The cellular base stations (BSs) have backup batteries to maintain uninterrupted power supply. Recent studies have shown that a backup battery may have some spare energy to act as a flexible resource. Similarly, electric vehicles (EVs) are also capable to give surplus energy stored in their batteries to other consumers or back to the grid. Therefore, both BSs and EVs can share energy among themselves through Telecom-to-Vehicle (T2V) and Vehicle-to-Telecom (V2T) exchange. However, the energy exchange is challenging in a disaster-affected region due to connectivity failures, power disruption and damaged routes. This paper proposes an energy exchange solution among BSs and EVs in a post disaster situation. We propose a digital-twin (DT) based solution which utilizes Artificial Intelligence (AI) algorithms to estimate energy consumption of BSs and EVs and identifies their role as energy buyers or sellers. It also models power disruption and disaster-affected blocked routes as Markov processes with parameters derived from real historic data of floods. Then, a reinforcement learning (RL) algorithm is proposed to match BSs and EVs which can feasibly take part in either T2V or V2T exchange. Performance of the proposed solution is compared with independent RL without DT and assisted by federated learning. Simulations show that energy exchanged by RL algorithm doubles with the utilization of DT.

With the rapid development of the Internet of Vehicles (IoV), the tight coupling between In-Vehicle Networks (IVN) and External Vehicle Networks (EVN) has made vehicular systems vulnerable to sophisticated cross-network attack chains. Existing Intrusion Detection Systems (IDS), however, typically operate in isolation on either IVN or EVN, and lack effective context-aware mechanisms for capturing inter-domain dependencies. To overcome this limitation, we propose CATwin-IDS, a context-aware intrusion detection framework that integrates digital twin technology with a lightweight Distilled Bidirectional Encoder Representations from Transformers (DistilBERT) model. In our design, Conditional Mutual Information (CMI) and Borderline Synthetic Minority Over-sampling Technique (Borderline-SMOTE) are applied for feature optimization and data balancing, while Temporal Self-Attention (TSA) enhances the modeling of spatiotemporal dependencies across heterogeneous traffic. The digital twin provides real-time bidirectional synchronization and a simulation environment, enabling proactive adaptation to dynamic threats. Experimental results on benchmark datasets (Car-Hacking, CICIoV2024, CICIDS2018, CICIoT2023) demonstrate that CATwin-IDS achieves higher accuracy and real-time efficiency compared with state-of-the-art methods, providing a holistic solution for securing IoV against cross-network intrusions.

Secure and trustworthy computation offloading is essential in vehicular edge computing to ensure reliability and efficiency. Existing algorithms often emphasize efficiency over security, leaving systems exposed to malicious providers. This paper presents the Reputation-Enhanced Vehicle Selection (REVS) framework, which combines social trust-based initialization, direction alignment, and a weighted trust score based on provider reputation and stay time. To enhance provider selection reliability, REVS employs a lightweight consortium blockchain for decentralized and distributed reputation management, with a smart contract deployed at the edge RSU to automate the selection process. Simulations show that REVS improves task success rates by up to 40.85%, avoids 40.70% more malicious providers, and reduces latency by 20%, outperforming fixed-reputation and random selection methods that ignore trust.

Increasing developments in vehicle automation and traffic systems have brought renewed focus to the control and coordination of vehicle platoons. This paper focuses on a novel control architecture based on behavioral stability that integrates high-level vehicle scheduling with low-level real-time control to enhance the stability, robustness and safety of vehicle platooning. To address performance degradation caused by inaccurate perception of parameters such as position, a two-layer driving control framework is proposed, consisting of a high-level scheduling controller and a low-level real-time controller. Kalman filtering is employed to reduce state estimation errors and an improved Model Predictive Controller (MPC) is designed to ensure platoon stability under uncertainty. Simulation results demonstrate that the proposed method reduces the longitudinal velocity error by approximately 23% and achieves a faster convergence than the traditional approaches, thus improving the cohesion of the platoon and the resistance to disturbances.

Virtual coupling (VC) is perceived to be promising in raising rail traffic capacity. In a train-to-train (T2T) based VC system, a communication network that ensures high quality of service (QoS) plays a critical role in enhancing both the coupling efficiency and the safety of the train platoon. However, unreliable communication environments characterized by issues such as time delays, packet loss, and network attacks present significant security risks to virtually coupled train sets (VCTS). How to cope with the impact caused by unstable communication and realize safe and stable VCTS formation are an important challenge for the VC system. In this paper, we propose a model predictive control (MPC) based distributed bidirectional control (DBC) strategy to tackle these challenges. We propose a control framework that integrates MPC with linear feedback-feedforward control to achieve real-time optimal control of the VC system, utilizing a bidirectional communication topology. To stabilize the VCTS, we derive local and string stability conditions to be satisfied by the controller parameters under asymmetric time-lagged unreliable networks, and utilize them as real-time constraints for the MPC controller. Furthermore, an analysis of the scalability of the proposed strategy has been conducted to improve its adaptability. Simulation results demonstrate that the proposed MPC-based DBC strategy significantly reduces the VCTS formation time and the maximum fluctuation of VCTS by 28.57% to 41.86%, and 28.84% to 52.10%, respectively, across various unreliable communication scenarios.

With the development of hardware and communication technology, human action recognition (HAR) in the Internet of Things (IoT) environment is gradually becoming the solution to problems such as long-term healthcare, security surveillance, etc. However, HAR in IoT faces challenges due to heterogeneous, uncertain, and resource-constrained sensing conditions. To address this, we propose FIFIAR, a lightweight adaptive fuzzy neural network (FNN)-based decision fusion framework designed for image-based HAR in IoT systems. FIFIAR learns fuzzy relationships across multiple modalities (RGB, depth, IR, skeleton) to reduce decision uncertainty and support efficient, real-time inference on edge devices. Experiments on MSR Daily Activity and NTU RGB+D 120 datasets show that FIFIAR achieves 99.8% and 96.36% accuracy, respectively, outperforming conventional fusion methods and demonstrating strong potential for real-world IoT deployments.

With increasing emphasis on vehicular automation and traffic efficiency, the management and coordination of platoon-based systems have become important. This research introduces a unique control framework based on a behavioral stability strategy, designed to enhance the cohesion of vehicle platoons and improve their ability to resist disturbances. Our approach integrates a vehicle scheduling system with a real-time platoon control mechanism to enhance the behavioral stability, robustness, and safety of the platoon. Given the heterogeneous nature of vehicles, we propose an optimal platoon formation model. This model strategically determines the number of platoons, arranges the sequence of vehicles within each platoon, and selects optimal cruising speeds to maximize platoon cohesion. To further enhance system robustness, a centralized robust model predictive controller is deployed for each platoon, ensuring stability against stochastic perturbations in vehicle dynamics and guaranteeing platoon safety. Finally, we conduct a simulation study involving multiple platoons with 20 heterogeneous vehicles to validate the effectiveness of the multi-layer optimization model.

Platoon-based connected vehicles have recently received increasing attention from academia and industry since they are considered promising solutions to transform our mobility society into the next generation. Vehicular communication and platoon coordination are two aspects of enabling technologies for mobile edge computing (MEC)-enabled cooperative vehicle-infrastructure systems (CVIS), while few efforts have incorporated these two dimensions into a joint implementation framework. In this paper, we investigate the problem of joint car-following coordination and data transmission scheduling of vehicle platoons. We develop a two-tier hierarchical framework for vehicle platooning: a fuel-efficient mobility optimization layer for car-following coordination and a reliable vehicle-to-infrastructure (V2I) communication layer for data transmission scheduling. Specifically, we present a platoon-based fuel consumption minimization model and a car-following control protocol to derive fuel-efficient control inputs. We also propose a reliability-oriented and delay-constrained data transmission scheduling model that is driven by upper-layer car-following coordination. We derived a closed-form expression for the reliability-optimal data transmission scheduling solution, which incorporates platoon mobility, channel characteristics, and application requirements. With simulations, we show that our joint method improves fuel efficiency and communication reliability for platooning vehicles. In particular, the proposed method reduces the platoon’s fuel consumption per time slot by 16.4%, meanwhile making the communication reliability 1.31 times higher than other traditional methods.

