Unsupervised anomaly detection in multivariate time series is of significant practical importance in industrial monitoring and IoT device management. However, existing methods still face significant challenges in modeling complex temporal patterns. Although Transformer models have demonstrated notable potential in point-wise representation learning, their attention mechanisms primarily focus on direct dependencies between time steps, making it difficult to capture structural features at the segment level. To address these issues, we propose PETformer, a prototype-enhanced multi-scale unsupervised Transformer architecture. PETformer introduces the Multi-Head Scale Convolutional Attention (MHSCA) module, which employs parallel convolutional kernels to extract multi-scale features. This design enhances the model’s ability to capture both local and global dependencies. Additionally, it incorporates Multi-Scale Cosine Prototypes (MSCP) as inductive biases that interact with the input sequence, strengthening prior modeling of normal patterns across sequences and improving the model’s sensitivity to potential pattern deviations. Experimental results show that PETformer consistently outperforms existing state-of-the-art unsupervised anomaly detection methods on three widely used benchmark datasets.

In modern warfare, bionic robots are increasingly deployed to execute high-risk missions. To ensure robust remote control and command in complex urban environments, unmanned aerial vehicles (UAVs) are utilized as aerial relays to facilitate reliable data transmission. This paper investigates the joint optimization of UAV positioning and multi-hop relay path selection in UAV-assisted wireless networks. We formulate the problem as a graph-based optimization task and propose a two-stage Graph Neural Network (GNN) framework. In the first stage, a reinforcement learning (RL) enhanced Relay Path GNN (RPG) is developed to enable low-latency and efficient routing. In the second stage, a UAV Position GNN (UPG) determines near-optimal UAV deployment strategies. Both modules are designed to train without labeled data, relying instead on unsupervised and RL techniques tailored to the graph-structured problem, making the approach data-efficient and robust. Simulation results show that the proposed framework UPG-RPG achieves near-optimal performance with substantially reduced computational complexity and demonstrates superior scalability and adaptability compared to conventional heuristic or rule-based methods.

Unmanned aerial vehicles (UAV) swarm network (USNET) is a promising solution for diverse applications and usually works in harsh environments. However, it is challenging to rebuild the communication connectivity in USNETs with low time complexity under unpredictable UAV damages. In this paper, we present a graph attention network (GAT)-based deep reinforcement learning topology self-healing (GDR-TS) framework to minimize the topology self-healing (TSH) time of remaining UAVs. Specifically, we decompose the TSH problem into a neighbor selection problem and a trajectory planning problem. For the former, we present an edge update GAT-based actor-critic structure to find an optimal adjacency matrix; for the latter, we present a node update GAT-based position fine-tuning module to compute an optimal position matrix. Simulation results show that under various disruption setups, our GDR-TS framework outperforms the other four baselines in terms of the average TSH time and the response time.

Anomaly detection of Key Performance Indicators (KPIs) e.g. response latency, network throughput, etc., is one of the key techniques to ensure the quality and security of network services. However, state-of-the-art unsupervised deep learning algorithms present limitations: they are sensitive to noise and demand extensive KPI data for training, complicating the detection process. This paper proposes an Unsupervised Deep Transfer Learning (UDTL) approach. First, UDTL uses self-training preprocessing that generates reliable, high-quality samples for model training. Then, UDTL calculates the correlation based on the shape of the KPIs and the deviation scores of the KPIs, and clusters these KPIs into different clusters via correlation. At last, UDTL selects the KPI closest to each cluster’s centroid to train a base anomaly detection model for this cluster. The anomaly detection model of other KPIs adaptively choose the parameters to transfer based on correlation. Our experiments conducted on several public datasets highlight UDTL’s effectiveness. UDTL enhances the F1 score by 15.24% and reduces the training time by 22.17 times compared to baselines.

