The integration of wireless power transfer (WPT) and mobile edge computing (MEC) provides an effective solution for overcoming the energy and computational limitations of Internet of Things (IoT) devices by enabling them to harvest energy from radio frequency signals and offload data to edge servers. A crucial challenge in wireless powered MEC (WP-MEC) networks is how to efficiently optimize offloading decisions and resource allocation to enhance overall system performance. In this paper, we investigate the partial offloading strategy within a WP-MEC network consisting of multiple HAPs. The optimization problem is formulated as a Mixed-Integer Non-linear Programming (MINLP) problem with variables of WPT duration, offloading decisions and energy allocation. To solve this problem, we propose a deep reinforcement learning (DRL)-based framework, which employs a neural network architecture combining convolutional and fully connected layers to output offloading decisions. Additionally, we design an optimization algorithm for joint optimization of WPT duration and offloading proportions. Numerical results demonstrate the proposed method achieves better performance than the existing DRL methods, which demonstrates the efficiency of the proposed method.

The substantial heterogeneity in energy and communication protocols among IoT devices leads to highly diversified image resolutions, which severely constrain the reliability of downstream visual analysis tasks in applications like intelligent transportation and smart buildings. While recent deep learning-based SR methods have achieved remarkable success, the majority are typically designed for specific integer scaling factors, requiring separate models for different scales, which is impractical for real-world IoT applications. To address this, we propose SafaSR, an arbitrary-scale image super-resolution network based on multi-domain feature fusion. Our key innovation lies in a Multi-domain Multi-level Feature Fusion(M2F2 )mechanism, which is driven by a scale-aware feature learning (SFL) model that adaptively extracts features from both spatial and frequency domains. TheM2F2mechanism is designed to reduce correlations between different feature domains, allowing a more effective integration of complementary information. Extensive experiments show that our proposed network outperforms the most advanced image SR algorithms in terms of PSNR and SSIM metrics on the benchmark datasets, with fewer network parameters and less runtime.

To address the challenges of weak identity binding between sensors and users, as well as insufficient data security in the Internet of Medical Things (IoMT), this paper proposes a blockchain-based certificateless aggregate signcryption scheme. The scheme integrates dynamic anonymous identity generation with aggregate signcryption techniques to achieve lightweight encryption while enhancing protocol-level identity binding between sensors and device users. By storing anonymous identities, public keys, and signcrypted data on the blockchain, the proposed approach enables verifiable identity auditing and tamper-proof data preservation, thereby improving the overall trustworthiness and traceability of the system. Under the random oracle model, we formally prove the confidentiality and unforgeability of the scheme against both external and internal adversaries. Experimental evaluations are conducted in the Hyperledger Fabric environment and on an NVIDIA Jetson Nano Developer Kit, demonstrating the proposed scheme’s superior performance in computation efficiency, communication overhead, and practical deployability.

The massive low earth orbit (LEO) satellite-empowered Internet of Things (MLS-IoT) network has many advantages including high capacity, low latency, large network coverage, and high reliability. However, the openness and broadcasting on satellite communication links pose huge challenges to protect the security of data delivery, especially to defend against eavesdropping. Leveraging the density of LEO satellite deployment and developing efficient secure communication schemes with the aid of physical layer security (PLS) technique deserve further exploration. To the best of our knowledge, it is an almost untouched issue to employ satellites to transmit jamming signal in MLS-IoT systems since lots of available researches in this line mainly considered cooperative jamming only on ground networks. For this purpose, we propose in this paper a PLS based secure communication enhancement scheme in MLS-IoT network. A jamming scheduling and resource allocation problem is presented for the secrecy rate maximization by dynamically switching the roles of satellites on sub-channels for transmitting private information or jamming signal. An iterative strategy is devised to jointly optimizing jamming scheduling, device association, channel assignment, and power allocation. Numerical results validate that our presented optimization approach could significantly improve the system secrecy rate and achieve the optimal jamming scheduling and resource allocation decisions.

A covert ISAC transmission scheme based on an innovative heterogeneous sub-connected hybrid analog and digital (H2AD) multiple-input multiple-output (MIMO) transceiver is investigated in this paper. Specifically, H2AD ISAC system possesses the capability to detect the point-like target while covertly transmitting confidential information to a singleantenna legitimate user, and enabling secure transmission without detection by the warden. The objective is to maximize the covert transmission rate for legitimate users while adhering to the Cramér-Rao bound (CRB) threshold. However, due to the coupling of multiple variables under the H2AD transceiver framework, the optimization problem becomes non-convex. To tackle the challenging, an alternating optimization algorithm based on Dinkelbach’s transformation and semidefinite relaxation (DTSDR) is proposed to design the analog and digital beamforming along with the sensing signal. Then, by utilizing historical system states and optimizing for long-term returns, an improved distributional soft Actor-Critic with three refinements (DSACv2) algorithm framework based on deep reinforcement learning (DRL) is proposed. Simulation results demonstrate that incorporating the novel H2AD MIMO antenna array into ISAC system design enhances the covert performance while ensuring target sensing performance.

Covert communication can reduce the security overhead of messages in New Radio (NR) Vehicle-to-Everything (V2X) and provide a higher level of privacy assurance. However, it also introduces a tradeoff between covertness and transmission. In this paper, we explore the tradeoff between covertness and transmission in NR V2X communications and propose a covert scheme, Covert Semi-Persistent Scheduling (C-SPS). To elucidate this tradeoff in the traditional case, we derive the theoretical results based on stochastic geometry, which accounts for the fundamental processes of semi-persistent scheduling, including transport block re-selection and resource collision. Based on our derivation, the optimal setting of C-SPS is obtained to maximize the covertness of message delivery while satisfying the transmission requirements of NR V2X. More importantly, we assess the covert threat posed by intelligent adversaries equipped with the Large Language Model (LLM) capable of reasoning based on the tailored chain of thought. Finally, we evaluate the effectiveness of C-SPS in balancing the tradeoff under both traditional case and LLM-assisted case.

In 6G vehicle-to-everything (V2X) scenary, precise millimeter-wave beam prediction under dynamic environmental disturbances faces critical challenges of latency-accuracy trade-offs and cross-scenario generalization. It not only requires accurate and prompt determination of the vehicle’s position, but also needs to overcome external environmental disturbances. However, most existing schemes suffer from critical limitations such as poor adaptability in extreme scenarios, prolonged decision latency, and restricted cross-scenario generalization capability. Towards this end, we propose LLM-MM: an end-to-end robust multimodal beam prediction framework for 6G V2X networks via MoE-LoRA Adaptation. Specifically, we first construct a distinctive beam prediction architecture to leverage the powerful inference capabilities of the Large Language Model (LLM), thereby shortening the inference time while ensuring the inference accuracy. Secondly, by integrating the Mixture-of-Experts with Low-Rank Adaptation model, LLM-MM not only ensures its generalization ability across multiple scenarios but also reduces the training cost. Our framework integrates a rigorously evaluated open-source LLM, selected through systematic comparison of multiple candidates to achieve optimal performance. Extensive numerical results demonstrate the advantages of proposed framework from multiple perspectives.

At complex intersections, it has become a consensus to address the perception limitations (such as blind spots, the difficulty of accurately obtaining road condition information in distant areas or under adverse weather conditions) faced by single-vehicle intelligence through vehicle-infrastructure cooperation. However, under the constraints of limited spectrum resources, the sharply rising in the number of connected vehicles, and the pressing requirement for effective utilization of electric power, there has been no study on how to design a scheme that assists roadside unit (RSU) completing roadside cooperative message delivery (RCMD) with minimal transmit power. Towards this end, this paper proposes a general and energy-efficient RCMD scheme based on multicarrier multigroup multicasting rate-splitting multiple access. We formulate a joint optimization problem involving the RSU’s transmit power, along with the power and size coefficients of each message. Then we design a deep reinforcement learning-assisted bilevel resource allocation algorithm to solve this problem. Finally, we collect extensive numerical results, using the message delivery success probability and RSU’s transmit power as performance metrics, from the proposed scheme and multiple benchmarks. Results indicate that the proposed scheme not only exhibits strong adaptability (i.e., it can be deployed at any intersection regardless of the number of lanes, vehicles, or traffic complexity), but also significantly reduces the RSU’s transmit power while maintaining high message delivery success probability.

The distributed edge computing network has been proposed as a promising approach to accelerate task computation. Incorporating parallel edge computing, we investigate a parallel distributed edge computing network where each edge device is allowed to receive one task and compute another task simultaneously, and meanwhile, edge devices are allowed to compute their respectively received tasks simultaneously. We minimize the total task completion time (TCT) of source nodes by jointly optimizing offloading selections of source nodes and scheduling order of task offloading, i.e., MTOS problem, which is proved to be NP-hard. To tackle it, we first study the MTOS problem with one edge device (MTOS-1), establish three task offloading rules to minimize the total TCT, and propose a priority-based scheduling order of task offloading algorithm. Based on the established task offloading rules for the MTOS-1 problem, we study the MTOS problem withMedge devices and additional offloading-adjacency constraint (MTOSO-M), establish another two task offloading rules for scheduling order, and propose an automatic adjustment-based joint offloading selections and scheduling order algorithm. By relaxing the offloading-adjacency constraint of the MTOSO-Mproblem, we further study the general MTOS problem withMedge devices (MTOS-M), derive a lower bound of the total TCT, and propose a queue jumping-based joint offloading selections and scheduling order algorithm. Extensive numerical results are conducted to discuss impacts of vital network parameters on the total TCT, verify the superiority of the proposed algorithms, and show that the communication-computation parallelism for edge devices and the computation parallelism among edge devices further reduce the total TCT.

Cooperated with simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) and unmanned aerial vehicle (UAV), a non-orthogonal multiple access (NOMA) covert communication network is conceived. However, open channels are more vulnerable to eavesdropping byWardens, overlying that the channel state information (CSI) may be unstable with UAV’s time-varying deployments. In this paper, a deep reinforcement learning (DRL)-based and instantaneous channel-deducted framework is investigated for resolving the cutting-edge maximization problem of covert communication rate, subjected to the UAV flight, QoS requirement, and communication covertness. Given the channels instability caused by time-varying UAV flight, we design a channel deduction network by integrating complex-domain multi-layer perceptron (CMixer) and recurrence-based bidirectional long-short term memory (BiLSTM) to exploit the nonlinear correlations of channels in time, spatial location, and antenna domains. Relying on the states with deducted channels, the Twin Delayed Deep Deterministic policy gradient (TD3) as a proactive and policy-based DRL algorithm is used to iteratively train an agent responsible for adaptive adjusting UAV deployment and STAR-RIS beamforming. Simulation results demonstrate the effectiveness of the proposed channel deduction scheme, covert communication mechanism, and their synthesis.

Intelligent transportation plays a vital role in modern urban sustainability by enhancing traffic efficiency, ensuring safety, and mitigating environmental impact. Vehicle-to-Everything (V2X) technology, particularly Vehicle-to-Infrastructure (V2I), is fundamental to this vision, enabling seamless collaboration between vehicles and infrastructure. However, reliable communication in V2I faces challenges due to high-speed mobility, dynamic environments, and stringent latency requirements. While channel alignment methods such as scanning, tracking, and prediction offer some solutions, they struggle with efficiency and adaptability in real-world conditions. To address these limitations, this paper proposes a channel prediction model based on generative learning with implicit state evolution, capturing nonlinear mappings between channel state information and communication dynamics. Additionally, a side information module incorporating temporal and spatial data enhances adaptability to varying traffic and environmental conditions. Extensive evaluations across different weather and traffic densities demonstrate the model's robustness and superior predictive accuracy. The proposed approach provides a reliable and efficient solution for dynamic V2I communication, offering new insights for future intelligent transportation systems.

In the new radio (NR) vehicle-to-everything (V2X), congestion control (CC) is adopted to reduce channel occupancy and improve delivery performance. However, most existing CC schemes decide to adjust the number of messages while ignoring the throughput requirements, which may lead to serious traffic accidents due to the loss of information. In light of this, we investigate the performance of cooperative awareness messages (CAMs) delivery via NR sidelink under different CC configurations, including the modulation and coding scheme, the semi-persistent scheduling-based resource allocation, as well as the CAM transmission period. Specifically, taking into account these configurations, we develop a theoretical framework to capture two metrics of CAM delivery, namely the effective throughput and the channel busy ratio, based on the stochastic geometry approach. With the help of this framework, we propose the CC-(m,n,$\tau $) scheme, which is able to optimize the two metrics simultaneously by properly tuning the parameters m, n, and$\tau $. Extensive numerical results show that the proposed scheme is highly beneficial for efficient and reliable CAM delivery via NR sidelink, particularly in scenarios with high vehicle densities.

Vehicular platoon communication demands high reliability and low latency to ensure safe and coordinated operations. However, the Semi-Persistent Scheduling (SPS) protocol in Cellular Vehicle-to-Everything (C–V2X) Mode 4 often results in persistent packet collisions in resource contention scenarios, greatly undermining the stability of the platoon. This paper proposes a Echelon-based Collaborative Resource Allocation (ECRA) protocol that conceptualizes the platoon structure as a three-tier communication hierarchy and implements refined resource management strategies. ECRA introduces four mechanisms to enhance the reliability of platoon communication. Specifically, a vehicle role-aware resource evaluation mechanism achieves differentiated assessment and allocation of resource quality by considering the functional importance of vehicles within the platoon. A hybrid error classification mechanism effectively differentiates communication errors within the platoon by integrating deterministic decision making with fuzzy logic. A echelon-based error response mechanism provides customized resource selection strategies and error response mechanisms for vehicles of different roles, ensuring efficient allocation of communication resources according to importance. A echelon-based waiting window mechanism dynamically optimizes the detection frequency based on vehicle roles, thereby balancing real-time requirements with system overhead. Finally, a theoretical model of packet collision probability and average delay is developed to quantify improvements in reliability and latency. Simulation results demonstrate that the proposed ECRA enhances platoon communication reliability and reduces latency more effectively than traditional SPS and other existing solutions. In particular, ECRA exhibits superior robustness in dynamic environments, especially under high-density and complex interference conditions.

As an essential technology for constructing green passive Internet of Things (IoT) network, backscatter communication (BackCom) enables battery-free IoT devices to deliver information by modulating and reflecting incident carriers. Nevertheless, the energy harvesting at an IoT device is constrained by its distance from the radio-frequency (RF) emitter, and the double-fading phenomenon significantly restricts the achievable performance of BackCom-based IoT network. Existing research has revealed that intelligent reflecting surface (IRS) and dynamic unmanned aerial vehicle (UAV) are promising solutions to overcome the current bottleneck, and their combined application in IoT network for BackCom remains in the preliminary exploration phase. Most studies focus more on traditional fixed RF emitters, which lack the flexibility needed for efficient energy transmission and are not suitable for infrastructure blank areas or hard-to-maintain regions. Motivated by this, an IRS-aided UAV-powered green IoT network is proposed in this paper, where the UAV acts as a mobile power beacon to energize multiple IoT nodes on the ground in a time-division multiple address manner, and an IRS is deployed in the scenario to enhance the BackCom performance. To guarantee reliable data transfer under energy harvesting constraint, we maximize the minimum average throughput among all IoT nodes for BackCom during the UAV flight duration by optimizing the node communication scheduling, power splitting coefficient, UAV trajectory and IRS phase shift. Specifically, we employ a four-stage alternating optimization method to decouple the optimization variables and solve for each variable independently. Finally, extensive experimental results verify the superior performance of our proposal in improving throughput over other benchmark schemes.

Collaborative perception enables connected and autonomous vehicles (CAVs) to overcome individual viewpoint limitations by exchanging perception data, making effective resource allocation crucial for timely transmission. However, existing studies focus on resource allocation within a single collaborative perception group (CPG), limiting their effectiveness in multi-group collaborative perception systems. In such a system, each CPG contributes differently to the overall collaborative perception performance, and it is challenging to evaluate and represent CPG system-level utilities. Meanwhile, competition for shared spectrum resources leads to interference and complicates the joint optimization of collaboration mechanisms and spectrum allocation, which is intensified by their temporal scale misalignment. To address these challenges, we propose a Utility-Aware Hierarchical Reinforcement Learning method (UAHRL) to jointly optimize collaboration mechanisms and spectrum allocation. Specifically, we introduce a hierarchical framework to handle temporally misaligned decisions through joint training. The upper layer optimizes the collaborative relationship and granularity over a longer time scale to enhance system-level collaborative performance, while the lower layer allocates spectrum resources over a shorter time interval to fulfill individual CPG transmission demand and enhance system transmission efficiency. To represent and utilize system-level utility, we leverage a feature-based confidence map to assess CAVs’ perception capability and complementarity. A mixing network in the upper layer further decomposes global performance into individual CPG utilities, enabling utility-aware resource allocation. Simulations show that UAHRL outperforms baseline methods in system-level collaborative perception in multi-group systems.

With the advantages of seamless coverage and ubiquitous connections, Non-Terrestrial Networks (NTNs) composed of Low Earth Orbit (LEO) satellites and Unmanned Aerial Vehicles (UAVs) can provide content caching services for future Connected Automated Vehicles (CAVs) to satisfy onboard collaborative viewing, traffic sensing, and metaverse entertainments in remote areas. However, the heterogeneous caching hardware, communication environments, and frequent network dynamics make the optimization of content caching policy highly complicated. Firstly, considering all LEO satellites as caching satellites can lead to content duplication and radio interference, causing storage waste and NTN transmission quality deterioration. Secondly, how to provide customized QoS by intra-layer and inter-layer cooperative caching in such complicated environments remains an open issue. Thus, we propose a Delay-Motivated Ant Colony Optimization (DM-ACO) scheme to select caching LEO satellites with reduced system propagation delay. Then, the Multi-Agent Deep Reinforcement Learning-based Hierarchical Caching and Asynchronous Updating (MADRL-HCAU) strategy is designed to manage the caching capacity of LEO satellites and UAVs, providing customized services for CAVs and dispensing the peak traffic. Simulation results illustrate that the proposed scheme can not only effectively accelerate the caching refreshing and content downloading process but also significantly reduce the packet drop and improve the cache hit ratio.

The development of 6G should enable users in remote and harsh areas to enjoy computation-intensive services including metaverse entertainment, intelligent transportation, and immersive communications. Low Earth Orbit (LEO) satellite constellations widely constructed in recent years have been recognized as an efficient solution to complement the terrestrial infrastructure with seamless coverage and decreasing expenses for both communication and computation services. However, the widely studied Federated Reinforcement Learning (FRL) based task offloading strategies neglect the potential trust concerns like malicious satellites and buffer pollution, while 6G service providers may rent the LEO satellites belonging to different companies to minimize the expense. To address these issues, blockchain has been considered in the Zero Trust (ZT) scenario, with the group consensus mechanism through the smart contract. Moreover, we propose a Constrained Correction Voting Mechanism (CCVM) to give punishing correction to the aggregation weight of malicious voting satellites. Furthermore, a Cold Start Reputation Aggregation (CSRA) scheme is adopted to first severely degrade and then gradually recover the weight of Federated Learning (FL) sub-models trained by malicious satellites. Thus, the Blockchain-enabled Cold Start Aggregation FRL (BCSA-FRL) scheme is proposed to make effective and secure offloading decisions in the ZT LEO satellite Networks. The numerical results illustrate the advantages of our proposal.

Roadside units (RSUs) play a vital role in intelligent transportation systems (ITS), working as critical elements in delivering superior Internet of Vehicles (IoV) services. A large service coverage and fast accident information diffusion RSU deployment solution can reliably ensure the ITS’ quality of service. Simultaneously, with the development of the city and the ITS, changes in traffic flow lead to RSU load imbalance, which will reduce the benefit of the original RSU deployment, and it is necessary to adjust RSU locations with minimal cost. Besides, due to the high visibility of the ITS, RSUs are highly susceptible to external attacks, which is commonly overlooked in existing RSU deployment work. Specifically, Sybil attack is one of the most dangerous attacks against ITS, and it can reshape the network state by forging multiple identities, interfering with risk sensing, etc. Motivated by these, we respectively propose the PSO-meme joint heuristic deployment algorithm (PJHDA) and the heuristic RSU multi-objective adaptation adjustment algorithm (HRMA3) to carry out deployment and adaptation adjustment of the city’s RSUs, taking into account the constraint of Sybil attack detection. Numerical results demonstrate that the multi-attribute performance of PJHDA is superior to the existing schemes. Compared with benchmark schemes, the HRMA3 excels in achieving advanced service coverage and load balancing while controlling costs, and both proposed schemes exhibit higher Sybil attack detection rate.

Hybrid Automatic Repeat reQuest (HARQ) feedback and retransmission, as one of the crucial technologies for reliability enhancement, may cause a security-reliability tradeoff in New Radio (NR) Vehicle-to-Everything (V2X) communications. Specifically, for NR V2X messages, the HARQ feedback and retransmission improve its success delivery probability but endanger its confidentiality. This paper proposes a secure scheme, namely SecHARQ, to maximize the security performance of HARQ-assisted NR V2X while satisfying the reliability requirement. To describe the performance of SecHARQ accurately, a stochastic geometric based theoretical framework is developed with the consideration of three specified factors, namely transmission rounds, resource allocation, and feedback option selection. With the help of this framework, we can formulate the SecHARQ scheme as a tractable optimization problem. Extensive numerical results show that the proposed scheme is beneficial for secure communication in HARQ-assisted NR V2X by carefully tuning the optimal parameters of SecHARQ.

Traditional single-vehicle intelligence system faces challenges such as undetectable spots and perception performance bottlenecks due to limitations in sensor perception angles, ranges, and accuracy, which are particularly pronounced in complex intersection. Vehicle-infrastructure cooperative mechanism has been widely recognized as a promising solution to address challenges faced by single-vehicle intelligence. However, against the backdrop of limited spectrum resources and the sharply rising in the number of connected vehicles, how to efficiently deliver cooperative messages from roadside unit to vehicles is often overlooked. Towards this end, we propose a roadside cooperative message delivery scheme based on multicarrier multigroup multicast rate-splitting multiple access, considering the rarely explored case of transmitting messages with limited size under delay constraint. Then we focus on the critical joint optimization problem of message size and power allocation, with consideration for imperfect channel state information at the transmitter. Subsequently, a multi-agent deep reinforcement learning based resource allocation algorithm is designed to solve this joint optimization problem, exhibiting robustness to dynamic changes in vehicle density and message size. Finally, we analyze through extensive numerical results the impacts of various factors on message delivery success probability.

In the context of 5G and beyond 5G, radio access network (RAN) slicing emerges to enable differentiated services via the instantiation of virtualized logical networks. Despite its promising potential, the resource optimization of RAN slicing confronts significant challenges stemming from the scarcity of spectrum resources, the intricacies of tradeoff between slice service quality and slice security, the mobility of users, and the complexity of wireless interference in multi-cell environments. To address these challenges, we propose a heuristic-assisted multiagent deep reinforcement learning-based resource allocation scheme for RAN slicing. This scheme aims to augment inter-slice resource isolation for security (quantified as isolation rate) while efficiently accommodating diverse requirements across slices (quantified as satisfaction rate). Through extensive numerical results, we exhibit that our proposed scheme adeptly adapts to multiple user mobility patterns, achieving superior performances in terms of satisfaction rate and isolation rate.

The development of Autonomous Vehicles (AVs) provides users with high-quality services and convenient travel experiences. As one of the most important functions in the automotive field, mobile positioning has attracted widespread attention from scholars. However, using a single-modal sensor (LiDAR or camera) poses challenges for precise localization due to their measurement flaws. Therefore, some scholars have proposed Visual-LiDAR Odometry (VLO). Nevertheless, most of the existing VLO solely use a single-modal sensor as their main framework and utilize another sensor for optimization, which does not fully leverage the complementary behavior of sensors in different environments. Thus, this paper presents a novel LiDAR-camera fusion method for improving the odometry estimation. Firstly, we employ a depth completion network to convert the image into pseudo-LiDAR to compensate for the missing depth values in the LiDAR point clouds. Then, we adopt Bayesian inference to enhance the robustness of the fusion method in different environments. Finally, evaluations on the public KITTI odometry show that the proposed method outperforms several state-of-the-art methods.

In New Radio (NR) Vehicle-to-Everything (V2X), the congestion control is adopted to reduce channel occupancy and improve delivery performance. However, most available congestion control schemes choose to reduce the number of messages while ignoring the throughput requirements, which may lead to serious traffic accidents due to the loss of information. This paper investigates the delivery performance of Cooperative Awareness Messages (CAMs) in NR V2X and proposes an Optimizing Throughput Congestion Control (OTCC) scheme to ensure that more information is successfully exchanged between vehicles. The OTCC is able to analytically provide the optimal CAM generation frequency and maximize the effective throughput of CAM delivery with the help of a carefully derived stochastic geometry based theoretical framework. Extensive numerical results show that the OTCC scheme is highly beneficial for effective information exchange, especially in complex traffic environments with highly dense vehicles.