Platooning has been proven to dramatically increase traffic flow and reduce fuel consumption, and vehicle-to-vehicle (V2V) communication and sensing are requisite for platooning stability. However, most of the existing works only address V2V communication or sensing functions respectively, which is far away from meeting the 6G requirements in availability and synchronization for platooning applications. Inspired by the recent advanced integrated sensing and communication (ISAC), in this paper, we propose VehicleTalk, a lightweight V2V communication and sensing framework. Essentially, VehicleTalk reuses the head/tail LED lights of the vehicles to construct communication/sensing channels for achieving concurrent message exchange and status awareness between adjacent vehicles. In particular, VehicleTalk innovates in both message transmission and vehicle sensing to improve communication robustness and lower system latency for platooning. It leverages Raptor Codes to combat serious packet loss in the V2V network. We also engineer a fast risk detection algorithm by simply monitoring the strength change of the received optical signals from the head/tail lights to improve safety in extreme cases such as emergency brake and cutting-in. Finally, we build a prototype of VehicleTalk with low-cost Commercial Off-The-Shelf (COTS) devices to quickly verify its effectiveness, and the extensive experimental results demonstrate the promising performance of VehicleTalk.

Modern in-vehicle networks are shifting towards an Ethernet-based backbone where high bandwidth and low latency can be guaranteed. However, this comes with the cost of exposing the vehicle to more IP-based attacks such as blackholes and denial of service (DoS) attacks. To better secure the in-vehicle network, it is essential to provide efficient monitoring and anomaly detection mechanisms in order to detect such attacks. Software-defined networking (SDN) facilitates these tasks by providing a global view of the underlying network available at the SDN controller. To this end, we propose in this work an anomaly detection solution for SDN-enabled in-vehicle networks. In particular, we use deep learning and network tomography to monitor the network. The deep learning model used in this paper is based on deep autoencoder neural networks. Moreover, network tomography is leveraged so that only a subset of the network is monitored while the remaining can be inferred using the available measurements. We investigate anomaly detection using two types of statistics: flows and ports. We found that the flows' stats outperform the ports' stats in detecting anomalies. Moreover, by only monitoring selected flows, the proposed solution can detect anomalies with an accuracy of up to 99%. In addition, our approach can classify the type of attack, whether it is DoS, SYN flooding, or ARP spoofing attack with only 3% maximum error.

Virtual coupling is expected to primarily improve the capacity of a railway system. Virtual coupled systems are affected by multi-source disturbances due to the complex operating environment. However, existing research only partially considers the effects of structural or external disturbances, which limits the stability and robustness of the virtually coupled train set (VCTS). In this paper, we aim to tackle the challenges arising from both structural and external disturbances in virtual coupling. We specifically propose a distributed robust model predictive control (DRMPC) solution based on a linearized model by joining linear feedback and feedforward control into a model predictive control (MPC) framework with a discrete Kalman filter (DKF). We also theoretically derive and prove a set of sufficient conditions for both local and string stabilities under structural uncertainty. The stability conditions are incorporated into the constraint space of the distributed MPC framework in order to guarantee system stability in the presence of structural and external uncertainties. The simulation results validate that our proposed control method can stabilize train platooning under both structural and external disturbances. Our control method particularly reduces the spacing and velocity tracking errors by approximately 97.55% and 99.97% on average, respectively, as compared to several baselines.

Energy-efficiency (EE) optimization of long-term evolution (LTE) networks dedicated to vehicle-to-vehicle communications (LTE-V2V) is critical for connected vehicles. In this paper, we integrate perimeter control methodologies from transportation science into EE optimization to make vehicular communications adaptive to temporal-spatial dynamics of macroscopic traffic flows in multiple urban regions. Specifically, we develop a hierarchical framework of joint LTE-V2V EE optimization and perimeter traffic flow control. Its goal is to minimize the total traffic network delay, defined as the integral of the vehicle accumulations in the urban regions over a prediction horizon time, meanwhile maximizing the energy efficiency of the LTE-V2V communications in the same regions. We propose a model predictive perimeter controller at a low level, using a macroscopic fundamental diagram (MFD) to capture the relationship between the traffic density and the outflow of each urban region. We also propose a high-level EE optimization model and an iterative algorithm, considering the multi-region coordinated traffic dynamics, to jointly optimize vehicular transmission power and beacon frequency. Simulation results validate our proposed models and show that our method outperforms the latest solutions by improving at least 9.57% EE of the multiple regions. Our method can also provide 27.69% improvement in resource utilization fairness, indicating a fairer EE performance distribution among these regions.

Energy efficiency is a significant requirement of resource management and design optimization in information networks. In this article, we propose an iterative fractional programming framework embedded with a distributed primal-dual extra-gradient projection algorithm, which addresses a wide class of the energy-efficiency optimization problems in wireless ad hoc networks with full-duplex radios and multi-packet reception capability. Specifically, we propose a model convexification mechanism by joining an affine transformation and an exponential transformation into the nonlinear fractional programming, which enables us to deal with the challenge arising from the complexity and non-convex structure of the original problem. With the model convexification, we can map the non-convex power control space into a convex space and equivalently derive a sequence of convex subproblems, which relaxes the convexity assumption widely adopted in the existing literature. We further propose a distributed primal-dual algorithm based on extra-gradient projection to solve the convex subproblem at each iteration of the fractional programming. The convergence of the proposed iterative fractional programming and the distributed optimization method is theoretically proven. Numerical results also verify the proposed method and demonstrate its superior performance over other representative distributed and centralized schemes in terms of achieving global energy efficiency.

With the rapid development of the Internet of Things (IoT) related technologies, the application of digital twins (DT) in industry and healthcare becomes possible. Human activity recognition (HAR) is emerging as a hot research area with great potential in healthcare. Activity recognition systems combined with DT will make it easier to monitor human health conditions to improve the quality of life and happiness with individualized healthcare. In this paper, we design an effective HAR system, called HAR-Net, which uses WiFi time series data collected by sensors to train a deep learning network. Deep learning’s great learning ability is utilized to extract features of various human activities for activity recognition. We built the DT system with Unity, which is combined with the HAR system. In the DT system, real-world physical activities are mapped onto human models. The results of activity prediction can be evaluated in real-time in DT, and warnings can be issued quickly when dangerous activities occur. To make our human activity recognition system more adaptive, we propose a one-shot recognition method based on meta-learning. Specifically, we design a Bi-path basic network that extracts features in the time-domain and frequency-domain, and a meta-learning framework with a classification module and a WiFi metric module. Using datasets from different environments, we conducted various experiments on HAR-Net, and the results proved that our presented method was superior to the baseline network.

The high mobility feature of vehicular networks poses tremendous challenges to maintaining network connectivity. In this paper, we investigate the possibility of enhancing the connectivity of Cellular Vehicle-to-Everything (C-V2X) networks through distributed trajectory adjustment. Based on a physical layer abstraction model, we characterize the network connectivity enhancement problem as a network utility maximization and study its concavity. We propose a distributed trajectory updating algorithm that dynamically adjusts the trajectory of vehicles on top of their planned trajectory. The algorithm is distributed and requires only geo-location exchanges, which are readily available in V2X networks. Simulation results show that the mobility updating algorithm converges and improves the aggregated network utility by up to 48% compared to the scenarios without mobility tuning.