Reinforcement learning (RL)-based collaborative perception in vehicular networks chooses the sub-frame of radio channel resources for connected autonomous vehicles (CAVs) to exchange sensing data to enhance the perception performance, but leads to inaccurate detection in the light detection and ranging (LiDAR)-based object detection due to the asynchronous scan period of the LiDAR point clouds. This paper proposes a RL-based collaborative perception scheme to choose the transmit power and sub-frame to share the feature maps extracted from point clouds. Based on the estimated packet timestamp, the network topology and the channel gains among CAVs, this scheme enhances the perception accuracy and latency against path-loss and interference. The collaborative risk in the policy distribution is formulated as a weighted sum of the perception latency and packet loss rate to avoid the time asynchronization and information loss of the feature map exchange. The performance bound of the perception accuracy and latency is provided based on a Nash equilibrium of the cooperative game among CAVs. Simulation results based on five CAVs show the performance gain of the perception accuracy and latency over the benchmarks.

Anomaly detection based on key performance indicator (KPI) is an important topic in the field of intelligent operation and maintenance. The problem of insufficient annotated samples is widespread in the industrial Internet, and it severely impairs the performance of data-driven anomaly detection. Previous methods tackled the problem mainly through unsupervised methods, which rely heavily on the ability of the algorithm to extract features. In this work, we propose an unsupervised hybrid model to address these issues. Technically, we capture the long-term dependencies of time series by stacked BiLSTM and use the self-attention mechanism to adaptively select the most noteworthy information in the time series data for global consideration. To effectively distinguish anomalies, we propose a dynamic threshold method that takes into account the context of the data being measured. By doing so, we aim to minimize false positives and missed positives, thus significantly enhancing the overall performance. Extensive experiments on various public benchmarks and real-world measured data demonstrate that our method offers advanced performance and practicality.

In this paper, we investigate a mixed-traffic control problem considering uncertainties of HDVs flow. The challenges mainly lie in modeling the stochastic characteristics of mixed-traffic flow and developing less-conservative algorithm to deal with the uncertainties. To tackle the problem, we propose a stochastic model predictive control (MPC) strategy based on data-driven distributionally robust optimization (DRO). First, a stochastic mixed-traffic model, extended from cell transmission model, is proposed to describe the traffic dynamics. Then, utilizing historical traffic data, an incremental principal component analysis (IPCA) based method is given to construct ambiguity set and incorporate generalized moment information of uncertainties. Based on the above predictive model and ambiguity set, a DRO-based MPC problem is formulated and further converted into an equivalent dual form for efficient solutions, i.e., ramp metering and variable speed limit control. Finally, simulation results based on real data collected in Shanghai, China, demonstrate that our proposed strategy can significantly reduce traffic congestion, achieving 5.74 % total travel time reduction compared to robust MPC.

Due to the highly dynamic network topology in Flying Ad hoc Networks (FANETs), topology-based routing protocols are impractical, while known geographic routing protocols suffer from routing holes. In this paper, a Q-learning-based adaptive geographic routing protocol is presented, in which the impact of delay, mobility, and energy consumption on routing is comprehensively considered. To overcome the dynamic changes in routing caused by the high mobility of Unmanned Aerial Vehicles (UAVs), reinforcement learning parameters and HELLO message interval are adaptively adjusted by sensing local topology changes. Meanwhile, a new routing hole avoidance mechanism is proposed, which involves a scheme of broadcasting routing hole information and an approach of data forwarding route selection based on the node degree of UAVs and the distance to the destination node. To enable the routing protocol to adapt to highly dynamic changes in FANETs topology, link pre-learning and multi-Q learning methods are used to speed up the learning process. We use NS-3 to evaluate the proposed routing protocol. The results show that our protocol improves throughput by 6% and reduces delay by 20% compared to Q-learning-based Multi-objective Optimization Routing (QMR). It is also far superior to Q-learning-based Geographic Routing (QGeo) and Greedy Perimeter Stateless Routing (GPSR).

The jamming and interference in vehicular ad hoc networks (VANETs) depend on the channel states of vehicles from the ambient radio transmitter, which in turn result from the topologies and radio features. In this paper, we propose an environment-aware reinforcement learning (RL)-based VANET communication scheme against jamming and interference that applies the post decision state algorithm to optimize the power allocation and channel selection without relying on the jamming attack model. This scheme exploits the environment information in the state formulation due to the traffic density and their locations reflect the interference level, as well as the location of transmission vehicle combined with building structure and heights indicate the channel gain and shadowing. The proposed post decision state-based RL method employs the estimated future communication distances of the moving vehicles to accelerate the learning process. We provide the performance bounds of the energy consumption, bit error rate (BER), and utility based on a Nash equilibrium. Simulation results show that the proposed scheme significantly reduces the BER with less energy consumption compared with the benchmark.