The development of 5G-vehicle-to-everything (5G-V2X) technology makes vehicle-road-cloud collaboration possible. Vehicles and roads transmit sensor data to the cloud via 5G-V2X technology and then the cloud sends the data to the target vehicle. The target vehicle utilizes dynamic environmental data from surrounding vehicles and roadside units to predict the driving trajectory of surrounding vehicles in order to ensure its own safety. However, many existing trajectory prediction schemes are based on incomplete single-vehicle perception and ignore surrounding road conditions, which will greatly limit their value in real-world scenarios. Therefore, this paper proposes a flexible multi-channel vehicle trajectory prediction scheme based on vehicle-road collaboration. Specifically, we first design a flexible multi-channel vehicle trajectory prediction scheme that can extract different vehicle and map features from various information sources. Then, we use the Transformer model to generate predicted trajectories of surrounding vehicles by fusing features from different sources, and achieve parallel computing effects. The most popular dataset, INTERACTION, is used to evaluate the proposed scheme. The results show that our scheme is robust across different scenarios and possesses better accuracy.

Network slicing is a virtualization paradigm that divides multiple isolated logical networks on the physical infrastructure to meet different service requirements. Due to the dynamic changes in service requirements, reconfiguration of slice resources is essential to ensure the performance of slicing. However, most of the existing work focuses on reconfiguring slice through the migration of virtual network functions (VNFs, the virtual nodes that constitute the slice), which leads to large reconfiguration overhead. In view of this, we concentrate on the vertical scaling of VNF and propose a dynamic slice resource reconfiguration scheme based on multi-agent deep deterministic policy gradient (MADDPG), which can flexibly adjust multidimensional slice resources, as well as reduce or avoid costly VNF migration operations. In addition, we improve the proposed scheme using incremental learning to adapt to the different number of VNFs on each physical node and accelerate the training speed. Experimental results show that our proposed scheme has significant advantages over several benchmark schemes in VNF and slice resource satisfaction ratio, and can converge quickly through incremental learning.

As a critical element in intelligent transportation systems (ITS), roadside units (RSUs) are pivotal in delivering superior Internet of Vehicles (IoV) services encompassing intelligent traffic management, accident prevention, and emergency rescue. Considering the high deployment and maintenance costs of RSUs, many studies focus on the efficient RSU deployment issues. However, due to the high visibility of the ITS system, RSUs are highly susceptible to external attacks, which is commonly overlooked in existing RSU deployment researches. Specifically, the Sybil attack is one of the most dangerous attacks against ITS, it can reshape the network state by forging multiple identities, interfering with the operator’s reputation assessment or causing severe DDoS. Therefore, we propose a joint heuristic scheme that combines the advantages of particle swarm optimization and double local-search memetic algorithm to solve the city RSU deployment problem in Sybil attack environments. It can find solutions with higher fitness values and guarantees that the IoV has the capability to detect Sybil attacks. Numerical results show that our proposed scheme not only outperforms other traditional solutions regarding signal validity coverage, overlap rate, and initial propagation speed of accident information, but also performs satisfactorily in Sybil attack detection.

Recent studies have shown that cooperative multi-agent deep reinforcement learning (c-MADRL) is under the threat of backdoor attacks. Once a backdoor trigger is observed, it will perform abnormal actions leading to failures or malicious goals. However, existing proposed backdoors suffer from several issues, e.g., fixed visual trigger patterns lack stealthiness, the backdoor is trained or activated by an additional network, or all agents are backdoored. To this end, in this paper, we propose a novel backdoor attack against c-MADRL, which attacks the entire multi-agent team by embedding the backdoor only in a single agent. Firstly, we introduce adversary spatiotemporal behavior patterns as the backdoor trigger rather than manual-injected fixed visual patterns or instant status and control the attack duration. This method can guarantee the stealthiness and practicality of injected backdoors. Secondly, we hack the original reward function of the backdoored agent via reward reverse and unilateral guidance during training to ensure its adverse influence on the entire team. We evaluate our backdoor attacks on two classic c-MADRL algorithms VDN and QMIX, in a popular c-MADRL environment SMAC. The experimental results demonstrate that our backdoor attacks are able to reach a high attack success rate (91.6%) while maintaining a low clean performance variance rate (3.7%).

To further enhance the reliability and throughput of the Vehicle-to-Everything (V2X) communications via New Radio (NR) sidelink, the flexible Modulation and Coding Scheme (MCS) was approved by 3GPP in Release 16. However, most available works neglect the variations in message data size and adopt fixed MCS, which unavoidably limits the NR sidelink spectral efficiency. Using the theory of stochastic geometry, we are able to present a mathematical framework and analytically investigate the impact of MCS selection on the achievable message delivery probability in NR sidelink, following the general message data size in 3GPP. Then, the optimal MCS is selected by enlarging the surplus between the Signal to Interference-plus-Noise Ratio (SINR) measured at the receiver and the SINR Threshold Required for Successful Delivery (TRSD). The numerical results prove that the optimal MCS is extremely beneficial for the V2X message transmission.

Network slicing, as the backbone technique of evolving B5G/6G, consists of Network Function Virtualization Infrastructure (NFVI) layer, network slice instance layer, service instance layer and management and orchestration module. Given the lessons learned from recently reported nationwide and long-lasting global telecommunication disasters with severe service degradation or even outages, we find that many of them orig-inated from a simple faulty entity in the NFVI layer at the very beginning. To the best of our knowledge, as the very first attempt, we propose Zoom-inRCL, which enables us to quickly and accurately find the root cause entity at the NFVI layer once observing the slice service degradation. Specifically, it first filters abnormal NF call graphs at the graph level based on deep support vector data description (Deep SVDD) algorithm and then filters faulty entity candidates at the node level by novelly designed rules, and finally infers the suspicious entity rank list according to the ratio of affected NFs. Evaluations on a real-world dataset show that for over 80% of fault cases, the top-ranked entity identified by Zoom-inRCL is the actual root cause of the service degradation. We believe that our work can provide useful guidance for the design of future B5G/6G networks.

Hybrid Automatic Repeat reQuest (HARQ) feedback is one of the key technologies for enhancing the performance of New Radio (NR) Vehicle-to-Everything (V2X) communication. However, for NR V2X messages, HARQ-based retransmission not only improves its delivery performance, but also poses a significant threat to its confidentiality. This paper investigates the Security-Reliability Tradeoff (SRT) of HARQ-assisted NR V2X. To accurately describe the SRT behavior in the V2X scenario, a theoretical framework based on stochastic geometry is developed, carefully considering the random device distributions and physical layer settings of NR V2X. Numerical results prove that this theoretical framework can effectively capture the success probability and secrecy probability of V2X communications under different configurations, including transmission rounds, resource selection, and power control. Furthermore, our analysis shows that the SRT of HARQ-assisted NR V2X can be adjusted by finely tuning the considered configurations.

With the advantages of seamless coverage and ubiq-uitous connections, Non-Terrestrial Networks (NTNs) composed of Low Earth Orbit (LEO) satellites and Unmanned Aerial Vehicles (UAVs) can provide content caching services to reduce End-to-End (E2E) delay and alleviate the network traffic for future 6G applications including autonomous driving, eHealth, and metaverse. However, the ultra-density of LEO satellites complicates the selection of caching nodes, while the heteroge-neous caching hardware and communication environments make optimization of content deployment highly difficult. To address these issues, we propose an intelligent hierarchical caching and asynchronous updating scheme. Specifically, a Delay-Motivated Ant Colony Optimization (DM-ACO) scheme is first adopted to select the caching LEO satellites to reduce the system propagation delay. Then, the Multi-Agent Reinforcement Learning-based Hi-erarchical Caching and Asynchronous Updating (MARL-HCAU) strategy is proposed to meet caching service demands. Simulation results illustrate that compared with the benchmarks, the overall cache hit ratio increases by 14.2 % with the reduced packet drop rate and transmission delay by 8.78 % and 0.94s, respectively.

With the increase of the near field communication (NFC) function deployment on smartphones, the mobile NFC card system becomes a solution to inadequacies of conventional physical authentication in accommodating modern Internet of Things (IoT) scenarios involving shared access, like shared family bank cards and household vehicles. While NFC technology brings convenience to these scenarios, it also introduces security vulnerabilities, including relay attacks and man-in-the-middle attacks. Given the multi-user context, these vulnerabilities are further amplified. Therefore, it is urgently needed to design an authentication for securing the mobile NFC card system. However, existing security approaches, such as protocol authen-tication and supervised radio frequency (RF) authentication, face challenges in computational resource allocation and data labeling, making them inadequate for the multi-user mobile NFC card system. To address these issues, this paper proposes NFC-RFAE, a semi-supervised authentication system based on RF signals, demonstrating high accuracy, high recall rate, low latency, and minimal space occupancy in real-life scenarios, including a vehicle keyless system and a mobile bank system.

As the evolution of 3GPP specification and the deployment of 5G network, Internet of Vehicles (IoVs) boom fireworks. However, its attack surface is expanded with the increased fusion of various functional interfaces, leading to easier penetration of vehicles. To deal with endless vehicle attacks, scholars propose many methods, where intrusion detection system (IDS) is an important branch. However, many IDSs are based on characteristics of single or specific types of vehicles, which limits model transplantation. Besides, 1-D features are usually utilized in existing IDSs, such as time, traffic, or voltage, etc., limiting the ability to detect attacks related to other dimensions. What is more, many IDSs harness machine learning algorithms and are deployed in vehicles simultaneously, which aggravates the computational burden. Therefore, we devise a cloud-vehicle collaborative IDS based on multidimensional features (CVMIDS) for IoV, called CVMIDS. It solves the problem of data heterogeneity by abstracting different vehicle data to the same feature space. Thus, data sets from different vehicles can be fed into one model for multiclassification, which naturally solves the problem of model transplantation. The feature space is established by combining features in dimensions of time, traffic, and voltage, thereby extending the types of attacks that CVMIDS can detect. Due to the deviated location of abnormal data in feature space compared with normal data, CVMIDS will misclassify vehicle data. Hence, CVMIDS can detect intrusions based on multiclassifying vehicles. Extensive experiments are conducted on three vehicles with different brands and numerical results corroborate the robustness and efficiency of CVMIDS.

In this article, channel estimation (CE) of an active intelligent reflecting surface (IRS) aided uplink Internet of Things (IoT) network is investigated. First, the least square (LS) estimators for the direct channel and the cascaded channel are presented, respectively. The corresponding mean-square errors (MSEs) of channel estimators are derived. Subsequently, in order to evaluate the influence of adjusting the transmit power at the IoT devices or the reflected power at the active IRS on Sum-MSE performance, two situations are considered. In the first case, under the total power sum constraint of the IoT devices and active IRS, the closed-form expression of the optimal power allocation (PA) factor is derived. In the second case, when the transmit power at the IoT devices is fixed, there exists an optimal reflective power at active IRS. To further improve the estimation performance, the convolutional neural network (CNN)-based direct CE (CDCE) algorithm and the CNN-based cascaded CE (CCCE) algorithm are designed. Finally, simulation results demonstrate the existence of an optimal PA strategy that minimizes the Sum-MSE, and further validate the superiority of the proposed CDCE/CCCE algorithms over their respective traditional LS and minimum MSE (MMSE) baselines.

With the advances in artificial intelligence, robotics, and data fusion, large numbers of drones operating in a coordinated manner will become commonplace for a wide range of commercial and military uses. At present, the application methods of drone swarms are mainly divided into fully autonomous methods and controlled methods with human participation. Because of the limited level of artificial intelligence, controlled drone swarms will be the main way for the application of large-scale drone swarms for a long time. However, there is less research on achieving global coordination in a limited time for a controlled large-scale drone swarm. Therefore, a new large-scale drone swarm framework is proposed firstly in this paper, which achieves global coordination through local interaction and reduces the impact of limited channel resources. Secondly, this paper proposes a local interaction-based fast coordination method and introduces a prediction mechanism, to ensure that large-scale drone swarms can quickly achieve coordination even in the presence of node loss. Moreover, the numerical integration method is used to update the consensus state, so that the drones can increase the iteration period, reduce the number of packets, and further reduce the channel burden. Finally, considering that large-scale drones swarm are usually composed of drone swarms launched at different locations and times, a consensus algorithm considering the merging behavior of drone swarms is also proposed. The simulation results show that the large-scale drone swarm using the proposed architecture can achieve the leader-follower consensus in a very short time and even in a confrontational environment with poor communication conditions. Besides, after the merger of multiple drone swarms, the consensus problem can still be solved in very few iteration cycles.

As an essential 6G component, the Low Earth Orbit (LEO) satellite communication has aroused increasing attentions from academia and industry to provide seamless and highly-efficient networking services. However, existing routing strategies are primarily designed for terrestrial networks or small-scale satellite networks, making it inapplicable to future LEO satellite constellations of ultra density, high dynamics, and large scale. Moreover, since Free Space Optical (FSO) communications have been expected for Inter-satellite Links (ISLs) and the number of constructed FSO ISLs depends on the Acquisition, Pointing, and Tracking (APT) terminals and geometric visibilities, the routing algorithm needs to be adaptive. To address these issues, this paper considers the dual-layer network architecture composed of Medium Earth Orbit (MEO) satellites and LEO satellites, where the regional network division is adopted for the LEO satellite layer to alleviate the complexity and improve the routing efficiency. Then, a multi-objective reinforcement learning-based routing strategy with local information considered is proposed to meet the differentiated Quality of Service (QoS) requirements of diversified terrestrial applications. A cooperative mechanism is also designed to address the conflicts caused by the routing design for different applications. The simulation results demonstrate the proposal is applicable to varying numbers of APT terminals and outperforms benchmark algorithms in terms of diversified QoS metrics.

Behavior-oriented decision-makers are critical components in generating intelligent decisions for user virtual interactions in metaverse. In this work, we study the efficiency and security of behavior-oriented decision-makers in metaverse from perspective of autonomous driving (AD), where modeling human uncertain driving behaviors is the key factor of their performance. We first explore the ability of different deep-neural-network-based decision-makers used in deep reinforcement learning for efficient autonomous vehicle control, and then we propose a novel neural backdoor attack against them using spatiotemporal driving behaviors, rather than an immediate state. With our attack, the adversary acts as a normal driver and can trigger attacks by driving his vehicle following specific spatiotemporal behaviors. Extensive experiments show that our proposed backdoor attack can achieve high stealthiness and effectiveness (less than 1% clean performance variance rate and more than 98% attack success rate) on behavior-oriented decision-makers, and is sustainable against existing advanced defenses.

As innovative technologies such as autonomous driving, over-the-air technology, and vehicle-to-everything are widely applied to intelligent connected vehicles, people can gain a more convenient and safer driving experience. Although the application of these technologies facilitates our lives, they also bring a series of vulnerable interfaces (such as 5G, Bluetooth, and WiFi), which pose a significant security threat to existing in-vehicle networks. To address these threats, researchers have proposed two mainstream schemes, including message authentication and intrusion detection system (IDS), where the scheme of message authentication needs to occupy the limited bandwidth of controller area network (CAN) bus. Furthermore, most IDSs either cannot locate the sender of the attack, fail to detect aperiodic malicious frames, or require prior knowledge of which CAN identifiers (IDs) belong to which electronic control units (ECUs). To address these weaknesses, we propose a practical voltage-based IDS named IdentifierIDS for real in-vehicle networks. To the best of our knowledge, it is the first scheme to detect intrusions by establishing a voltage fingerprint for each ID without the need for prior knowledge. This allows IdentifierIDS to detect both periodic and aperiodic malicious frames without occupying the limited bandwidth of the CAN bus. As a self-learning IDS, it can adapt to different in-vehicle networks without the need for customization for them. Experiments on three real vehicles demonstrate the robustness of our scheme in different in-vehicle networks.

Dual-functional radar and communication (DFRC) system is capable of sensing potential eavesdroppers close to the DFRC base station (BS) and further ensuring secure transmission using physical layer security technologies based on the obtained location information of eavesdroppers. However, such security can be threatened by a malicious intelligent reflecting surface (IRS) that simultaneously changes both radar and communication channels. To reveal this threat, an IRS-based active attack scheme is proposed in this paper under the assumption of a prevalent DFRC framework. In this scheme, the attacker, equipped with a malicious IRS, carefully controls and optimizes the IRS phase shifts in each stage of the DFRC framework to reduce its reflected radar echo power to the BS and/or strength the wiretap link gain for pilot spoofing attack according to the statistical channel state information. Numerical results show that our proposed attack scheme can significantly reduce the secrecy rate of the legitimate user, while avoid being detected by the DFRC BS.

The Internet of Vehicles (IoV) is a large-scale interactive network that operates in a dynamic and changeable environment, encompassing diverse types of private information. In recent years, safeguarding vehicle location privacy in IoV has been a topic of concern. However, the independent location privacy protection mechanisms cannot consistently meet the rigorous security requirements of various IoV scenarios, which will pose a significant threat to the location privacy of IoV users. In contrast, risk sensing as a preventive security strategy needs lower computing costs and is more suitable for the intricacies of complex city environments. Unfortunately, the existing works lack that combination of risk assessment with trust assessment to conduct a comprehensive study on location privacy risk sensing. Therefore, considering the city vehicles’ spatial clustering phenomenon and the strong regularity of traffic flow, we propose a risk-sensing approach to vehicle location privacy based on the continuous adaptive risk and trust assessment strategy. This approach employs the Ripley method to analyze space clustering characteristics and combines the traffic flow prediction model to establish the risk assessment scheme. Furthermore, to enable our risk-sensing approach to have historical memory that can identify and continuously track malicious users, we incorporate a penalty factor into the trust assessment scheme that updates in a time iterative format. Extensive numerical results demonstrate the adaptability and reliability of our proposed risk-sensing approach to vehicle location privacy.

Controller Area Network (CAN) is the most wide-used bus system in Intra-Vehicle Networks(IVN). However, the nature of broadcast communication and the lack of security mechanisms make the CAN bus extremely fragile against malicious attacks. Although there are works protecting IVN, most of them are not feasible when applied to real vehicles because they do not consider the IVN node capability. In this paper, we propose a security framework for the CAN bus, covering ECU entity identity management and authentication, symmetric key generation and update, intra-domain, cross-domain secure transmission, and sensitivity-based security classification methods. We formally verify our protocols using the up-to-date tool Tamarin and simulate real attacks in a simulation environment and the results show that the proposed protocol can resist these attacks. By the use of speck encryption and the Chaskey MAC algorithm in our schemes, the analysis results show that the increased time of a frame for a single ECU in our proposed intra-domain scheme is$2.09~ms$to$2.78~ms$on Arduino Mega, and$121.65 \mu s$to$152.15 \mu s$on Arduino DUE, which takes up$6.08\%$to$7.61\%$of a 10ms cyclic time frame. And in the cross-domain scheme is$2.55~ms$to$3.24~ms$on Arduino Mega, and$134.30 \mu s$to$164.80 \mu s$on Arduino DUE, which takes up$6.72\%$to$8.24\%$of a 10ms frame. To the best of our knowledge, this is the first time an IVN cross-domain secure transmission protocol has been proposed without changing the IVN network topology or the CAN protocol. Our work brings practical protection to IVN.

Sidelink relay, authorized as an independent work item in both 3GPP R17 and R18, is a promising technology for facilitating future V2X communication in 5G New Radio (NR). As specified by 3GPP, several important factors of NR sidelink, including flexible hop-by-hop operation mode selection, sidelink path loss based power control, and resource sharing with uplink communications, were partially (if not totally) ignored in available studies. In light of this, we propose a general multi-hop NR sidelink relay scheme, namely, NSR-$(\rho,\omega,m)$, for flexible dissemination of a typical Decentralized Environmental Notification Message (DENM). To efficiently characterize the multi-hop DENM dissemination process under NSR-$(\rho,\omega,m)$, we further develop a stochastic geometry-based theoretical framework, by carefully taking into account the above 3GPP specified important factors. With the help of the theoretical framework, we are able to derive the end-to-end delivery probability and the total dissemination distance of a given DENM. Extensive numerical results are presented to verify the effectiveness of the theoretical framework. For a given DENM with a limited lifetime, it is also proved that the NSR-$(\rho,\omega,m)$scheme is of great potential in significantly expanding the achievable performance region, by properly tuning the parameters$\rho $,$\omega $, and$m$.

In 5G advanced and beyond, latency-critical and computation-intensive applications require more communication and computing resources. However, remote areas without available terrestrial edge/cloud infrastructure fail to satisfy these applications’ demands. This motivates the emergence of the UAV-enabled aerial computing paradigm. Single UAV-enabled aerial computing (SUEAC) is limited by small coverage area and insufficient resources, which cannot meet the application requirements. Multiple UAV-enabled aerial computing (MUEAC) has broken through the limitation of SUEAC and has attracted wide attention. Cooperation among multiple UAVs in MUEAC can fully utilize UAV resources and achieve load balancing. Furthermore, for divisible tasks with data-dependent characteristics, using partial offloading makes task scheduling more flexible compared to binary offloading, thus reducing task processing delay. Therefore, we propose a software defined networking enhanced cooperative MUEAC system. To minimize the processing delay of divisible tasks, we study the problem of joint task scheduling and computing resource allocation under task data dependency and UAV energy consumption constraints. To solve the non-convex problem, a multi-UAV cooperative communication and computing optimization (MCCCO) scheme is proposed. Experimental results corroborate that MCCCO can achieve better performance in task processing delay reduction and load balancing on UAV energy consumption than the traditional schemes.

Recently, Reduced Capability (RedCap) New Radio (NR) User Equipment (UE), which has smaller cost, lower complexity, and longer battery life than normal NR UE, was introduced by 3GPP in R17. In particular, the UE energy consumption is an important metric of interest. Small Data Transmission (SDT) technology has been proposed by 3GPP to save UE power, which allows UEs to directly transmit small-sized uplink payloads inInactivestate without transitioning toConnectedstate. However, for a large number of RedCap UEs with bursty traffic, how to minimize UE energy consumption while ensuring the reliability of uplink payload transmission is still a challenge. Towards this end, we propose a novel Non-Orthogonal Multiple Access (NOMA)-enhanced SDT scheme to tackle this challenge. Considering that the power level pool is an important component of NOMA to achieve performance gains and the power level pool design problem in multi-cell scenarios is still rarely studied, we present a Multi-Agent Dueling Double Deep Q Network (MAD3QN)-based algorithm to further improve the performances of the proposed scheme by optimizing the power level pool for each gNB with practical imperfect Successive Interference Cancellation (SIC). Extensive numerical comparisons among the proposed scheme and multiple benchmarks show that the proposed scheme (with appropriate user pairing) can significantly improve the transmission reliability of large-scale RedCap UEs with a small cost of energy consumption.

This paper proposes to leverage intelligent reflecting surface (IRS) backscatter to implement downlink communications for a multi-cell multiple-input multiple-output (MIMO) network, which represents a brand-new communication framework. In such a network, one active macro cell base station (MBS) is deployed for radiating energy signal, while each IRS acts as a small cell base station to realize its own information transmission by modulating and reflecting the signal from the MBS. Under this paradigm, we investigate two optimization problems, namely weighted sum rate maximization problem and max-min fairness problem, aiming at satisfying different communication requirements. To seek their optimal solutions, Lagrangian dual transform and alternate methods are employed to optimize the active beamforming vector at the MBS and the passive beamforming vectors at all the IRSs. Additionally, an element clustering scheme is developed to reduce computation and control complexity, in which each element cluster works like a unit and all clusters can collaborate to communicate with users. Extensive simulations are conducted to evaluate the achievable communication performance and to verify the feasibility of the proposed IRS backscatter enabled downlink multi-cell MIMO network.

5G core network slicing is an important part of 5G end - to-end slicing, which can provide customized services by tailoring network functions for diverse application scenarios. In order to implement core network slicing efficiently and make full use of network resources, slice admission control that selectively accepts or rejects slice establishment requests is crucial. However, existing related works mainly focus on optimizing the revenue of mobile operators, and lack consideration of dynamic resource scheduling and recycling under limited resources. To this end, we propose a dynamic slice admission control mechanism with a warm-up resource recycling method, which uses the adaptability of reinforcement learning to improve the slice admission rate and ensure the efficient utilization of resources. Also, considering the differentiated demands of typical application scenarios, a slice request dataset construction rule is designed and a dataset is established to evaluate the effectiveness of the proposed mechanism. Experimental results demonstrate the superiority of the proposed core network slice admission control mechanism in guaranteeing high slice admission rate under various simulation settings.