The rapid development of Internet of Things has led more realization of digital twins (DT), such as healthcare, smart homes, virtual reality, etc., gesture recognition is a fundamental component of DT. Its implementation can provide users with personalized services or improved human-computer interaction, such as smart home control, in-car interaction, etc., most of existing gesture recognition methods are based on vision or wearable device. However, the vision-based methods face the problem of privacy breach, whereas the wearable-based methods may bring inconvenience to users. With the wide deployment of Wi-Fi networks, lots of consumer devices are widely accessible in people’s homes. Motivated by the fact that Wi-Fi signal propagation can be affected by human motion, the opportunity to use Wi-Fi signals for gesture recognition can be further explored. However, the challenge is that the received Wi-Fi signal shows great differences when the same person performs the same gesture in different environments or different person performs the same gesture in the same environment. Therefore, the signal alignment across different domain needs to be solved. In this paper, we propose a gesture recognition system named Phase-Attention-based-Conv-CSI (PAC-CSI), which consists of two modules: data processing and gesture recognition. In the data processing module, we eliminate random phase noise in channel state information (CSI) and perform phase calibration. In the gesture recognition module, we feed the processed phase sequence into a lightweight deep neural network for gesture recognition. PAC-CSI can obtain the gesture category in about 200ms, which can meets the real-time requirements of DT. The gesture recognition accuracy of our proposed system in a single domain is 99.46%, and its performance across new locations, orientations, users, and environments is 98.77%, 98.90%, 97.54%, and 96.47%, respectively.

Radio frequency identification technology has been widely used to verify the presence of items in many applications such as warehouse management and supply chain logistics. In these applications, the challenge of how to timely identify the missing tags (namely tag searching or missing tag identification) is a key focus. Existing missing tag identification solutions have not achieved their full potentials because collision slots have not been well explored. In this paper, we propose an approach named collision resolving based missing tag identification (CR-MTI) to break through the performance bottleneck of existing missing tag identification protocols. In CR-MTI, multiple tags are allowed to respond with different binary strings in a collision slot. Then, the reader can verify them together by using the bit tracking technology and particularly designed string, thereby significantly improve the time efficiency. CR-MTI also reduces the number of messages transmitted by the reader using customized coding. We further explore the optimal parameter settings to maximize the performance of our proposed CR-MTI. Extensive simulation results show that our proposed CR-MTI outperforms prior art in terms of time efficiency, total executive time and communication complexity.

How to obtain the information from massive tags is a key focus of RFID applications. The occurrence of collisions leads to problems such as reduced identification efficiency in RFID networks. To tackle such challenges, most tag collision arbitration protocols focus on scheduling tag identification with collision avoidance. However, how to effectively identify tags in collisions to improve identification efficiency has not been well explored. In this paper, we propose a group query allocation method to divide the string space into mutually disjoint subsets which contains several strings. Each string can be viewed as a full ID or partial ID of a tag. When multiple string from a subset are sent simultaneously, the reader can identify all of them in a time slot. Based on the group query allocation method, a segment detection based characteristic group query tree (SD-CGQT) protocol is presented for fast tag identification by significantly reducing the collision slots and transmitted bits. Numerous experimental results verify the superiority of the proposed SD-CGQT, compared to prior arts in system efficiency, total identification time, communication complexity and energy consumption.

Due to the increased complexity of new in-vehicle networking architectures, which makes direct monitoring of internal network components intractable, alternative solutions are required to tackle this issue. One solution is to leverage the end-to-end measurements to estimate the internal network performance. To this end, we propose to employ network tomography as a monitoring approach for in-vehicle networks. Network tomography can infer the overall network performance by measuring only subset of the network. We investigate the use of network tomography in in-vehicle network by analysing network identifiability of three main architectures: bus-based, central-gateway, and Ethernet-based architectures. Our analysis results indicate the applicability of network tomography in in-vehicle networks based on certain topological and monitors' conditions. Furthermore, we validate our analytical results through simulation which shows a maximum error of only$174\mu s$. Moreover, we compare the proposed approach with one of existing solutions and show that network tomography achieves better bandwidth and latency performance with monitoring overhead saving up to 52.2% and$782.3\mu s$, respectively.

Vehicular networks are vulnerable to large scale attacks. Blockchain, implemented upon application layer, is recommended as one of the effective security and privacy solutions for vehicular networks. However, due to an increasing complexity of connected nodes, heterogeneous environment and rising threats, a robust security solution across multiple layers is required. Motivated by the Physical Layer Security (PLS) which utilizes physical layer characteristics such as channel fading to ensure reliable and confidential transmission, in this paper we analyze the impact of PLS on a blockchain-enabled vehicular network with two types of physical layer attacks, i.e., jamming and eavesdropping. Throughout the analysis, a Full Duplex Non-Orthogonal Multiple Access (FD-NOMA) based vehicle-to-everything (V2X) is considered to reduce interference caused by jamming and meet 5G communication requirements. Simulation results show enhanced goodput of a blockckchain enabled vehicular network integrated with PLS as compared to the same solution without PLS.

The real-time intelligent perception and prediction of traffic situation can assist connected automated vehicles (CAVs) in path planning and reduce traffic congestion in Cognitive Internet of Vehicles (CIoVs). The centralized traffic congestion prediction solutions generally fail to adapt to the dynamic traffic environment and lead to significant communication overheads. Blockchain technology has attracted great attention in the information sharing of vehicular networks for its advantages in decentralization, transparency, traceability, and tamper-proof capability. However, due to the bottlenecks, such as high computational cost, current blockchains are incapable actuate on efficient online traffic situational cognition and prediction for CIoVs. Motivated by this, we propose a blockchain-enabled cognitive segments sharing framework for online multistep congestion duration prediction. We design a cognitive model of traffic situation based on anomaly detection and filtering mechanism to guarantee the accuracy of the cognitive segments before being packaged into the block. Furthermore, to improve the consensus efficiency, we design a credit evaluation mechanism and propose a credit-based delegated Byzantine fault tolerance (CDBFT) algorithm. Finally, we propose an online multistep prediction algorithm based on long short-term memory (LSTM) to predict future traffic congestion duration. Experimental results demonstrate that the proposed algorithms achieve shorter consensus latency and higher predictive accuracy than the existing algorithms.

With the rapid development of mobile computing, mobile-edge computing (MEC) has increasingly become an essential means to meet the computing power requirements of intelligent networked vehicles. However, users with high mobility and coupled dynamics are rarely considered in the edge computing paradigms. In this article, we studied a UAV-assisted MEC system with multiplatoon vehicles. Our article aims to maximize the system’s weighted global energy efficiency, which can flexibly adjust each vehicle’s energy consumption according to user preferences and system needs. In particular, we design a controller for platooning vehicles based on a 2-D path-following model and Frenet frames, and model the coupled characteristics of air-to-ground communications and onboard computation. Furthermore, due to the nonconvexity of the objective function and constraints of the optimization problem, we propose an optimization algorithm based on the sequential quadratic programming (SQP) method. The simulation results show that the proposed method significantly surpasses conventional schemes.

For vehicularad hocnetworks (VANETs), edge caching has attracted considerable research attention to maximize the efficiency and reliability of infotainment applications. In this article, we propose a two-layer distributed content caching scheme for VANETs by jointly exploiting the cache at both vehicles and roadside units (RSUs). Specifically, we formulate the content caching problem to minimize the overall transmission delay and cost as a nonlinear integer programming (NLIP) problem and propose an alternate dynamic programming search (ADPS)-based algorithm to solve it. In ADPS, we divide the original problem into three subproblems and then we use the dynamic programming (DP) method to solve each subproblem separately. To reduce the complexity, we further propose a cooperation-based greedy (CBG) algorithm to solve the large-scale original problem. Both numerical simulation results and experiments in the testbed show that the proposed caching scheme outperforms existed caching schemes, and the transmission delay and cost can be reduced by 10% and 24%, respectively, while the hit ratio can be increased by 30% in a practical environment, as compared to the popularity-based caching scheme.

