The Internet of Vehicles (IoV) connects roadside units, vehicles, and pedestrians into an efficient communication network. However, ensuring honesty among vehicles in such a decentralized system remains a challenge, especially for intersection management. To address this issue, we propose a blockchain-based IoV credit system. Drivers’ rushing behavior is a primary contributor to accidents at intersections. To solve this problem and enhance traffic efficiency, we propose a strategy that allows time-sensitive convoys to pass through an intersection ahead of others by paying traffic tokens via IoV while still complying with traffic regulations. To protect the privacy of the yielding vehicles, we develop an optimal pricing strategy for the rushing convoys using a dynamic game model with incomplete information, which is solved using a refined Bayesian Nash equilibrium. Furthermore, we introduce a blockchain consensus mechanism that employs a sequence chain to ensure a unique and consistent order for passing. Both theoretical analysis and simulations show that the proposed strategy is feasible and effective in meeting the practical need for low latency at intersections. It increases the likelihood of successful transactions, protects yielding convoys from lower benefits, and encourages broader participation.

As the mainstream architecture for 6G networks, the Space-Air-Ground Integrated Network (SAGIN) faces dual challenges of security threats introduced by Network Function Virtualization and inherent topology dynamics, necessitating a secure and flexible Service Function Chain (SFC) deployment paradigm. This paper proposes a novel SFC security framework integrating Deception Defense (DD) and Moving Target Defense (MTD). A security assessment model is introduced to quantify virtualization risks, complemented by a DD-driven deployment strategy that incorporates security awareness and load balancing to enhance attack obfuscation. Furthermore, to mitigate the impact of topology dynamics, a link-stability-aware migration algorithm is designed with MTD, effectively balancing security and service continuity. Extensive simulations demonstrate that compared to conventional methods, the framework improves SFC security by approximately 102%, optimizes network load balancing by 23%, and reduces migration frequency by 31%, thereby ensuring service stability in highly dynamic scenarios. This work effectively bridges the critical gap between proactive security enforcement and adaptive resource orchestration in SAGIN environments.

With the development and popularization of virtualization technology, it offers efficient, flexible services in cloud computing, data centers, etc. However, the sharing of underlying physical devices makes the isolation between different virtual machines (VMs) relatively fragile, leading to new security challenges. Co-resident attack is one of the most representative types. In order to facilitate the construction of side channels to achieve sensitive data theft and other malicious operations, attackers try to co-locate their VMs with target VMs on the same physical device. This paper proposes a VM placement method for virtualization cloud platforms to mitigate the risks posed by co-resident attacks. This study model the VM placement process in the virtualization cloud platform as a Markov Decision Process (MDP) and construct an objective function to minimize co-resident attacks under various constraints. Furthermore, it is believe that after the co-residence occurs, it is equally important to prevent attackers from constructing side channels against the co-resident targets. Therefore, the concept of deceptive defense is introduced to mislead attackers. In addition, this study introduces reinforcement learning and designs a deceptive VM deployment strategy generation method based on PPO (DD-PPO), considering platform security, load balance, and power consumption. Finally, CloudSim is used to build the simulation environment, and the performance of the proposed algorithm is evaluated through experiments. Simulation results show that the proposed algorithm effectively mitigates the threats of co-resident attacks compared to baselines.

High-speed railway (HSR) communication is subject to unauthorized eavesdropping, and the acquisition of perfect channel state information (CSI) is difficult, which increases the secrecy outage probability of the system and poses a threat to secure data transmission. In addition, the train’s operating environment is complex. Deviations in train speed can increase Doppler shift compensation errors, further increases the secrecy outage probability. This paper constructs a train speed prediction model based on the L-Ns-Transformer. By compensating for Doppler shift, the model achieves more accurate channel modeling. Furthermore, an intelligent reflecting surface (IRS)-assisted beamforming method for HSR secure communication is proposed when the eavesdropper’s (Eve) CSI is unknown. We propose a two-stage deep learning (TS-DL)-based approach to design transmitter beamforming and IRS jointly, where the precoding vector and phase shift matrix are designed to minimize the secrecy outage probability. Simulation results demonstrate that the proposed TS-DL approach has lower computational complexity and can effectively reduce the system secrecy outage probability, thereby enhancing the security of HSR wireless communication.

