The emerging Internet-of-Things (IoT) network is expected to connect billions of devices, creating intelligent environments and transforming our daily lives. However, due to large-scale deployment, the broadcast nature of wireless channels, and the limited energy and computation capabilities of IoT devices, such networks are highly vulnerable to cyber-attacks and malware infiltration. In the presence of eavesdroppers, establishing a giant connection where devices can securely and privately communicate is particularly challenging. This motivates our study of a physical layer security (PLS) approach that introduces artificial noise (AN) into transmissions. Using tools from percolation theory, we analyze the probability of legitimate devices forming a giant component in both signal-to-noise ratio (SNR)- and signal-to-interference-plus-noise ratio (SINR)-based connectivity and intrinsically secure communication graphs. We show that by carefully designing the AN power fraction, legitimate devices can be protected from eavesdropping regardless of the eavesdroppers’ density and capabilities, leading to a significant reduction in the critical percolation density. We also prove the existence of an optimal AN fraction that minimizes this critical density, which remains consistent across both SNR and SINR graph models. Furthermore, we characterize the percolation behavior in SINR-based graphs and reveal that, while AN reduces the percolation threshold, efficient interference cancellation is necessary to counteract the reduced data transmission power and maintain robust connectivity.

The Digital Twin (DT) system has become a new paradigm to empower Unmanned Aerial Vehicles (UAV) networks for low-altitude applications, such as parcel delivery. However, due to high computing complexity, traditional DT technology might confront challenges to imitating highly dynamic UAVs in large-scale parcel delivery scenarios. It causes a negative influence on low-latency and high-accuracy delivery. To address the issue, we propose a terminal-edge cooperative multi-scale DT framework. It can perform a cooperative DT implementation with a cross-layer computing resource orchestration based on a multi-scale imitation manner. Explicitly, we propose a graph matching network based DT algorithm to run macro-scale DTs at the edge. It can assist edge UAVs in exploring feasible delivery associations among UAV groups and parcel clusters based on information on UAV topology and parcel destinations for a high successful delivery ratio. We then propose a Competitive and Cooperative Reinforcement Learning (CCRL) based DT algorithm to implement micro-scale DTs at the terminal. It can enable UAVs to implement low-latency delivery by optimizing delivery paths with low energy consumption. We demonstrate the effectiveness of the proposed framework with verifications under multiple metrics. The results show that our solution provides a real-time UAV delivery performance, with up to 94% successful delivery ratio, under a low system latency compared to the state-of-the-art solutions.

Large Language Models (LLMs) have been increasingly applied to intelligent vehicular systems for tasks such as scene understanding, intent reasoning, and natural language interaction. However, their inference demands exceed onboard processing capabilities, making low-latency on-vehicle inference impractical. Although edge computing can partially offload computation, the prolonged nature of LLM inference often causes execution to exceed the residence time of vehicles within edge coverage areas, leading to frequent service interruption. To address these challenges, we propose a collaborative spatiotemporal resource orchestration architecture for LLM inference in vehicular-edge networks (CoInfer). CoInfer exploits the intrinsic decomposability of LLM inference by modeling each request as a Directed Acyclic Graph (DAG) of interdependent subtasks, which are then scheduled, migrated, and aggregated along the road network to preserve end-to-end inference continuity. To improve latency and resource efficiency, CoInfer integrates multi-agent reinforcement learning for coarse-grained task orchestration with a reactive scheduler for fine-grained resource adaptation, forming a closed-loop service optimization under dynamic resource conditions. The simulation results demonstrate that CoInfer achieves a task success ratio of up to 96.0% and reduces the end-to-end inference latency by 35.7% compared to representative baselines.