Connected and autonomous vehicles (CAVs) are recently envisioned to provide a tremendous social impact, while they put forward a much higher requirement for both vehicular communication and computation capacities to process resource-intensive applications. In this paper, we study unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) for a platoon of wireless power transmission (WPT)-enabled vehicles. Our objective is to maximize the system-wide computation capacity under both communication and computation resource constraints. We incorporate the coupled effects of the platooning vehicles and the flying UAV, air-to-ground (A2G) and ground-to-air (G2A) communications, onboard computing and energy harvesting into a joint scheduling optimization model of communication and computation resources. To tackle the resulting optimization problem, we propose a successive convex programming method based on a second-order convex approximation, in which feasible search directions are obtained by solving a sequence of quadratic programming subproblems and used to generate feasible points that can approach a local optimum. We also theoretically prove the feasibility and convergence of the proposed method. Moreover, simulation results are provided to validate the effectiveness of our proposed method and demonstrate its superior performance over other conventional schemes.

Platoon-based vehicular cyber-physical systems have gained increasing attention due to their potentials in improving traffic efficiency, capacity, and saving energy. However, external uncertain disturbances arising from mismatched model errors, sensor noises, communication delays and unknown environments can impose a great challenge on the constrained control of vehicle platooning. In this paper, we propose a closed-loop min-max model predictive control (MPC) with causal disturbance feedback for vehicle platooning. Specifically, we first develop a compact form of a centralized vehicle platooning model subject to external disturbances, which also incorporates the lower-level vehicle dynamics. We then formulate the uncertain optimal control of the vehicle platoon as a worst-case constrained optimization problem and derive its robust counterpart by semidefinite relaxation. Thus, we design a causal disturbance feedback structure with the robust counterpart, which leads to a closed-loop min-max MPC platoon control solution. Even though the min-max MPC follows a centralized paradigm, its robust counterpart can keep the convexity and enable the efficient and practical implementation of current convex optimization techniques. We also derive a linear matrix inequality (LMI) condition for guaranteeing the recursive feasibility and input-to-state practical stability (ISpS) of the platoon system. Finally, simulation results are provided to verify the effectiveness and advantage of the proposed MPC in terms of constraint satisfaction, platoon stability and robustness against different external disturbances.

Radio frequency identification (RFID) has been widely applied in large-scale applications such as logistics, merchandise and transportation. However, it is still a technical challenge to effectively estimate the number of tags in complex mobile environments. Most of existing tag identification protocols assume that readers and tags remain stationary throughout the whole identification process and ideal channel assumptions are typically considered between them. Hence, conventional algorithms may fail in mobile scenarios with unreliable channels. In this paper, we propose a novel RFID anti-collision algorithm for tag identification considering path loss. Based on a probabilistic identification model, we derive the collision, empty and success probabilities in a mobile RFID environment, which will be used to define the cardinality estimation method and the optimal frame length. Both simulation and experimental results of the proposed solution show noticeable performance improvement over the commercial solutions.

In the era of the Internet of Everything, autonomous driving has put forward a higher ambition for data transmission capabilities. This paper studies joint scheduling of computation and communication resources in the collaborative networking of unmanned aerial vehicles (UAV s) and platooning vehicles in mobile edge computing (MEC) framework to maximize the energy efficiency. Considering the movement characteristics of vehicles, we integrate mobility, communication, computation, and energy consumption to establish a collective optimization problem. Since this multivariate coupled model is non-convex, we further propose a joint optimization method (JOM) algorithm based on the convex approximation theory, particularly quadratic programming. Experimental results verify that this algorithm converges quickly within a dozen iterations and proves to be superior to several other benchmark schemes.

Blockchain applications in vehicular networks can offer many advantages, including decentralization and improved security. However, most of the consensus algorithms in blockchain are difficult to be implemented in vehicular ad hoc networks (VANETs) without the help of edge computing services. For example, the connectivity in VANET only remains for a short period of time, which is not sufficient for highly time-consuming consensus algorithms, e.g., Proof of Work, running on mobile-edge nodes (vehicles). Other consensus algorithms also have some drawbacks, e.g., Proof of Stake (PoS) is biased toward nodes with a higher amount of stakes and Proof of Elapsed Time (PoET) is not highly secure against malicious nodes. For these reasons, we propose a voting blockchain based on the Proof-of-Quality-Factor (PoQF) consensus algorithm, where the threshold number of votes is controlled by edge computing servers. Specifically, PoQF includes voting for message validation and a competitive relay selection process based on the probabilistic prediction of channel quality between the transmitter and receiver. The performance bounds of failure and latency in message validation are obtained. This article also analyzes the throughput of block generation, as well as the asymptotic latency, security, and communication complexity of PoQF. An incentive distribution mechanism to reward honest nodes and punish malicious nodes is further presented and its effectiveness against the collusion of nodes is proved using the game theory. Simulation results show that PoQF reduces failure in validation by 11% and 15% as compared to PoS and PoET, respectively, and is 68 ms faster than PoET.

Task offloading decision making plays a key role in enabling mobile-edge computing (MEC) technologies in Internet of Things (IoT). However, it meets the significant challenges arising from the stochastic dynamics of task queueing in the application layer and coupled wireless interference in the physical layer in a distributed multiagent network without any centralized communication and computing coordination. In this article, we investigate the distributed task offloading optimization problem with consideration of the upper layer queueing dynamics and the lower-layer coupled wireless interference. We first propose a new optimization model that aims at maximizing the expected offloading rate of multiple agents by optimizing their offloading thresholds. Then, we transform the problem into a game-theoretic formulation, which further leads to the design of a distributed best-response (DBR) iterative optimization framework. The existence of Nash equilibrium strategies in the game-theoretic model has been analyzed. For the individual optimization of each agent's threshold policy, we further propose a programming scheme by transforming a constrained threshold optimization into an unconstrained Lagrangian optimization (ULO). The individual ULO is integrated into the DBR framework to enable agents to cooperate and converge to a global optimum in a distributed manner. Finally, simulation results are provided to validate the proposed method and demonstrate its significant advantage over other existing distributed methods. The numerical results also show that the proposed method can achieve comparable performance to a centralized optimization method.

This paper comprehensively discusses the cooperative communication and computation of vehicular system. Based on the cooperative transmission, an stochastic model of vehicle-to-vehicle (V2V) communication reliability is established using probability theory. Furthermore, the computation reliability is defined as a new metric for computation offloading, and a vehicle computational performance evaluation model is also established. In order to effectively compute the required data, we combine V2V communication and vehicle computing to further characterize the coupling reliability of cooperative communications and computation systems. In addition, we propose a virtual queue model that combines queue length and vehicle privacy entropy to optimize partitioning. Finally, considering the amount of processing data and cut-off time of vehicle applications, we establish the optimal partition model of vehicle computing with the goal of maximizing the coupling reliability, and propose the coupling-oriented reliability calculation for vehicle collaboration using dynamic programming methods. Simulations show that the proposed scheme outperforms traditional approaches in terms of coupling reliability and completion rate. In addition, the allocation between local computing and data offloading is controlled by the server’s privacy perception of collaboration events.

Secure message dissemination is an important requirement of intelligent transportation systems (ITS). Existing solutions, such as broadcasting, are effective in flooding a message to a wider area, however, they are inherently unreliable and bandwidth inefficient. Furthermore, it is difficult to both assess the authenticity of a message and maintain the privacy of sender in a single solution. Moreover, as a practical solution, there is a need of economic modeling to incentivise vehicles for safe driving and cooperation. This paper proposes a blockchain based message dissemination approach which utilises incentive distribution and reputation management to overcome these challenges. Specifically, with the proposed voting based consensus algorithm, it can assess the authenticity of a message and select the most suitable relay node for its dissemination in a completely decentralised fashion. Meanwhile, the blockchain based integrated incentive and reputation scheme encourages the cooperation among vehicles and strengthens its ability to deliver authentic messages. The security capacity of the proposed solution is demonstrated by a game theoretic analysis. Simulation results show that the proposed approach can save average consensus time by 11% and improve success rate of authentic message dissemination by 17% with less number of hops as compared to the existing solutions.