The deep integration of the vehicular computing power network (VCPN) and artificial intelligence offers the potential to meet the computation-intensive and low-latency demands of emerging vehicular applications. However, due to the dynamic VCPN scenarios, task heterogeneity gives rise to differentiated and time-varying task requirements, while node mobility and wireless channel fluctuations further exacerbate scheduling complexity, which jointly hinder efficient and on-demand task scheduling. In this paper, we propose a metric termed the age of task (AoT), which characterizes the differentiated service requirements of tasks in VCPN. A utility-driven scheduling model is developed that jointly considers AoT reduction and computing cost of concurrent tasks, and a task utility maximization problem is formulated. Due to the complexity of directly solving the problem, the original problem is decomposed into a joint multi-task scheduling and matching subproblem and a resource allocation subproblem. To address the dynamic scheduling challenges in VCPN, including task heterogeneity, vehicle mobility, and fluctuating wireless channels, we model the joint task scheduling and matching subproblem as a Markov decision process. An age-aware, multi-dimensional double-deep Q-learning algorithm is designed to handle discrete action spaces and mitigate the overestimation bias in traditional DQN methods. Additionally, we develop an improved interior point-based resource allocation algorithm to obtain the optimal solution. Numerical results show that the proposed algorithm effectively adjusts learning strategies to maximize the task utility.

With the rapid advancements in intelligent transportation systems, autonomous driving technologies, and V2X communications, the volume of data generated by vehicles has surged. Effectively leveraging this data for training machine learning models while ensuring privacy protection has become a pressing challenge. Federated learning, as a distributed learning framework, facilitates collaborative model training across multiple parties while preserving data locality. However, privacy risks, particularly those arising from inference attacks, remain a significant concern. In such attacks, adversaries can deduce sensitive information from the model updates shared by participants. This paper proposes a novel proactive defense algorithm, FL-VAD, designed to empower vehicles to autonomously adopt privacy protection strategies that actively mitigate the risk of data leakage. Specifically, vehicles reduce the likelihood of inference attacks and enhance data security through techniques such as data perturbation, model update control, and dynamic defense mechanisms. By integrating V2X technology, vehicles can collaborate more efficiently in the learning process while safeguarding privacy. Experimental results demonstrate that the proposed approach effectively reduces privacy leakage risks, offering robust privacy protection with minimal impact on model performance. This research introduces innovative solutions for enhancing privacy protection in federated learning within the context of intelligent transportation systems and autonomous driving.

With the advancement of intelligent driving technology, vehicular networks generate vast amounts of decentralized data that need to be processed. As a distributed paradigm, Federated Learning (FL) enables data integration and processing across various vehicles. However, traditional FL methods face significant challenges in vehicular networks, including high communication overhead and the difficulty of meeting strict latency and reliability requirements. To address these challenges, we propose a Multi-scale Semantic Selection-based FL (MSSFL) scheme, which integrates multi-scale semantic parameter selection and transmission optimization to reduce the system's communication cost. The proposed scheme selects parameters with high semantic importance and allocates bandwidth proportionally based on their quantity to enhance communication efficiency. We further formulate an optimization problem to minimize both parameters' transmission cost and upload delay. To solve this problem, we develop an alternating iterative solution using the block coordinate descent (BCD) method, which alternately optimizes the semantic parameter selection and bandwidth allocation strategy. Experimental results validate the effectiveness of the proposed framework in enhancing both communication efficiency and model accuracy.