Backscatter communication, as a key technique to achieve low-power device transmission in Internet of Things, has aroused wide concern all over the world in recent years. Since the double fading effect limits the backscatter communication range and the achievable system performance, intelligent reflecting surface (IRS) appears to be introduced into backscatter communication to raise the propagation quality. However, the signal model considered in the available researches with only a single-reflection path is not complete. Motivated by this, we first formulate the achievable rate maximization problem for IRS-enabled backscatter communication system in this work, with consideration of two-reflection characteristic at the IRS through designing the beamforming vector of carrier transmitter, the phase shift of IRS, and the power reflection coefficient of backscatter device jointly. Note that the complex superposition of two-reflection signal links makes the formulated problem highly non-convex, we utilize an iterative algorithm combined with alternating optimization approach and minorization-maximization method to derive the approximate solution to each optimization variable. Simulation and experimental results corroborate the remarkable performance improvements created by the considered signal model, as well as the excellence of our proposed scheme with joint optimization over other benchmarks.

5G and beyond are required to support a variety of emerging services that impose different requirements on the network. Radio Access Network (RAN) slicing is a promising candidate technology, and resource allocation issues related to it have received extensive attention. However, most existing literature on RAN slicing resource allocation either ignores actual network interference or focuses primarily on the QoS of slices but not slicing security. Towards this end, we propose a QoS and security-oriented slicing resource allocation scheme in a multi-cell and multi-slice scenario, where actual link interference is carefully considered. We then formulate a problem of maximizing security (i.e., isolation rate) subject to QoS (i.e., satisfaction rate) constraint. Finally, a multi-agent deep reinforcement learning-based solution is designed to solve this problem, and extensive numerical results demonstrate the superior performance of the proposed scheme.

Mobile edge computing (MEC) has emerged as a promising approach to address the time-sensitive requirements of mobile Internet of Vehicles (IoVs) systems. Unfortunately, the current deployment density of roadside units (RSUs) is relatively sparse, and the direct V2I communication coverage is limited, making it impossible to meet the communication and computing requirements of all vehicles. There is an urgent need for V2V communication to assist V2I communication, which can achieve a wider coverage of RSUs, a diversified selection of task processing locations, and even load balancing between RSUs. However, V2V communication also faces a series of challenges. On the one hand, due to the sparsity, time-varying, and high-speed mobility of vehicle nodes in IoVs, the selection of collaborative communication paths becomes more difficult. On the other hand, there are inevitably malicious vehicles in IoVs, and how to achieve efficient task processing while ensuring privacy and driving safety is also a problem worth studying. Existing research generally optimized the delay of direct V2I task offloading, ignoring the necessity of V2V-assisted communication and the presence of malicious communication nodes. To address the above challenges, we present a vehicular edge computing network structure with multiple communication modes, including V2V, V2I, etc, and use a recommended trust model to analyze the trust degree between the nodes in IoVs. Then, we discuss the issue of trusted task offloading for IoVs and propose a Deep Deterministic Policy Gradient (DDPG) scheme. The numerical results indicate that our proposed strategy outperforms current methods in terms of task offload latency and credibility.

In this paper, we introduce two double-IRS strategies to enhance proactive eavesdropping, namely Double-IRS Enhancing Strategy (DES) and Double-IRS Enhancing-Jamming Strategy (DEJS). In the former strategy, both cooperative IRSs reflect the incident signal passively. In the latter strategy, one IRS performs passive reflection while the other IRS acts as a passive jammer to modulate the incident signal into the jamming signal to deteriorate the suspicious link. Based on these two strategies, the semidefinite relaxation (SDR) technique and the bisection search method are applied to optimize the reflection coefficients at two IRSs, aiming at maximizing the effective eavesdropping rate. Finally, numerical evaluations are conducted to verify the effectiveness of our proposed schemes.

Sidelink relay enhancement, as a promising item in 3rd Generation Partnership Project (3GPP) R18, can support further vertical domain expansion of New Radio (NR). However, existing research on performance analysis of NR sidelink relay, either considered simple cases of single lane model or partially (if not totally) ignored the important factors of NR sidelink, such as resource allocation, power control, and coexistence with the cellular network. In this paper, a general stochastic geometry theoretical framework for performance analysis is developed to evaluate the success probability, time cost, and transmission distance of NR sidelink multi-hop relay in a typical unicast scenario, closely following the 3GPP specifications in both physical layer characteristics and parameters. As verified by extensive experiments, the considered performance metrics under different parameter settings can be accurately described through this theoretical framework, which holds great prospects for NR sidelink network design and deployment.

Sybil attack is one of the most serious attacks in vehicular ad hoc network (VANET), where a malicious node forges many illegal identities to interfere with or even control the network, which brings severe security threats to the intelligent transportation system. Received signal strength indicator (RSSI) is highly relative to the positions of communication nodes, and can be applied for sybil attack detection according to the fact that two real nodes cannot be in the same position. However, the high mobility of nodes in VANET makes it more difficult to be applied directly. In this paper, an improved RSSI-based sybil attack detection method is proposed against the fading channel and the high mobility of nodes in VANET. This method first estimates the distance between each communication node through maximum likelihood estimation, and then detects sybil nodes through mean-shift clustering algorithm. The numerical simulation results show the propose sybil attack detection has high accuracy in VANET by appropriately setting the detection time and the searching radius of the mean-shift algorithm.

The rapid development of 5G and battery has enabled the electricity-driven intelligent connected vehicles to become the focus of current automobile industry. With automobiles growing intelligent, convenient, and entertaining, the computation tasks generated by various vehicle-integrated applications significantly increase. Cloud servers far away from the vehicles cannot complete the users' tasks in time, while the energy and computing resources on current Electric Vehicles (EVs) are very limited. Multi-Access Edge Computing (MEC) has been proposed to process the tasks generated by vehicles, which can reduce the latency and save the battery energy of EVs. However, the computing resource of MEC servers is still limited and cannot meet the delay requirements if massive EVs all offload the tasks. In addition, due to the uneven spatial and temporal distribution of vehicle arrivals, some MEC servers are busy, while some others are idle, resulting in the low resource efficiency and task completion ratio. In this paper, we propose the mobility-aware task offloading strategy method to allocate the computation resource of roadside servers for multiple EVs. We formulate the mathematical model of task offloading and resource allocation to jointly optimize computation latency and EV energy. Finally, the discrete particle swarm optimization algorithm is used to solve the problem. Simulation results show that the proposed method significantly alleviates the energy consumption and reduce the latency compared with conventional methods.

Network virtualization technology is envisioned as the new paradigm for modern Internet by virtue of its flexible management and allocation of physical resources, as well as fast provisioning of customized network services. Virtual network embedding (VNE), one of the main issues faced by network virtualization, has attracted interests of numerous researches due to its importance and proven NP-hardness. However, existing works addressing this issue have limitations such as inadequate generality, heavily relying on hand-craft features, inefficient to the changeable action space of the VNE problem, etc. Towards this end, we propose a VNE scheme in this paper with a new environment interpretation mechanism and a duel network based decision making architecture, which has the automatically feature extraction ability for both physical and virtual networks, and the capability of adapting to the VNE environment with changeable action space. Comparison results with existing works demonstrate the superiority of our proposal, which can bring higher acceptance ratio and larger average revenue on both synthetic and real physical networks.

Benefiting from New Radio (NR) sidelink based Vehicle-to-Everything (V2X) communication, Vulnerable Road Users Protection (VRUP) can be further developed in intelligent transport systems. However, considering limited resources in the V2X network, the interference caused by resource collisions may restrict this development. This paper propose a spatial reuse scheme of frequency resources, namely the Per-Zone Resource Reuse (PZRR) scheme, for NR sidelink based VRUP enhancement. To efficiently characterize the VRUP behavior in the PZRR scheme, a stochastic geometry based theoretical framework is provided by carefully taking into account interference from multiple zones. With the help of this framework, the delivery probability of the VRUP message can be accurately described under various parameter settings. Furthermore, numerical results show that the VRUP message delivery probability can be significantly improved by properly tuning parameter settings.

The near field communication (NFC), as one of the most widely used radio frequency identification (RFID) technologies, has been applied to intelligent devices to replace the traditional key, bringing convenience to people’s lives. While the appearance of NFC keys facilitates users’ lifestyles, it also increases the risk of being stolen for intelligent devices. So, it is urgently needed to take action to protect the NFC security of equipment. There are abundant researches on NFC security, which can be divided to protocols authentication and data analysis two main defense methods. However, the way of protocols authentication is limited by the space available and real-time communication of devices. The way of data analysis can not identify the malicious NFC devices from outside. Thus, we propose an intrusion detection system (IDS) based on radio frequency (RF) signals for NFC security, called NFC-IDS. We use the random forests algorithm to select the four most important feature extracted from RF signals and compare random forests, support vector machine (SVM), and k-Nearest Neighbor (k-NN) algorithms to detect intrusions. The experimental results on two real electric motorcycles show that every NFC device has its unique physical signal characteristics, which can be used to detect intrusions with high accuracy and robustness.

Unmanned Aerial Vehicles (UAVs), also known as drones, have recently been proposed as flying base stations for providing reliable service to IoT devices. However, due to the lack of effective authentication schemes, UAVs are often hijacked by adversaries, which raises a high potential for sensitive information leakage. Therefore, designing a real-time authentication scheme is essential to enhance UAV safety. Up to the present, several works exist about pilot authentication by classifying radio-control signals. As propagating through the open environment, radio-control signals can be sniffed, analyzed, and simulated, posing significant threats to UAV security. For this reason, we propose a novel deep learning-based drone pilot authentication scheme, DP-Authentication, to protect UAVs from malicious radio-manipulated attacks. Specifically, we collect UAV flight data from the onboard PX4 flight stack and feed them into the authentication scheme to validate pilot legal status dynamically. As verified by comprehensive experiments, the proposed authentication scheme can authenticate pilots with an accuracy of 95.24% and detect malicious hijacking with an accuracy of 96.82%. Thanks to the low system overhead, it holds great promise for deployment on the UAV side to monitor pilot legal status in real-time.

The Industrial Internet of Things (IIoT) network is composed of devices that contain sensitive data, which makes them vulnerable to various security threats. Digital Certificates can be used to reinforce the security of the IIoT network, however, their management remains a major issue. Hence, in this article, we rely on trust management to deal with the whole certificate management process in IIoT networks, from revocation to verification. For this purpose, we organize the IIoT network into a clustering architecture where each cluster head (CH) hosts an agent, called CH-UR agent, that renews/revokes the certificates of its cluster member nodes. We apply signaling game theory to build a Certificate Revocation Game modeling the interactions between a member IIoT node and the CH-UR agent. Thus, upon the belief on the member node, updated by using the Bayesian rules, the best response strategy for the CH-UR agent can be obtained. Further, we propose a new efficient certificate verification scheme based on short-lived certificates (SLCs) and suitable for IIoT network requirements. The performance evaluation of our framework proves, first, the accuracy and convergence speed of our revocation mechanism to detect untrusted devices and on-off attacks. Second, the effectiveness of our clustering architecture to reduce the resource consumption resulting from the management of SLCs to 60% even with the increase of network density. Third, the effectiveness of the proposed certificate verification scheme to reduce the time needed to obtain the revocation information as well as the resulting storage and communication overhead to achieve this purpose.

This article proposes a novel intelligent reflecting surface (IRS) backscatter enhancing strategy to secure multi-input multioutput (MIMO) transmission in the presence of an eavesdropper and a malicious jammer. To be specific, the IRS is employed to backscatter the jamming signal into the desired signal to enhance the reception of the user. Utilizing this strategy, we maximize the system secrecy rate by jointly designing the reflection coefficients of IRS and active beamforming at the base station (BS). To efficiently handle this nonconvex optimization problem, we adopt an iterative block coordinate descent (BCD)-based algorithm, where the active beamforming is optimized through the Lagrange multiplier method and the backscatter coefficient matrix of IRS is optimized via the majorization-minimization (MM) method. Then, we examine the robustness of the proposed scheme when considering channel estimation errors. Extensive simulations confirm the secrecy performance gains achieved by our proposed strategy and verify its superiority compared to conventional IRS-based physical layer security (PLS) strategy, IRS backscatter-aided anti-eavesdropping strategy, and other baselines.

As one widely applied wireless technique, WiFi has the potential to execute noncontact monitoring of vital signs based on channel state information (CSI). However, due to the dynamic of the surrounding environment, the bandwidth of the WiFi channel is not enough to identify the respiration-induced path from other movement-induced paths and this seriously limits the accuracy of respiration rate detection. In this article, we propose ExRadio, a system that can monitor respiration in high-dynamic environments via combining multiple WiFi channels. Specifically, the receiver synchronously switches the channels with the transmitter and samples CSI at multiple channels, and then the CSI data are combined and regarded as CSI data of one extended-bandwidth channel. However, the hardware-related noises from multiple channels are also accumulated. Eliminating these noises causes too heavy computation overhead to be afforded by the embedded devices and affects the real-time performance of respiration monitoring. To address this problem, we propose an effective approach that employs the ratio of CSI readings from the wireless channel and wire direct connection channel to shorten the time of eliminating the hardware-related noise. We deploy the ExRadio in commercial off-the-shelf embedded devices and conduct a series of experiments. The experimental results demonstrate that reducing the execution time is beneficial to respiration rate detection under high-dynamic environments, and the overall detection error of ExRadio is less than 0.5 bpm even when multiple persons who are 1.5-m away from the monitored person are fast walking.

We investigate in this article joint trajectory design and resource allocation for secure air–ground integrated Internet of Things (IoT) networks with unmanned aerial vehicle (UAV) jamming and device-to-device (D2D) enhancement. By jointly optimizing ground user (GU) scheduling, UAV flight trajectory, and transmit power, we are able to maximize the minimum system secrecy rate of UAV and D2D communications. The formulated optimization problems of the two typical network scenarios, i.e., with and without UAV jamming, are challenging to be solved due to the corresponding nonsmooth and nonconcave objective functions. Therefore, we propose alternating iterative algorithms to solve the problems by employing the successive convex approximation and block coordinate descent methods. Extensive results indicate that the proposed joint optimization schemes can effectively improve the secrecy communication performance under different spatial distributions of GUs.

To meet the future demands for ubiquitous communication coverage and temporary / unexpected computing resources, aerial computing networks have been envisioned as a new paradigm. Nevertheless, dynamic changes on the network make it particularly challenging to achieve global optimal resource allocation. As an emerging technology, digital twin (DT) can represent real objects in physical network by creating virtual models. With the help of DT, we can easily obtain comprehensive real-world high-fidelity state information for model training, so as to achieve intelligent efficient decision-making. Accordingly, DT-based aerial computing networks have emerged as a potential solution. Note that available researches mostly assumed simple ground user distribution like uniform distribution, and adopted binary / partial offloading in task processing, neglecting the task separability and data inter-dependency among subtasks. Toward this end, we introduce DT into aerial computing networks, and study the problem of intelligent UAV deployment and resource allocation. Specifically, we firstly propose a DT-assisted UAV deployment strategy and model the data inter-dependency among subtasks. After that, two DT-assisted hybrid (binary and partial) task offloading schemes are presented, i.e., heuristic greedy and DQN-based schemes. Extensive analysis and numerical results confirm the effectiveness of our proposed DT-assisted UAV deployment and hybrid task offloading strategies.

This paper investigates a novel framework of intelligent reflecting surface (IRS) backscatter enabled multi-tier computing system. In such a hierarchical network, the data bits of computational task requested by each user equipment (UE) are broken up into three parts, which are respectively computed at tier-1 UEs, tier-2 access points (APs) and a tier-3 central server. Distinguished from conventional active antennas, IRS backscatter at the UEs is leveraged to offload data bits to the APs. Based on the established network framework, an optimization problem is formulated, which aims at maximizing the sum computational bits of system during the considered time block by jointly optimizing the active beamforming at the power beacon, the passive beamforming at the UEs, the active beamforming at the APs, the bandwidth and power allocation among all the UEs, as well as the time of local computing. To seek the optimal solution, the optimization problem is decomposed into two, namely the maximization of stage-1 sum computational bits and the minimization of stage-2 delay. By the objective function conversion and alternative optimization methods, the two problems are addressed. Extensive simulations are performed to confirm the feasibility of the proposed system and show the achievable performance in processing computational bits.

Power-domain Non-Orthogonal Multiple Access (NOMA) and Ultra-Dense Network (UDN) are promising candidates to cope with the massive access challenge of Machine-Type Communications (MTC). The power level pool is crucial for NOMA to bring performance gains. The existing related literatures rarely consider the power level pool design problem, or only resolve it in the single-cell scenario. However, this problem in multi-cell scenario is more complex and difficult to solve due to the presence of inter-cell interference. Towards this end, we propose a Non-Orthogonal Random Access (NORA) scheme to enable the coexistence of Human-Type Communications (HTC) and MTC for 5G and beyond UDN, where the power level pool design problem in multi-cell scenario is our focus. In order to deal with the complexity caused by multiple optimization objectives and inter-cell interference, we present a Multi-Agent Reinforcement Learning (MARL)-based solution to solve this problem, where each small base station acts as an agent to learn a suitable gap between adjacent power levels. Extensive numerical comparisons demonstrate the superior performances of our proposed scheme in multiple perspectives.

Wireless power transfer (WPT) and edge computing have been validated as effective ways to solve the energy-limited problem and computation-capacity-limited problem of wireless devices (WDs), respectively. This paper studies the wireless-powered multi-access edge computing (WP-MEC) network, where WDs conduct either local computing or task offloading for their individable computation tasks. We aim to minimize total computation delay (TCD) when each WD has a computation task to execute, referred to as the total computation delay minimization (TCDM) problem, by jointly optimizing the offloading-decision, WPT duration, and transmission durations of offloading WDs. The TCDM problem is a mixed integer programming (MIP) problem that is challenging to efficiently obtain the optimal or near-optimal solution. To tackle this challenge, we decompose the TCDM problem into the sub-problem of optimizing the WPT duration and transmission durations, and the top-problem of optimizing the offloading decision. For the nonconvex sub-problem, we design a worst-WD-adjusting (WDA) algorithm to efficiently obtain its optimal solution. For the top-problem, under the time-varying channel conditions, traditional optimization methods are hard to determine the optimal or near-optimal offloading decision within the channel coherence duration. To fast obtain the near-optimal offloading decision, we propose a deep neural networks (DNN)-based deep reinforcement learning (DRL) model, which takes the sub-problem solving as one component for utility evaluation. Finally, numerical results demonstrate that the proposed online DRL-based offloading algorithm achieves the near-minimal TCD with low computational complexity, and is suitable for the fast-fading WP-MEC network.

With the fast development of emerging Internet of Everything applications, human-type communications (HTC) and machine-type communications (MTC) will inevitably coexist in future 6G cellular networks. To support massive connectivity while fulfilling diverse requirements of both HTC and MTC, we present a device-to-device aided rate splitting multiple access (RSMA) scheme by encoding both the MTC devices’ messages and HTC users’ common messages into a general common data stream in each cell (or group). Nevertheless, such deploying strategy may bring challenges in complex resource allocation and transmission modes selection. In view of this, we introduce a simple received signal strength (RSS) based transmission modes selection scheme, through which the RSS-threshold selection problem is formulated to maximize the HTC and MTC sum rates. Considering the computational complexity and scalability, we employ a multi-agent reinforcement learning based algorithm for each small base station to choose the optimal RSS threshold thus to achieve maximum sum rate while maintaining massive connectivity. Simulation results reveal our proposed RSMA deploying strategy outperforms non-orthogonal multiple access (NOMA) in system coverage while maintaining high-level system rate. Besides, the proposed MARL based scheme can further improve the system sum rate and coverage for both the HTC and MTC.

To fulfill the stringent requirements brought by human-type communication (HTC) along with massive machine-type communication (MTC), device-to-device (D2D) and non-orthogonal multiple access (NOMA) techniques will inevitably be incorporated into dense cellular networks to cater massive connectivity and maintain high spectral efficiency. However, such combination may lead to very complex network topologies and bring challenge in resource allocation, interference management and transmission mode selection. Note the received signal strength (RSS) is an important factor for cellular and D2D mode selection, it can affect multi-access mode determination in D2D-aided HTC/MTC dense NOMA systems. Therefore, the RSS threshold of each cell has great impact on system performance and should be carefully tuned. To this end, we formulate the RSS-threshold selection problem as a decentralized partially observable Markov decision process to maximize the performance for downlink and uplink communications. Accordingly, we employ a multi-agent reinforcement learning based scheme wherein each small base station acts as an agent and chooses the optimal RSS threshold to achieve maximum sum rate by interacting with the environment continuously. Extensive simulation results reveal our proposed scheme can improve the system sum rate and coverage by enhancing the connectivity of massive HTC and MTC devices via D2D and NOMA techniques.

In order to meet the ever-increasing task processing demands of computation-intensive and delay-sensitive applications in the era of autonomous driving, a promising approach is to adopt nearby roadside units (RSUs) or/and vehicles passing by to provide edge computing services, i.e., vehicular edge computing (VEC). However, due to the untrustworthiness of fast-moving vehicles, the vehicles' tasks may face false result attacks or processing timeout. Note that there is little research on the vehicle trust evaluation in VEC networks, especially taking processing delay minimization into consideration. Toward this end, this paper studies the joint optimization problem of vehicle trust evaluation and task processing delay, aiming to ensure the security of the vehicles with tasks and minimize the task offloading delay. To solve this problem, we propose an efficient and trusted VEC offloading scheme based on fuzzy comprehensive strategy (FCS) and adopt the concept of game theory to motivate nearby vehicles to share computing resources. Experimental results corroborate that our proposed scheme can accurately evaluate the trustworthiness of vehicles and improve service security in VEC networks. Moreover, it can significantly reduce task offloading delay.

As emerging technologies such as mobile communication, vehicle to everything, and artificial intelligence are widely used in intelligent connected vehicles, drivers can gain a convenient and colorful driving experience. While these tech-nologies enrich the driving experience, they also bring a series of vulnerable interfaces to the vehicle. These interfaces can be used by hackers to attack other nodes of in-vehicle network that lack authentication and encryption. For this, researchers design scheme to encrypt and authenticate messages to protect in-vehicle networks, but this scheme would occupy the bandwidth resources of in-vehicle network. Therefore, researchers propose parameter monitoring-based intrusion detection system (IDS), information theory-based IDS, and fingerprint-based IDS, which do not occupy bandwidth. However, most IDSs either cannot locate the source of the attack, cannot detect aperiodic frames, or need to know the non-public mapping between electronic control units (ECUs) and identifiers (IDs) of in-vehicle network. To solve these weaknesses, we propose a novel IDS that establishes voltage fingerprints for each ID. This system can detect period and aperiodic malicious frames and locate the source of attack without knowing the mapping between ECUs and IDs. The experimental results on actual vehicles demonstrate that our scheme is robust against real scenarios.

Deep reinforcement learning (DRL) has made sig-nificant achievements in many real-world applications. But these real-world applications typically can only provide partial ob-servations for making decisions due to occlusions and noisy sensors. However, partial state observability can be used to hide malicious behaviors for backdoors. In this paper, we explore the sequential nature of DRL and propose a novel temporal-pattern backdoor attack to DRL, whose trigger is a set of temporal constraints on a sequence of observations rather than a single observation, and effect can be kept in a controllable duration rather than in the instant. We validate our proposed backdoor attack to a typical job scheduling task in cloud computing. Numerous experimental results show that our backdoor can achieve excellent effectiveness, stealthiness, and sustainability. Our backdoor's average clean data accuracy and attack success rate can reach 97.8% and 97.5%, respectively.

5G and artificial intelligence greatly promote the development of intelligent and connected vehicle (ICV). However, ICV opens more ports to the outside world, making it easy for hackers to intrude controller area network (CAN) and control ICV. Therefore, many researchers design intrusion detection systems (IDSs) to detect vehicle intrusion in real-time. In this paper, we propose a highly camouflaged attack method called the same origin method execution (SOME) attack. The intrusion messages of this attack have the same characteristics as normal messages and can bypass most existing IDSs. To detect this attack, we design a reliable IDS for ICV based on a generative adversarial network (GAN) called GVIDS. It takes CAN messages as the input sample and trains the IDS model to distinguish the legality of messages. Experiments on two real vehicles show that GVIDS can detect most existing attacks, including spoofing, bus-off, masquerade, and SOME attacks. The average detection accuracy of GVIDS is 96.64%, and the average running time of each detection is only 0.18 ms. In addition, the experiment also shows that the detection performance of GVIDS is not affected by the value of identifiers in CAN messages.