The tag collision avoidance has been viewed as one of the most important research problems in RFID communications and bit tracking technology has been widely embedded in query tree (QT) based algorithms to tackle such challenge. Existing solutions show further opportunity to greatly improve the reading performance because collision queries and empty queries are not fully explored. In this paper, a bit query (BQ) strategy based M-ary query tree protocol (BQMT) is presented, which can not only eliminate idle queries but also separate collided tags into many small subsets and make full use of the collided bits. To further optimize the reading performance, a modified dual prefixes matching (MDPM) mechanism is presented to allow multiple tags to respond in the same slot and thus significantly reduce the number of queries. Theoretical analysis and simulations are supplemented to validate the effectiveness of the proposed BQMT and MDPM, which outperform the existing QT-based algorithms. Also, the BQMT and MDPM can be combined to BQ-MDPM to improve the reading performance in system efficiency, total identification time, communication complexity and average energy cost.

Identification efficiency is a key performance metrics to evaluate the ultra high frequency (UHF) based radio frequency identification (RFID) systems. In order to solve the tag collision problem and improve the identification rate in large scale networks, we propose a collision arbitration strategy termed as group-based binary splitting algorithm (GBSA), which is an integration of an efficient tag cardinality estimation method, an optimal grouping strategy and a modified binary splitting. In GBSA, tags are properly divided into multiple subsets according to the tag cardinality estimation and the optimal grouping strategy. In case that multiple tags fall into a same time slot and form a subset, the modified binary splitting strategy will be applied while the rest tags are waiting in the queue and will be identified in the following slots. To evaluate its performance, we first derive the closed-form expression of system throughput for GBSA. Through the theoretical analysis, the optimal grouping factor is further determined. Extensive simulation results supplemented by prototyping tests indicate that the system throughput of our proposed algorithm can reach as much as 0.4835, outperforming the existing anti-collision algorithms for UHF RFID systems.

The evolution of Internet of Things (IoT) devices and their adoption in new generation intelligent systems has generated a huge demand for wireless bandwidth. This bandwidth problem is further exacerbated by another characteristics of IoT applications, i.e., IoT devices are usually deployed in massive number, thus leading to an awkward scenario that many bandwidth-hungry devices are chasing after the very limited wireless bandwidth within a small geographic area. As such, cognitive radio has received much attention of the research community as an important means for addressing the bandwidth needs of IoT applications. When enabling IoT devices with cognitive functionalities including spectrum sensing, dynamic spectrum accessing, circumstantial perceiving, and self-learning, one will also need to fully study other critical issues such as standardization, privacy protection, and heterogeneous coexistence. In this article, we investigate the structural frameworks and potential applications of cognitive IoT. We further discuss the spectrum-based functionalities and heterogeneity for cognitive IoT. Security and privacy issues involved in cognitive IoT are also investigated. Finally, we present the key challenges and future direction of research on cognitive-radio-based IoT networks.

Cognitive radio-enabled vehicular nodes as unlicensed users can competitively and opportunistically access the radio spectrum provided by a licensed provider and simultaneously use a dedicated channel for vehicular communications. In such cognitive vehicular networks, channel access optimization plays a key role in making the most of the spectrum resources. In this paper, we present the competition among self-interest-driven vehicular nodes as an evolutionary game and study fundamental properties of the Nash equilibrium and the evolutionary stability. To deal with the inefficiency of the Nash equilibrium, we design a delayed pricing mechanism and propose a discretized replicator dynamics with this pricing mechanism. The strategy adaptation and the channel pricing can be performed in an asynchronous manner, such that vehicular users can obtain the knowledge of the channel prices prior to actually making access decisions. We prove that the Nash equilibrium of the proposed evolutionary dynamics is evolutionary stable and coincides with the social optimum. Besides, performance comparison is also carried out in different environments to demonstrate the effectiveness and advantages of our method over the distributed multi-agent reinforcement learning scheme in current literature in terms of the system convergence, stability and adaptability.

The emergence of vehicular networking enables distributed cooperative computation among nearby vehicles and infrastructures to achieve various applications that may need to handle mass data by a short deadline. In this paper, we investigate the fundamental problems of a cooperative vehicle-infrastructure system (CVIS): how does vehicular communication and networking affect the benefit gained from cooperative computation in the CVIS and what should a reliability-optimal cooperation be? We develop an analytical framework of reliability-oriented cooperative computation optimization, considering the dynamics of vehicular communication and computation. To be specific, we propose stochastic modeling of V2V and V2I communications, incorporating effects of the vehicle mobility, channel contentions, and fading, and theoretically derive the probability of successful data transmission. We also formulate and solve an execution time minimization model to obtain the success probability of application completion with the constrained computation capacity and application requirements. By combining these models, we develop constrained optimizations to maximize the coupled reliability of communication and computation by optimizing the data partitions among different cooperators. Numerical results confirm that vehicular applications with a short deadline and large processing data size can better benefit from the cooperative computation rather than non-cooperative solutions.

Radio-frequency identification (RFID) is a major enabler of Internet of Things (IoT), and has been widely applied in tag-intensive environments. Tag collision arbitration is considered as a crucial issue of such RFID system. To enhance the reading performance of RFID, numerous anti-collision algorithms have been presented in previous literatures. However, most of them suffer from the slot efficiency bottleneck of 0.368. In this paper, we revisit the performance of tag identification in Aloha-based RFID anti-collision approaches from the perspective of time efficiency. Based on comprehensive reviews and analysis of the existing algorithms, a novel partitioning approach is proposed to maximize identification performance in framed slotted Aloha based UHF RFID systems. In the proposed approach, the tag set is divided into many groups which only contains a few tags, and then each group is identified in sequence. Benefiting from the optimal partition, the proposed algorithm can achieve a significant performance improvement. Simulation results supplemented by prototyping tests show that the proposed solution achieves an asymptotical slot efficiency up to 0.4348, outperforming the existing UHF RFID solutions.

Radio frequency identification (RFID) is widely applied in massive items tagged domains. Existing medium access control (MAC) solutions primarily focus on improving slot efficiency or reducing the total number of slots. However, with pervasive applications of RFID, the time and energy consumption are increasingly important and should be considered in the new design. In this paper, we re-exam the problem of tag identification in UHF RFID system from the perspective of time and energy consumption. The presented work comprehensively reviews and analyzes the prior tag reading protocols. Based on prior art, we further discuss a novel design of tag reading algorithm to improve both time and energy efficiency of EPC C1 Gen2 UHF RFID standard. By exploring the effectiveness of embedding slot-by-slot mechanism in a sub-frame observation phase and combine the sub-frame and slot-by-slot observation in the proposed algorithm, which can achieve more fine-grained frame size adjustment with time and energy-efficiency. Moreover, the cardinality estimation function of the algorithm is implemented by the look-up tables, which allows dramatically reduction in computational complexity and energy consumption. Both simulation results and experiments show clear performance improvement over the commercial solutions.

Computation offloading is critical for mobile applications that are sensitive to computational power, while dynamic and random nature of vehicular networks makes it challenging to guarantee the reliability of vehicular computation offloading. In this letter, we propose a reliability-oriented stochastic optimization model based on the dynamic programming for computation offloading in the presence of the deadline constraint on application execution. Specifically, a theoretical lower bound of the expected reliability of computation offloading is derived, and then an optimal data transmission scheduling mechanism is proposed to maximize the lower bound with consideration of randomness in vehicle-to-infrastructure communications. Experimental results demonstrate that our mechanism can outperform the conventional scheme and benefits vehicular computation offloading in terms of reliability performance in stochastic situations.

Efficient and effective objects identification using radio frequency identification (RFID) is always a challenge in large-scale industrial and commercial applications. Among existing solutions, the tree-based splitting scheme has attracted increasing attention because of its high extendibility and feasibility. However, the conventional tree splitting algorithms can only solve tag collision with counter value equals to zero and usually result in performance degradation when the number of tags is large. To overcome such drawbacks, we propose a novel tree-based method called fast splitting algorithm based on consecutive slot status detection (FSA-CSS), which includes a fast splitting (FS) mechanism and a shrink mechanism. Specifically, the FS mechanism is used to reduce collisions by increasing commands when the number of consecutive collision is above a threshold, whereas the shrink mechanism is used to reduce extra idle slots introduced by the FS. Simulation results supplemented by prototyping tests show that the proposed FSA-CSS achieves a system throughput of 0.41, outperforming the existing ultra high frequency RFID solutions.