With the rapid advancement of intelligent railway systems, a high-quality train-ground communication system is crucial. However, ensuring reliable wireless communication in ultra-high-speed environments remains a significant challenge due to severe Doppler effects, frequent inter-cell handovers, and diverse QoS demands. Existing solutions, such as soft/hard handover schemes, lack the flexibility to adapt to dynamic conditions, leading to service interruptions and suboptimal performance. In this paper, we propose a dynamic resource allocation strategy based on dual base station coordination, utilizing millimeter-wave (mmWave) technology and real-time train position prediction. This approach dynamically optimizes user association and spectrum allocation to accommodate the rapid movement of trains, reducing service interruptions and ensuring continuous high-quality communication. Simulation results show that our algorithm improves system QoS satisfaction by 37.5%-68.8% and achieves spectrum utilization rates between 76% and 90%, outperforming the comparison schemes. These results validate the effectiveness of our approach in addressing high-speed mobility challenges.

Vehicular digital twin networks (VDTN) is an emerging paradigm integrating physical vehicular networks with their virtual digital twins (DT) mirror, enabling real-time mapping, simulation, and optimization of complex systems. However, constrained resources, high data synchronization costs, and dynamic network conditions in vehicular networks may degrade the performance of DT. We consider the interaction between task performance guarantees and node resource constraints to adaptively determine collaborative task computation transfer optimization in VDTN. In this paper, we design a semantic-aware multi-task vehicular digital twin network model, where vehicles extract semantic representations to achieve lightweight data transmission and efficient DT synchronization. We formulate a problem of maximizing the average utility of DT tasks by jointly considering the synchronization performance of DT tasks and the resource consumption among heterogeneous nodes. To solve the formulated problem, we develop a dynamic collaborative task computation transfer algorithm involving the high mobility of vehicles and heterogeneous resources of nodes. The algorithm is optimized in two phases to maximize average utility. A coarse-grained policy space is first obtained through an adaptive multi-agent deep reinforcement learning approach, aiming to alleviate the policy space explosion caused by dynamic task requirements and heterogeneous node collaboration. Subsequently, a fine-grained policy is derived via a resource-aware refinement mechanism. Numerical results validate the effectiveness and robustness of our proposed algorithm.

With the increasing number of Internet of Things devices and sensors in vehicular network, a huge amount of data is generated. Vehicle Edge Computing (VEC) utilises the computation resources at the edge of the network and can efficiently process this big data through computational offloading techniques. However, due to the neglect of communication network relationships among vehicles, current Vehicle-to-Vehicle (V2V) computation offloading schemes encounter challenges such as high communication latency, substantial communication overhead, and the wastage of computation resources. To address these challenges, we design a computation offloading mechanism based on Federated Graph Neural Network (GNN) for vehicular networks, that is, vehicular FedGNN (V-FedGNN). Firstly, our modeling approach considers features including vehicle speed, location, available resources, and wireless network, which are embedded in the graph structure. Secondly, we design weighted vehicular communication network topology and propose weighted total delay optimization problem. Finally, this paper proposes a prediction model based on Federated Learning (FL) and GNN to minimize the weighted computation offloading delays among vehicle nodes. Experimental results demonstrate that our proposed scheme achieves high offloading prediction accuracy, with an average value of 98.1% and achieves low offloading latency, correspondingly.

Emerging as an important enabling technology for smart factories, ultra-reliable low latency communications (URLLC) have attracted extensive attention from academia and industry. In this paper, we aim to improve the performance of downlink URLLC in a smart factory. We first construct the system model based on the 5G New Radio (NR) standard, which specifies the modulation scheme, resource block structure and achievable data rates under finite blocklength codes (FBC). Next, since it is challenging to fulfill all transmission requests with limited radio and power resources, we formulate the problem to maximize the network throughput while considering delay and reliability constraints. This is a mixed integer non-convex nonlinear problem that is difficult to solve directly. To be tractable, we decompose it into two sub-problems, and apply the alternating optimization to obtain a sub-optimal solution. Specifically, the flow scheduling sub-problem is transformed into a matching game (MG) and solved by a delayed acceptance-based algorithm. Also a local water-filling algorithm is utilized to solve the power allocation sub-problem. Simulation results reveal that our proposed scheme outperforms other benchmark schemes.