With emerging technologies such as 5G, artificial intelligence, and other emerging technologies widely used in intelligent connected vehicles (ICVs), users can obtain more personalized service and more comfortable experiences. Although these technologies significantly facilitate our quality of life, they also bring a series of vulnerable interfaces, which threaten the security of in-vehicle networks, such as the controller area network (CAN) bus. Therefore, many researchers design intrusion detection systems (IDSs) to detect malicious frames. However, most IDSs cannot locate the sender electronic control unit (ECU) of the malicious frames, the compromised ECU. This means vehicles cannot take timely defensive measures against the ECU, which seriously endangers the safety of users. In order to identify the sender more accurately, we design a lightweight sender identification scheme based on the physical layer characteristics of vehicles. It does not increase the load and calculation burden of the CAN bus, and it can accurately map multiple identifiers (IDs) to each ECU without developer documentation. When compromised ECUs send malicious frames to attack vehicles by spoofing or masquerading, the scheme is able to accurately identify the sender, with an average accuracy rate of over 95%.

The appearance of autonomous vehicle prompts the development of multi-access edge computing. In cellular vehicle-to-everything (C-V2X) system, autonomous vehicle can offload complicated tasks to multi-access edge server (MES) due to the shortage of computation resource. However, because of eavesdroppers, there exists security problem in wireless communication between vehicles and MES. Except for security problem, autonomous vehicle also casts stringent requirements on latency and energy consumption for task offloading, which motivates plenty of scholars to study. Most of the existing studies cover one or two dimensions of aforementioned three problems, which limits their practicability. Therefore, we put forward a multi-access edge task offloading scheme with carefully considering information security, offloading latency and energy consumption. To solve the security problem at physical layer, small-cell base station proactively sends artificial noises to degrade the decoding ability of wiretappers. On the premise of secure wireless communication, the optimal task offloading proportion is calculated through one dimension search algorithm in the scheme, which ensures the minimum weighted sum of offloading latency and energy consumption. Numerical results validate the feasibility and effectiveness of the proposed scheme.

With the rapid development of ultra-dense networks (UDNs) and random access technologies, device-to-device (D2D) and non-orthogonal multiple access (NOMA) techniques will incorporated into future UDNs supporting both human-type communications (HTC) and machine-type communications (MTC) to fulfill the stringent requirements brought by various potential Internet of Everything (IoE) applications. Nevertheless, the combination of D2D and NOMA will make the network management more complicated. In view of this, we optimize the received signal strength (RSS) threshold value of each small base stations (SBSs) in the UDN where HTC and MTC coexist. Considering the computational complexity, we employ a multi-agent reinforcement learning based RSS threshold value selection scheme, in which each SBS acts as an agent and chose the optimal RSS threshold value to achieve maximum system throughput performance by interacting with the environment. Extensive numerical results show our proposed scheme can greatly improve the system throughput by enhancing the connectivity of massive HTC users and MTC devices via D2D and NOMA techniques.

With the popularity of personal computing devices, people often keep long-term head immobility in front of screens, resulting in the emergence of “phubbers” and “office workers.” The early warning solutions in the Internet of Healthcare Things (IoHT) have brought hope to protect users’ health and safety. However, most existing works cannot recognize the different head gestures during walking, which is also a common cause of text neck and traffic accidents. In addition, they also need a large amount of data to update the model to adapt to the new environment, which reduces the practicality of the model. To solve these problems, we propose a system, CSEar, based on built-in accelerometers of off-the-shelf wireless earphones, which can recognize 12 kinds of head gestures both in resting and walking states. First, an innovative algorithm is designed to detect head gesture signals, especially for the signals mixed with gait. Then, we propose the MetaSensing, a head gesture recognition model that can improve the recognition ability with few samples compared with the existing metalearning algorithms. Finally, the experimental results prove the effectiveness and robustness of the CSEar.

The rapid development of the information-centric wireless sensor network (ICWSN) has solved the challenges of information transmission and processing caused by the accelerated growth of wearable devices and the wide deployment of the Internet of Things (IoT) recently. The privacy security is also a growing problem. The existing works use earphones, covert, and user-friendly wearable devices, for user authentication. However, some of the earphone-based authentication solutions need to customize special earphones, which are not universal. Other solutions use microphones and speakers of earphones for authentication, which are susceptible to changes in the auricle’s internal environment, resulting in a decline in performance. To solve this problem, a new authentication solution based on the existing commercial earphones is proposed to authenticate a user by tapping on the earphone rhythmically. This rhythmic tap behavior causes a change of the signal waveform of the built-in accelerometer in the earphone. Based on this, we design a pipeline to authenticate the user’s identity. We first design an event detection algorithm to segment the tap signal accurately. Then, we use the global features calculated based on the event detection algorithm and local features extracted from the convolutional neural network (CNN) for building an authentication model using the Naive Bayes (NB) classifier. Finally, 20 users are recruited to evaluate the experiment and the recognition accuracy reaches 98%. Moreover, we extend the experiment to prove that it has a good performance against the different attacks and is robust in different scenarios.

With the gradual commercialization of 5G, especially the widespread application of artificial intelligence (AI) technology, the Internet of Things (IoT) continues to expand and has integrated into every aspect of our lives. While enjoying the convenience brought by IoT, we also face unprecedented challenges, including ubiquitous and unpredictable demands for communication and computing resources. In consideration of their flexible deployment, low cost, and easy expansion, UAV edge computing IoT networks (UECINs), which adopt unmanned aerial vehicles (UAVs) to provide fast communication and computing services, have emerged as a promising solution. Note that there have been a number of studies focusing on UAV’s position deployment and trajectory design, resource allocation in UECIN. However, most existing works proposed short-term service provisioning systems with a fixed number of UAVs, ignoring the problem of UAVs’ limited battery power and the possible changes of ground users’ number, locations, and resource requirements. To address these issues, we present a dynamic UECIN framework with autonomous prediction characteristics, aiming to stably provide mobile-edge computing services for ground users in a certain area over a long period of time. This framework can not only support UAV’s dynamic entry and exit according to the real-time needs of ground users but also update their position deployment based on the distribution of ground users. As we know, we are the first to propose UECIN with a dynamic entry and exit mechanism. Besides, an efficient and load-balancing task allocation scheme is further given, and extensive analysis and numerical results corroborate the feasibility and superior performance of our framework.

While providing significant convenience for people, mobile applications (Apps) bring serious privacy leakage and invasion threats over certain platforms (e.g., Android) due to privacy violations. To protect users from these threats, a lot of works related to privacy violation detection have been proposed. However, few of them particularly check the violations, including lacking privacy policy, collecting privacy before statement, lacking account cancelation service, and stubborn permission request. Toward this end, we design an automatic detection tool namedPVDetectorto detect these violations in Android Apps. We extract and construct relevant threat forms by statically and dynamically analyzing Apps’ behaviors, and then fine tune these forms through threat-form-matching methods on problematic Apps. Finally, a comprehensive experiment is conducted to detect privacy violations on different Android application markets byPVDetector. Specifically, we detect 16 162 Android Apps (involving people’s various aspects of life) collected from six popular official application markets and three special categories. The experiment results indicate that the situation that Apps contain privacy violations is greatly serious in these markets and categories. We also randomly check the experiment results of 385 Apps. The check results illustrate that the detection accuracy ofPVDetectorcan reach 93%.

Unmanned aerial vehicles (UAVs) have shown great potential in benefiting industries due to their good features, such as the ease of deployment and low maintenance cost. However, the communication security issue remains a serious challenge before the large-scale application of industrial UAVs. The untrusted communication environment can cause the leakage of valuable industrial data or the losing of important cargos that carried by UAVs. Traditional authentication mechanisms for protecting communications include public-key infrastructure-based, ID-based, and certificateless authentication. These mechanisms rely on a central authority and some of them may introduce high-complexity computation that is not suitable for industrial drones. Therefore, aiming at these challenges, we design a blockchain-assisted distributed and lightweight authentication service for industrial UAVs. The blockchain technology supports the distributed and immutable storage of industrial UAVs’ authentication information, and smart contracts enable convenient operations for drones to acquire or update the corresponding information. Security evaluation demonstrates that our scheme is resistant to various attacks and can guarantee trustworthy communications for industrial drones. Extensive experiments also show that our designed authentication service can not only achieve low computation and communication cost for industrial UAVs but also remain robust even if a small proportion of drones are compromised.

Internet of Vehicles (IoV) is an emerging technology in automotive field, in which vehicles can communicate with other vehicles and roadside infrastructures to improve information acquisition ability as well as obtain various services to elevate the security and comfort level. To cope with the increasingly complex vehicular network, software-defined networking (SDN) architecture with advantages of centralized management and flexible control becomes a promising solution. However, in application scenarios, the security of SDN is rarely concerned. If attackers exploit the vulnerabilities of SDN to hijack the network location of the servers or vehicles, vehicles may not be able to access the services they need timely and effectively, which will pose a great threat to the benefit of vehicle users. In light of this, we focus on location hijacking attack against SDN in vehicular network. We perform this attack on five mainstream SDN controller platforms and analyse its impacts from multiple perspectives. As far as we know, this is the first study of such attack in vehicular network. Furthermore, using the advantages of the software-defined space–air–ground-integrated vehicular network and the characteristics of high altitude platform (HAP), such as wide coverage and high load capacity, we put forward the attack recovery scheme based on deep$Q$-learning (DQL) to supplement existing defence mechanisms that always have counter attacks and endow the vehicular network with a certain resilience.

Software-defined networking (SDN) has become an attractive solution to carry out centralized and efficient control in Industrial Internet of Things (IIoT). However, its security has received little attention when applied to IIoT, and no comprehensive consideration has been given to attacks against forwarding nodes (FNs), the basic elements of the data plane. Therefore, in this article, we aim to investigate attacks against FNs from multiple perspectives in software-defined IIoT. To the best of our knowledge, we are the first to systematically consider this kind of attacks. Since it is difficult to predeploy all defense methods against various attacks, we propose a deep reinforcement learning (DRL)-based general attack tolerance scheme to guide the benign traffic flow bypass the attacked FNs. Furthermore, in view of the situation that the real data set is rare and the standard model-based data set is likely to be impractical, we use generative adversarial network (GAN), a representative deep generative model (DGM), to flexibly generate real-like network traffic for more sufficient and effective experimental verification on the attack tolerance scheme. Experimental results show that our proposed scheme can significantly improve the successful arrival rate of IIoT traffic and achieve near-optimal results.

This article pioneers an unprecedented strategy of secure transmission, in which intelligent reflecting surface (IRS) is used as a backscatter device to form and scatter jamming signal while the transmitter (Alice) is regarded as a radio-frequency (RF) source. Specifically, Alice transmits confidential signal to a single-antenna legitimate user (Bob) while the transmission is overheard by multiple single-antenna illegitimate users (Eves). The beamformer at Alice is designed to align with the estimated channel vector from Alice to Bob, in order that the proposed strategy is completely compatible with the common communication system without respect to wiretap. To achieve secure transmission, IRS is deployed to modulate the received confidential signal to jamming signal and reflect it so as to deteriorate the reception at Eves. Based on this model, the reflection coefficient vector of IRS is optimized to minimize the eavesdropped information amount while guaranteeing the reliable communication at Bob. By comparing with the familiar IRS-based beamforming scheme and the cooperative jamming scheme in extensive simulations, the feasibility and secrecy performance gain are confirmed for the proposed strategy of IRS-based backscatter jamming.

To the best of our knowledge, this article represents the first attempt toward robust beamforming design for multiuser downlink communications in intelligent reflecting surface (IRS)-enhanced satellite (SAT) and high altitude platform (HAP) integrated network. Such network configuration is mainly composed of a single-antenna SAT, a multiple-antenna HAP, multiple single-antenna SAT and HAP terminals and an IRS. Although sharing the same spectrum resource with the SAT, the HAP suspended in the near-space intends to offer temporary higher-speed and lower-delay multiuser communication connections than the SAT. Therefore, the power budget should be carefully tuned at the HAP. Toward this end, we first formally formulate the transmit power minimization problem at the HAP under the constraints of signal-to-interference-plus-noise-ratio-outage-probability (SINR-OP) at each SAT and HAP terminal by taking into account the imperfect channel state informations and their Gaussian channel estimation errors. Note that the complicated superposition of direct SAT/HAP links and cascaded IRS links, makes the optimization problem rather challenging. Specifically, we transform the probabilistic constraints into approximate deterministic ones, by performing rank relaxation. Based on this, we are able to solve the optimization problem by alternately optimizing the two ensuring subproblems. As verified by extensive simulation results, the proposed IRS-enhanced beamforming schemes can substantially diminish the transmit power at the HAP compared to the beamforming counterparts without IRS.

Intelligent and connected vehicles (ICVs) have become the mainstream in the development of automobile industry. Many emerging technologies have been proposed to provide users with comfortable and convenient driving experience. However, even though these technologies significantly improve the quality of service, some of the communication interfaces they used are vulnerable and easily attacked. Note that although many malicious attacks can be carried out in various ways, their final step must be in the in-vehicle network, i.e., the controller area network (CAN) bus. In order to protect the security of the CAN bus, it is of great importance to design an intrusion detection system (IDS), which can monitor the message transmission in real time. In this article, we design a novel external IDS based on vehicle voltage signals, named VehicleEIDS. It does not occupy the bandwidth or computing resources of the CAN bus and maintains the original CAN bus protocol as well. The system can be directly installed in the automobile gateway to monitor the external intrusion, and can be connected to the CAN bus as an independent external device to protect the automobile security. In addition, VehicleEIDS is robust against the factors of vehicle status, the number of attacking electronic control units (ECUs), and the sending frequency of attack data. It is only related to the voltage signals of external intrusion device. Once external intrusion devices send attack data to the CAN bus, VehicleEIDS can quickly identify its abnormal voltage signals, with the accuracy of more than 97%.

By providing loosely coupled, lightweight, and independent services, the microservice architecture is promising for large-scale and complex service provision requirements in the Internet of Things (IoT). However, it requires more fine-grained resource management and orchestration for service provision. Most of the existing microservice orchestration solutions are based on those designed for the traditional cloud. They can only provide coarse-grained resource allocation using possibly conflicting weighted objectives. In this article, we present a fine-grained microservice orchestration approach to provide services online for dynamic requests of IoT applications. By using a fine-grained resource model of energy cost and service end-to-end response time of orchestrated microservices, we formulate the microservice orchestration problem as a multiobjective Markov decision process. We then propose a multiobjective optimization solution based on deep reinforcement learning (DRL) to simultaneously reduce energy consumption and response time. Through extensive experiments, our proposed algorithm presents significant performance results than the state of the art. To the best of our knowledge, this is the first work that addresses microservice orchestration using DRL for multiple conflicting objectives.

The Internet of Things (IoT) provides convenience for our daily lives via a huge number of devices. However, due to low-resource and poor computing capability, these devices have a high number of firmware vulnerabilities. Software verification is a powerful solution to ensure the correctness and security of IoT firmware programs. Unfortunately, due to the complex semantics and syntax of program languages (typically C), applying software verification in IoT firmware faces the tradeoff between efficiency and accuracy. One of the fundamental reasons is that verification methods cannot support verifying state transitions on the memory space caused by pointer operations well. To this end, by combining sparse value flow (SVF) analysis into model checking and optimizing computational redundancy among them, we design a novel points-to-sensitive model checker, called PCHECKER, which can provide a highly precise and efficient verification for IoT firmware programs. We first design a spatial flow model to effectively describe state behaviors of a C program both on the symbolic and memory space. We then propose a counterexample-guided model checking algorithm that can dynamically refine abstract precisions and update nondeterministic points-to relations. With a set of C benchmarks containing a variety of pointer operations and other complex C features, our experiments have shown that compared with state of the art (SOTA), PCHECKER can achieve outstanding results in the verification tasks of C programs that its verification accuracy is 95.9%, and its average verification time of each line of code is 1.27 ms, which are both better than existing model checkers.

Although intelligent connected vehicles (ICVs) can better assist drivers and improve their driving experience, they have huge network security problems and are frequently attacked. This is because the vehicle network is connected to the Internet, which expands the attack surface of ICV, and attackers have more ways to launch attacks. In recent years, many security experts fight against attackers and propose various types of vehicle intrusion detection systems (IDSs) to protect the controller area network (CAN). However, with the continuous enhancement of attack means, especially the appearance of the masquerade attack, most IDSs are no longer applicable. In this article, we design a new fingerprint-based vehicle IDS to protect the CAN, called ClockIDS. It establishes a unique fingerprint for each electronic control unit (ECU) based on clock skew. On this basis, ClockIDS realizes the functions of intrusion detection and attack source identification by utilizing the empirical rule and dynamic time warping. It neither occupies the bandwidth of CAN bus nor needs to modify the CAN protocol. Our experiments on two real vehicles show that ClockIDS can establish a unique fingerprint for ECU without being affected by the size of message period, and can detect three types of attack with a detection accuracy of 98.63%. In addition, this system can identify the attack source, and the average recognition accuracy is 96.77%. Furthermore, ClockIDS has high real-time performance, and the average time cost of each detection is only 1.99 ms.

Millimeter wave (MmWave) dual-functional radar-communication (DFRC) technology is believed to hold the ability to alleviate spectrum congestion and inter-radar interference in 5G vehicle-to-everything (V2X) systems. The radar target sizes in V2X system may not be ignored in views of the demands of short-range sensing and ultra-narrow beams supported by massive MIMO mmWave beamforming. Under such scenario, a novel single-target-multi-beams (STMB) radar beam alignment scheme is proposed to acquire more accurate information on estimated ranges and velocities by allocating multiple radar beams to a certain target. For instance, the relative velocity direction can be accurately estimated based on STMB scheme by using a weighted linear estimation methods. Then, the hybrid analog-digital beamforming under STMB scheme is formulated and optimized by maximizing transmission rate subject to radar signal-to-interference-and-noise (SINR) constraints, where a radar beam cancellation algorithm is proposed to adjust adaptively the radar beam number pointing to a certain target, which can guarantee strict radar SINR constraint under different transmission power levels. The numerical results verify the effectiveness and reliability of STMB scheme and show that the proposed beamformer outperforms the benchmark in both spectral efficiency and minimum radar SINR.

In 5G vehicle-to-everything (V2X) systems, the scarcity of spectrum resources and the inefficiency of resource allocation make vehicle-to-vehicle (V2V) communications that require stringent latency and high reliability still a challenge. Existing relevant literatures either focus on vehicle-to-infrastructure (V2I) communications, or consider scenarios where the vehicle roles (transmitter or receiver) are fixed in V2V communications, or study the resource allocation within a single cell. What’s more, considering the high-speed movement of vehicles across the coverage regions of multiple cells, the above resource allocation problem becomes even more challenging. Toward this end, we consider in this paper a multi-cell 5G V2X system where V2V links and V2I links coexist and the vehicle roles are not fixed, and propose a sparse code multiple access-based centralized resource allocation scheme, so as to address the above challenges. In view of the fact that our formulated maximizing packet reception ratio problem is a combinatorial optimization problem and is NP-hard, we design a three-stage heuristic yet joint alternating optimization approach to obtain a suboptimal solution. Extensive numerical results demonstrate the superior performances of the proposed scheme in multiple perspectives.

This paper investigates intelligent reflecting surface based backscatter communication (IRS-BackCom), in order to realize computational task offloading of energy-constrained mobile edge computing network in a self-sustainable manner. Specifically, the system operation is divided into two phases. In the first one, the ambient signal energy from a power beacon (PB) either provides the energy supply of local computing and energy harvesting circuits, or flows into the energy storage, when reaching the IRS. In the second one, the stored energy is used to enable IRS-BackCom for partial computational data offloading and energize local computing circuit. Based on this, the maximization problem of sum computational bits is formulated. By jointly optimizing the beamforming vector at the PB, the backscatter matrix at the IRS, the time scheduling of two-phase process, as well as the time of local computing, sum computational bits are maximized. In addition, this paper proposes element clustering to realize BackCom, so as to reduce the control and computation complexity of IRS. According to different operating mechanisms, two cluster operation modes are considered, namely independent cluster operation mode and joint cluster operation mode. Simulation results demonstrate the achievable sum computational bits by the proposed IRS-BackCom schemes.

Multiple unmanned aerial vehicles (UAVs) can function as aerial base stations to provide flexible and reliable communication services for massive ground devices (GDs). It is quite a challenging task to analyze such multi-UAV networks when considering practical mutually exclusive relationships among UAVs. Based on the tools of stochastic geometry, we in this paper develop a theoretical framework for modeling and analyzing aerial networks with UAVs following Matérn hard-core point process (MHCPP). As the tractable probability generating functional (PGFL) of repulsive point processes is unavailable, we employ an approximate approach based on the Poisson point process to analyze the cumulative interference and the signal-to-interference ratio (SIR) of a typical GD. By considering both line-of-sight (LOS) and none-line-of-sight (NLOS) communications, we obtain the approximation expressions of the network coverage probability and average rate. Finally, extensive simulation results are presented to validate the efficiency and accuracy of our proposed framework.

Multi-access edge computing (MEC) has been one promising solution to reduce the computation delay of wireless devices. Due to the high spectrum efficiency of non-orthogonal multiple access (NOMA), this paper studies the single-user multi-edge-server MEC system based on downlink NOMA, aiming to minimize task computation delay by jointly optimizing the NOMA-based transmission duration (TD) and workload offloading allocation (WOA) among edge computing servers. This task computation delay minimization (CDM) problem is formulated as a nonconvex optimization problem. To solve the CDM problem efficiently, we decompose it into the sub-problem of determining the optimal WOA with a given TD and the top-problem of optimizing the TD. For the sub-problem, we first derive its some important properties and then design an efficient channel quality ranking based algorithm to obtain the optimal WOA. We solve the top-problem for the static-channel and dynamic-channel scenarios, respectively. For the static-channel scenario, we design an optimal algorithm which only apply once the golden section search method to obtain the optimal TD of first task and directly obtain the optimal offloading solution for any consequently arrived task with different workloads. For the dynamic-channel scenario where the channel qualities from the wireless device to the edge-computing servers are varying, it is critical to quickly determine the current task’s offloading solution under the current channel state and task workload, which is very challenging for the traditional optimization methods. In order to conquer this challenge, we propose the deep reinforcement learning (DRL) based algorithm, which can obtain the near-optimal offloading solution instantly after enough learning. Finally, we validate through simulations the advantages of NOMA over frequency division multiple access (FDMA).

Trigger-action programming (TAP) is a popular end-user programming framework that can simplify the Internet of Things (IoT) automation with simple trigger-action rules. However, it also introduces new security and safety threats. A lot of advanced techniques have been proposed to address this problem. Rigorously reasoning about the security of a TAP-based IoT system requires a well-defined model and verification method both against rule semantics and physical-world features, e.g.,concurrency, rule latency, extended action, tardy attributes,andconnection-based rule interactions, which has been missing until now. By analyzing these features, we find 9 new types of rule interaction vulnerabilities and validate them on two commercial IoT platforms. We then present TAPInspector, a novel system to detect these interaction vulnerabilities in concurrent TAP-based IoT systems. It automatically extracts TAP rules from IoT apps, translates them into a hybrid model by model slicing and state compression, and performs semantic analysis and model checking with various safety and liveness properties. Our experiments corroborate that TAPInspector is practical: it identifies 533 violations related to rule interaction from 1108 real-world market IoT apps and is at least 60000 times faster than the baseline without optimization.

With the development of the industrial Internet of Things (IIoT), intelligent healthcare aims to build a platform to monitor users’ health-related information based on wearable devices remotely. The evolution of blockchain and artificial intelligence technology also promotes the progress of secure intelligent healthcare. However, since the data are stored in the cloud server, it still faces the risk of being attacked and privacy leakage. Note that little attention has been paid to the security issue of privacy information mixed in raw data collected from large number of distributed and heterogeneous wearable healthcare devices. To solve this problem, in this article, we design a deep learning-based privacy preservation and data analytics system for IoT enabled healthcare. At the user end, we collect raw data and separate the users’ privacy information in the privacy-isolation zone. At the cloud end, we analyze the health-related data without users’ privacy information and construct a delicate security module based on the convolutional neural network. We also deploy and evaluate the prototype system, where extensive experiments prove its effectiveness and robustness.

The development of software-defined industrial networks (SDIN) promotes the programmability and customizability of the industrial networks and is suitable to cope with the challenges brought by new manufacturing modes. For building more scalable and reliable SDIN, a distributed control plane with multicontroller collaboration becomes a promising option. However, as the brain of SDIN, the security of the distributed control plane is rarely considered. In addition to suffering direct attacks, each controller is also subjected to attacks propagated by other controllers because of information sharing or management domain takeover, resulting in the spread of attacks in a wider range than a single controller. Therefore, in this article, we study attacks against SDIN with distributed control plane, demonstrate their propagation across multiple controllers, and analyze their impacts. To the best of our knowledge, we are the first to study the security of SDIN with distributed control plane. In addition, since the existing defense mechanisms are not specifically designed for distributed SDIN and cannot defend it perfectly, we propose an attack mitigation scheme based on deep reinforcement learning to adaptively prevent the spread of attacks. Specifically, the novelty of our scheme lies in its ability of learning from the environment and flexibly adjusting the switch takeover decisions to isolate the attack source, so as to tolerate attacks and enhance the resilience of SDIN.