Mobile Edge Computing (MEC) enables vehicles to flexibly obtain computing, content storage and other services by deploying at the network edge, which is of great research significance. The application tasks in vehicular networks can be distributed to MEC or task vehicle for collaborative processing. Aiming at the situation that the task dependency graph with multiple corresponding relationships is represented by Directed Acyclic Graph (DAG), this paper considers caching the program data of the corresponding node, and explores the problem of task scheduling and resource allocation in the cache enhancement scenario. Firstly, the task scheduling and cache placement decision are modeled as the optimization problem of minimizing the completion time. Then, through the analysis of the optimization problem, the problem is divided into two sub problems: task scheduling and cache decision. For the two sub problems, the task scheduling algorithm based on the latest start time and the cache decision algorithm based on dynamic programming are designed respectively. Simulation results show that the proposed algorithms and scheme can significantly reduce the completion time and task failure rate compared with the benchmark scheme.

Virtualized network function (VNF) orchestration dynamically deploys network slices, which provides an effective means of customized service provision. To achieve a realistic and comprehensive perspective of the decision process for customized service provision, we propose a virtualized resource orchestration strategy in the radio access network (RAN) of Internet of Things (IoT) based on chemical reaction optimization (CRO). Specifically, we apply particle swarm optimization (PSO), a Gaussian process, random walk model, and$Q$-learning to enhance the CRO algorithm to quickly obtain the approximate optimal solution for the proposed CRO-based resource orchestration strategy (CROROS). The simulation results show that compared with existing access methods, CROROS can reduce the service rejection rate of a virtualized RAN and improve the utilization rate of network system resources. Compared with other heuristic algorithms [e.g., PSO, genetic algorithm (GA), and CRO], CROROS can accelerate the global approximate optimal solution and improve the approximate fitness of the approximate optimal solution within a specified time.

Wireless caching improves the efficiency of content delivery in vehicular networks. However, the cached content needs to be frequently updated to adapt to dynamic service requirements, which is challenging due to high vehicle mobility and scarce radio resources. This paper investigates the online joint scheduling strategy of content files (CF) and server vehicles (SV) for cache content updating in a vehicle collaborative transmission scenario, where SVs act as content servers to deliver cached files to customer vehicles. The non-orthogonal multiple access (NOMA) technology is applied to push more CFs to SVs during a limited time interval, compared with the conventional orthogonal multiple access principle. Particularly, we define a utility function to reflect file value from local content popularity, user vehicle distribution, and file updating demand. By solving an "inversed" 0-1 knapsack problem, a suboptimal SV-CF scheduling strategy with the purpose of maximizing system utility is obtained, where the "inversed" means that the knapsack capacity represents current reserved power value. Simulation results illustrate the superiority of our proposed caching strategy.

Driven by the tremendous in vehicular networks computation-intensive application demands, the incorporation of mobile edge computing (MEC) and vehicular cloud is convinced as a promising paradigm to fulfill computation offloading requirements. However, the changing vehicular communication topology (CVCT) poses a significant challenge for offloading directed acyclic graph (DAG) model application. Due to the precedence and connection constraint between different sub-jobs, the successful offloading of DAG-enabled apllication will be disturbed even interrupted without considering CVCT. To address this problem, we propose a topology-aware dynamic computaion offloading mechanism and adopt simulated annealing algorithm (TASA) to jointly optimize the energy consumption and completion time under dynamic environment, while guaranteeing the convergence of the proposed method. Simulation results reveal the effectiveness of the proposed method in overcoming CVCT’s influence.

Resource scheduling directly affects the resource utilization and network capacity in Internet of Vehicles (IoV). However, it’s challenging while vehicles are moving at high speed. We propose a reservation based resource blocks (RBs) allocation scheme for IoV networks. In the proposed scheme, Road Side Units (RSUs) dynamically allocates resources to users considering number of available RBs and arrival rate of service requests. We use inventory theory to formulate an optimization problem to decrease the rejection rate of service requests and improve RBs utilization. Simulation results show that the proposed scheme can achieve lower rejection rate of service requests and higher RBs utilization than the existing schemes.