Mobile Crowd Sensing (MCS) networks aim to leverage users’ mobile devices to collect environmental or activity information, thereby supporting a wide range of applications. Although large-scale participation of mobile users drives the application and development of MCS networks, it also introduces new security challenges. Due to the diversity and uncertainty of users in MCS networks, trust relationships are difficult to evaluate accurately. This paper introduces the concept of Zero Trust and designs a Zero Trust-based architecture for MCS networks to enhance system security. To address the issue of Zero Trust continuous authentication that consumes network resources and impacts system transmission efficiency, this paper proposes the concept of Age of Trust (AoT), where a higher trust level corresponds to a lower AoT value. The paper tackles the bi-objective optimization problem between the average AoT and throughput on a single link in MCS networks. The results demonstrate that the proposed scheme achieves a reasonable trade-off between security performance and transmission efficiency, showcasing broad application potential in various zero-trust architectures.

With the increasing demands from passengers for data-intensive services, millimeter-wave (mmWave) communication is considered as an effective technique to release the transmission pressure on high speed train (HST) networks. However, mmWave signals encounter severe losses when passing through the carriage, which decreases the quality of services on board. In this paper, we investigate an intelligent refracting surface (IRS)-assisted HST communication system. Herein, an IRS is deployed on the train window to dynamically reconfigure the propagation environment, and a hybrid time division multiple access-nonorthogonal multiple access scheme is leveraged for interference mitigation. We aim to maximize the overall throughput while taking into account the constraints imposed by base station beamforming, IRS discrete phase shifts and transmit power. To obtain a practical solution, we employ an alternating optimization method and propose a two-stage algorithm. In the first stage, the successive convex approximation method and branch and bound algorithm are leveraged for IRS phase shift design. In the second stage, the Lagrangian multiplier method is utilized for power allocation. Simulation results demonstrate the benefits of IRS adoption and power allocation for throughput improvement in mmWave HST networks.

As research on integrated sensing and communication (ISAC) progresses, it has been discovered that ISAC can be effectively utilized to enhance the security of wireless communications. Its sensing function can assist in both eavesdropping detection and physical-layer security techniques. In this article, our focus lies on addressing the security challenges associated with high-speed train-to-ground communication using ISAC technology. We explore a novel secure communication scheme. Specifically, we exploit the sensing capabilities of ISAC to detect eavesdropping at the receiving end and subsequently establish a signal blind zone at the location where eavesdropping occurs through beamforming and waveform optimization techniques. This approach ensures the achievement of secure wireless communication. Mathematically modeling the problem as an optimization problem, we derive a lower bound for simplification purposes. Subsequently, we employ an alternating optimization algorithm to iteratively find suboptimal solutions for the optimization variables. Through extensive simulation experiments and comparative analysis, we demonstrate that our proposed algorithm not only guarantees communication security but also outperforms existing algorithms in terms of efficiency.

Federated learning is an emerging distributed learning paradigm which brings an efficient and privacy-preserving intelligent model for the Internet of Vehicles (IoV). Unfortunately, federated learning is vulnerable to abnormal model attacks as it is hard to authenticate model parameters. Abnormal local models may slow down the convergence rate, reduce the accuracy of global models, and even deliberately control the global model in the attackers' chosen way. Furthermore, an abnormal global model may deduce sensitive information about vehicles and hinder the execution of genuine tasks. Therefore, in this paper, we propose a parameter-authentication federated learning (PAFL) scheme that can protect privacy of vehicles, such as driving habits, and defend against abnormal model attacks simultane-ously. Concretely, we equip the federated learning framework with the zero knowledge proof and Pedersen commitment to prove and authenticate the reliability of model parameters. Security and privacy analysis, as well as performance evaluation show that the PAFL scheme can successfully detect abnormal models with higher detection rate and achieve more secure global aggregation than existing representative schemes.