Increasingly serious data security and privacy protection issues make federated learning (FL) gradually evolve to be an important technology in the field of artificial intelligence (AI). Meanwhile, in consideration of the huge demands for network access and computing resources from massive IoT devices, ultra-dense edge computing (UDEC), which integrates mobile edge computing (MEC) and ultra-dense network (UDN), has turned out to be a promising network architecture in the era of 5G and even 6G. Facing requirements on ultra-low processing latency, performing FL for UDEC confronts many challenges, one of which is how to relieve the barrel effect caused by the difference in computing power of local devices while ensuring overall FL efficiency. Nevertheless, little work can be found in this area. Toward this end, the paper takes the lead in studying FL for UDEC, and proposes an inter-server collaborative federated learning method by grouping the servers and clients. Theoretical analysis and numerical results corroborate that our proposed inter-server collaborative method can significantly reduce the waiting time during local training without reducing the learning accuracy, thus improving the overall efficiency.

The communication systems for Poisson channels require long pilot for channel estimation and may result in large percentage of overhead due to the signal-dependent noise of Poisson-distributed signal, i.e., both signal part and noise part experience random processes. In this paper, both blind and semi-blind channel estimation methods are studied to shorten the overhead and increase the transmission efficiency for Poisson channels. First, fractionally spaced equalizers are proposed based on modified constant modulus algorithm (CMA) and subspace (SS). Second, a data-aided iterative channel estimation (ICE) is designed and analyzed in terms of its asymptotic unbiasedness and convergence. The proposed methods are evaluated based on both a constant channel scenario and a varying channel scenario. Numerical simulation results show that the modified CMA has the worst bit-error rate performance but requires the lowest computational complexity. Besides, both SS based channel estimation and ICE have negligible overhead and comparable bit-error rate performances with respect to the conventional periodic pilot based channel estimation having 50% overhead.

With the rapid development of machine-type communications (MTC), the future communication architecture needs to provide services for both human-type communications (HTC) and MTC with unique characteristics. The huge connections from MTC bring serious challenges to the existing wireless network. Ultra-dense network (UDN), a promising candidate technology, can support massive device access through dense deployment of small base stations (SBSs). Different from the resource management in traditional wireless network with single base station (BS), the resource allocation problem at BS level is more prominent in UDN, and the diversity of devices will make this problem more complicated. In view of this, we investigate the joint optimization of massive access and resource management in the UDN where HTC and MTC coexist. Considering the computational complexity and scalability, we propose a multi-agent deep reinforcement learning based SBS state selection scheme, in which each SBS acts as an agent and selects the optimal state between active and idle by continuously interacting with the environment. In addition, we adopt the power-domain non-orthogonal multiple access to further improve system throughput, and use grant-based and grant-free access manners for HTC and MTC respectively, so as to meet their unique characteristics. Extensive numerical results demonstrate the superior performances of proposed scheme in multiple perspectives.

In this paper, we study a joint user pairing and power allocation problem in the 5th generation (5G) satellite random access (RA) networks, where some user equipments (UEs) are assisted by relay satellite UEs to establish satellite access. We aim to maximize the total sum rate of the RA system by jointly optimizing user pairing and power allocation. The above joint optimization problem is a non-convex mixed-integer problem, which is challenging to solve. To solve this problem, we decompose it into two subproblems. Firstly, a problem for optimal user pairing is formulated to find the optimal user pairing relationship. To solve this subproblem efficiently, a Q-learning based distributed user pairing algorithm (QL-DUPA) is proposed, which converts the user pairing problem to a Q-learning process. The Q-learning process can achieve a near-optimal solution and is practically feasible. Then, a problem for optimal power allocation is formulated to find the optimal power allocation coefficients in each user pair. The subproblem is convex and the optimal solution is obtained using convex optimization. Next, a satellite RA scheme with collision resolution is proposed based on the joint optimization of user pairing and power allocation, and we analyze its total sum rate. Simulation results show that the proposed satellite RA scheme with collision resolution greatly outperforms the existing schemes in terms of total sum rate.

Due to the outstanding characteristics of unmanned aerial vehicles (UAV), i.e., maneuverability and flexibility, UAV enabled mobile edge computing (MEC) has become a widely attractive research direction. However, single-UAV cannot be qualified for numerous tasks and application scenarios in view of its limited computing capacity, while multi-UAV enabled MEC is still in the initial stage, and most existing work transformed the problem of multi-UAV enabled MEC into multiplied single-UAV. The UAV swarm can make UAVs cooperate intelligently, and accomplish diversified tasks in complex environments at low cost, which is regarded as a promising development direction of UAV technology. Nevertheless, it is inefficient since each UAV node is responsible for both communication and computation, and multi-hop transmission among UAVs may lead to a very high delay. Toward this end, the paper takes the lead in studying the problem of grouping and role division in UAV swarm-based edge computing, and puts forward a grouping and role division algorithm to solve it. Final experimental results corroborate that the complexity of our algorithm is less than that of the traditional algorithm, and role division can maximize the use of communication and computing resources.

Mobile edge computing (MEC) has been envisioned as a promising technology to meet ever-increasing demands on computational resources. Due to the fixed deployment and limited coverage of conventional MEC, unmanned aerial vehicle (UAV) edge computing began to receive attention with the advantage of flexibility and controllability. However, single UAV edge computing is not competent for complex scenarios with the limitations of computing capability and coverage. Further-more, although multi-UAV edge computing could improve the situation, the long processing delay and insufficient utilization of resources still restrict the communication and cooperative computation among UAVs. Characterized by the unique swarm cooperative communication and computing, UAV swarm-based edge computing can realize more complex task computing and higher computational efficiency. Toward this end, we provide this paper to study the cooperative task offloading problem in UAV swarm-based edge computing, aiming to minimize the overall task processing delay. To solve this problem, we adopt an optimal cooperative computation offloading method. Experimental results demonstrate the importance and high performance of UAV swarm-based edge computation and the low complexity of our proposed method.

The utilization of UAV may suffer severe security problems due to the inherent characteristics of wireless air-to-ground (A2G) channels. To this end, researchers have drawn much attention on utilizing physical layer security (PLS) techniques to maintain secrecy data transmission in UAV enabled networks. Different from previous works, we jointly optimize user scheduling strategy, signal transmission power and 3D flying trajectory of the UAV to maximize the minimum system secrecy rate by considering UAV position and an eavesdropper with partial location information simultaneously. Because the formulated problem is intractable and non-convex, we in this paper develop an iteration approach to solve the problem based on the successive convex approximation (SCA) method. Finally, experimental results are further derived to validate the performance gains of our scheme.

Nowadays, more and more external interfaces are added into intelligent and connected vehicles. The in-vehicle network, especially the controller area network (CAN), is no longer a closed environment, which provides more approaches for attackers to invade. To resist attacks, numerous researchers have proposed intrusion detection systems (IDSs). However, attackers can intrude CAN bus in a more advanced way, such as masquerade attack, which leads to failures of most IDS. To counter masquerade attacks, we propose an efficient vehicle IDS based on clock behavior, called VehicleCIDS. First, the system uses recursive least squares (RLS) algorithm to estimate the clock behavior of each electronic control unit (ECU). Then, a statistical method called empirical rule is used to detect attack messages. Finally, it utilizes dynamic time warping (DTW) to identify attackers. The experimental results on real vehicles show that the recognition rate of VehicleCIDS can achieve 98.52% in intrusion detection and 87.71% in attacker identification.

Multiaccess edge computing is an essential technology that academia and industry have recognized as fundamental for the future of the Internet of Things. Current research on the subject utilizes virtual machines as the intermediary between end devices and cloud servers. However, recently a new framework was proposed that utilizes Cybertwins instead of virtual machines for the same function. Such framework comes with a myriad of advantages but, most importantly, in this case, it includes a control plane capable of enabling cooperation between the Cybertwins. In this article, we present a mathematical model of the total service delay of a Cybertwin-based multiaccess edge computing system that includes user mobility, migration of virtual servers, multiple physical servers at different network tiers, fronthaul and backhaul communication, processing, and content request/caching. We also propose algorithms for guiding the operation of Cybertwins and the control plane in a multiaccess edge computing scenario. Finally, a performance analysis between Cybertwin and a virtual machine-based scheme is offered. Simulations show that Cybertwin brings significant improvement for the assumed scenario in the form of a faster overall service due to the higher cooperation. The models and simulations here were designed with the characteristics of future networks, beyond the current 5G, in mind, making them likely relevant for future networks, where multiaccess edge computing and the Internet of Things should play an even more important role.

The explosive growth of machine-type communications (MTC) devices poses critical challenges to the existing cellular networks. Therefore, how to support massive MTC devices with limited resources is an urgent problem to be solved. Bursty traffic is an important characteristic of MTC devices, which makes it difficult for agents to learn useful experience and has a negative impact on model convergence. However, most existing reinforcement learning-based literatures assume that devices have saturate data. Towards this end, we propose two distributed Q-learning aided uplink grant-free non-orthogonal multiple access (NOMA) schemes (including all-devices distributed Q-learning (ADDQ) scheme and portion-devices distributed Q-learning (PDDQ) scheme) to maximize the number of accessible devices, where the bursty traffic of massive MTC devices is carefully considered. In order to reduce the dimension of scheduling space and mitigate the impact of bursty traffic, the idea of grouping devices as well as transmission resources and the intermittent learning mode are adopted in our schemes. Extensive numerical results demonstrate the advantages of proposed schemes from multiple perspectives.

Reconfigurable intelligent surface (RIS), as a revolutionary technique, appears to ameliorate undesirable propagation environment in a controllable manner, which has the potential to substantially boost security of private information in the future smart radio environment. Much recent attention has been directed to the RIS-enhanced secure communication, among which the terrestrial network scenarios are generally concerned, while the exploration of aerial-ground communication integrated with RIS remains an open issue. Furthermore, in the available research, the ideal assumption of line-of-sight channel for aerial-ground link cannot be exploited in complex urban environment. Inspired by this, we provide in this paper the secrecy rate maximization problem under a complex urban scenario, where the drone's high mobility and RIS's tunable capability are utilized to against the potential eavesdropper. Although the established problem is intractable to tackle, we devise an iterative algorithm combining alternating optimization and successive convex approximation method as the solution, which jointly optimizes the phase shifts, the drone's trajectory along with transmit power for the single-user scenario. The proposed designs are also extended to the multi-user multi-eavesdropper system. Eventually, extensive simulation experiments indicate the remarkable benefits brought by our proposed scheme on secrecy performance and the necessity of optimizing phase shifts.

The densification of base station (BS) deployments is driving the evolution of network structures towards heterogeneous ultra-dense networks (UDN), making coordinated multipoint (CoMP) a viable and promising transmission solution. However, the BS cooperation regions formed by applying CoMP in the UDN are small and irregular, which causes frequent handover for mobile users. Different from most existing work that focus on the trigger time of handover, we explore how to choose the appropriate BS cooperation set to reduce handover rate. In this paper, we consider movement aware CoMP handover (MACH). By estimating cell dwell time, a user would be intelligently assigned to macro cell or small cell according to its movement trend. To enhance reliability, we further proposed improved MACH (iMACH) to achieve a trade-off between BSs with long dwell time and the current best performed BS for multipoint cooperation while user moving. Using stochastic geometry method, expressions of coverage probability, handover probability and throughput that characterize performance of the proposed schemes are derived. The numerical results indicate that the theoretical analyses fit the simulation results well and the proposed schemes surpass the existing schemes in terms of the aforementioned metrics, and more intelligent and suitable for ultra-dense scenarios.

Unmanned aerial vehicle (UAV) is recognized as one of the best sensing tools for gathering data in the industrial Internet of things sector. Besides, the flying ad hoc network (FANET) with multiple drones shows significant advantages in complicated task performing of large area. However, as an important part of communication security, key management for FANET currently depends heavily on the base station or infrastructures, which may easily become the attack target or increase the communication overheads of drones. Therefore, we propose a blockchain-based distributed key management scheme for heterogeneous FANET in this article, based on which drones can autonomously distribute cluster keys, update their public/private key pairs, migrate between clusters, and revoke malicious UAVs in a secure way. Security analysis and performance evaluation prove that our scheme can resist against a variety of external and internal attacks, and guarantee lightweight energy consumption for ordinary drones in the network.

The industrial Internet of Things has become the new driving force for the automobile industry, making people's travel increasingly convenient. However, there are still a multitude of challenges that need to be tackled, including but not limited to illegal driver detection, legal driver identification, and driving behavior evaluation. At present, many researchers have attempted to solve issues of illegal driver detection and legal driver identification by using deep learning network, but there are still quite a few limitations in the collection and analysis of driving behavior data. Moreover, the problem of driving behavior evaluation has been paid little attention. Therefore, in this article we conduct a comprehensive study on driving behavior habits and establish a multitask learning (MTL) network to solve the abovementioned problems. First, we collect original data from a real vehicle and extract the driving behavior characteristics. Then, a novel MTL network composed of long short-term memory network, support vector domain description model and feedforward neural network is established, which achieves illegal driver detection, legal driver identification, and driving behavior evaluation. Extensive experiments illustrate that the proposed MTL network not only supports parallel learning to reduce time and space costs, but also has excellent performances and robustness for the three tasks.

Large-scale connectivity support is a critical challenge in the massive machine-type communications scenario. Grant-free random access (RA) is a promising solution because it can reduce severe signaling overhead in contention-based RA procedure. However, there will still be collisions due to the random selection of spectrum resources by the devices. Therefore, we propose a distributed Q-learning-assisted grant-free RA scheme to alleviate the collisions between devices. Considering the characteristic of the machine-type communications devices with bursty traffic, the random packet arrival model is adopted in this paper. In order to cope with the difficulties brought by the random transmission of devices to Q-learning, an action reward based on the active probabilities of devices is designed. In addition, we introduce the power domain nor-orthogonal multiple access to further enhance the number of accessible devices. Numerical results demonstrate the advantages of the proposed scheme from the devices' successful access probability.

The development of vehicular networks spawns various service scenarios, whether safety-related or infotainment-related, making people's life and travel more comfortable and efficient. As an innovative network architecture to realize centralized control, Software-Defined Networking (SDN) is very beneficial to the management of complex vehicular networks. Nevertheless, its security has received little attention. If the core SDN controller is threatened, the entire vehicular network can be seriously affected. To this end, we focus on the vulnerability of SDN controller, successfully perform topology poisoning attacks on four mainstream controllers, analyze the attack impacts hierarchically, and discuss the countermeasures for security improvement. As far as we know, we are the first to explore the security of SDN controller in vehicular networks.

Map information is of crucial importance to ensure the safety and reliability of vehicle, no matter indoor or outdoor, it should reflect the real-time changes of environment. Existing indoor map update mechanisms have several common limitations such as small update range, long cycle, large amount of update data, high cost and poor currency. Therefore, we present a multi-vehicle collaborative indoor map update scheme based on edge-cloud architecture to realize real-time autonomous map updating. This scheme can be achieved through continuous monitoring, tagging, identification, and layering of the environment during driving process. Compared with traditional map update schemes, experimental results show that our scheme can effectively realize the collaborative map update in indoor environment, enhance the map update efficiency, reduce the update delay, and improve the adaptability of vehicles.

Covert communication can prevent an adversary from knowing that a transmission has occurred between two users. In this article, we consider covert wireless communications in an Internet-of-Things (IoT) network with dense deployment, where an IoT device experiences not only the background noise but also the aggregates interference from other Tx devices. Our results show that in a dense IoT network with lower frequency AWGN channels, when the distance between Alice and the adversary Willie da,w= ω(n1/(2α)), Alice can reliably and covertly transmit O(log2√n) bits to Bob in n channel uses. In an IoT network with terahertz (THz) band, covert communication is more difficult because Willie can simply place a receiver in the narrow beam between Alice and Bob in order to detect or block their line-of-sight communications. We demonstrated that covert communication is still possible in this occasion by utilizing the reflection or diffuse scattering from a rough surface. From the physical-layer security perspective, covert communication can enhance the security of IoT network from the bottom layer.

With the development of Internet and communication technologies, various information can easily spread to children via applications (Apps) on Internet-of-Things (IoT) devices (e.g., emerging smart toys, watches, and phones), especially, the Apps on smart phones based on Android. While greatly bringing up convenience for children's lives and studies, these Apps also make illegal and inappropriate contents (such as violence, pornography, gambling, and drug) more accessible to kids, which is harmful to minors' growth. To keep children away from inappropriate contents in applications, previous researches mainly focused on detecting unsuitable videos and advertisements in children applications or designing App maturity rating methods and parental control software. There are few literature that specially investigate the inspection of inappropriate contents in children Android Apps. Toward this end, we propose a novel automatic content inspection and the forensics framework to identify children Android Apps which are not proper for kids under 12. In addition, this framework offers evidence to make users understand why the inspected App is judged as unsuitable. In experiments, we apply this framework on some specially chosen Android Apps which distinctly include inappropriate contents to verify its performance. The results show that it can successfully identify those applications with high precision that reaches 85.7%. Besides, by analyzing the collected children's Android Apps through our framework, we find that 40% of them are identified to be improper, which illustrates the serious issue of unsuitable children Android Apps.

The development of the Internet of Vehicles (IoV) has made people's lives and travels safer, more efficient, and more comfortable. The combination of edge computing and IoV can provide processing and storage capabilities close to vehicles, thus becoming a potential paradigm. At this time, the software-defined networking (SDN) architecture is extremely necessary to realize centralized control and convenient management for complex and dynamic vehicular edge networks. However, as the brain of the SDN architecture, little attention has been paid to the security of the SDN controller. Once the controller is threatened, severe global chaos may happen. Therefore, in this article, we study the attack against the SDN controller, which is the topology poisoning attack. We successfully implement this attack in four mainstream controllers and analyze its impact from multiple levels. To the best of our knowledge, we are the first to study this attack in the vehicular edge network. In addition, in view of the counter-attacks of the existing defence mechanisms, we propose an attack-tolerance scheme based on deep reinforcement learning (DRL) to enhance the vehicular edge network with a certain degree of self-recovery.

With the development of hardware and communication technology, Internet-of-Things (IoT) devices have become more powerful in computing, caching, and communication. The enhancement of equipment makes it possible for IoT devices to act as caching helpers in cache-enabled networks to address the conflict between the limited caching resources and the sharply increasing data traffic. However, how to efficiently utilize the caching resources of IoT devices and edge servers is a challenging problem especially when contents are of different sizes. In this article, we focus on the probabilistic caching for contents of different sizes in heterogeneous IoT networks, aiming at improving the offloading rate for backhaul links. We present a mathematical framework to formulate the hit probability of cache-enabled IoT networks based on stochastic geometry and propose an improved caching probability conversion (CPC) algorithm to derive the closed-form solutions of optimal caching probabilities. Moreover, we further extend the network model with serving capacity constraint, i.e., caching devices can only serve a limited number of users simultaneously and solve the corresponding nonconvex problem by the difference of convex (DC) programming. We validate our analytical framework by the Monte Carlo method and give extensive numerical results to compare the performance of the proposed strategy with that of two existing caching strategies.

Smart grid delivers power with two-way flows of electricity and information with the support of information and communication technologies. Electric vehicles (EVs) with rechargeable batteries can be powered by external sources of electricity from the grid, and thus charging scheduling that guides low-battery EVs to charging services is significant for service quality improvement of EV drivers. The revolution of communications and data analytics driven by massive data in smart grid brings many challenges as well as chances for EV charging scheduling, and how to schedule EV charging in a smart and resilient way has inevitably become a crucial problem. Toward this end, we in this paper leverage the techniques of software defined networking and vehicular edge computing to investigate a joint problem of fast charging station selection and EV route planning. Our objective is to minimize the total overhead from users' perspective, including time and charging fares in the whole process, considering charging availability and electricity price fluctuation. A deep reinforcement learning (DRL) based solution is proposed to determine an optimal charging scheduling policy for low-battery EVs. Besides, in response to dynamic EV charging, we further develop a resilient EV charging strategy based on incremental update, with EV drivers' user experience being well considered. Extensive simulations demonstrate that our proposed DRL-based solution obtains near-optimal EV charging overhead with good adaptivity, and the solution with incremental update achieves much higher computation efficiency than conventional game-theoretical method in dynamic EV charging.

As a powerful tool, the vehicular network has been built to connect human communication and transportation around the world for many years to come. However, with the rapid growth of vehicles, the vehicular network becomes heterogeneous, dynamic, and large scaled, which makes it difficult to meet the strict requirements, such as ultralow latency, high reliability, high security, and massive connections of the next-generation (6G) network. Recently, machine learning (ML) has emerged as a powerful artificial intelligence (AI) technique to make both the vehicle and wireless communication highly efficient and adaptable. Naturally, employing ML into vehicular communication and network becomes a hot topic and is being widely studied in both academia and industry, paving the way for the future intelligentization in 6G vehicular networks. In this article, we provide a survey on various ML techniques applied to communication, networking, and security parts in vehicular networks and envision the ways of enabling AI toward a future 6G vehicular network, including the evolution of intelligent radio (IR), network intelligentization, and self-learning with proactive exploration.

Automobile manufacturers around the world are increasingly deploying Industrial Internet of Things (IIoT) devices in their factories to accompany the Industrial Revolution 4.0. Security and privacy are the main limitations to the integration of Internet of Things (IoT) into industrial processes. Therefore, it is necessary to protect industrial data contained in IIoT devices and keep them confidential. As a step towards this direction, in this paper, we propose a dynamic trust management model suitable for industrial environments. We propose also to change the traditional centralized architecture of IIoT networks in automotive plants into a hybrid architecture based on a set of new industrial relationship rules. The performance evaluation in this work is done in two parts. In the first part, we compare our proposed architecture with the traditional architecture of the plant's IIoT network. The results of this comparison show that our architecture is more suitable to simplify trust management of IIoT devices. In the second part, we demonstrated the ability, the adaptiveness and the resiliency of our proposed trust model against behavioral changes of IIoT nodes in malicious environments.

With the explosive growth of diverse Internet of Things (IoT) applications, mobile edge computing (MEC) has been brought to settle the conflict between computation-intensive applications and resource-limited IoT mobile devices (IMDs). Note that the assistance of unmanned aerial vehicles (UAVs) is of great importance in providing reliable connectivity in areas with limited or no available communication infrastructure. To cope with the surging demands for Big Data processing from UAV-aided IoT applications, combining UAV-aided communication and MEC has been envisioned to be a promising paradigm, which gives rise to the so-called UAV-enhanced edge. In consideration of IMDs' limited battery capacity and UAV energy budget, in this article we study the energy reduction problem in UAV-enhanced edge by smartly making offloading decisions, allocating transmitted bits in both uplink and downlink, as well as designing UAV trajectory. This joint optimization problem is formulated as a mix-integer nonconvex optimization problem, and an alternative optimization algorithm based on block coordinate descent and successive convex approximation techniques is proposed as our solution. Extensive numerical results demonstrate that the overall energy consumption for accomplishing the tasks can be effectively reduced by adopting our joint optimization scheme, and the necessity of task offloading, UAV trajectory design, and bit allocation during transmission is validated.

The industrial Internet of Things (IIoT), as one of the indispensable paradigms of the future network, challenges existing computing network architectures by supporting computational-intensive applications. In the IIoT, resource-rich industrial devices may share idle computing resource to lightweight nodes through device-to-device (D2D) technology, whereas such resource sharing is under social and locality constraints. When industrial devices are carried by human or installed on manned machines, resource sharing more likely occurs among social-trustworthy and locality-adjacent devices. In this article, we propose two social-aware incentive mechanisms for D2D resource sharing in the IIoT, namely one-hop-based social-aware incentive mechanism (OSIM) and relay-based social-aware incentive mechanism (RSIM). In the OSIM, resource-constrained devices bid for offloading tasks using a Vickrey-Clarke-Groves auction, while the RSIM relaxes the locality constraint to two hops to achieve a higher resource utilization ratio. Extensive simulation results show that the performance of the proposed mechanisms can significantly improve the system efficiency while maintaining truthfulness.