While Internet of Vehicles (IoV) networks enhance road safety and driving intelligence through collaborative sensing, reliable data sharing remains a critical challenge due to severe inter-vehicle interference and scarce wireless bandwidth. Conventional orthogonal multiple access often imposes bottlenecks in multiuser semantic communication by failing to accommodate the high heterogeneity of semantic representations among different users. To overcome this, we propose a new context-aware hybrid access framework for Cooperative Semantic Communication (CoSC), which strategically aligns access modes with semantic attributes to maximize fusion efficiency. The fundamental design of CoSC decouples transmission by broadcasting critical semantic information via reliable orthogonal modes to serve as semantic context, while fusion features are aggregated through high-capacity concurrent modes. Within this framework, we first design a context-attentive cooperative channel encoder to align semantic features from different vehicles under the shared context. Next, a semantic fusion-oriented precoding algorithm resolves potential feature conflicts in concurrent channels. Finally, to recover semantic signals from hybrid access modes with a unified receiver, we design a Bayesian-guided score-diffusion algorithm to reconstruct semantic information adaptively from heterogeneous signals. Extensive simulations show that CoSC supports scalable multiuser fusion, yielding a 34.2% improvement in average precision on the OPV2V dataset relative to state-of-the-art methods.

Unmanned Aerial Vehicles (UAVs) are extensively used to collect sensing data from Internet of Things (IoT) networks. Facing with widely distributed Sensor Nodes (SNs), multiple UAVs collaborate to accomplish coverage access of SNs. Age of Information (Aol) is utilized to evaluate the timeliness of sensing data. However, SNs encounter a trade-off between remaining inactive to extend their lifespan and data generation to ensure task sensing requirements. Limited coverage capabilities lead to UAVs frequently traveling between SNs, consuming energy and reducing collection efficiency. Delays in information updates arise from the unsynchronized between UAV cruising times and the dynamic sensing frequencies of SNs. To tackle these challenges, we propose an multi-UAV collaboration framework that integrates coordinated data generation from SNs with UAV data collection. Additionally, we design an AoI and Energy Consumption Minimization (AECM) algorithm to jointly optimize sensing frequencies, UAV state switching, trajectory planning, and SN association. Simulation results demonstrate that our algorithm reduces energy consumption by 30 % while achieving a significantly lower AoI compared to traditional methods.

Unmanned Aerial Vehicles (UAVs) have been an attractive device to serve target tracking scenarios, such as hit- and-run tracking and border patrol. Nonetheless, it is difficult to implement real-time UAV topology optimization due to communication resources of UAVs and random moving speeds of targets. To address the problem, we propose a Digital Twins-assisted topology optimization framework (TopoDT). We formulate a UAV topology optimization model based on Lyapunov theory in the framework. The model is decoupled into two subproblems using our proposed TopoDT topology optimization algorithm. The DT model can allow UAVs to implement neighbor selection to construct and optimize small-scale local topologies for tracking low-speed moving targets. In addition, it allows UAVs to construct large-scale global topologies for tracking high-speed moving targets based on trajectory derivation. The system simulation results demonstrate that our solution reduces the end-to-end latency by 63.0% while decreasing the hop counts by 50% compared to state-of-the-art benchmarks.

Unmanned Aerial Vehicle (UAV) networks, renowned for their rapid deployment and high mobility, are well-suited for emergency communications that require swift responses to dynamic tasks. However, complex operational environments and frequent target movements cause service clusters to move dynamically, leading to constant changes in communication demands and service types, as well as highly dynamic distributions of service source and destination clusters, which can result in service interruptions and delays. To address these challenges, this paper proposes a dynamic task UAV network slicing architecture, comprising multiple UAV task clusters and a UAV physical infrastructure, designed to meet diverse service requirements. A slice resource reconfiguration model is developed to assess communication terminal issues caused by dynamic tasks, considering factors such as service interruptions, traffic rate, routing, node allocation, and spectrum allocation. The model aims to minimize service interruptions while considering constraints on limited spectrum, node resources, and flow capacities. To solve it, an algorithm based on the Deep Deterministic Policy Gradient (DDPG) is introduced. Simulation results show that this algorithm effectively reduces service interruptions, decreases reconfiguration needs, and enhances resource utilization in dynamic environments.