Semantic communication provides a new paradigm that aims at serving upcoming intelligent transportation applications including autonomous driving and real-time video monitoring. However, the problem of computing efficiency and data privacy during semantic extraction and transmission remains unsolved that need to be further investigated. In this paper, an efficient federated learning-empowered vehicular semantic communication(FVSCom) framework is proposed by jointly considering computing efficiency and data privacy, where federated learning is used to perform semantic extraction. To measure the performance of FVSCom, a metric of semantic utility that jointly considers semantic timeliness and semantic fidelity is proposed. We further analyze the end-to-end delay of the FVSCom network and formulate the semantic utility maximization problem. A DRL-driven dynamic semantic-aware algorithm for semantic utility optimization in FVSCom is proposed. The proposed algorithm can guide the agent to approach the suitable policy of semantic extraction and resource allocation, and dynamically respond to the leave or exit of vehicles. Experimental results showcase the potential of the proposed method for achieving substantial advantages over comparison algorithms and demonstrate strong robustness concerning the departure or exit of vehicles.

Federated learning is a common distributed machine learning framework. Through the training of the global model, the problems of large communication overhead and data privacy protection in traditional centralized machine learning are solved. But in real distributed scenarios, Non-Independent and Identically Distributed(Non-IID) of data reduces the speed of learning and the accuracy of global model. To solve this problem, this paper proposes a federated learning client selection algorithm based on cluster label information(FedCLS). FedCLS realizes efficient federated learning by optimizing the selection of clients in each round of training. Through extensive simulations, we demonstrate that compared with traditional FedAVG based on random extraction, FedCLS has better learning performance and less resource overhead.

For safety applications in intelligent transportation system (ITS), it is essential for vehicles and pedestrians to decode the safety messages from nearby moving vehicles through direct sidelink in the presence of cellular link. This article presents the coverage probability analysis of vehicular safety messages-prioritized cellular vehicle-to-everything (C-V2X) communications. We model the spatial layout of macro base stations (MBSs) and vehicles as a 2-D Poisson point process (PPP) and a Poisson line Cox point process (PLCPP). Since vehicles can be regarded as mobile base stations, we assume that the MBSs and vehicles share the same spectrum. In a similar way, we consider two kinds of users, i.e., planar users and linear users, which are also modeled by a 2-D PPP and a PLCPP, respectively. Using a stochastic geometry tool, we derive the signal-to-interference ratio (SIR)-based coverage probability of four links (i.e., downlink of planar user, sidelink of planar user, downlink of linear user, and sidelink of linear user) according to the vehicle-prioritized association scheme. We assume that the users are required to decode the vehicular safety messages if they are within a certain distance from vehicles. Then, we derive the conditioned coverage probability of four links to study their reliability. In addition, we explore the impacts of several key parameters on the coverage probability and provide some design insights.

A novel paradigm that combines federated learning with blockchain to empower edge intelligence over vehicular networks (FBVN) can enable latency-sensitive deep neural network-based applications to be executed in a distributed pattern. However, the complex environments in FBVN make the system latency much harder to minimize by traditional methods. In this article, we model the training and transmission latency of each autonomous vehicle (AV) and consensus latency of the blockchain in-edge side in FBVN. Considering the dynamic and time-varying wireless channel conditions, unpredictable packet error rate, and unstable data sets quality, we adopt duel deep$Q$-learning (DDQL) as the solving approach. We propose a federated DDQL algorithm, in which the learning agent is deployed on each AV side, and the sensing states on each AV do not need to be shared so that it increases scalability and flexibility for practical implementation. Simulation results show that the proposed algorithm has better performance in reducing system latency compared with the other schemes.