Advanced technologies are constantly emerging in automobile industry, which not only provides drivers with a comfortable driving experience, but also enhances the safety of passengers. However, there are still some security issues need to be solved in automobiles, such as automobile driver fingerprinting. At present, identification technologies, such as fingerprint recognition and iris recognition, cannot monitor the driver's identity in real-time manner. Therefore, it is of great significance to design a real-time automobile driver fingerprinting scheme to ensure the safety of people's properties and even lives. Different from previous work concerning automobile driver fingerprinting, in this article, we conduct a comprehensive study on behavioral characteristics of drivers in two vehicles, namely Luxgen U5 SUV and Buick Regal. We exploit the actual data of the controller area network to construct a driver identity comparison library by extracting and processing the feature data. Then, we construct a combined model based on convolutional neural network and support vector domain description to achieve efficient automobile driver fingerprinting. Extensive experimental results show that the proposed driver fingerprinting scheme can dynamically match the driver's identity in real time without affecting the normal driving.

Deep learning has emerged as a popular Artificial Intelligence (AI) technique to make conventional cyber physical systems become intelligent and sustainable. Recently, deep learning has been widely used in the network domain. With the aid of powerful deep neural networks, the communication network can carry out packets forwarding actions intelligently to avoid possible failure and congestion. However, with the high computing cost and process limitation in only the static network scenario, the existing deep learning based network traffic control algorithms cannot satisfy the sustainable requirement of next generation large scale dynamic network. To conquer the existing problems, a novel spatial-temporal value network aided deep learning based intelligent traffic control algorithm referred as ST-DeLTA is proposed in this paper. In ST-DeLTA, the value matrix and spatial temporal training model (ST model) are employed to intelligently extract the spatial as well as temporal features of traffic patterns and make adaptive packets forwarding decision in large scale and dynamic networks. The mathematical analysis gives the computing cost reduction of our proposal, and the computer simulation demonstrates that our proposal has significantly better training and network performance compared with traditional algorithms in terms of training accuracy, transmission throughput, and average packets loss rate.

In this paper, multiple machine learning-enabled solutions are adopted to tackle the challenges of complex sensing model in cooperative spectrum sensing for non-orthogonal multiple access transmission mechanism, including unsupervised learning algorithms (K-Means clustering and Gaussian mixture model) as well as supervised learning algorithms (directed acyclic graph-support vector machine, K-nearest-neighbor and back-propagation neural network). In these solutions, multiple secondary users (SUs) collaborate to perceive the presence of primary users (PUs), and the state of each PU need to be detected precisely. Furthermore, the sensing accuracy is analyzed in detail from the aspects of the number of SUs, the training data volume, the average signal-to-noise ratio of receivers, the ratio of PUs' power coefficients, as well as the training time and test time. Numerical results illustrate the effectiveness of our proposed solutions.

Mobile edge computing (MEC), as a promising technology, provides proximate and prompt computing service for mobile users on various applications. With appropriate incentives, profit-driven users can offload multi-task requests across heterogeneous edge servers. However, such incentive trade lacks a trustworthy platform. Due to the decentralized nature of MEC, trading information from players is easily tampered with by edge servers, which poses a threat to cross-server resource allocation. In this paper, we jointly consider incentives and cross-server resource allocation in blockchain-driven MEC, where the blockchain prevents malicious edge servers from tampering with player information by maintaining a continuous tamper-proof ledger database. Particularly, we propose two double auction mechanisms, namely a double auction mechanism based on breakeven (DAMB) and a more efficient breakeven-free double auction mechanism (BFDA), in which users request multi-task service with claimed bids and edge servers cooperate with each other to serve users. A delegated proof of stake (DPoS) based blockchain technology is leveraged to realize decentralized, untampered, safe and fair resource allocation consensus mechanism. The simulation results show that the proposed DAMB and BFDA can significantly improve the system efficiency of MEC.

Radio link quality is highly influenced by changes in the physical environment. To sustain reliable and efficient data delivery, link quality estimation is essential for Cyber-Physical Systems (CPSs) or Internet of Things (IoT). Network-based link quality estimation methods estimate the link quality by monitoring data transmissions. In a dynamic environment, the accuracy of link quality so estimated may become degraded because the accuracy must be balanced against the overhead of data transmissions. In this work, we propose to incorporate sensor readings available in a CPS/IoT system to augment existing link quality estimation. We call this a Physical-Assisted Channel Estimator (PACE). By analyzing sensor readings that are highly correlated to the link quality, PACE may detect the change of link quality in real-time. Evaluation conducted on a real intelligent parking system shows that compared to existing network-based methods, PACE reacts to persistent disturbances much more quickly without sacrificing robustness to transient fluctuations, and achieves higher accuracy even under a low data transmission rate. With PACE, the data delivery performance of routing protocols can be significantly improved. We expect PACE to be the first milestone towards Physical-Assisted Cyber Systems (PACSs) for fulfilling the vision of environment-aware computing and communication.

The densification of small cells, ultimately towards ultra-dense networks (UDN), makes coordinated multipoint (CoMP) a feasible transmission solution for mobile users. However, CoMP may increase the handover rate, as users move across small and irregular BS cooperation regions. In this paper, we consider movement aware CoMP handover (MACH) in heterogeneous UDNs. Unlike most prior works, which focus on the handover trigger time, we explore the appropriate selection of BS cooperation set to reduce handover rate. By estimating cell dwell time, a user would be intelligently assigned to macro cell or small cell according to its movement trend. Moreover, we achieve a balance between BSs with long dwell time and the current best performed BS for multipoint cooperation while user moving. The performance of the proposed MACH is analyzed in terms of coverage probability and handover probability using stochastic geometry. Through extensive simulations, we show that the analytical results fit well with simulations, and the proposed MACH outperforms the existing works in both handover probability and coverage probability.

The auxiliary functions of modern vehicles have increased the number of attack surfaces. As the most important information exchange channel for vehicles, Controller Area Network (CAN) attracts more and more attention. Due to no encryption and authentication, CAN bus is vulnerable to attack. Thus, the attacker can manipulate the message with serious consequences. Several methods have been proposed to improve the security of vehicles. However, most of them have their own deficiencies. In this paper, we propose an LOF-based attack detection scheme which utilizes the voltage physical characteristics of the CAN frame to judge whether a message is transmitted by a legitimate Electronic Control Unit (ECU). As validated by extensive experiments on two vehicles, our scheme can identify the attacker with an average detection rate of 98.9\%, and the false detection rate is less than 0.5\%. In addition, our scheme can accurately identify attacks from external devices.

In conventional terrestrial cellular networks, mobile devices at the cell edge often suffer from poor channel conditions, and thus unmanned aerial vehicles (UAVs) are introduced in recent years to improve the reliability of communication links. However, with the rapid development of Internet of Things (IoT) technology, the emerging IoT applications have blooming demands for high computation capacity from the resource-constrained IoT mobile devices (IMDs), motivated by which, mobile edge computing has been envisioned as an appealing solution to the resource bottleneck problem of IMDs. In order to cope with poor communication performance and high computation demands of cell-edge IMDs, we in this paper leverage UAV-aided edge computing to collaboratively assist computation offloading, taking account of the limited battery life of both IMDs and the UAV. We investigate a joint optimization problem of collaborative computation offloading, bandwidth portion, bit allocation, and UAV trajectory design, aiming to minimize the weighted energy consumption of IMDs and the UAV. Extensive numerical results validate the necessity of introducing UAV-aided edge computing to cellular networks, and the advantages of our proposed scheme on energy savings.

Nowadays, almost all fields extremely rely on Global Positioning System (GPS) which provides accurate and reliable position as well as time information free of charge. While bringing up convenience, GPS also has a number of flaws which bring people nonnegligible security threats. Existing works primarily focused on GPS signal attacks at the physical layer in GPS systems without considering the road navigation scenario. In this paper, we present a novel attack scheme targeting the road navigation systems by injecting the malicious codes into a map application and falsifying the GPS location remotely. Successful attacks are launched to induce victims into arriving at the wrong destination or passing a malicious place.

Android applications (Apps) have penetrated almost every aspect of our lives, bring users great convenience as well as security concerns. Even though Android system adopts permission mechanism to restrict Apps from accessing important resources of a smartphone, such as telephony, camera and GPS location, users face still significant risk of privacy leakage due to the overprivileged permissions. The overprivileged permission means the extra permission declared by the App but has nothing to do with its function. Unfortunately, there doesn't exist any tool for ordinary users to detect the overprivileged permission of an App, hence most users grant any permission declared by the App, intensifying the risk of private information leakage. Although some previous studies tried to solve the problem of permission overprivilege, their methods are not applicable nowadays because of the progress of App protection technology and the update of Android system. Towards this end, we develop a user-friendly tool based on frequent item set mining for the detection of overprivileged permissions of Android Apps, which is named Droidtector. Droidtector can operate in online or offline mode and users can choose any mode according to their situation. Finally, we run Droidtector on 1000 Apps crawled from Google Play and find that 479 of them are overprivileged, accounting for about 48% of all the sample Apps.

With the continuous expansion and deepening of Intelligent and Connected Vehicles (ICVs), advanced technology continues to emerge, making ICVs more intelligent to provide services for drivers and protect them. It is noticed that the emergence of almost all advanced technologies is based on the use of automotive data. Using automobile data can not only restore the current driving state, but also realize the identification of the driver. Different from previous works about driver identification, we don't use any manufacturer's Controller Area Network (CAN) protocol to parse vehicle's data and don't use any external sensor data. We first rely on the broadcast feature of the CAN bus, and use the automotive diagnostic tool to get all real-time data from the On-Board Diagnostic (OBD-II) port. Then, we use Feature scaling and Principal Component Analysis (PCA) algorithm to preprocess the data. Finally, we use k-Nearest Neighbor (k-NN) algorithm and Naive Bayes algorithm combined with voting mechanism to successfully identify the driver's identity. The experimental results show that the recognition rate of ten drivers is 100%.

Mobile edge computing (MEC) enables a distributed computing environment closer to mobile devices (MDs) and substantially reduces the response time for a MD computing task. However, lightweight servers may be incapable of keeping up with all the tasks from MDs due to the limited resources. Therefore, how to effectively allocate resources of edge servers for profit-driven multi-task users is a key issue in MEC. In this paper, we study the cross-server resource allocation scheme in MEC from the perspective of network economics. Because of the supply-demand relationship between the edge servers providing services and the MDs requesting the services, we regard the resource allocation as an auction problem in the network economics. In particular, we propose a multi-task resource allocation algorithm based on double auction (MADA) to maximize the system efficiency. The simulation results indicate that MADA can efficiently allocate resources while maintaining the economic properties of individual rationality, truthfulness and weakly balanced budget.

Heterogeneous caching network consisting of edge servers and caching helpers is proposed to meet the huge amount of mobile traffic and provide low-latency caching services for users. The problem of caching redundancy caused by the overlapping of edge servers and caching helpers is neglected by most researchers. In this paper, we propose a spatially cooperative caching strategy to avoid the caching redundancy and improve the hit probability of the heterogeneous caching network. We analyze the performance of this caching strategy by using stochastic geometry, derive the optimal caching probability and make a comparison with available caching strategies. The numerical results show that, the proposed caching strategy has a better performance under the normal parameter settings.

With the evolution of wireless networks, new techniques including massive multiple-input multiple- output (MIMO) and millimeter wave are adopted to satisfy the demands for diversified services. However, it has been verified by field tests that the traditional wide sense stationary assumption for wireless channel does not hold anymore. As a result, traditional channel state information (CSI) acquisition methods, especially the statistical CSI acquisition, cannot be applied straightforwardly in such a circumstance. In this paper, we propose a pre-processing method for channel sensing in the non-stationary environment. Specifically, the data sampled from channel training is treated as a channel image, where the statistical channel state is represented by gray-scale. Then the computer vision technique, specifically, the edge detection method, is used on the channel image to detect the homogeneous sub-regions. Within each sub-region, the channel is statistically stationary, and then the CSI can be obtained by existing methods. It is verified by simulation results that, the proposed method can help to improve the CSI acquisition accuracy in the non- stationary environment.

The prompt development of wireless communication network and emerging technologies such as Internet of Things (IoT) and 5G have increased the number of various mobile devices (MDs). In order to enlarge the capacity of the system and meet the high computation demands of MDs, the integration of ultra-dense heterogeneous networks (UDN) and mobile edge computing (MEC) is proposed as a promising paradigm. However, when massively deploying edge servers in UDN scenario, the operating expense reduction has become an essential issue to be solved, which can be achieved by computation offloading decision-making optimization and edge servers' computing resource configuration. In consideration of the complicated state information and ever-changing environment in UDN, applying reinforcement learning (RL) to the dynamical systems is envisioned as an effective way. Toward this end, we combine the deep learning with RL and propose a deep Qnetwork based method to address this high-dimensional problem. The experimental results demonstrate the superior performance of our proposed scheme on reducing the processing delay and enhancing the computing resource utilization.

Crowdsourcing, as an essential part in Internet of Vehicles (IoV), can provide vehicles with various functions such as road condition monitoring and path planning. The prevalence and heterogeneity of crowdsourcing devices, although enabling various emerging applications in IoV, makes it challenging to yield intelligent and flexible incentive and management framework, while ensuring optimal choice for all entities. Note that artificial intelligence (AI) algorithms could automatically select the significant features in the underlying data and globally find optimal solutions even for non-convex object functions. In this paper, we propose an AI-driven incentive scheme using a deep learning based reverse auction scheme, in order to achieve revenue-optimal, dominant-strategy incentive compatible objectives. The effectiveness of the proposed framework has been verified through extensive simulations.

Mobile Edge Computing (MEC) is one of the promising solutions for delay-sensitive emerging applications. There are multiple available options to provide wireless access and computing service for users in the dense deployment of MEC-enabled small base stations (SBSs). It makes the mobility management (MM) more complicated. To this, we study the MM problem during the users' movement in the ultra-dense edge computing scenario to minimize the delay with handover cost as a penalty term of the offloading tasks. In this paper, we propose an online learning optimization scheme based on reinforcement learning to optimize handover decision-making by predicting the upcoming future information. Simulation results show that the proposed scheme can effectively reduce the average delay of users' computing tasks and the handover rate compared with the available conventional handover schemes.

The ongoing increasing number of mobile devices (MDs) with innovative applications yields unprecedented demands for user experience and network capacity expansion. The combination of ultra-dense network (UDN) and mobile edge computing (MEC) has been envisioned as a promising futuristic technology. It can remarkably improve the capacity of system and extend cloud-computing capabilities to proximate edge servers, by which the growing computation requirements of MDs will be met. However, what is not addressed well is how to optimize the configuration of computing resources with varying computation demands of MDs being satisfied, aiming at maximizing the operating earnings of operators while decreasing the cost of MDs. Considering the diverse computation demands in different regions and variational computing resources of edge servers, it is hard to solve this problem by traditional methods. To address this issue, an effective solution is generating an optimal computing resource configuration strategy and task offloading profile in time-varying UDN scenarios. Toward this end, a deep Q-network based scheme is proposed to achieve maximum long-term weighted network utility in such ever-changing environments. Simulation results validate the significant performance improvement of our scheme in weighted network utility and task offloading compared to conventional game-theoretical solution.

In IoT, a flexible and trustworthy access control framework is of significance to ensure the security of lightweight IoT devices. The conventional centralized access control framework is no longer fit for the open and large-scale IoT environments. In this paper, we propose an attribute-based distributed access control framework (ADAC) for IoT using blockchain technology. The attributes, such as manufacturer and object-specified attribute, are considered in the proposed ADAC for more fine-grained access control in the open and lightweight IoT devices. Particularly, we design a smart contract system, which includes a subject contract (SC), an object contract (OC), an access control contract (ACC) and multiple policy contracts (PCs), to manage and access attributes of IoT devices for distributed and trustworthy access control (DTAC). SC and OC are responsible for managing subject attribute and object attribute information, respectively. PCs are used to manage access control policies. ACC performs authorization judgment by accessing attributes and policies. Finally, a case study is performed to demonstrate the workflow and show that ADAC could achieve fine-grained and flexible access control for IoT.

To meet the surging demands on network throughput and spectrum resources arising with billions of Internet-of-Things mobile devices (IMDs), ultradense networks are envisioned to be a promising technology, which gives rise to the so-called ultradense Internet-of-Things (IoT) networks. Meanwhile, with the constant emergence of new IoT applications, the conflict between computing-intensive applications and resource-constrained IMDs is increasingly prominent. By offloading computing-intensive tasks to the edge servers in close proximity, mobile-edge computing is expected as an effective solution to address this issue. However, computation offloading research in ultradense IoT networks is still scarce until now. Toward this end, we provide this paper to study the energy-aware task offloading problem with multiple edge servers in ultradense IoT networks, where diverse kinds of computation tasks are randomly requested by the IMDs and the computing resources at the edge servers change dynamically. An iterative searching-based task offloading scheme is proposed as our solution, which jointly optimizes task offloading, computational frequency scaling, and transmit power allocation. Extensive numerical results demonstrate the superior performance of conducting task offloading among multiple edge servers, and corroborate the advantages of our scheme over existing works which either fixed computational frequency and transmit power, or neglected the impact of the IMDs' residual battery.

Smart city is coming into urban life with the development of information and communication technologies. As an integral part of the smart city, massive heterogeneous Internet of Things devices face enormous challenges in multiple accessing and signal processing. In this paper, we propose an innovative and flexible scheduling method under the channel field multiple access framework to meet these requirements. In particular, artificial intelligence (AI) technology is adopted by the proposed solution. It is proved that the proposed AI-based scheduling method can obtain better performance than the existing methods by low complexity, especially, such performance gain becomes more significant as the number of devices increases.

Due to the advantages of large area coverage, low capital cost and fast deployment, unmanned aerial vehicles (UAVs) are believed to play a key role in the emerging Internet of Things (IoT). In this paper, we first develop an effective analytical approach to characterize the properties of UAV-assisted communications over a large number of IoT devices by introducing average channel access delay for packets that can be successfully transmitted. Specifically, an IoT device is said to establish a full transmission to a UAV, only if its time duration covered by the UAV is greater than the specified average channel access delay. Then, we present a stochastic geometry based mathematical framework to analyze the coverage probability and average achievable rate for a multi-UAV assisted downlink network. Different from previous works: 1) we consider a flexible multi-UAV deployment strategy connecting IoT devices to the Internet via sky-haul links to the satellite, where the altitudes of the UAVs can be adjusted to fulfill the requirements of various IoT applications and 2) we derive analytical expressions, in particular integral form, for the coverage probability and average achievable rate. Our results indicate that the developed framework is very helpful for network designers to efficiently determine the optimal network parameters at which the optimum IoT system performances can be achieved.

Recently, the newly emerging wireless powered communication network (WPCN) has drawn significant interests, where the network nodes are powered by the energy harvested from the radio-frequency (RF) signal. This paper focuses on the widely studied WPCN, where one hybrid sink (H-sink) coordinates the wireless energy/information transmissions to/from a set of one-hop nodes powered by the harvested RF energy only, and aims to minimize the network-throughput constrained H-sink's energy provision (EP). Specifically, we investigate the performance of two important MAC protocols: time-division multiple access (TDMA) and non-orthogonal multiple access (NOMA). For both the TDMA-based WPCN (T-WPCN) and NOMA-based WPCN (N-WPCN), we first formulate the EP minimization problems as the non-linear optimization problems, then transform them into convex problems, and finally propose an efficient algorithm, which jointly uses the golden-section search and bisection search methods to determine the optimal time allocation of H-sink's energy transfer and each node's information transmission as well as the optimal H-sink's transmit power. Furthermore, for the scenarios where the circuit power is negligible, we first prove that the optimal H-sink's transmit power is the maximum allowable value, then prove theoretically that the NOMA and TDMA achieve the same EP, and also present a more efficient algorithm for the EP minimization problem. Simulation results demonstrate that the TDMA outperforms NOMA when the circuit power is non-negligible because the circuit energy consumption of NOMA accounts for a large percentage of the total energy consumption.

In order to meet the ever-increasing demands on computational and spectrum resources in the era of 5G and Internet of Things (IoT), mobile-edge computing (MEC) and ultra-dense heterogeneous network (UDN) have been envisioned as two promising technologies, which gives rise to the so-called ultra-dense edge computing. Note that existing works on task offloading for ultra-dense edge computing mostly considered simple task offloading scenarios, ignoring the random request for types of computation tasks from the mobile devices (MDs) and the random arrival of the tasks at the edge servers. Toward this end, we provide this paper to study the multi-user task offloading problem in ultra-dense edge computing with multiple types of tasks requested by the MDs. To minimize the MDs' energy consumption and thus prolong their battery lifetime, task offloading, computation frequency scaling, and transmit power allocation are jointly optimized in this paper. After that, the problem is divided into two subproblems, i.e., local energy minimization, and joint task offloading and transmit power allocation. A game-theoretical joint offloading scheme is proposed as our solution. Extensive numerical results corroborate the superior performance of our proposed scheme rather than those with single edge server, fixed computation frequency and transmit power at the MDs.

As the mainstream of future automotive industry, Intelligent and Connected Vehicles (ICVs) have versatile connections between themselves and external devices, although able to provide more conveniences and better driving experiences for the users, also bring forward lots of intrusion portals for the malicious attackers. It is noticed that the final step of almost all attacks in available works, must be at the in-vehicle network, i.e., the CAN bus. Actually, the characteristics of CAN data, specifically, the broadcast transmission on the CAN bus, as well as the unencrypted authentication strategy make the CAN bus vulnerable to various attacks. Different from previous works about CAN bus, we present in this paper a comprehensive study on the in-vehicle network of a modern ICV (a Luxgen SUV), from the perspective of the vehicle auxiliary system. We first clarify the complicated communication process among the smart key, Body Control Module (BCM), and Key Control Unit (KCU), identify the loophole among the Luxgen auxiliary system, and then introduce a practical method to utilize this vulnerability. Finally, extensive experiments have been conducted on the Luxgen SUV where a wireless diagnostic equipment was utilized to achieve successful remote invasion in road tests.

The general tendency that cellular networks evolve toward small cells and ultimately ultra-dense networks (UDNs) will bring paradigm shift in network design. Cooperating multiple BSs for the service of a user, referred to as coordinated multipoint (CoMP), becomes feasible in UDNs to provide high quality of experience (QoE) for mobile users. To the best of our knowledge, we are the first to consider CoMP-based uplink transmission in UDNs. A simple CoMP uplink transmission scheme is firstly proposed to enable each user to transmit to multiple cooperating BSs with power control. The performance of the proposed scheme is analyzed using tools of stochastic geometry, in terms of outage probability and ergodic capacity, considering the effect of BS intensity, power control, and the number of cooperating nodes. Extensive simulation has been done and it is found that the proposed scheme could significantly improve outage probability and ergodic capacity of mobile users, as compared with non-CoMP scheme, especially in UDNs. Indications are also provided on network settings and parameter selection in CoMP-based uplink transmission in UDNs.

To meet the mobile devices' surging demands for throughput and computation resources in 5G networks, heterogeneous networks (HetNets) and multi-access edge computing (MEC) are expected to be two key distinct but complementary technologies. By offloading the computation tasks of the mobile devices (MDs) to the nearby MEC servers at the edge of radio access networks, MEC can largely augment the MDs' computation resources and battery lifetime. However, existing research on mobile-edge computation offloading only focused on multi-user single-MEC scenarios and little work can be found on designing computation offloading schemes for the case of multi-user multi-MEC. Toward this end, we investigate the problem of collaborative mobile-edge computation offloading in 5G HetNets and propose a game-theoretical computation offloading scheme. Numerical results corroborate that our collaborative computation offloading scheme for multiple MEC servers can not only reduce the overall computation overhead efficiently, but also achieve a Nash equilibrium in a finite number of steps.

Covert communication can prevent the opponent from knowing that a wireless communication has occurred. If only the additive white Gaussian noise (AWGN) channels and ambient noise are taken into consideration, a square root law was obtained and the result shows that the privacy rate approaches zero asymptotically. In this paper, we consider the covert communication in large-scale wireless networks, where the transmitters form a stationary Poisson point process, and Alice wishes to communicate covertly to Bob without being detected by warden Dave. In this scenario, Bob and Dave not only experience the ambient noise, but also the aggregate interference simultaneously. Although the interference sources are not in collusion with Alice, and Bob's noise increases as well, our results show that, the measurement uncertainty of Dave will increase along with the increase of interference, and interference can indeed improve the performance of covert communication.

Inter-segment gateway selection, as a critical issue for data delivery from ground segment to satellite via air network segment, is of great challenges for the design of space-air-ground converged networks (SAGCNs), especially for the transmission energy optimization due to the presence of unreliable or lossy wireless link in air network. To the best of our knowledge, it is an entirely new problem, since existing works on gateway selection mainly focused on one single network segment and gave no consideration to other segments. It is also noted that, we are the first to study the gateway selection problem with the objective of minimizing the transmission energy. Toward this end, in this paper, we formulate the issue of inter-segment gateway selection as a constrained optimization problem and propose two algorithms, i.e., an optimal enumeration algorithm (OEA) and a simulated annealing based algorithm (SOA). Extensive experiments based on different link error rate and relative velocity settings have been conducted and as validated by our numerical results, OEA can obtain an optimal result with extremely high computational complexity and SOA is able to achieve a near-optimal solution with much lower computational complexity.