With energy scarcity and environmental pollution becoming increasingly serious, the accurate estimation of fuel consumption of vehicles has been important in vehicle management and transportation planning toward a sustainable green transition. Fuel consumption is calculated by fuel-level data collected from high-precision fuel-level sensors. However, in the vehicle monitor platform, there are many types of error in the data collection and transmission processes, such as the noise, interference, and collision errors that are common in the high speed and dynamic vehicle environment. In this paper, an effective method for cleaning and repairing the fuel-level data is proposed, which adopts the threshold to acquire abnormal fuel data, the time quantum to identify abnormal data, and linear interpolation based algorithm to correct data errors. Specifically, a modified Gaussian mixture model (GMM) based on the synchronous iteration method is proposed to acquire the thresholds, which uses the particle swarm optimization algorithm and the steepest descent algorithm to optimize the parameters of GMM. The experiment results based on the fuel-level data of vehicles collected over one month prove that the modified GMM is superior to GMM-expectation maximization on fuel-level data, and the proposed method is effective for cleaning and repairing outliers of fuel-level data.

Vehicular ad hoc networks (VANETs) have a potential to promote vehicular telematics and infotainment applications, where a key and challenging issue is the design of robust and efficient vehicular content transmissions to combat the lossy inter-vehicle links. In this paper, we focus on the robust optimization of content transmissions over cooperative VANETs. We first derive a stochastic model for estimation of time-varying inter-vehicle distance, which is dependent of the vehicle real-time kinematics and the distribution of the initial space headway. With this model, we analytically formulate the transient inter-vehicle connectivity assuming Nakagami fading channels for the physical (PHY) layer. We also model the contention nature of the medium access control (MAC) layer, on which we are based to evaluate the throughput achieved by each vehicle equipped with dedicated short-range communication (DSRC). Combining these models, we derive a closed-formed expression for the upper bound of the probability of failure in intact-content transmissions. Based upon this theoretical bound, we develop a robust optimization model for assigning content data traffic among different cooperative transmission paths, where the objective is to minimize the maximum likelihood of unsuccessful content transmissions over the cooperative VANET. We mathematically transform the optimization model to another equivalent form, such that it can be practically deployed. Finally, we validate our theoretical development with extensive simulations. Numerical results are also provided to confirm the power of cooperation in boosting the VANET performance as well as demonstrate the advantage of the proposed robust optimization in terms of content data reception reliability.

Vehicular ad hoc networks (VANETs) can help reduce traffic accidents through broadcasting emergency messages among vehicles in advance. However, it is a great challenge to timely deliver the emergency messages to the right vehicles which are interested in them. Some protocols require to collect nearby real-time information before broadcasting a message, which may result in an increased delivery latency. In this paper, we proposed an improved position-based protocol to disseminate emergency messages among a large scale vehicle networks. Specifically, defined by the proposed protocol, messages are only broadcasted along their regions of interest, and a rebroadcast of a message depends on the information including in the message it has received. The simulation results demonstrate that the proposed protocol can reduce unnecessary rebroadcasts considerably, and the collisions of broadcast can be effectively mitigated.

We present a bio-inspired unicast routing protocol for vehicular ad hoc networks which uses the cellular attractor selection mechanism to select next hops. The proposed unicast routing protocol based on attractor selecting (URAS) is an opportunistic routing protocol, which is able to change itself adaptively to the complex and dynamic environment by routing feedback packets. We further employ a multiattribute decision-making strategy, the technique for order preference by similarity to an ideal solution, to reduce the number of redundant candidates for next-hop selection, so as to enhance the performance of attractor selection mechanism. Once the routing path is found, URAS maintains the current path or finds another better path adaptively based on the performance of current path, that is, it can self-evolution until the best routing path is found. Our simulation study compares the proposed solution with the stateof-the-art schemes, and shows the robustness and effectiveness of the proposed routing protocol and the significant performance improvement, in terms of packet delivery, end-to-end delay, and congestion, over the conventional method.

The technology of Internet of Things (IoT) has been gaining great popularity in recent years, as it provides an effective and immediate bridge between the physical world and the virtual objects in the cyber space, which can lead to innovative applications and services with high efficiency and productivity. However, IoT is just at the beginning stage of a longer journey. In-depth research and development efforts on systems, networks and architectures of IoT for efficient large-scale deployments are still required to fill the gaps between the current performance and service requirements, particularly with the predicted importance of IoT in the upcoming years, improved connectivity and communication among numerous devices will become necessary and critical.

In recent years, Industrial Internet of Things (IIoT) has attracted growing attention from both academia and industry. Meanwhile, when traditional wireless sensor networks are applied to complex industrial field with high requirements for real time and robustness, how to design an efficient and practical cross-layer transmission mechanism needs to be fully investigated. In this paper, we propose a Q-learning-based dynamic spectrum access method for IIoT by introducing cognitive self-learning technical solution to solve the difficulty of distributed and ordered self-accessing for unlicensed terminals. We first devise a simplified MAC access protocol for unlicensed users to use single available channel. Then, a Q-learning-based multi-channels access scheme is raised for the unlicensed users migrating to other lower cells. The channel with most Q value will be considered to be selected. Every mobile terminals store and update their own channel lists due to distributed network mode and non-perfect sensing ability. Numerical results are provided to evaluate the performances of our proposed method on dynamic spectrum access in IIoT. Our proposed method outperforms the traditional simplified accessing methods without self-learning capability on channel usage rate and conflict probability.

Advances in future computing to support emerging sensor applications are becoming more important as the need to better utilize computation and communication resources and make them energy efficient. As a result, it is predicted that intelligent devices and networks, including mobile wireless sensor networks (MWSN), will become the new interfaces to support future applications. In this paper, we propose a novel approach to minimize energy consumption of processing an application in MWSN while satisfying a certain completion time requirement. Specifically, by introducing the concept of cooperation, the logics and related computation tasks can be optimally partitioned, offloaded and executed with the help of peer sensor nodes, thus the proposed solution can be treated as a joint optimization of computing and networking resources. Moreover, for a network with multiple mobile wireless sensor nodes, we propose energy efficient cooperation node selection strategies to offer a tradeoff between fairness and energy consumption. Our performance analysis is supplemented by simulation results to show the significant energy saving of the proposed solution.

With market-driven secondary spectrum trading, licensed users can receive benefits in terms of monetary rewards or various transmission services, thus setting a fair pricing structure by suitably defining spectrum quality characteristics and accurately addressing participant's requirement is a key issue. In this paper, we investigate the pricing-based spectrum access by casting the problem of spectrum pricing into a Hotelling game model according to spectrum quality diversity. Particularly, we first build a pricing system model where unused spectrum from primary systems with different qualities forms a spectrum pool and can be divided into a number of uniform channels. A secondary user purchases a channel for usage according to its selection preference which is closely related to the channel quality and spectrum evaluation. The secondary user not only needs to consider the channel's quality and price, but also the interference cost on primary system. Detailed analysis on the policy preference of both primary system and secondary buyer are provided. By forming a game problem of spectrum pricing between primary and secondary users, we apply the Hotelling game model to handle the interaction between the participants. Specifically, by fixing Nash equilibrium of the game, an iterative algorithm for spectrum pricing is proposed based on the distribution characteristics of secondary user's preference. Essential analysis for the existence and uniqueness of the Nash equilibrium along with algorithm's convergence conditions are provided. Numerical results are also supplemented to show the effectiveness of the proposed algorithm in ensuring spectrum owner's profit.