In this paper, we present the success probability analysis of cooperative cellular vehicle-to-everything (C-V2X) communications, i.e., cellular-relay V2X communications. We model the spatial layout of macro base stations (MBSs) as a 2D Poisson point process (PPP) and roads as a Poisson line process (PLP), with road wireless nodes (including vehicles and roadside units) modeled as a 1D PPP on each road. For a typical source node, we calculate the signal-to-interference ratio (SIR)-based success probability of transmitting a packet to its closest destination node with the assist of its nearest MBS. We take into account three cooperative transmission schemes and derive their expressions of joint success probability in two consecutive phases considering the correlation of road topology and nodes’ locations, respectively. We verify the accuracy of our analytical results through Monte-Carlo simulations. In addition, we explore the impacts of several key parameters on the success probability and discuss the selection of transmission schemes.

To compensate for the limitations of existing dedicated short range communications (DSRC), cellular vehicle-to-everything (C-V2X) has been proposed recently, which is also a promising technology for future intelligent transportation systems (ITS). Using stochastic geometry approach, this paper presents the modeling and analysis of success probability in multi-relay cooperative C-V2X networks. The spatial distribution of base stations (BSs) and vehicles in$\mathbb {R}^{2}$are modeled as a 2D Poisson point process (PPP) and a Poisson line Cox point process (PLCPP), respectively. We focus on the success probability of a source vehicle sending a message to the nearest destination vehicle assisted by the closest BS. Each vehicle is equipped with single antenna whereas each BS is equipped with multiple antennas, which act as independent relays. We consider two decoding schemes, i.e., selection combining (SC) and maximum ratio combing (MRC), and obtain the analytical expressions for joint success probability during two continuous time slots, taking into account the interference correlation (i.e., the spatial correlation of vehicle location). The analytical model is validated using Monte Carlo simulations in MATLAB, and the effects of major parameters on success probability are investigated.

To overcome the shortcomings of Dedicated Short Range Communications (DSRC), cellular vehicle-to-everything (C-V2X) communications have been proposed recently, which has a variety of advantages over traditional DSRC, including longer communication range, broader coverage, greater reliability, and smooth evolution path towards 5G. In this paper, we consider an LTE-based C-V2X communications network in irregular Manhattan grids. We model the macrobase stations (MBSs) as a 2D Poisson point process (PPP) and model the roads as a Manhattan Poisson line process (MPLP), with the roadside units (RSUs) modeled as a 1D PPP on each road. As an enhancement architecture to DSRC, C-V2X communications include vehicle-to-vehicle (V2V) communication, vehicle-to-infrastructure (V2I) communication, vehicle-to-pedestrian (V2P) communication, and vehicle-to-network (V2N) communication. Since the spectrum for PC5 interface in 5.9 GHz is quite limited, cellular networks could share some channels to V2I links to improve spectral efficiency. Thus, according to Maximum Power-based Scheme, we adopt the stochastic geometry approach to compute the signal-to-interference ratio- (SIR-) based success probability of a typical vehicle that connects to an RSU or an MBS and the area spectral efficiency of the whole network over shared V2I and V2N downlink channels. In addition, we study the asymptotic characteristics of success probability and provide some design insights according to the impact of several key parameters on success probability.

Location-based service (LBS) has been widely used and brings convenience to people's lives. In order to obtain the desired services, a user must report its current location information to the LBS provider. If this information falls into the hands of malicious adversaries, users may even face serious threats. Researchers have proposed some LBS-based vehicle location privacy protection methods. However, these methods are vulnerable to attackers with background knowledge. In this paper, we propose a privacy protection method based on the background knowledge of reliable servers, and use correlation probabilities and correlation transition probabilities to achieve ε-differential privacy geography indistinguishability. The Laplace scheme is used to add noises to the query results. At the same time, this method provides different levels of privacy protection. The simulation results compare and explain the incompleteness of the consideration of this algorithm.

