practice, the proposed system can be applied to bus lane occupancy control. For example, when a vehicle illegally occupies the bus lane, buses nearby can help recognize the vehicle and transmit the detected results through the VANET to patrol cars for the enforcement.

Information exchange in a vehicular network between autonomous vehicles and the roadside infrastructure is important for improving road safety. These autonomous vehicles, equipped with a sensor suite, are capable of obtaining road map data that can be used to inform other vehicles and update the central road map repository through roadside units. The roadside infrastructure nodes act as local databases for distributing regional 3D road map data in form of point clouds to autonomous vehicles passing by. Since the vehicles might have various side information regarding the road network and traffic condition, minimizing the required number of transmissions to satisfy the demand of participating vehicles through network coding is an interesting research problem in road map data dissemination. In this paper, we propose the Road Map Data Encoding and Dissemination System (REDS) and evaluate its performance in a four-way junction scenario. It is based on index coding for broadcasting road map data from a centrally-managed roadside node to vehicles. REDS uses the data availability and demand knowledge for encoding and transmitting 3D point cloud road map data from different road segments. The data availability information helps prevent the transmission of duplicated road map data and provides the sets of side information in the index coding problem, while the data demand information further defines the message transmission priority based on the data demand of different road segments. Simulation results indicate that REDS reduces the average number of transmissions and transmitted point cloud data size by around 30% when the data availability probability is about 0.5 under random mobility in all simulated scenarios when compared to the traditional broadcasting approach.

The increasingly dense deployments of wireless CSMA networks arising from applications of Internet-of-things call for an improvement to mitigate the interference among simultaneous transmitting wireless devices. For cost efficiency and backward compatibility with legacy transceiver hardware, a simple approach to address interference is by appropriately configuring the carrier sensing thresholds in wireless CSMA protocols, particularly in dense wireless networks. Most prior studies of the configuration of carrier sensing thresholds are based on a simplified conflict graph model, whereas this paper considers a realistic signal-to-interference-and-noise ratio model. We provide a comprehensive study for two effective wireless CSMA protocols: Cumulative-interference-Power Carrier Sensing and Incremental-interference-Power Carrier Sensing, in two aspects: (1) static approach that sets a universal carrier sensing threshold to ensure interference-safe transmissions regardless of network topology, and (2) adaptive approach that adjusts the carrier sensing thresholds dynamically based on the feedback of nearby transmissions. We also provide simulation studies to evaluate the starvation ratio, fairness, and goodput of our approaches.

Routing plays an important role in the overall architecture of the Internet of Things. IETF has standardized the RPL routing protocol to provide the interoperability for low-power and lossy networks (LLNs). LLNs cover a wide scope of applications, such as building automation, industrial control, healthcare, and so on. LLNs applications require reliable and energy-efficient routing support. Point-to-point (P2P) communication is a fundamental requirement of many LLNs applications. However, traditional routing protocols usually propagate throughout the whole network to discover a reliable P2P route, which requires large amount energy consumption. Again, it is challenging to achieve both reliability and energy-efficiency simultaneously, especially for LLNs. In this paper, we propose a novel energy-efficient region-based routing protocol (ER-RPL), which achieves energy-efficient data delivery without compromising reliability. In contrast of traditional routing protocols where all nodes are required for route discovery, the proposed scheme only requires a subset of nodes to do the job, which is the key of energy saving. Our theoretical analysis and extensive simulation studies demonstrate that ER-RPL has a great performance superiority over two conventional benchmark protocols, i.e., RPL and P2P-RPL.

Due to the limitation of GPS in indoor environment and the rapid growth of Wi-Fi hotspots and mobile devices, indoor Wi-Fi-based positioning has been attracting growing interest. In this paper, we implement a practical and convenient indoor positioning system based on the fingerprint method and Kalman filter on Android mobile devices. This paper not only discusses the positioning algorithms, but also addresses various challenges in practical application, such as the effect of antenna orientation and signal fluctuation. Specifically, an improved mapping algorithm based on k-nearest neighbors (K-NN) is introduced to tackle the orientation effect, and an orientation-based fingerprint database is established through studying the received signal strength patterns in different directions to handle the large fluctuation caused by orientation change. Finally, our experimental result indicates that the proposed indoor positioning system can achieve up to 1.2 meters accuracy in 90 percent of time, which is sufficient for supporting various navigation and infotainment services in large-scale indoor environments (e.g., shopping malls).