The increasing complexity of automotive electronics has put considerable pressure on automotive communication networking to accommodate in-vehicle information flows. The use of power lines has been a promising alternative to in- vehicle communications because of elimination of extra data cables. In this paper, we focus on the latest HomePlug Green PHY (HPGP) which has been promoted by major automotive manufacturers for green communications with electric vehicles, and study its worst-case access delay performance in supporting Delay-critical in-vehicle applications using both theoretical analysis and the simulation. Specifically, we apply Network Calculas as a deterministic modeling approach to evaluate the worst delay and further verify its performance using the OMNeT++ simulation. Evaluation results are also supplemented to compare with legacy methods and provide useful guidelines for developing HPGP based vehicular power line communication systems.

The tag collision problem is considered as one of the critical issues in RFID system. Recently, bit tracking technology has been proposed for query tree (QT) based protocols to resolve tag collision efficiently. However, the performance of these protocols remain to be improved due to unused collided bits and idle slots. In this paper, a query method Bit query is presented, which requires the tag to respond a mapped bit string instead of its ID sequence. Compared with traditional ID query, it not only can eliminate idle queries, but also can separate collided tags into many small subsets and make full use of the collided bits as well. Based on this method, a novel query tree protocol Bit Query based M-ary Tree (BMQT) protocol is proposed, which recursively resolves collisions by forming a M-ary tree, and optimally switches from Bit query mode to ID query mode for quickly identifying the tags when tag is readable. Theoretical analysis and simulation results show that the system efficiency of BMQT is closed to 0.89, which outperforms the other existing QT-based and hybrid algorithms.

Many vehicular ad hoc applications rely on vehicular broadcasting-based multi-hop routing to disseminate messages. In this work, we study the question of vehicular broadcasting-based routing. In particular, by modelling the vehicular message dissemination with a limited-time epidemic dynamics, we propose an online self-adaptive beaconing method to dynamically learn the optimal beaconing policy for vehicular broadcasting with consideration of varying opportunistic contacts between vehicles. The vehicular broadcasting incorporated within the proposed method can ensure message delivery with low dissemination delay and routing cost. Both theoretical analysis and simulation results are provided to exhibit the robustness and effectiveness of the proposed solution and the significantly performance with respect to the conventional solution.

With growing needs to better understand our environments, the Internet-of-Things (IoT) is gaining importance among information and communication technologies. IoT will enable billions of intelligent devices and networks, such as wireless sensor networks, to be connected and integrated with computer networks. In order to support large scale networks, IETF has defined the routing protocol for low power and lossy networks (RPL) to facilitate the multihop connectivity. In this paper, we provide an in-depth review of current research activities. Specifically, the large scale simulation development and performance evaluation under various objective functions and routing metrics are pioneering works in RPL study. The results are expected to serve as a reference for evaluating the effectiveness of routing solutions in large scale IoT use cases.

In this paper, we study the problem of distributed information estimation that is closely relevant to some network-based applications, such as distributed surveillance, cooperative localization, and optimization. We consider a problem where an application area containing multiple information sources of interest is divided into a series of subregions in which only one information source exists. The information is presented as a signal variable, which has finite states associated with certain probabilities. The probability distribution of information states of all the subregions constitutes a global information picture for the whole area. Agents with limited measurement and communication ranges are assumed to monitor the area, and cooperatively create a local estimate of the global information. To efficiently approximate the actual global information using individual agents' own estimates, we propose an adaptive distributed information fusion strategy and use it to enhance the local Bayesian rule-based updating procedure. Specifically, this adaptive fusion strategy is induced by iteratively minimizing a Jensen-Shannon divergence-based objective function. A constrained optimization model is also presented to derive minimum Jensen-Shannon divergence weights at each agent for fusing local neighbors' individual estimates. Theoretical analysis and numerical results are supplemented to show the convergence performance and effectiveness of the proposed solution.

In this paper, we propose an efficient identification algorithm for RFID systems based on EPC C1 Gen2 RFID standard1. Specifically, the proposed anti-collision algorithm is based on the observation of sub-frame during an identification process, and makes effective use of idle and collision statistics to accurately estimate the tag backlog and determine the proper frame size for the next inventory round. Simulation results are supplemented to demonstrate the advantages of the proposed algorithm in achieving time and computation efficiency.

There is an urgent need for smart transportation, to enable safer, efficient and more enjoyable journeys, and connected vehicles are a way to realise that. Modern cars feature embedded systems that monitor and manage all the critical sensors and actuators. The vehicle connectivity can be further extended with Vehicle-to-Vehicle (V2V) technology, which allows cars to exchange that collected information and even act on it. In this demo we present our latest simulation environment, that takes advantage of the in- vehicle (e.g, PLC, Ethernet) and inter-vehicle communications (e.g., DSRC and LTE-V) for the exchange of data between cars or infrastructure in a city scale. The simulator is an essential tool for our study in connected vehicle communications.

Tag collision severely affects the performance of radio-frequency identification (RFID) systems. Most anti-collision algorithms focus on preventing or reducing collisions but waste lots of idle slots. In this letter, we propose a time efficient anti-collision algorithm based on a query tree scheme. Specifically, the dual prefixes matching method is implemented based on the traditional query tree identification model when the reader detects the consecutive collision bits, which can significantly remove idle slots. Moreover, the proposed method can also make extensive use of collision slots to improve the identification efficiency. Both theoretical and simulation results indicate that the proposed algorithm can achieve better performance than existing tree-based algorithms.

The user behavior is one of the key factors to impact the communication performance of vehicular ad hoc networks. Generally, more active vehicles can enhance the connectivity performance. Nevertheless, an increasing number of active vehicles may bring about significant interference and thus negatively affect the connectivity. In this paper, a highway model is presented to study the effects of active vehicle ratio on connectivity performance in the presence of co-channel interference. A closed form expression of the inter-vehicle connectivity probability is also derived to evaluate the impacts of active ratio, traffic flow, and fading factors under Nakagami fading channel. Analytical results of inter-vehicle connectivity probability are shown to be consistent with the simulation results, and can be applied to estimate connectivity performance with interference in the network design for highway vehicular scenarios.

This paper proposes a new media access control (MAC) protocol for in-vehicle power line communications (PLC). Specifically, the proposed protocol is based on the HomePlug Green PHY (HomePlug GP) which is a modern PLC protocol standard for Smart Grid applications on the Home Area Network [1], however, it has been found that current HomePlug protocols would not cope with the strict timing requirements of in-vehicle communication systems. This paper suggests improvements and modifications to enhance the real-time MAC performance of HomePlug GP in an in-vehicle network through additional priority levels and reduced message length. Simulation results based on the open source network simulator OMNeT++ are provided to show advantages of the proposed protocol.

It is predicted that billions of intelligent devices and networks, such as wireless sensor networks (WSNs), will not be isolated but connected and integrated with computer networks in future Internet of Things (IoT). In order to well maintain those sensor devices, it is often necessary to evolve devices to function correctly by allowing device management (DM) entities to remotely monitor and control devices without consuming significant resources. In this paper, we propose a lightweight RESTful Web service (WS) approach to enable device management of wireless sensor devices. Specifically, motivated by the recent development of IPv6-based open standards for accessing wireless resource-constrained networks, we consider to implement IPv6 over low-power wireless personal area network (6LoWPAN)/routing protocol for low power and lossy network (RPL)/constrained application protocol (CoAP) protocols on sensor devices and propose a CoAP-based DM solution to allow easy access and management of IPv6 sensor devices. By developing a prototype cloud system, we successfully demonstrate the proposed solution in efficient and effective management of wireless sensor devices.