With the development of the autonomous hybrid plug-in electric vehicles (AHEVs), majority of AHEVs are equipped with bidirectional chargers. However, during the energy trading process, the centralized schemes are exposed to many flaws like the high energy transmission loss and cannot resist the single-point failure. Therefore, it is more important to use the decentralized manner to keep the safety energy trading between AHEVs. In this paper, we propose a decentralized energy trading architecture in which AHEVs can broadcast their requirements to neighbors and negotiate with other AHEVs on the transmission parameters without a transaction server. The smart contract (SC) is deployed among RSUs which can be automatically executed after the negotiation process. To promote AHEVs to behave honestly on the road, we associate the trust value of AHEVs with the charging price. We use proof-of-concept to theoretically demonstrate the feasibility of the development of the SC and the security properties of the proposed scheme. The simulation results demonstrate the proposed negotiation scheme outperforms the non-negotiation scheme in the aspect of efficiency.

Public blockchain network (PBN) has been widely used in wired networks such as bitcoin network, in which proof-of-work (PoW) algorithm is deployed among miners to reach consensus on users data during the mining process. However, the PoW consensus mechanism is computation-consuming which obstacles the application of PBN in wireless mobile networks since most Internet of Things/mobile devices (IMDs) are resource limited. Recently, mobile edge computing (MEC) has been regarded as a promising technology which can allow IMDs to offload their computation tasks to the edge nodes. Although IMDs can offload their computation tasks to the edge nodes, there is still lots of competition among enormous solo mining IMDs when reaching consensus. In this paper, we first formulate the computation resource allocation problem of PBN from the viewpoint of coalition game theory under the MEC environment. Then, we propose a coalition formation game-based algorithm to maximize the system sum utility and take both the individual profit of IMD and coalition profit into consideration. Furthermore, we prove the proposed algorithm converges to a Nash-stable partition in a fast convergence rate and finally reaches the near-optimal solution with low computational complexity. The simulation results demonstrate the optimality and convergence of the proposed algorithm, and the proposed algorithm outperforms other schemes in terms of system sum profit and ratio of rewarded IMDs to overall IMDs.

This paper analyzes the network connectivity in vehicular networks with slight traffic interference, i.e., small‐scale traffic accident. In this paper, we develop an analytical model for highway scenarios or sparse urban scenarios, where the slight traffic interference can make the vehicles slow down rather than block the traffic flow. When a traffic accident occurs, it is necessary to inform the nearby vehicles to slow down in order to reduce congestion at the accident location. Once they pass by, they can return to the normal value. Consequently, we can divide an entire road into two or three subsections, which helps us to analyze the connectivity performance. In addition, we analyze the impact of several key parameters on connectivity probability, including vehicle arrival rate, vehicle communication range, length of road, vehicle normal speed, and safe speed. All analytical results are verified through Monte Carlo simulation experiments. The simulation results are very close to analytical results, which means that the analytical results are accurate and the analytical model we propose is effective.

In Intelligent Transportation System (ITS), the safety and non-safety related message are delivered based on the wireless communications through Vehicle-to-Vehicle (V2V) and Vehicle-to- Infrastructure (V2I) in Vehicular Ad Hoc Networks (VANETs) environments. The connectivity performance between vehicles and infrastructures are critical for providing high-quality service. The user behavior is a critical factor for analyzing system performance in VANETs. In this paper, we analyze the influence of the user behavior and other system parameters on the connectivity probability. The results can help control and adjust the traffic on the highway to satisfy the connectivity requirement. Therefore, the user behavior can not be neglected when designing the network connectivity model.