Mobile edge computing (MEC), as an emerging technique, brings computing resource near to the user end, and thus offers prompt and high-bandwidth service. In MEC, edge servers typically provide their limited computing resource to an appropriate set of users and expect to be rewarded for that, while users pay for the service. In this paper, We utilize the network economics to solve the problem of resource allocation in MEC to maximize system efficiency. We consider a multi-user and multi-server scenario with locality-awareness, i.e., an edge server can only serve multiple MDs in the vicinity, and propose a single-round double auction scheme based on breakeven in MEC (SDAB), which is proved to be individual rationality and truthful. The simulation results indicate that SDAB can significantly improve the system efficiency of MEC as compared with the existing works, while maintaining the economic property of budget balance.

As a new application of modern traffic, vehicle networks need to deal with more and more information which makes it urgent for messages to access the channel in an orderly and reasonable way. However, most of the existing message access mechanisms used static queue allocation, which cannot meet the current requirement of vehicle networks. In light of this, we propose an improved Enhanced Distributed Channel Access (EDCA) protocol of IEEE 802.11p called DQ-EDCA by introducing the dynamic priority queue management mechanism. DQ-EDCA can dynamically adjust priority queue allocation for data frames by prioritizing different message into a reasonable queue according to its access type and the real-time monitoring and pre-estimating the transmission delay, which can achieve the effects of sending important and urgent messages in a timely manner and preventing low real-time messages from being suspended for a long time. Finally, we evaluate the performance of throughput and transmission delay of the proposed scheme compared to other available schemes, and results show that the proposed scheme can improve the efficiency of the network significantly.

Heart rate is an important physiological sign that can reflect the human body burden and exercise intensity. Due to the sensor faults, energy saving requirement and other factors, heart rate prediction become very important for the monitoring of human body during exercise. Most of the available researches using single model for prediction of heart rate have a bad performance with a greater prediction error. In light of this, we formalize a multi-step prediction scheme for heart rate during running using the Bayesian combined predictor. We first construct a Neural network predictor and a Linear regression predictor as the basic prediction models by parameter learning with training data, and then construct a Bayesian combined predictor by training the weights of the basic predictors to give the multi-step prediction process of heart rate during running. Finally, we evaluate the performance of the proposed scheme using actual measurement data of wearable devices of several runners, and the simulation results tell us that the proposed scheme can achieve better performance of prediction error compared to the available schemes.

The emergence of massive Internet of Things (IoT) mobile devices (MDs) and the deployment of ultradense 5G cells have promoted the evolution of IoT toward ultradense IoT networks. In order to meet the diverse quality-of-service and quality of experience demands from the ever-increasing IoT applications, the ultradense IoT networks face unprecedented challenges. Among them, a fundamental one is how to address the conflict between the resource-hungry IoT mobile applications and the resource-constrained IoT MDs. By offloading the IoT MDs’ computation tasks to the edge servers deployed at the radio access infrastructures, including macro base station (MBS) and small cells, mobile-edge computation offloading (MECO) provides us a promising solution. However, note that available MECO research mostly focused on single-tier base station scenario and computation offloading between the MDs and the edge server connected to the MBS. Little works can be found on performing MECO in ultradense IoT networks, i.e., a multiuser ultradense edge server scenario. Toward this end, we provide this paper to study the MECO problem in ultradense IoT networks, and propose a two-tier game-theoretic greedy offloading scheme as our solution. Extensive numerical results corroborate the superior performance of conducting computation offloading among multiple edge servers in ultradense IoT networks.

Energy harvesting techniques extend the lifetime of Internet of Things (IoT), whereas yield an unprecedented paradigm shift in network design. Base stations (BSs), being powered by self-contained energy harvesting modules, may keep OFF during recharging, leading to super-frequent handovers for nodes and high network dynamics. Under such dynamics, multiple cooperating BSs for the service of a “smart thing,” referred to as coordinated multipoint (CoMP), become a feasible solution by effectively improving communication reliability. To the best of knowledge, this is the first work to consider CoMP uplink transmission to alleviate outage caused by energy harvesting of BSs in IoT. A simple CoMP uplink transmission scheme is proposed for each node of IoT to transmit to two cooperating BSs in a K-tier heterogeneous network. The performance of the proposed scheme is analyzed using tools of stochastic geometry, in terms of availability and average rate, considering the effect of energy capacity, energy charging rate, in a K-tier heterogeneous network. Performance evaluation through extensive simulations are conducted and it is shown that the simulation results fit well with the analytical ones. The performance of the proposed scheme is then compared with that of the non-CoMP scheme, and it is found that the proposed scheme can significantly improve availability and average rate.

The widespread use of Internet of Things (IoT), especially smart wearables, will play an important role in improving the quality of medical care, bringing convenience for patients and improving the management level of hospitals. However, due to the limitation of communication protocols, there exists non unified architecture that can connect all intelligent things in smart hospitals, which is made possible by the emergence of the Narrowband IoT (NB-IoT). In light of this, we propose an architecture to connect intelligent things in smart hospitals based on NB-IoT, and introduce edge computing to deal with the requirement of latency in medical process. As a case study, we develop an infusion monitoring system to monitor the real-time drop rate and the volume of remaining drug during the intravenous infusion. Finally, we discuss the challenges and future directions for building a smart hospital by connecting intelligent things.

With the development of Internet of Things and intelligent transport system, the intelligent connected vehicle (ICV) represents the future direction of the vehicle industry. Due to the open wireless medium, high speed mobility and vulnerability to environmental impact, vehicle data faults are inevitable, which may lead to traffic jam or even accident threatening the life of the driver and passengers. At present, there are few studies for fault detection and repairing of ICV while using traditional methods directly for ICV has a low accuracy. In this paper, we propose a threshold-based fault detection and repairing scheme using a dynamic Bayesian network (DBN) model, which can obtain the temporal and spatial correlations of vehicle data for accurate real-time or history fault detection and repairing. In addition, we give an algorithm of how to select the threshold to achieve the best effect by history data before fault detection and repairing process. Finally, simulation results show that the proposed scheme possesses a good fault detection and repairing accuracy as well as a low false alarm rate compared to other available methods.

Given the highly dynamic traffic loads of mobile Internet of Things (IoT) devices and their stringent quality-ofservice requirements, i.e., access delay particularly, as well as the heterogeneous infrastructures among IoT networks, it is a nontrivial task to efficiently deploy cloudlets among large number of access points (APs) in IoT networks, especially for the access delay and network reliability, since different placement schemes would produce various network performances. To combat this issue, we are motivated to investigate in details the optimal placement of cloudlets to minimize the average access delay by applying software-defined networking (SDN) techniques to provide flexible and programmable management for cloudlets deployment in IoT networks with considering the complicated queuing process at numerous SDN-based APs. An enumerationbased optimal placement algorithm (EOPA) is first proposed as benchmark. Then we propose a ranking-based near-optimal placement algorithm (RNOPA) which is able to dynamically adapt to mobile IoT devices and their traffic loads, by treating each AP as a single server queue and adopting an efficient ranking mechanism. As corroborated by extensive simulation results, RNOPA reports access delay very close to that of EOPA. Note that RNOPA outperforms the famous K-medians clustering algorithm (KMCA) in both of average cloudlet access delay and reliability, while at the cost of a much lower computational complexity than KMCA.

Leveraging the concept of software-defined network (SDN), the integration of terrestrial 5G and satellite networks brings us lots of benefits. The placement problem of controllers and satellite gateways is of fundamental importance for design of such SDN-enabled integrated network, especially, for the network reliability and latency, since different placement schemes would produce various network performances. To the best of our knowledge, it is an entirely new problem. Toward this end, in this paper, we first explore the satellite gateway placement problem to obtain the minimum average latency. A simulated annealing based approximate solution (SAA), is developed for this problem, which is able to achieve a near-optimal latency. Based on the analysis of latency, we further investigate a more challenging problem, i.e., the joint placement of controllers and gateways, for the maximum network reliability while satisfying the latency constraint. A simulated annealing and clustering hybrid algorithm (SACA) is proposed to solve this problem. Extensive experiments based on real world online network topologies have been conducted and as validated by our numerical results, enumeration algorithms are able to produce optimal results but having extremely long running time, while SAA and SACA can achieve approximate optimal performances with much lower computational complexity.

Mobile edge computing (MEC) yields significant paradigm shift in industrial Internet of things (IIoT), by bringing resource-rich data center near to the lightweight IIoT mobile devices (MDs). In MEC, resource allocation and network economics need to be jointly addressed to maximize system efficiency and incentivize price-driven agents, whereas this joint problem is under the locality constraints, i.e., an edge server can only serve multiple IIoT MDs in the vicinity constrained by its limited computing resource. In this paper, we investigate the joint problem of network economics and resource allocation in MEC where IIoT MDs request offloading with claimed bids and edge servers provide their limited computing service with ask prices. Particularly, we propose two double auction schemes with dynamic pricing in MEC, namely a breakeven-based double auction (BDA) and a more efficient dynamic pricing based double auction (DPDA), to determine the matched pairs between IIoT MDs and edge servers, as well as the pricing mechanisms for high system efficiency, under the locality constraints. Through theoretical analysis, both algorithms are proved to be budget-balanced, individual profit, system efficient, and truthful. Extensive simulations have been conducted to evaluate the performance of the proposed algorithms and the simulation results indicate that the proposed DPDA and BDA can significantly improve the system efficiency of MEC in IIoT.

The ongoing densification of small cells, i.e., ultimately evolving toward ultra-dense networks (UDNs), yields an unprecedented paradigm shift in network design by bringing the base stations (BSs) and users to approximate the spatial scale and number magnitude. To address the super-frequent handovers of mobile users in such UDNs, the coordinated multipoint (CoMP) then becomes a feasible solution by effectively improving communication reliability. In this paper, we consider CoMP-based uplink transmission in heterogeneous UDNs. As our first step, a CoMP uplink transmission scheme is proposed for each user to transmit to multiple (2 to M) cooperating BSs with channel inversion power control in a multi-channel scenario. The performance of the proposed uplink CoMP is analyzed in the context of UDNs using stochastic geometry, in terms of outage probability and ergodic capacity, considering the effects of BS intensity, power control, number of cooperating nodes, and network tiers, first in a single-tier network, and later extended to a K-tier heterogeneous network. Performance evaluation through extensive simulations is conducted and it is shown that the simulation results fit well with the analytical ones. The performance of the proposed scheme is then compared with that of the non-CoMP scheme, and it is found that the proposed scheme can significantly improve outage probability and ergodic capacity of mobile users.

In order to address the conflict between resource- hungry mobile applications and resource-constrained mobile devices (MDs), mobile-edge computing (MEC), which offers cloud computing capabilities at the edge of networks in close proximity to the MDs, is envisioned to be a promising approach. However, existing mobile-edge computation offloading studies only took the resource allocation between the MDs and MEC servers into consideration, and ignored the resource allocation between MEC and centralized cloud computing servers. Moreover, current MEC Hosted Networks mostly adopt the networking technology integrating cellular and core networks, which has the shortcomings of single networking mode, high congestion, high latency and energy consumption. Toward this end, we provide in this paper an architecture of centralized cloud and distributed MEC over hybrid fiber-wireless network, which has the features of supporting diverse network techniques, easy expansibility, high capacity and reliability, low latency and energy consumption. The problem of cloud-MEC collaborative computation offloading is studied and an approximation collaborative computation offloading scheme is proposed as our solution. Numerical results corroborate the energy efficiency of our proposed collaborative scheme.

Mobile edge computing (MEC), as an extension of the cloud computing paradigm to the edge network, is a promising solution to provide resource-intensive and time-critical applications to mobile users. It overcomes some obstacles of traditional mobile cloud computing by offering ultra-short latency and less core network traffic. This paper proposes a new framework based on the architecture of MEC to deliver cloud services to the edge. We introduce enumeration based optimal placement algorithm (EOPA) and divide-and- conquer based near-optimal placement algorithm (DCNOPA) to attain minimal data traffic by distributing virtual machine replica copies (VRCs) of applications to the edge network. Simulation results show that compared to the famous K-medians clustering algorithm (KMCA), the performance of DCNOPA is much closer to that of EOPA with lower computational complexity. Furthermore, we investigate the optimal number of VRCs within a given limitation of benefit-to-cost ratio.

With the advance of wireless technologies, the rapid mobile data traffic growth will lead to severe network resource consumption and exceptionally long latency to access services, especially for cloud-based applications. To tackle these issues, Fog Radio Access Network (F-RAN) is recently emerged for next generation cellular networks. F-RAN is considered as an extension of the cloud computing paradigm to the edge of the network and a highly virtualized platform that provides computing, storage, and network services for mobile devices. However, how to appropriately place virtual machines (VMs) into fog nodes in F-RAN systems is very challenging, and will significantly affect the bandwidth consumption of backhaul links. Thus this paper studies the replication-based VM placement problem, and aims at minimizing the total backhaul traffic generated by VM migrations and data transmissions. We observe that the VM placement operation should not be frequently executed and practically considers the problem in a long term aspect. Then we propose a heuristic algorithm to solve the problem. The simulation results agree our observation and show that compared with a greedy approach and an optimal algorithm, the proposed algorithm demonstrates with favorable results for the overall backhaul network usage.

This paper analyzes the information theoretic secrecy performance in finite-area wireless networks based on a stochastic geometry framework. Unlike most prior works, which explored the physical layer security with a large number of transmitters, legitimate receivers and eavesdroppers in infinite regions, we consider a finite downlink wireless network composing of a transmitter, a legitimate receiver and several eavesdroppers. The legitimate receiver attempts to receive confidential data from the transmitter in the presence of the eavesdroppers. We present the probabilistic characteristics of the achievable secrecy rates and average secrecy rates in both disk regions and regular L-sided convex polygon regions. As shown by extensive numerical results, the proposed framework could be leveraged to efficiently analyze the secrecy performance of finite-area networks, and give insights for network designers on how to achieve good secrecy performance in finite-area networks.

In the process of cities informatization, the smart cities use of information technology and service system to handle of urban problems, improving people's lives. We study the problem of indoor signal coverage in smart cities. Indoor signal coverage inside large buildings is important to provide guaranteed communication quality for indoor mobile users. Specifically, traditional cellular coverage is deficit for indoor communications in the following three aspects: the emergence of the mobile signals are weak, or even blind at indoor environment, due to the complicated interior building structure; in some area with high traffic, traditional network is far from sufficient to meet the capacity needs of users; radio frequency interference problem between floors seriously a2642ects the stability of mobile signals and communication quality. Previous research mainly focuses on the outdoor macro cell coverage, and fails to meet the surge demand of indoor communication demand. Indoor coverage system is therefore demanded to solve the issue. In this paper, we investigate on the planning of an indoor distribution system. Based on the analysis of an indoor coverage system, an TD-LTE system is developed to provide indoor signal coverage. We implement our design in a real-world scenario. Using realworld experiments, we verifies the performance of the proposed system.

The technological evolution of the Internet of Things (IoT) and of the related devices, and their increasing diffusion, give mobile network providers the opportunity to come up with more advanced and innovative services. Among these are the so-called context-aware services: highly customizable services tailored to the user’s preferences and needs, which rely on real-time knowledge of the user’s surroundings, without requiring complex configuration on the user’s part. Examples of context-aware services are user profile changes that result from context changes, user proximity-based advertising, or media content tagging, etc. Applications based on the information acquired, processed and distributed by the IoT can answer the following questions about the objects’ surroundings: what, who, where, when, why, and how.

In this paper, we present a stochastic geometry based framework to analyze the coverage probability and ergodic rate with different channel allocations for device-to-device (D2D) communications. Different from existing works, we assume there are two different kinds of users, cellular users and D2D users, in the muti-channel uplink cellular network. Specifically, cellular users can upload data to the nearest base station (BS) directly through cellular channels. However, D2D users must upload data to their own D2D relays through D2D channels and then the D2D relays communicate with the nearest BS through cellular channels. There is no overlapping between cellular channels and D2D channels. Each cellular user and D2D relay adopt the channel inversion power control with maximum transmit power limit. Our results indicate that the framework can help to find the optimal channel allocation to achieve the optimal system performance in terms of coverage probability and average rate.

Due to limited resource, noise and unreliable link, data loss and sensor faults are common in medical body sensor networks (BSN). Most available works used data reconstruction to improve data quality in traditional wireless sensor networks (WSN). However, existing data reconstruction schemes using redundant information of WSN can not provide a satisfactory accuracy for BSN. In light of this, a Bayesian network based data reconstruction scheme is formalized in this paper, which rebuilds data using conditional probabilities of body sensor readings to recover missing data and sensor faults, rather than the redundant information collected from a large number of sensors. Experiments on extensive online data set show that the performance of our scheme outperforms all available data reconstruction schemes.

This paper investigates cooperative jamming for secure connection in wireless networks. A cooperative jamming strategy is proposed to thwart eavesdroppers anywhere in the network, even if they are located close to the source or the destination. The basic idea is to defeat eavesdroppers by a divide and conquer strategy, and exploit the helpful interference from the source and the destination to circumvent the nearby eavesdropper problem. Analysis and simulation results reveal that this strategy can enhance the secure connection probability and can tolerate any number of independent eavesdroppers provided that the number of legitimate nodes satisfies certain condition.

This paper investigates divide-and-conquer based cooperative jamming for physical-layer security enhancement in the presence of multiple eavesdroppers. Different from previous works, we consider a scenario where the eavesdroppers can be located anywhere inside the communication region of the source, no location information of the eavesdroppers is available and no constraint on the number of eavesdroppers is presupposed. The basic idea is to transmit the message in multiple rounds and exploit the helpful interference from the source and the destination to jam the eavesdroppers in close proximity. Stochastic geometry based analytic results as well as Monte Carlo simulations are presented to illustrate the achievable secrecy performances.

In this paper, a new framework based on the tool of stochastic geometry is proposed to analyze the coverage probability and average rate with different channel allocations in a D2D multi-channel downlink cellular network. We consider a network with two types of users: cellular users and D2D users, where each D2D user has its own D2D relay and can only receive data from the relay. Cellular users and D2D relays can establish cellular links with the nearest base station through cellular channels only if the SINR is above a threshold. D2D users communicate with their own D2D relays to form D2D links through specific D2D channels. As validated by extensive numerical results, we are able to find the optimal channel allocation for D2D communications, to achieve the optimal system performance in terms of coverage probability and average rate.

Internet of Things (IoT), as an ecosystem that interconnects physical objects with telecommunication networks, introduces a tighter connection between cyber space and physical reality. It has been widely recognized that our daily lives will be fundamentally improved with various IoT applications in environmental surveillance, healthcare, agriculture, public security, supply chain management, etc. Building on these successes and recent advances in information and communication technologies, IoT will develop toward largescale and ubiquitous directions, imposing increasingly higher requirements on safety, reliability, security, energy efficiency, performance, robustness, and cost efficiency. This introduces many unprecedented challenging issues across the disciplines of embedded system, manufacturing, telecommunication, computing, sensing, software engineering, data management, and analysis. Emerging and advanced communication technologies introduce many new opportunities to tackle the challenging issues in large-scale ubiquitous IoT.

In this paper, we study the outage probability of device-to-device (D2D)-communication-enabled cellular networks from a general threshold-based perspective. Specifically, a mobile user equipment (TIE) transmits in D2D mode if the received signal strength (RSS) from the nearest base station (BS) is less than a specified threshold β ≥ 0; otherwise, it connects to the nearest BS and transmits in cellular mode. The RSS-threshold-based setting is general in the sense that by varying β from β = 0 to β = ∞, the network accordingly evolves from a traditional cellular network (including only cellular mode) toward a wireless ad hoc network (including only D2D mode). We provide a unified framework to analyze the downlink outage probability in a multichannel environment with Rayleigh fading, where the spatial distributions of BSs and TIEs are well explicitly accounted for by utilizing stochastic geometry. We derive closed-form expressions for the outage probability of a generic TIE and that in both cellular mode and D2D mode and quantify the performance gains in outage probability that can be obtained by allowing such RSS-thresholdbased D2D communications. We show that increasing the number of channels, although able to support more cellular TIEs, may result in an increase of outage probability in the D2D-enabled cellular network. The corresponding condition and reason are also identified by applying our framework.

With the emergence of cloud storage, enabling cloud clients to securely store and efficiently retrieve ciphertext is a fundamental issue in cloud computing as data outsourcing can greatly ease heavy computation and management burden locally. Unfortunately, two challenging issues (i.e., data security and privacy) dramatically impede the adaption and practicability of cloud storage due to honest-but-curious cloud service provider (CSP). Furthermore, data encryption mechanism seriously makes the information retrieval over ciphertext extremely difficult. Besides, dealing with single CSP is predicted to become less popular with cloud customers for fear of risks of single-point failure threats and potential malicious insiders. To this end, we propose two efficient keyword search over encrypted data in multi-cloud setting schemes which exploit Identity-Based Encryption (IBE) and Key-Policy Attribute-Based Encryption (KP-ABE), respectively. Formal security analysis proves that our schemes can guarantee data privacy and reliability. As a further contribution, experimental results over real-world dataset show that our proposed schemes are feasible and efficient in practical applications. Copyright © 2016 John Wiley & Sons, Ltd.

Device-to-device (D2D) communication, which utilizes mobile devices located within close proximity for direct connection and data exchange, holds great promise for improving energy and spectrum efficiency of mobile multimedia in 5G networks. It has been observed that most available D2D-based works—considered only the single-cell scenario with a single BS. Such scenario-based schemes, although tractable and able to illustrate the relationship between D2D links and cellular links, failed to take into account the distribution of surrounding base stations and user equipments (UEs), as well as the accumulated interference from ongoing transmissions in other cells. Furthermore, the single-tier network with one BS considered in available works is far from the real 5G scenario in which multi-tier BSs are heterogeneously distributed among the whole network area. In light of such observations, we present in this article a model for network coverage probability and average rate analysis in a D2D communication overlaying a two-tier downlink cellular network, where nineteen macro base stations (MBSs) with pico base stations (PBSs) placed at the end point of macro cell (hexagons) borders are employed according to the 3GPP specifications, and mobile users are spatially distributed according to the homogeneous Poisson Point Process model. Each mobile UE is able to establish a D2D link with adjacent UEs or connect to a nearby macro or pico base station. Stochastic geometric analysis is adopted to characterize the intratier interference distribution within the MBS-tier, PBS-tier, and D2D-tier based on which network coverage probability and per-user average rate are derived with a careful consideration of important issues such as threshold value, SINR value, user density, content hit rate, spectrum allocation, and cell coverage range. Our results show that, even for the overlaying case, D2D communication can significantly improve network coverage probability and per-user average downlink rate. Another finding is that the frequency allocation for D2D communications should be carefully tuned according to network settings, which may result in totally different varying behaviors for the per-user average rate.

There has been surging research interest in utilizing mobile phones for information relaying in postdisaster areas lacking infrastructure support. A common complication for such postdisaster ad hoc communication is how to efficiently control the forwarding behaviors of relay nodes so as to save their energy consumption and buffer usage while simultaneously guaranteeing the desired delivery performance. Different from previous studies, we consider in this paper an explicit probabilistic stopping mechanism, where a relay node that is actively disseminating a message will stop spreading the message with a certain probability, after meeting another node having already received the message. Besides developing a two-dimensional Markov chain framework to characterize the message propagation process, we also derive the average time required for completion of message propagation, the probability distribution, the expectation, the variance of the fraction of nodes finally receiving the message, etc. Our results reveal that the explicit probabilistic stopping mechanism is very desirable for postdisaster communication, even being able to guarantee a majority of nodes in final message reception. What is more, the developed framework provides us a deeper understanding on how network parameters may affect these important performance metrics, which further enables network designers to accordingly tune controllable parameters.

In this paper, we present a model for average rate analysis in a D2D communication overlaying two-tier downlink cellular network. Each mobile UE is able to establish D2D link with adjacent UEs or connect to a nearby macro or pico base station. Stochastic geometry analysis is adopted to characterize the medium contentions within macro and pico cells, as well as the D2D pair distributions, based on which closed-form per user average rate is derived with a careful consideration of the important issues such as frequency allocation, UE density, content hit rate, and cell coverage radius. Our results show that even for the overlaying case, D2D communication can significantly improve the per user average rate. Another finding is that the frequency allocation for D2D pairs should be carefully tuned according to network settings, which may result in totally different varying behaviors for the per user average rate.