This paper considers physical-layer network coding (PNC) in vehicular ad-hoc network (VANET) to solve the problem of short contact time between fast-moving vehicles. PNC enables data exchange between nodes in a relay network within a short airtime, e.g., twice faster than relay networks based on traditional communication, and can be a powerful performance booster in VANET. One of the most important challenges in applying PNC to VANET, however, is the Doppler shift caused by vehicular motions. Doppler shift leads to carrier frequency offset (CFO) that induces inter-carrier interference (ICI) in OFDM systems. The ICI destroys the orthogonality of modulated symbols, causing degradation in PNC signal detection. This paper puts forth a detection method to mitigate the CFO/ICI effect on PNC. The method, referred to as BP-VPNC, makes use of a belief propagation (BP) algorithm to process the outputs of the OFDM correlators. BP extracts useful hidden information embedded in ICI to improve signal detection in VANET PNC. Our study shows that the BER performance of PNC VANET operated with BP-VPNC can be achieved close to that of traditional VANET at various CFO levels. These results suggest that with BP-VPNC, a potential shortcoming of PNC, vulnerability to CFO, can be circumvented, and that PNC can be used to overcome the short vehicular contact time in VANET.

To exchange information between two sources in a two‐way relaying network with multiple potential relays, most researches focus on two‐hop relay system with single‐relay‐selection (SRS) scheme. Comparing with SRS scheme, the authors first design a paired‐relay‐selection (PRS) scheme in which a pair of ‘best’ relays broadcast network‐coded information to other nodes (source or relay). They propose an optimal selection algorithm and a suboptimal algorithm that selects the pair of ‘best’ relays in the PRS scheme and they describe how the nodes exchange information in a frame consisting of four timeslots. Both the analytical and simulation results show that when the pathloss exponent is large and/or there is a sufficient number of relays to choose from, using two relay nodes can provide a lower outage compared with using only one relay node even under the same total transmit power in uniformly distributed relay networks. In addition, to reduce the overhead of the PRS scheme, they propose an iterative‐PRS (I‐PRS) scheme in which the paired relay is selected in an iterative and opportunistic way. Simulation results show that the I‐PRS scheme has nearly the same outage performance as the PRS scheme under time‐invariant channels and significantly outperforms the PRS scheme under time‐varying channels.

Vehicular Ad-hoc Network (VANET) is expected to play a major role in improving road safety and traffic efficiency in people's daily life. However, the main issue in VANETs remains to be intermittent node connectivity and relatively short contact duration due to the high mobility of vehicles. Physical-layer Network Coding (PNC) that enables data exchange within a much shorter airtime (e.g., twice faster than traditional scheduling) favors the highly-dynamic link condition in vehicular environments and hence appears to be a powerful tool in VANETs. One of the most important challenges in applying PNC to VANETs comes from the Doppler shift due to high-speed vehicle motion, which leads to carrier frequency offset (CFO) and hence introduces inter-carrier interference (ICI) that degrades the bit error rate performance. In this paper, we investigate the impact of motion-induced CFO/ICI on the overall signal detection. In particular, we study whether PNC in VANETs can be made feasible with conventional equalization techniques that suppress the effect of CFO. We found that PNC suffers only a 3 dB SINR penalty in the worst case compared with generic point-to-point (P2P) communications, and generally PNC is feasible in vehicular environments even if the transmission powers of source nodes cannot be finely controlled.

There has been an increasing interest in deploying wireless mesh networks (WMNs) for communication and video surveillance purposes thanks to its low cost and ease of deployment. It is well known that a major drawback of WMN is multihop bandwidth degradation, which is primarily caused by contention and radio interference. The use of mesh nodes with multiple radios and channels has been regarded as a straightforward solution to the problem in the research community. However, we demonstrate in this paper through real-world experiments that such an approach cannot resolve the multihop TCP throughput degradation problem in IEEE 802.11n mesh networks. With extensive experimentation, we verify that the degradation is principally caused by the increase in TCP Round-Trip Time (RTT) when the number of hops increases. TCP throughput is fundamentally limited inversely by the RTT. We find that the multihop TCP throughput (up to five hops) when using 802.11n is no better than when using 802.11a, despite the much higher data rate 802.11n. We attempt to use multiple parallel TCP connections as a remedy to the problem, and it turns out that the wireless bandwidth can be fully utilized with a sufficient number of parallel streams. In general, our results give a key message that TCP tuning (e.g., setting the correct TCP buffers and use of parallel streams) is of paramount importance in high-bandwidth multihop wireless mesh networks that employ the latest wireless standards. These tuning techniques have to be implemented into commercial products to fully leverage the ever advancing wireless technologies to support the growing demand of multihop communications in wireless mesh networks.