5G and Internet of things (IOT) develop rapidly, but the major applications of IOT-wireless sensor networks(WSNs) have numerous data, resulting in serious transmission load. In order to reduce the number of transmitted packets, this paper focuses on data aggregation for WSNs and proposes a novel event-based data aggregation mechanism using both principle component analysis(PCA) and support vector regression(SVR). The proposed method first uses correlation to achieve event checker at data aggregation node. Then when the state is normal, PCA is performed for data aggregation to reduce data dimensionality. When the state is urgent, data aggregation node receives changing data and transmits the sensing data to base station instantly, meanwhile, the data aggregation node performs SVR-based prediction. According to the prediction accuracy, data aggregation node adjusts the data transmission time interval adaptively. The simulation results show that the proposed scheme reduces the amount of transmission data among base station and one or more data aggregation nodes and decreases energy consumption compared with Adaptive-PCA.

Wireless communication link quality can be determined by the transmission protocol design, the interference level, the channel fading properties, and the mobility characteristics etc. As one of the most essential features for vehicular networks, high mobility brings in intermittent connectivity and relative short link lifetime. Therefore, an analytical model on link lifetime can be of great help for an efficient and effective vehicular communication protocol design. In this paper, the impact of mobility on link lifetime in the highway environment is modeled and analyzed by utilizing the discrete-time Markov chain (DTMC). Specifically, two link lifetime theoretical models, the first-order Markov model and the second-order Markov model, are studied and analyzed for effectively predicting the proprieties of a vehicular communication link. It is shown that our proposed models, especially the second-order Markov model, can provide more accuracy in performance prediction. Additionally, extensive simulations are carried out to verify our analytical results on link lifetime.

The link duration property of Vehicular Ad Hoc Networks (VANETs), which is influenced by vehicle mobility and signal propagation environment, is studied in this paper. For our investigation, the inter-vehicle distance is partitioned into several non-overlapping segments as different transition states. After that, a modified Markov model is proposed, and the distance transition probability matrix is also derived to theoretically describe the effects of dynamically changed inter-vehicle distance under the pathloss transmission model. For the more complex fading channel model, we defined the virtual transmission range and considered the possible situations for breaking link connection, based on which an revised Markov model is also proposed to describe the joint effects of random movements and fading channel fluctuations. Finally, the numerical results obtained from simulation and our analytical model are compared to verify the accuracy of our work.

A novel cluster-head selection and update algorithm "Type-based Cluster-forming Algorithm (TCA)" is proposed, which outperforms both the lowest node ID (LID) and the Weighted Clustering Algorithm (WCA) in the ad hoc network scenario considered. The system's performance is investigated in a scenario, when the 50 communicating nodes belong to three different groups, for example, a group of rescue workers, fire-fighters and paramedics. It is demonstrated that the carefully designed protocol is capable of outperforming the above-mentioned benchmarkers both in terms of a reduced number of cluster-head updates and cluster-change events. Hence its quality-of-service may be deemed higher.

This paper proposes an Energy Efficient Clustering Scheme for mobile ad hoc networks. In the initial clustering stage, a node's residual energy, nearby topology, relative location and relative mobility are used for determining whether the node is suitable for being a cluster head. In the cluster maintaining stage, a strategy called Distance Estimation Broadcasting is designed to help a cluster member to estimate the distance between itself and its cluster head. Thus, the cluster members can use less energy to accomplish the data transmission. Moreover, a dynamic calculated Off-Duty Threshold is proposed to trigger the re-clustering operation when needed. Simulation results show that the proposed clustering scheme performs better than the previous Weighted Clustering Algorithm and Distributed Weighted Clustering Algorithm.

This paper addresses the clustering problem for highly mobile ad hoc networks. In the proposed scheme, Doppler shifts associated with received signal are used to estimate the relative speed between cluster head and cluster members. With the estimated speed, a node can predict its stay time in every nearby cluster. In the initial clustering stage, a node joins a cluster that can provide it with the longest stay time in order to reduce the number of re-affiliations. In the cluster maintaining stage, strategies are designed to help node cope with connection loss caused by channel fading and node mobility. Simulation results show that the proposed clustering scheme can reduce the number of re- affiliations and the average disconnection time compared with previous schemes.