Based on the tool of stochastic geometry, we present in this paper a framework for analyzing the coverage probability and ergodic rate in a D2D overlaying multi-channel downlink cellular network. Different from previous works, 1) we consider a flexible new scheme for mobile UEs to select operation mode individually, under which a mobile UE decides to establish a cellular link (with a BS) or a D2D link (with a neighboring UE) based on the pilot signal strength received from its nearest BS; 2) we allow a mobile UE which is located far from BSs to connect to a nearby BS via another intermediate UE in a two-hop manner. Our results indicate that the developed framework is very helpful for network designers to efficiently determine the optimal network parameters at which the optimum system performance can be achieved. Furthermore, as corroborated by extensive numerical results, enabling the D2D link based two-hop connection can significantly improve the network coverage performance, especially for the low SIR regime.

Different from previous works, in this paper, we adopt D2D communication as a routing extension to traditional cellular uplinks thus enabling a two-hop route between a user and the serving BS via a D2D relay. Specifically, a BS establishes a cellular link with a mobile user only if the pilot signal strength received from the user is above a specified threshold; otherwise, the user may establish a D2D link with a neighboring user and connect to a nearby BS in a two-hop manner. We present a stochastic geometry based framework to analyze the coverage probability and average rate in such a two-hop multi-channel uplink cellular network where mobile users adopt the fractional channel inversion power control with maximum transmit power limit. As validated by extensive numerical results, the developed framework enables network designers to efficiently determine the optimal control parameters so as to achieve the optimum system performance. Our results show that employing D2D link based two-hop connection can significantly improve both the network coverage and average rate for uplink traffic.

We propose a Bayesian network based method for the fault diagnosis problem of medical body sensor networks used to collect physiological signs to monitor the health of patients. We formalize a Bayesian network to describe the body sensor network considering both the spatial and temporal correlation in measurements at different sensors. Then we give the theoretical analysis of the fault detection, false alarm of this method, and the error probability after executing the fault diagnosis algorithm. Finally, Experiments carried out on synthetic medical datasets by injecting faults into real medical datasets show that the simulation performance matches the theoretical analysis closely, and the proposed approach possesses a good detection accuracy with a low false alarm rate.

How to minimize the energy dissipation and extend the lifetime of wireless sensor networks (WSNs) is still an active research topic nowadays. Hierarchical routing based on node clustering is an effective method, while using mobile elements (MEs) to gather data can prevent huge energy consumption of the sensors from long-distance transmission. Considering that both methods have pros and cons, this paper presents a hybrid approach, called Node Density based Clustering and Mobile Collection (NDCM), to combine the hierarchical routing and ME data collection in WSNs. A number of Cluster Heads (CHs) first gather information from the cluster members and then the ME visits these CHs to collect data. A new CH selection scheme based on the node density is proposed. Thus, a node at the center of an area where nodes are densely deployed is more likely to be a CH, which can improve the efficiency of both intra-cluster routing and ME data collection. We also introduce a simple Random Clustering and Mobile Collection (RCM) scheme according to which a number of CHs are selected randomly throughout the network. In addition, the nodes which are covered by the radio range of the ME, called Virtual Heads (VHs), can also send/relay packets directly to the ME. The different mobility schemes are compared through extensive simulations and the results show that the proposed hybrid NDCM scheme leads to remarkable improvement in network lifetime and convenient trade off between the network energy saving and packet latency.

In this paper, we explore the throughput-delay tradeoff in a mobile ad hoc network (MANET) operating under the practical reference point group mobility model and also a general setting of node moving speed. In particular, we consider a MANET with unit area and n nodes being divided evenly into Θ(nα) groups, α ∈ [0, 1], where the center of each group moves according to a random direction model with speed of no more than υ ∈ [0, 1]. We determine the regions of per-node throughput and average delay and their tradeoffs that can be achieved (in order sense) in such a network. For the regime of v = 0, we first prove that the per-node throughput capacity is Θ(n-α/2) and then develop a routing scheme to achieve this capacity, resulting in an average delay of Θ(max{n1/2, n1-α}) for any α ∈ [0, 1]. Regarding the regime of v > 0, we prove that the per-node throughput capacity can be improved to Θ(1), which is achievable by adopting a new routing scheme with an average delay of Θ(max{n1-α, nα/2/v}) for υ = o(1) and Θ(n) for v = Θ(1). The results in this paper help us to have a deep understanding on the fundamental performance scaling laws and also enable an efficient throughput-delay tradeoff to be achieved in MANETs with correlated mobility.

Reference Point Group Mobility (RPGM) has been a practical mobility model used to efficiently capture the potential correlation among mobile nodes in many important applications. In this paper, we explore the throughput-delay tradeoff in a mobile ad hoc network (MANET) operating under the RPGM model and also a general setting of node moving speed. In particular, we consider a MANET with unit area and n nodes being divided evenly into Θ(nα) groups, a Є [0,1], where the center of each group moves according to a random direction model with speed no more than v e [0,1]. We determine the regions of per node throughput, average delay and their tradeoffs that can be achieved (in order sense) in such a network. For the regime of v =0, we first prove that the per node throughput capacity is Θ(n−α/2), and then develop a routing scheme to achieve this capacity, resulting an average delay of Θ (max1/2, n1-α) for any α Є [0,1]. Regarding the regime of v > 0, we prove that the per node throughput capacity there can be improved to Θ(1), which is achievable by adopting a new routing scheme with an average delay of Θ(max{n1-α, na/2/v}) for v = o(l) and Θ(n) for v = Θ(1). The results in this paper help us to have a deep understanding on the fundamental performance scaling laws and also enable an efficient throughput-delay tradeoff to be achieved in MANETs with correlated mobility.

In this paper, we focus on medical body sensor networks collecting physiological signs to monitor the health of patients. We propose a Hidden Markov Model (HMM) based method for fault diagnosis of ECG sensor data. We firstly verify the Markov property of heart rate sequences by medical datasets. Then we use the Baum-Welch algorithm to estimate parameters of HMMs by history training data, and the Viterbi algorithm to determine whether the new sensor reading is fault. Finally, we do experiments on both real and synthetic medical datasets to study the performance of our method. The result shows that the proposed approach possesses a good detection accuracy with a low false alarm rate.

In recent years, HETerogeneous NETworks (HET-NET) arises as a promising network technique to manage a large number of mobile devices. By using the networks having different coverage size in the HETNET, it enables to increase the network capacity drastically. However, sometimes variations in user distribution causes inhomogeneous traffic load among the networks having different coverage size in the HETNET. On the other hand, Device-to-Device (D2D) communication technologies have attracted much attention as another solution to increase the network capacity. The direct communication between user devices creates flexible networks. Thus, we focus on utilizing D2D communication technologies in HETNET to avoid the inhomogeneous load among the networks having different coverage size. In this paper, a traffic detouring method is proposed and the advantage of the proposed method is analyzed with some mathematical expressions. Additionally, numerical results demonstrate the effectiveness of our proposal.

In the last decade, there has been a tremendous increase in both the number of mobile devices and the consumer demand for mobile data communication. As a general network architecture, ad hoc mobile networks are expected to offload a large amount of mobile traffic in lots of promising application scenarios. However, how to achieve a good balance between delivery performances (like delivery delay and delivery probability) and network resource consumptions (like power energy and buffer storage) remains an extremely challenging problem. In this paper, we focus on the general k-hop relay routing, which covers a lot of popular routing schemes as special cases, such as the direct transmission (k = 1), the two-hop relay algorithm (k = 2), and the epidemic routing (k = n - 1). We first develop absorbing continuous-time Markov chain models to characterize the complicated message delivery process under the general k-hop relay routing, and then conduct Markovian analysis to derive all the above important performance metrics. Finally, extensive numerical results are presented to illustrate the achievable delivery performances under the general k-hop relay and the possible performance trade-offs there.

Available works either explore the order sense capacity scaling laws or derive closed-form throughput results for mobile ad hoc networks (MANETs) where a transmitter randomly probes only once a neighboring node for possible transmission. Obviously, such single probing strategy may result in a significant waste of the precious transmission opportunities in highly dynamic MANETs since the randomly selected node may already get the packets that the transmitter hopes to deliver. In this paper, we consider a two-hop relay MANET where each transmitter may conduct multiple rounds of probing so as to identify a possible receiver. We first develop closed-form expressions for per node throughput capacity in such probing-based network, with a careful consideration of the time cost taken to probe for an eligible receiver in each time slot. Extensive numerical results are further presented to explore the possible maximum per node throughput capacity, the corresponding optimum setting of probing round limit, and also their relationships with the network control parameters, like the probing time limit, the redundancy limit and the number of users, etc.

A common complication for routing in mobile sensor networks is how to efficiently control the forwarding behaviors of relay nodes so as to save their energy consumption and buffer usage while simultaneously satisfy the specified delivery performance requirement. Available works either assign each message with a lifetime, a maximum number of copies, or a sequence number, or flush special feedback information among the whole network after the message reception. In the former case, a relay node has no idea of the message reception status and will carry and forward the message until meeting the destination, while the latter could efficiently notify all relay nodes but demands extra communication resources. Different from previous studies, we consider in this paper an explicit probabilistic stopping mechanism for relay nodes. Under such mechanism, a relay node that is actively disseminating a message will stop spreading the message with a certain probability, after meeting another node having already received the message. We first develop a two-dimensional Markov chain framework to characterize the highly complicated dynamics until the end of message propagation, then conduct Markovian analysis to derive the associated important performance metrics, including the average time required for the completion of message propagation, the expectation and variance of the fraction of nodes finally receiving the message, and the probability that a given number of nodes end up with the message, etc. Finally, extensive numerical results are provided to analytically explore how the network parameter settings may affect these performance metrics.

A significant amount of works has been done to model the delivery performances in Intermittently Connected Mobile Networks (ICMNs). However, available works considered either the two-hop relay routing or the epidemic routing, which actually represent two extreme cases of the message delivery process in ICMNs. In this paper, we take one step ahead and focus on the three-hop relay routing where each message travels at most three hops to reach the destination. Under such a scheme, besides that the source can send a message copy to each node it meets, a relay which receives the message directly from the source can also replicate the message to other nodes, while a relay node which receives the message from another relay can only forward the message to the destination. In order to characterize the complicated message delivery process under the three-hop relay routing, a multidimensional Markov chain theoretical framework is developed. Based on the Markov chain framework and block matrix theory, closed-form expressions are further derived for the important message delivery delay and delivery cost. Extensive numerical results are also provided to explore the achievable delivery performances under the three-hop relay.

This paper focuses on the delivery probability performance in a two-hop relay mobile ad hoc network (MANET) with erasure coding. Available works in this line either considered a simple extreme case of achieving the delivery probability 1, or assumed a simple traffic pattern with only one source-destination pair, or studied a very special MANET scenario (i.e., the sparsely distributed MANET) by assuming that whenever two nodes meet together they can transmit to each other. Obviously, such models cannot be applied for an accurate delivery probability analysis in the general MANETs where the interference, medium contention and traffic contention issues are of significant importance. In this paper, a general finite-state absorbing Markov chain theoretical framework is first developed to model the complicated message spreading process in the challenging MANETs. Based on the theoretical framework, closed-form expressions are further derived for the corresponding message delivery probability under any given message lifetime and message size, where all the above important issues in MANETs are carefully incorporated into analysis. As verified through extensive simulation studies, the new framework can be used to accurately predict the message delivery probability behavior and characterize its relationship with the message size, replication factor and node density there.

This paper studies the exact per node throughput capacity of a MANET, where the transmission power of each node can be controlled to adapt to a specified transmission range υ and a generalized two-hop relay with limited packet redundancy f is adopted for packet routing. Based on the concept of automatic feedback control and the Markov chain model, we first develop a general theoretical framework to fully depict the complicated packet delivery process in the challenging MANET. With the help of the framework, we are then able to derive the exact per node throughput capacity for a fixed setting of both υ and f. Based on the new throughput result, we further explore the optimal throughput capacity for any f but a fixed υ and also determine the corresponding optimum setting of f to achieve it. This result helps us to understand how such optimal capacity varies with υ (and thus transmission power) and to find the maximum possible throughput capacity of such a network for any f and υ. Interestingly, our results show that increasing the transmission power of the nodes improves the capacity, which is the same as that proved in fixed networks.

A major obstacle stunting the application of mobile ad hoc networks (MANETs) is the lack of a general throughput capacity theory for such networks. Available works in this area mainly focused on exploring the order sense scaling laws of throughput capacity in MANETs with omnidirectional antennas or that of static ad hoc networks with directional antennas. Although the order sense results can help us to understand the general scaling behaviors, it tells us little about the exact throughput capacity. Another limitation of available works is that the impact of transmission power constraint on the throughput capacity is largely neglected. In most MANET applications, however, the mobile nodes are usually powered by batteries and have limited transmission power. In this paper, we study the exact throughput capacity of MANETs with directional antenna and transmission power constraint, where a generalized twohop relay algorithm with limited packet redundancy is adopted for packet routing. For given transmission power constraint, we first develop a model to map the omnidirectional transmission range to that of the directional one. We then explore the exact throughput capacity under directional transmission and group-based scheduling. Finally, numerical studies are provided to demonstrate the efficiency of these models and validate our theoretical results.

This paper focuses on the per node throughput capacity in mobile ad hoc networks (MANETs) with the general group-based two-hop relay algorithm. Under such an algorithm with packet redundancy limit f and group size g (2HR-(f, g) for short), each packet is delivered to at most f distinct relay nodes and can be accepted by its destination if it is a fresh packet to the destination and also it is among g packets of the group the destination is currently requesting. A general Markov chain-based theoretical framework is first developed to characterize the complicated packet delivery process in the challenging MANET environment. With the help of the new theoretical framework, closed-form expressions are further derived for the throughput capacity of the 2HR-(f, g) algorithm, from which one can easily recover the available throughput capacity results by proper settings of the redundancy limit f and group size g.

Due to their simplicity and efficiency, the two-hop relay algorithm and its variants serve as a class of attractive routing schemes for mobile ad hoc networks (MANETs). With the available two-hop relay schemes, a node, whenever getting an opportunity for transmission, randomly probes only once a neighbor node for the possible transmission. It is notable that such single probing strategy, although simple, may result in a significant waste of the precious transmission opportunities in highly dynamic MANETs. To alleviate such limitation for a more efficient utilization of limited wireless bandwidth, this paper explores a more general probing-based two-hop relay algorithm with limited packet redundancy. In such an algorithm with probing round limit τ and packet redundancy limit f, each transmitter node is allowed to conduct up to τ rounds of probing for identifying a possible receiver and each packet can be delivered to at most f distinct relays. A general theoretical framework is further developed to help us understand that under different setting of τ and f, how we can benefit from multiple probings in terms of the per node throughput capacity.

A lot of works have been dedicated towards understanding the relationship between throughput capacity and packet delay in mobile ad hoc networks (MANETs). However, nearly all these works either assume a localized transmission range, or report the relationship between throughput capacity and packet delay only in terms of the number of users. It remains largely unknown for such a fundamental relationship in terms of other network parameters, like the packet redundancy and node transmission range. As a first step towards this end, in this paper we derive closed-from expressions for throughput capacity and delivery delay under a general setting of node transmission range and also a generalized two-hop relay with limited packet redundancy. Extensive numerical results are further provided to explore how throughput capacity varies with delivery delay in terms of various network parameters, such as the number of users, the packet redundancy limit, and the node transmission range, etc.

A lot of work has been done to model and analyze the performances of two-hop relay algorithm and its variants. However, the delivery ratio, especially under limited message lifetime, has been largely neglected in literature, which is not only of significant importance for delay sensitive applications (where a message beyond some delay limit will typically be dropped) but also of practical interests for general MANET scenarios (where mobile nodes are usually both energy-constrained and buffer storage-limited). In this paper, we study the delivery ratio of a generalized two-hop relay with limited message lifetime and redundancy. In particular, a finite-state absorbing Markov chain-based theoretical framework is first developed to model the complicated message delivery process under the considered relay algorithm. Closed-form expressions are then derived for the message delivery ratio under any given message lifetime, where the important interference, medium contention and traffic contention issues are carefully incorporated into analysis. Finally, extensive simulations are conducted to validate the theoretical framework and corresponding delivery ratio results.

The lack of a general capacity theory on mobile ad hoc networks (MANETs) is still a challenging roadblock stunting the application of such networks. The available works on this line mainly focus on deriving order sense results, which are helpful for us to explore the general scaling laws of throughput capacity but tell us little about the exact achievable throughput. This paper studies the exact per node throughput capacity of a MANET, where the transmission power of each node can be controlled to adapt to a specified transmission range v and a generalized two-hop relay with limited packet redundancy f is adopted for packet routing. Based on the concept of automatic feedback control and the Markov chain model, we first develop a general theoretical framework to fully depict the complicated packet delivery process in the challenging MANET environment. With the help of the framework, we are then able to derive the exact per node throughput capacity for a fixed setting of both v and f. Based on the new throughput result, we further explore the optimal throughput capacity for any f but a fixed v and also determine the corresponding optimum setting of f to achieve it. This result helps us to understand how such optimal capacity varies with v (and thus transmission power) and to find the maximum possible throughput capacity of such a network for any f and v. Surprisingly, our results here indicate that usually such maximum throughput capacity can not be achieved through the local transmission, a fact different from what is generally believed in literature.

Many important real networks can be modeled as intermittently connected mobile networks (ICMNs), like the vehicular ad hoc networks, wildlife tracking and habitat monitoring sensor networks, military networks, etc. However, the fundamental performance limits of ICMNs are still largely unknown so far. This paper explores the capability of these networks to support multicast traffic, where each source node desires to send packets to k distinct destinations and all nodes move according to the generalized hybrid random walk mobility model. We show how the network capacity and related delay/delay jitter for supporting multicast in such ICMNs are scaling with the basic network parameters under three transmission protocols: one-hop relay, two-hop relay without packet redundancy and two-hop relay with packet redundancy.

One of the challenging roadblocks stunting the development and commercialization of mobile ad hoc networks (MANETs), is the lack of a thorough understanding of the fundamental performance limits in MANETs. Distinguished from available works which mainly focused on deriving order sense scaling laws of the delay performance in MANETs and usually assumed a localized transmission range, this paper examines the MANET packet delay from a much more detailed perspective. Specifically, we assume for each node a general transmission power control such that the transmission range can be flexibly adapted and adopt a generalized two-hop relay with limited packet redundancy for packet routing. For a tagged traffic flow in the MANET, we first develop a theoretical framework based on two correlated FIFO queues to fully characterize the complicated packet delivery process. Then for any feasible traffic input rate there, we derive closed-form expressions for the corresponding expected end-to-end packet delay. Extensive simulations are further conducted to validate our theoretical results.

This paper examines the optimal forwarding problem in mobile ad hoc networks (MANETs) based on a generalized two-hop relay with limited packet redundancy f (f-cast) for packet routing. We formulate such problem as a forwarding game, where each node i individually decides a probability τi(i.e., a strategy) to deliver out its own traffic and helps to forward other traffic with probability 1-τi, τi∈[0,1], while its payoff is the achievable throughput capacity of its own traffic. We derive closed-form result for the per node throughput capacity (i.e., payoff function) when all nodes play the symmetric strategy profiles, identify all the possible Nash equilibria of the forwarding game, and prove that there exists a Nash equilibrium strategy profile that is strictly Pareto optimal. Finally, for any symmetric profile, we explore the possible maximum per node throughput capacity and determine the corresponding optimal setting of f to achieve it.

The available two-hop relay protocols with out-of-order or strictly in-order reception cannot provide a flexible control for the packet delivery delay, which may significantly limit their applications to the future mobile ad hoc networks (MANETs) with different delay requirements. This paper extends the conventional two-hop relay and proposes a general group-based two-hop relay algorithm with packet redundancy. In such an algorithm with packet redundancy limit$f$and group size$g$(2HR-$(f,g)$for short), each packet is delivered to at most$f$distinct relay nodes and can be accepted by its destination if it is a fresh packet to the destination and also it is among$g$packets of the group the destination is currently requesting. The 2HR-$(f,g)$covers the available two-hop relay protocols as special cases, like the in-order reception ones$(f\geq 1,g=1)$, the out-of-order reception ones with redundancy$(f>1,g=\infty)$, or without redundancy$(f=1,g=\infty)$. A Markov chain-based theoretical framework is further developed to analyze how the mean value and variance of packet delivery delay vary with the parameters$f$and$g$, where the important medium contention, interference, and traffic contention issues are carefully incorporated into the analysis. Extensive simulation and theoretical results are provided to illustrate the performance of the 2HR-$(f,g)$algorithm and the corresponding theoretical framework, which indicate that the theoretical framework is efficient in delay analysis and the new 2HR-$(f,g)$algorithm actually enables both the mean value and variance of packet delivery delay to be flexibly controlled in a large region.

Due to their simplicity and efficiency, the two-hop relay algorithm and its variants serve as a class of attractive routing schemes for mobile ad hoc networks (MANETs). With the available two-hop relay schemes, a node, whenever getting an opportunity for transmission, randomly probes only once a neighbor node for the possible transmission. It is notable that such single probing strategy, although simple, may result in a significant waste of the precious transmission opportunities in highly dynamic MANETs. To alleviate such limitation for a more efficient utilization of limited wireless bandwidth, this paper proposes a more general probing-based two-hop relay algorithm with limited packet redundancy. In such an algorithm with probing round limit τ and packet redundancy limit f, each transmitter is allowed to conduct up to τ rounds of probing for identifying a possible receiver and each packet can be delivered to at most f distinct relays. A general theoretical framework is further developed to help us understand that under different setting of τ and f, how we can benefit from multiple probings in terms of the per node throughput capacity and the expected end-to-end packet delay.

Among the "store-carry-forward" kind of protocols, the two-hop relay and its variants have become a class of attractive routing protocols for the mobile ad hoc networks (MANETs) due to its efficiency and simplicity. This paper focuses on the performance modeling for two-hop relay with erasure coding, a promising technique for improving the delay performance of conventional two-hop relay with simple replication. A general Markov chain-based theoretical framework is first developed to model the complicated message delivery process in such a network, based on which not only the mean value but also the variance of message delivery delay are derived analytically. The important medium contention, interference and traffic contention issues are carefully incorporated into our analysis, so the new theoretical framework can be used to precisely predicate the message delivery delay performance of two-hop relay with erasure coding, as verified by extensive simulation results.

Two-hop relay is a class of attractive routing protocols for mobile ad hoc networks (MANETs) due to its efficiency and simplicity. This paper extends the conventional two-hop relay and proposes a more general group-based two-hop relay algorithm with redundancy. In such an algorithm with redundancy f and group size g (2HR-(f, g) for short), each packet is delivered to at most f distinct relay nodes and can be accepted by its destination if it is among the group of g packets the destination is currently requesting. The 2HR-(f, g) covers the available two-hop relay protocols as special cases, like the in-order protocols (f ≥ 1, g = 1), the out-of-order protocols with redundancy (f >; 1, g = ∞) or without redundancy (f = 1, g = ∞), and it enables a more flexible control of packet delivery process to be made in the challenging MANET environment. A general theoretical framework is further developed to explore how the control parameters f and g affect the expected packet delivery delay in an 2HR-(f, g) MANET, where the important medium contention, interference and traffic contention issues are carefully incorporated into the analysis. Finally, extensive simulation and theoretical results are provided to demonstrate the efficiency of the 2HR-(f, g) scheme and the corresponding theoretical framework.

The 2-hop relay algorithm and its variants have been attractive for ad hoc mobile networks, because they are simple yet efficient, and more importantly, they enable the capacity and delay to be studied analytically. This paper considers the 2-hop relay with f-cast (2HR-f) under i.i.d. mobility model, a general 2-hop relay algorithm that allows one packet to be delivered to at most f distinct relay nodes. The 2HR-f algorithm covers the available 2-hop relay algorithms (f = 1,√n) as special cases. Closed-form analytical models rather than order sense ones are developed for the 2HR-f algorithm with a careful consideration of important medium contention and queuing delay issues, which enable an accurate delay and capacity analysis to be performed for ad hoc mobile networks employing 2HR-f. Based on our models and some typical settings of f (say, f = 1,√n), one can easily derive the corresponding order sense results.

The two-hop relay algorithm and its variants have been attractive for ad hoc mobile networks, because they are simple yet efficient, and more importantly, they enable the capacity and delay to be studied analytically. This paper considers a general two-hop relay with f-cast (2HR-f), where each packet is delivered to at most f distinct relay nodes and should be received in order at its destination. We derive the closed-form theoretical models rather than order sense ones for the 2HR-f algorithm with a careful consideration of the important interference, medium contention, traffic contention and queuing delay issues, which enable an accurate delay and capacity analysis to be performed for an ad hoc mobile network employing the 2HR-f. Based on our models, one can directly get the corresponding order sense results. Extensive simulation studies are also conducted to demonstrate the efficiency of these new models.