In this paper, we study the packet routing process in communication networks. For efficient and reliable data transmission, the traffic load should be as uniformly distributed as possible in the network and the average distance travelled by the data should be short. The Internet has been demonstrated to have small-world and scale-free properties in its topology. Under the shortest path routing strategy, the traffic intensity of high degree nodes is much higher, thus causing congestion of the whole network. We propose an adaptive routing algorithm, which takes into consideration both the network structure property and the dynamic traffic information. Simulation results show that the proposed algorithm can effectively balance the traffic in the network and improve the overall traffic performance.

In this paper we study the performance of communication networks from a network science perspective. We consider in particular the effects of the choice of routing algorithms and the kind of network topology on selected areas of performance, and we also study the vulnerability of the networks when subject to selected attack strategies. Contrary to intuition, our results reveal that the removal of a certain number of high-degree nodes in a scale-free network with shortest path routing does not necessarily worsen the overall network performance. Moreover, the scale-free network can perform better when high-degree nodes are coordinated to provide exclusive service to a specific group of nodes.

The safety and commercial benefits of Intelligent Transportation System (ITS) raised interests towards inter-vehicle networking technologies such as Vehicular Ad-hoc Network (VANET). Being an approved standard for wireless access in vehicular environments, IEEE 802.11p attracts a lot of research attentions, especially on its broadcasting performance. However, most of the previous network performance models paid little attention to vehicle distribution, or simply assumed homogeneous car distribution. It is obvious that vehicles are distributed non-homogeneously along a road segment due to traffic controls and speed limits at different portions of the road. In light of the inadequacy, we present in this paper an original methodology to study the performance of 802.11p VANETs with practical vehicle distribution in urban environment. An empirically verified stochastic traffic model is adopted, which incorporates the effect of urban settings (such as traffic lights) on car distribution and generates practical car density profiles. Based on the knowledge of car density at each location from the traffic model, the 802.11p broadcasting model is developed and a new metric, Broadcasting Performance Index (BPI), is introduced to better characterize the broadcasting performance and packet collision probability in VANETs. Furthermore, the analytical closed form for BPI is derived and its accuracy is confirmed with extensive simulation. In general, our results demonstrate the applicability of the proposed methodology on modeling protocol performance, and shed insights into the performance analysis of other communication protocols and network configurations in urban vehicular networks.

The space and time dynamics of moving vehicles regulated by traffic signals governs the node connectivity and communication capability of vehicular ad hoc networks (VANETs) in urban environments. However, none of the previous studies on node connectivity has considered such dynamics with the presence of traffic lights and vehicle interactions. In fact, most of them assume that vehicles are distributed homogeneously throughout the geographic area, which is unrealistic. We introduce in this paper a stochastic traffic model for VANETs in signalized urban road systems. The proposed model is a composite of the fluid model and stochastic model. The former characterizes the general flow and evolution of the traffic stream so that the average density of vehicles is readily computable, while the latter takes into account the random behavior of individual vehicles. As the key contribution of this paper, we attempt to approximate vehicle interactions and capture platoon formations and dissipations at traffic signals through a density-dependent velocity profile. The stochastic traffic model with approximation of vehicle interactions is evaluated with extensive simulations, and the distributional result of the model is validated against real-world empirical data in London. In general, we show that the fluid model can adequately describe the mean behavior of the traffic stream, while the stochastic model can approximate the probability distribution well even when vehicles interact with each other as their movement is controlled by traffic lights. With the knowledge of the mean vehicular density dynamics and its probability distribution from the stochastic traffic model, we determine the degree of connectivity in the communication network and illustrate that system engineering and planning for optimizing both the transport (in terms of congestion) and communication networks (in terms of connectivity) can be carried out with the proposed model.