The high mobility of vehicles in mobile edge computing (MEC) enabled vehicular networks causes the channel estimation error which will influence the quality of service (QoS) of users. In this paper, we explore the computation offloading and resource allocation in orthogonal frequency-division multiple access (OFDMA) based vehicular networks considering the imperfect channel state information (CSI). A mixed integer non-linear programming (MINLP) is formulated to minimize the offloading latency. To tackle this NP-hard problem, we divide the offloading and resource allocation into two subproblems which are offloading decision subproblem and resource allocation sub-problem. Specifically, given the offloading decision, we design a coalition game based algorithm to solve the subcarrier assignment problem and a convex optimization method to solve the power allocation problem. Meanwhile, given the resource allocation, we finally get the offloading decision by solving the linear programme (LP) problem. Numerical results show that the proposed scheme can significantly reduce the offloading latency.

In order to ensure that the video service data with high bandwidth requirements can be reliably transmitted in Vehicular Ad Hoc Networks (VANETs), this paper studies the optimization problem of QoS and transmission delay of video streaming in the application scenario of unmanned aerial vehicle (UAV)-assisted VANET communication. We design a set of Multipath Scalable Video Coding (MultiSVC) routing protocol in VANETs. Using the theory of Scalable Video Coding (SVC), AODV-Multipath (AODVM) with disjoint nodes is extended so that routing nodes can obtain information such as link parameters. The proposed multi-link layer-level code stream allocation algorithm and the maximum exchange k matching algorithm will allocate code blocks to the specified link according to the obtained link parameter information. The simulation results show that, compared to the single-path SVC transmission method, MultiSVC can not only guarantee higher video quality, but also the transmission delay can be changed depending on the size of the video file. In particular, it can complete the transmission of large or small video files with only a low delay, which is very suitable for the changeable VANET environment.

Unmanned aerial vehicles (UAVs) that are widely utilized for video capturing, processing and transmission have to address jamming attacks with dynamic topology and limited energy. In this paper, we propose a reinforcement learning (RL)-based UAV anti-jamming video transmission scheme to choose the video compression quantization parameter, the channel coding rate, the modulation and power control strategies against jamming attacks. More specifically, this scheme applies RL to choose the UAV video compression and transmission policy based on the observed video task priority, the UAV-controller channel state and the received jamming power. This scheme enables the UAV to guarantee the video quality-of-experience (QoE) and reduce the energy consumption without relying on the jamming model or the video service model. A safe RL-based approach is further proposed, which uses deep learning to accelerate the UAV learning process and reduce the video transmission outage probability. The computational complexity is provided and the optimal utility of the UAV is derived and verified via simulations. Simulation results show that the proposed schemes significantly improve the video quality and reduce the transmission latency and energy consumption of the UAV compared with existing schemes.

The heterogeneous vehicular network (HetVNET), which consists of multiple radio access networks (RANs), is a promising paradigm to provide a variety of services on the road. However, with the diverse quality of experience (QoE) requirements for vehicles, how to choose the optimal network for the vehicles pose great challenges. In this paper, we investigate the network selection problem in a HetVNET, which includes LTE-vehicle-to-anything (LTE-V2X), dedicated short-range communications (DSRC), WiFi. The network selection problem is formulated as a stable matching between the vehicles and different RANs. A two-sided one-to-many matching algorithm is presented based on the preference lists of both vehicles and RANs. Numerical simulation results show that the proposed method can improve the total throughput of the system and reduce the total network switch time compared to some existing algorithms.

Recently, to achieve latency-sensitive services for the Internet of Vehicles, the Long Term Evolution-Vehicle(LTE-V)-based solution is considered very promising. In the Vehicleto-Infrastructure(V2I) scenario, the optimal resource allocation scheme of latency-sensitive and non-latency-sensitive services should be considered in the resource allocation between vehicles and RodeSide-Unit(RSU) to achieve higher wireless resource utilization and reliability. In this paper, we propose the LowLatency Resource Pre-allocation Algorithm(LLRPA) based on multiple service types in the V2I scenario. This algorithm can effectively maximize the utilization of wireless resources and reduce the rejection rate of service in order to guarantee the reliability of V2I communication. The effectiveness of our proposed LLRPA is validated via simulation experiments.