Technologies to support the Machine-to-Machine (M2M) is becoming more important as the need to better understand our environments and make them smart increases. As a result it is predicted that intelligent devices and networks, such as wireless network, will not be isolated but connected and integrated composing computer networks. So far, to enable an End-to-end M2M service, WebSocket has attracted lots of attentions because of its unique full-duplex communications features. Besides, ZigBee technology has widely been deployed in short-range wireless communication systems with its low-power dissipation and high transmission speed. In this paper, we focus on the emerging M2M gateway development for home and industry applications. Specifically, by providing the detailed system architecture and user cases, we give a specific analysis on environmental monitoring implemented with WebSocket and ZigBee technology. The ZigBee sensor network is used to collect the temperature and humidity information. The foreground of the system shows the related data through B/S (Browser/Server) mode by utilizing WebSocket to push the information received by a web server to the client browser.

Driving is an integral part of our everyday lives, but it is also a time when people are uniquely vulnerable. Poor road condition, traffic congestion and long driving time may bring negative emotion to drivers and increase the chance of traffic accidents. We propose SAfeDJ, a situation-aware in-car music delivery application, which turns people's trips into pleasant journeys and driving into a safe and enjoyable activity. SAfeDJ aims at helping drivers to diminish fatigue and negative emotion. It is built on a vehicular healthcare platform that enables communications among drivers and integrates with multiple types of sensors to promote safe driving. Prototype implementation and initial results of SAfeDJ have demonstrated its desired functionality in drivers' daily lives and feasibility for real-world deployment.

Demographic information is usually treated as private data (e.g., gender and age), but has been shown great values in personalized services, advertisement, behavior study and other aspects. In this paper, we propose a novel approach to make efficient demographic prediction based on smartphone application usage. Specifically, we firstly consider to characterize the data set by building a matrix to correlate users with types of categories from the log file of smartphone applications. By considering the category-unbalance problem, we predict users' demographic information and propose an optimization method to further smooth the obtained results with category neighbors and user neighbors. The evaluation is supplemented by the dataset from real world workload. The results show advantages of the proposed prediction approach compared with baseline prediction. In particular, the proposed approach can achieve 81.21% of Accuracy in gender prediction. While in dealing with a more challenging multi-class problem, the proposed approach can still achieve good performance (e.g., 73.84% of Accuracy in the prediction of age group and 66.42% of Accuracy in the prediction of phone level).

Cooperative communication has been shown to have great potential in improving wireless link quality. Incorporating cooperative communications in multi-hop wireless networks has been attracting a growing interest. However, most current research focuses on either centralized solutions or schemes limited to specific network problems. In this paper, we propose a distributed framework that uses Network Utility Maximization (NUM) to optimize the following joint objectives: flow control, routing, scheduling, and relay assignment; for multi-hop wireless cooperative networks with general flow and cooperative relay patterns. We define two special graphs, Hyper Forwarding Graphs (HFG) and Hyper Conflict Graphs (HCG), to represent all possible cooperative routing policies and interference relations among the cooperative relays respectively. Based on HFG and HCG, a stochastic mixed-integer non-linear programming problem is formulated. We then propose lightweight algorithms to solve these in a fully distributed manner, and derive the theoretical performance bounds of these proposed algorithms. Simulation results verify our theoretical analysis and reveal the significant performance gains of our framework, in terms of throughput, flexibility, and scalability. To our knowledge, this is the first distributed cross-layer optimization framework for multi-hop wireless cooperative networks with general flow and cooperative relay patterns.

Cooperative communication has been proven effective in enhancing the performance of wireless networks. In this paper, we propose an adaptive multi-relay selection with power allocation mechanism to offer energy fairness at each node for a cooperative network. Unlike traditional approaches where all nodes are considered to transmit in a collaborative manner, we explicitly consider the situation where each node exhibits some degree of selfishness behavior. By introducing novel concepts of the selfishness index and utility function which denote node's benefit from cooperation, we show that the proposed cooperation scheme can well balance the energy consumption among nodes as well as maximize the network lifetime. Theoretical analysis and extensive simulation results are supplemented to show advantages of the proposed approach in power saving and power allocation fairness.

In this paper, we employ a stochastic geometry model to analyze the transmission capacity of the Decode-and-Forward (DAF) cooperation scheme in an overlaid wireless network where a primary (PR) network and a secondary (SR) network coexist together. The PR users employ DAF scheme and have a higher priority to access the channel, whereas the SR users use only direct transmission. Because of the fact of coexistence, the interference from SR network seriously affects the performance of PR network. Assuming that simultaneous transmitters in both networks are randomly located in space according to Poisson point processes, we develop outage probabilities for both DAF and direct transmission schemes in both deterministic and Rayleigh fading channels. By defining transmission capacity in terms of the outage probability, a desired data rate and the density of transmissions, we further quantify transmission capacities for both schemes. It shows that the use of cooperative transmission achieves much better reliability and a larger transmission capacity than the use of direct transmission in the PR network. Furthermore, such performance gain can be manipulated to increase the transmission capacity of the SR network without deteriorating the performance of the PR network. Numerical results also demonstrate the significant improvement on the transmission capacity by using cooperative transmission.

In wireless networks, the broadcast nature of wireless transmission enables cooperation by sharing the same transmissions with nearby receivers and thus can help improve spatial reuse and boost network throughput along a multi-hop routing. The performance of wireless networks can be further improved if prior information available at the receivers can be utilized to achieve perfect interference subtraction. In this paper, we investigate performance gain on network throughput for wireless cooperative networks by using a simple MUD scheme, called overlapped transmission, in which multiple transmissions are allowed only when the information in the interfering signal is known at the receiver. It is shown that the scheme of cooperative transmission with overlapping increases network throughput by 24% compared to that of direct transmission with overlapping. We then propose a new cooperation scheme called supplementary cooperation, which improves the performance gain of direct transmission with overlapping by 42%. Analytical results are developed to show that in a general network scenario, supplementary cooperation achieves bit error rate (BER) reduction of 34.87%, compared with the conventional cooperative transmission. Furthermore, we proposed a criterion for finding the best cooperative route to achieve maximum network throughput in a general network.

Most ad hoc mobile devices in wireless networks operate on batteries and power consumption is therefore an important issue for wireless network design. In this paper, we propose and investigate a new distributed cooperative routing algorithm that realizes minimum power transmission for each composed cooperative link, given the link BER (bit error rate) constrained at a certain target level. The key contribution of the proposed scheme is to bring the performance gain of cooperative diversity from the physical layer up to the networking layer. Specifically, the proposed algorithm selects the best relays with minimum power consumption in distributed manner, and then forms cooperative links for establishing a route with appropriate error performance from a source to a destination node. Analytical results are developed to show that our cooperative transmission strategy (MPSDF) achieves average energy saving of 82.43% compared to direct transmission, and of 21.22% compared to the existing minimum power cooperation strategy. Furthermore, the proposed power efficient routing algorithm can also reduce the total power consumption by a couple dB compared to existing cooperative routing algorithms. Monte-Carlo simulation results are also provided for performance evaluation.

In this paper, we employ a stochastic geometry model to analyze transmission capacity in wireless cooperative networks. Assuming that simultaneous transmitters are randomly located in space according to Poisson point process with density ¿, we develop the bound performances on outage probability and outage capacity for both direct transmission and Decode-and-Forward (DAF) cooperative scheme. Due to the nature of multipath propagation of cooperative transmission, we define regional capacity as the multiplied product of average density of successful simultaneous transmissions, achieved outage capacity and transmission distance. It shows that the regional capacity for cooperative transmission scales as ¿(¿(¿)), which is the same as the transport capacity for wireless network. Furthermore, Monte Carlo simulations demonstrate the significant improvement on the transmission capacity by using cooperative transmission.

In this paper, a quality-of-service driven routing protocol is proposed for wireless cooperative networks. The key contribution of the proposed protocol is to bring the performance gain of cooperative diversity from the physical layer up to the networking layer. Specifically, the proposed protocol uses a distributed algorithm to select the best relays based on link quality to form cooperative links for establishing a route with appropriate error performance from a source to a destination node. Furthermore, analytical results are developed to show that the proposed distributed routing protocol can perform close to the optimal in terms of error performance, especially for linear network topologies. Monte-Carlo simulation results are also provided for performance evaluation.