Received power measurements at spatially distributed monitors can be usefully exploited to deduce various characteristics of active wireless transmitters. In this paper, we study the problem of “blind” estimation of a wireless node's transmit power utilizing solely received power measurements at spatially distributed monitors, without any prior knowledge about the transmitter's location or any statistical characterization of its transmit power. We first consider a deterministic setup and utilize a geometrical approach to obtain fundamental limitations on estimating the transmit power and location of an unknown wireless node. We show that a regular placement of monitors, though appealing, does not provide sufficient measurement diversity to yield a unique estimate. We then extend the setup to consider wireless fading and present a theoretical analysis of maximum likelihood (ML) estimate, which is analytically shown to be asymptotically optimal. Finally, we provide numerical results comparing the performance of the estimator through simulations and on a dataset of field measurements.

In the design and planning of vehicular ad-hoc networks, road-side infrastructure nodes are commonly used to improve the overall connectivity and communication capability of the networks, however, to determine the locations to install the infrastructure nodes for optimal performance based on the ever-changing density and connectivity dynamics of moving vehicles remains to be a challenging issue. In this paper, we introduce a stochastic traffic model to capture the space and time dynamics of vehicles in signalized urban road systems to identify poorly-connected regions for infrastructure node placements. To closely approximate the practical road conditions, we propose a density-dependent velocity profile to approximate vehicle interactions and capture platoons formation and dissipation at traffic signals. Numerical results are presented to evaluate the stochastic traffic model. In general, we show that the fluid model can adequately describe the mean behavior of the traffic stream, while the stochastic model can approximate the probability distribution well even when vehicles interact with each other as their movement is controlled by traffic lights. With the understandings of the vehicular density dynamics from the proposed model, we illustrate that connectivity dynamics of vehicles can be determined and consequent system engineering and planning can be carried out.

In a Vehicular Ad-hoc Network (VANET), the amount of interference from neighboring nodes to a communication link is governed by the vehicle density dynamics in vicinity and transmission probability of terminals. It is obvious that vehicles are distributed non-homogeneously along a road segment due to traffic controls and speed limits at different portions of the road, the common assumption of homogeneous node distribution in the network in most of the previous work in mobile ad-hoc networks thus appears to be inappropriate in VANETs. To capture the density dynamics in generic urban routes, we utilize a fluid model to characterize the general vehicular traffic flow, and a stochastic model to capture the randomness of individual vehicles, from which we can acquire respectively the densities of the mean number of vehicles along the road and the probability distribution. With the knowledge of the vehicular density dynamics from the stochastic traffic model, we determine the throughput and progress performances of a routing strategy, and confirm the accuracy of the analytical results through simulations. The analytical model proposed in this paper serves as a fundamental building block for performance analysis of other transmission protocols and network configurations, we also demonstrate that the optimal transmission probability for optimized network performance can be found from our results, which provides insights into system engineering and protocol designs in VANETs.

We study blind estimation of transmission power of a node based on received power measurements obtained under wireless fading. Specifically, the setup consists of a set of monitors that measure the signal power received from the transmitter, and the goal is to utilize these measurements to estimate the transmission power in the absence of any prior knowledge of the transmitter's location or any statistical distribution of its power. Towards this end, we exploit spatial diversity in received-power measurements and cooperation among the multiple monitoring nodes; based on theoretical analysis we obtain the Maximum Likelihood (ML) estimate, derive fundamental geometrical insights and show that this estimate is asymptotically optimal. Finally, we provide numerical results comparing the performance of the estimators through simulations and on a data-set of field measurements.

Transmit-power estimation is an important part in power-aware designs of mobile ad-hoc networks (MANETs). In this paper, we consider the cooperation among multiple monitor-nodes to estimate the transmit power of other nodes. Utilizing a geometric approach, we characterize the theoretical performance of such cooperative monitoring schemes and propose transmit-power estimation techniques with different number of cooperating nodes. We introduce the novel concept of confidence region that provides a fundamental confidence level for the accuracy of the power estimation and enables the development of techniques for allocating network monitors. Finally, we present a simple, distributed cooperative estimation scheme for a large-scale wireless network and give illustrative simulation results to quantify its performance.