Online video becomes a significant service in daily life, and it usually adopts a caching and playing mechanism. Due to high mobility and changeable topology, challenges of video downloading still exist in vehicular networks, especially in areas where the roadside units (RSUs) are not fully covered. The flexible deployment of the unmanned aerial vehicle (UAVs) compensate for the lack of RSU coverage, and thus this paper considers that a cyclic flight UAV to assist RSUs in providing video download services for vehicles. With the help of UAV, seamless communication coverage and stable transmission links ensure better service quality for vehicles. In addition, we propose a model-free algorithm based on a deep Q network to find the optimal UAV decision policy to achieve the minimized stalling time. Finally, the simulation results are given to demonstrate that the proposed solution can effectively maintain a high-quality user experience.

Graph jobs represent a wide variety of computation-intensive tasks in which computations are represented by graphs consisting of components (denoting either data sources or data processing) and edges (corresponding to data flows between the components). Recent years have witnessed dramatic growth in smart vehicles and computation-intensive graph jobs, which pose new challenges to the provision of efficient services related to the Internet of Vehicles. Fortunately, vehicular clouds (VCs) formed by a collection of vehicles, which allows jobs to be offloaded among vehicles, can substantially alleviate heavy onboard workloads and enable on-demand provisioning of computational resources. In this article, we present a novel framework for VCs that maps components of graph jobs to service providers via opportunistic vehicle-to-vehicle communication. Then, graph job allocation over VCs is formulated as a nonlinear integer programming with respect to vehicles' contact duration and available resources, aiming to minimize the job completion time and data exchange cost. The problem is addressed for two scenarios: 1) low-traffic and 2) rush-hour scenarios. For the former, we determine the optimal solutions for the problem. In the latter case, given the intractable computations for deriving feasible allocations, we propose a novel low complexity randomized graph job allocation mechanism by considering hierarchical tree-based subgraph isomorphism extraction. The evaluation of the performance of both optimal and proposed randomized algorithms with two greedy-based baseline methods is carried out through extensive simulations.

Unmanned aerial vehicles (UAVs) are widely utilized to capture and compress videos of the target area and then transmit the processed videos to the control station (CS) on the ground. The media transmissions in the UAV-aided network face many challenges due to the highly dynamic network topology and limited resources such as bandwidth and energy. This paper introduces a media transmission scheme in the UAV-aided network utilizing reinforcement learning algorithms to efficiently process and transmit the captured video, which is able to improve the quality-of-experience (QoE) and reduce the energy consumption. Exploiting the proposed reinforcement learning algorithm, the UAV dynamically selects the quantization parameter in the source coding process and determines the transmit power without knowing the video transmission model. Simulation results demonstrate that the proposed scheme is capable of achieving a higher video quality and utility with lower energy consumption compared with the state-of-the-art schemes.

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

Recent years have witnessed the emerging concept of Vehicular Cloud Networks (VCN) with the development of Internet of Vehicles and cloud computing. There is a common phenomenon that the loads and resources of local clouds (LCs) in different regions are seriously unbalanced in dynamic VCN as LCs are confronted with the shortage of resources and vehicles are featured by high mobility. As a consequence, we propose a cross-area resource management scheme (CRMS) based on semi-Markov decision process (SMDP) to alleviate this problem. In this scheme, a service migration mechanism plays an imperative role, which the local cloud needs to take the service requests from both local and neighboring cloud into account when allocating computing resources that we focus on in this paper. Considering the impact of different types of service requests on system revenue, we obtain the optimal policy of resource allocation adaptively through SMDP to maximize the long-term expected reward. Numerical results show the performance of the proposed CRMS has been significantly improved.