Vehicular Ad hoc NETworks (VANETs) is a subclass of Mobile Ad hoc NETworks (MANETs). However, automotive ad hoc networks will behave in fundamentally different ways than the predominated models in MANET research. Driver behaviour, mobility constraints and high speeds create unique characteristics in the network. All of these constraints have implications on the VANET architecture at the physical, link, network, and application layers. To facilitate the cross-layer designs for VANETs, understanding of the relationship between mobility and network connectivity is of paramount importance. In this paper, we focus on studying transport systems with structured mobility (e.g., bus systems), which have unique characteristics on the road such as fixed routes that have never been explored in previous work. The main contributions of this paper are three-fold: 1) we provide an analytical framework including the design requirements of the mobility model for realistic vehicular network studies, and metrics for evaluating node connectivity in vehicular networks; 2) we demonstrate, through simulation, the impacts of marco- and micro-mobility models, and various transport elements on network connectivity; and 3) we show that multi-hop paths perform dramatically poorer than single-hop links in vehicular networks. Specifically, two- hop and three-hop (communication) paths can only respectively achieve less than 27% and 13% of the average duration of single-hop links. Such kind of knowledge of the performance of multi-hop transmission will be significant for the studies of routing algorithm and other networking functions in vehicular networks.

Optimizing spectral reuse is a major issue in large-scale IEEE 802.11 wireless networks. Power control is an effective means for doing so. Much previous work simply assumes that each transmitter should use the minimum transmit power needed to reach its receiver, and that this would maximize the network capacity by increasing spectral reuse. It turns out that this is not necessarily the case, primarily because of hidden nodes. This paper shows that in a network with power control, avoiding hidden nodes can achieve higher overall network capacity compared with the minimum-transmit-power approach. It is not always best to use the minimum transmit powers even from the network capacity viewpoint. Specifically, we propose and investigate two distributed adaptive power control algorithms that minimize mutual interferences among links while avoiding hidden nodes. Different power control schemes have different numbers of exposed nodes and hidden nodes, which in turn result in different network capacities and fairness. Although there is usually a fundamental tradeoff between network capacity and fairness, we show that, interestingly, this is not always the case. In addition, our power control algorithms can operate at desirable network- capacity-fairness tradeoff points, and can boost the capacity of ordinary non-power-controlled 802.11 networks by two times while eliminating hidden nodes.

Optimizing spectral reuse is a major issue in large-scale IEEE 802.11 wireless networks. Power control is an effective means for doing so. Much previous work simply assumes that each transmitter should use the minimum transmit power needed to reach its receiver, and that this would maximize the network capacity by increasing spectral reuse. It turns out that this is not necessarily the case, primarily because of hidden nodes. In a network without power control, it is well known that hidden nodes give rise to unfair network bandwidth distributions and large bandwidth oscillations. Avoiding hidden nodes (by extending the carrier-sensing range), however, may cause the network to have lower overall network capacity. This paper shows that in a network with power control, reducing the instances of hidden nodes can not only prevent unfair bandwidth distributions, but also achieve higher overall network capacity compared with the minimum-transmit-power approach. We propose and investigate two distributed adaptive power control algorithms that minimize mutual interferences among links while avoiding hidden nodes. In general, our power control algorithms can boost the capacity of ordinary non-power-controlled 802.11 networks by more than two times while eliminating hidden nodes

The seminar work of Gupta and Kumar showed that multi-hop wireless networks with capacity scalable with the number of nodes, n, are achievable in theory. The transport capacity scales as /spl Theta/(/spl radic/n), while the capacity scales as /spl Theta/(n). A subsequent study, on the other hand, showed that the capacity of IEEE 802.11 networks does not scale with n due to its carrier-sensing mechanism. This prior work, however, has not considered the use of power control. The main contributions of this paper are three-folds: 1) we provide an analytical framework for deriving the design requirements of adaptive power control strategies; 2) we demonstrate that 802.11 networks are scalable with power control; 3) however, an enhanced MAC protocol called selective disregard of NAVs (SDN) can achieve substantially higher capacity with an adaptive power control scheme; in particular, adaptive power control allows SDN to achieve capacity within 75% of the theoretical optimal capacity of infrastructure-mode wireless networks. A reason why adaptive power control works well is that it takes into consideration the fundamental mutual-interference relationships between links in the vicinity of each other, and adjust their relative transmit powers to reduce these interferences to a large extent that is possible theoretically.