Narrowband Internet-of-Things (NB-IoT) is a competitive 5G technology for massive machine-type communication scenarios, but meanwhile introduces narrowband interference (NBI) to existing broadband transmission such as the Long Term Evolution (LTE) systems in enhanced mobile broadband (eMBB) scenarios. In order to facilitate the harmonic and fair coexistence in wireless heterogeneous networks, it is important to eliminate NB-IoT interference to LTE systems. In this paper, a novel sparse machine learning-based framework and a sparse combinatorial optimization problem is formulated for accurate NBI recovery, which can be efficiently solved using the proposed iterative sparse learning algorithm called sparse cross-entropy minimization (SCEM). To further improve the recovery accuracy and convergence rate, regularization is introduced to the loss function in the enhanced algorithm called regularized SCEM. Moreover, exploiting the spatial correlation of NBI, the framework is extended to multiple-input multiple-output systems. Simulation results demonstrate that the proposed methods are effective in eliminating NB-IoT interference to LTE systems, and significantly outperform the state-of-the-art methods.

The growth of smart vehicles and computation-intensive applications poses new challenges in providing reliable and efficient vehicular services. Offloading such applications from vehicles to mobile edge cloud servers has been considered as a remedy, although resource limitations and coverage constraints of the cloud service may still result in unsatisfactory performance. Recent studies have shown that exploiting the unused resources of nearby vehicles for application execution can augment the computational capabilities of application owners while alleviating heavy on-board workloads. However, encouraging vehicles to share resources or execute applications for others remains a sensitive issue due to user selfishness. To address this issue, we establish a novel computation offloading marketplace in vehicular networks where a Vickrey-Clarke-Groves based reverse auction mechanism utilizing integer linear programming (ILP) problem is formulated while satisfying the desirable economical properties of truthfulness and individual rationality. As ILP has high computation complexity which brings difficulties in implementation under larger and fast changing network topologies, we further develop an efficient unilateral-matching-based mechanism, which offers satisfactory suboptimal solutions with polynomial computational complexity, truthfulness and individual rationality properties as well as matching stability. Simulation results show that, as compared with baseline methods, the proposed unilateral-matching-based mechanism can greatly improve the system efficiency of vehicular networks in all traffic scenarios.

Edge computing for mobile devices in vehicular ad hoc networks (VANETs) has to address rogue edge attacks, in which a rogue edge node claims to be the serving edge in the vehicle to steal user secrets and help launch other attacks such as man-in-the-middle attacks. Rogue edge detection in VANETs is more challenging than the spoofing detection in indoor wireless networks due to the high mobility of onboard units (OBUs) and the large-scale network infrastructure with roadside units (RSUs). In this paper, we propose a physical (PHY)- layer rogue edge detection scheme for VANETs according to the shared ambient radio signals observed during the same moving trace of the mobile device and the serving edge in the same vehicle. In this scheme, the edge node under test has to send the physical properties of the ambient radio signals, including the received signal strength indicator (RSSI) of the ambient signals with the corresponding source media access control (MAC) address during a given time slot. The mobile device can choose to compare the received ambient signal properties and its own record or apply the RSSI of the received signals to detect rogue edge attacks, and determines test threshold in the detection. We adopt a reinforcement learning technique to enable the mobile device to achieve the optimal detection policy in the dynamic VANET without being aware of the VANET model and the attack model. Simulation results show that the Q-learning based detection scheme can significantly reduce the detection error rate and increase the utility compared with existing schemes.

While home users can easily retrieve all kinds of contents onto their laptops or smartphones, vehicular users are constrained by intermittent connectivity to roadside units (RSUs). In this paper, we propose a cooperative downloading mechanism in homogeneous vehicular networks. In this mechanism, RSUs act as traffic managers to fetch proper data from the Internet and then distribute to vehicles in an approximately optimal manner. Specifically, based on vehicular mobility prediction and inter-node throughput estimation, a storage time aggregated graph (STAG) is constructed for planning transmission scheme, then an iterative greedy-driven algorithm is designed for deriving a suboptimal solution. Simulation results show that our approach can reduce 5% ~ 20% downloading time in an uniform distributed deployment scenario where the spacing between RSUs is not greater than 1500m.

Abstract The Internet of vehicles (IoV) is an emerging networking technology, which can support information sharing and interactions among users, vehicles, and infrastructures. Various applications can be provided by IoVs, and they have very different quality‐of‐service (QoS) requirements. It is a great challenge to design an efficient MAC protocol to meet the different QoS demands of various applications in IoVs, because of unreliable links and high vehicle mobility. On the other hand, cooperative communication is effective in mitigating wireless channel impairments by utilizing the broadcast nature of wireless channels. In this paper, a multi‐channel cooperative clustering‐based MAC (MCC‐MAC) protocol, under the Dedicated Short Range Communication (DSRC) multi‐channel architecture, is presented to improve the transmission reliability of safety messages and provision QoS for different applications in IoVs. Further, we analyze the performance of MCC‐MAC, in terms of average transmission delay. In addition, extensive simulations with ns‐2 are conducted to demonstrate the performance of the proposed MCC‐MAC. Copyright © 2016 John Wiley & Sons, Ltd.

In this paper, we investigate the impact of jamming attacks on the performance of smartphones regarding their WiFi access and propose a real-time jamming detection method based on the received signal strength indicator and the packet loss rate of WiFi signals, which can be easily implemented on Android smartphones. Experiments are performed to evaluate the proposed jamming detection method, in which universal software radio peripherals are used as jammers to block the WiFi signals between smartphone phones and wireless routers. Experimental results show that the proposed application can detect jamming attacks with small false alarm rate and miss detection raaaaaate.

Owing to the advancement of wireless communication technologies, the vehicular ad‐hoc network (VANET) has experienced a rapid development in recent years. However, it is challenging to design a reliable and efficient medium access control (MAC) protocol for safety messages with strict quality of service demands, owing to unreliable wireless links and frequent changes of topology. On the other hand, cooperative communication can enhance the reliability of wireless links by exploiting the spatial diversity. The authors present here a cooperative clustering‐based MAC (CCB‐MAC) protocol for VANETs, in order to improve the transmission reliability of safety messages. In CCB‐MAC, the selected helpers relay the safety message to the nodes that have failed in reception during the broadcast period. In addition, cooperation is conducted in idle slots, without interrupting the normal transmission. Both mathematical analysis and numerical results demonstrate that CCB‐MAC increases the successful reception rate of safety messages significantly.

ABSTRACT The IEEE 802.16 mesh network is a promising next generation wireless backbone network. In the network, the allocation of minislots is handled by centralized scheduling and distributed scheduling, which are independently exercised. However, the standard does not specify how the frame can be partitioned among its centralized and distributed schedulers. Through efficient partitioning that dynamically adapts the partitioning based on demand, network can support more user applications. Although a dynamic frame partitioning scheme to use Markov model has been studied, the dynamic frame partitioning method has not been fully investigated. This paper proposes two novel and general dynamic frame partitioning scheme for IEEE 802.16 mesh networks so that the minislot allocation can be more flexible and the utilization is increased. The two schemes respectively use GM(1,1)‐Markov model and Grey–Verhulst–Markov model to predict efficient partitions for future frames according to the minislot utilization in current frames. Our study indicates that the two proposed schemes outperform the scheme of using Markov model. Copyright © 2012 John Wiley & Sons, Ltd.

As wireless networks have been widely deployed for public mobile services, predicting the location of a mobile user in wireless networks became an interesting and challenging problem. If we can predict the next cell which the mobile users are going to correctly, the performance of wireless applications, such as call admission control, QoS and mobility management, can be improved as well. In this paper, we propose a mobility prediction algorithm based on dividing sensitive ranges. The division is in accordance with the cell transform probability. Then different prediction methods are applied according to the sensitivity of the range to gain high precision. Simulations are conducted to evaluate the performance of the proposed scheme. As it turns out, the simulation results show that the proposed scheme can accurately predict the location for mobile users even in the situation of lacking location history.

The IEEE 802.16 standard, also known as Worldwide Interoperability for Microwave Access (WiMax), which provides a mechanism for deploying high-speed wireless mesh networks in metropolitan areas. Thus, Quality of Service (QoS) is very important for WiMax networks. Providing QoS in multi-hop WiMax mesh networks is challenging as multiple links can interfere with each other if they are scheduled at the same time. In this paper, we propose the MDFS (Maximum Degree First Select) algorithm for channel assignment in multi-channel single-transceiver WiMax mesh networks. The goal is to eliminate the interference, to allow multiple non-interfering links to be scheduled at the same time and to reduce delay of traffic flows. The simulation result shows this proposed algorithm greatly reduces the length of scheduling and improves the BS's aggregate throughput in insufficient channels.