With Internet of Vehicles and advanced onboard computing, connected autonomous vehicles (CAVs) can interact and process driving data in real time, enhancing safety and improving road efficiency. However, in mixed-autonomy traffic, the unpredictability of human-driven vehicles (HDVs) poses significant challenges for CAV control. Moreover, existing cooperative control methods often assume seamless real-time information sharing, leading to excessive communication demands that strain limited transmission resources. To address these issues, we propose a lightweight Digital Twin (DT) framework that models the traffic environment and surrounding vehicle behaviors to reduce uncertainty in CAV decision-making. The framework employs an attention-based multi-agent deep reinforcement learning method, enabling each CAV to dynamically adjust its communication frequency with neighboring vehicles according to the significance of their observations for its driving control decisions. Asynchronous updates in the DT space allow CAVs to expand their situational awareness, enabling lightweight coordination that mitigates HDV disturbances and fosters swarm intelligence. Simulations show our scheme improves traffic stability and achieves 30% faster high-speed flow than baselines, while maintaining strong performance under low bandwidth.

Cooperative platooning holds great potential for driving safety and road efficiency. However, limited communication resources and dynamic network topologies pose challenges to reliable and timely vehicular negotiation on joint platoon control (e.g., changing lanes and giving ways) in cooperative platooning. In this article, we propose a new hierarchical consensus (HC) framework to support reliable and fast coordination among multiple platoons for safe and efficient driving control. The HC framework consists of intraplatoon and interplatoon schemes. For the intraplatoon scheme, we propose a new practical Byzantine fault tolerance (PBFT) enabled intraplatoon consensus mechanism. An adaptive local consensus scheme is designed to reduce the local consensus delay and improve the successful local consensus ratio. For the interplatoon consensus, we develop a new Raft and 5G time sensitive networking (5G-TSN)-based scheme to enhance the responsiveness and scalability of multiplatoon negotiations. Furthermore, we design a dynamic prioritization scheme for 5G-TSN flows and develop an intelligent flow scheduling algorithm to improve interactions among platoons and shorten the total negotiation delay, while ensuring successful multiplatoon negotiations. Simulation results indicate that the proposed scheme can significantly enhance the multiplatoon negotiation performance for cooperative control, with more than 16.9% higher successful consensus ratio and 14% lower negotiation delay than existing approaches.

Along with the development of intelligent transportation system (ITS), artificial intelligence (AI)-based machine learning technologies have been widely utilized in Internet of Vehicles (IoV). Neural network (NN)-based knowledge sharing among vehicles and road side units (RSUs) presents considerable benefits for enhancing vehicle intelligence. However, it is challenging to ensure the efficiency of knowledge sharing under unstable connectivity among vehicles with different NN model architectures. In this article, we propose a new deep semantic communication framework for knowledge sharing (SCKS), enabling one-to-many NN model transmission and realizing efficient knowledge sharing in an IoV. Based on this framework, a generative distillation algorithm is designed to extract the semantic features of NN model, which can ensure the efficiency of the transmitter for knowledge sharing across different NN models and reduce communication bandwidth demand. In order to facilitate an effective understanding of semantic information by heterogeneous receivers, we design a generative adversarial networks (GAN)-based semantic decoding algorithm. Numerical results on CIFAR10 and ImageNet datasets show that the proposed SCKS outperforms the baseline, especially in the low-signal-to-noise (SNR) region. In particular, the simulation results demonstrate superiority of proposed SCKS scheme in terms of bandwidth requirements and computational efficiency for knowledge sharing cross different NN architectures than the state-of-art scheme, including DeepJSCC and knowledge distillation (KD).

Deep learning-based applications have great potential for providing intelligent and personalized services in the Internet of Things (IoT). However, the resource limitation in IoT devices may significantly hinder deep learning applications in IoTs, especially when infrastructures are absent for critical environments. Unmanned Aerial Vehicle (UAV) based split learning can alleviate this problem, by offloading the major part of the deep learning training tasks from IoT devices to the UAV. Whereas, current studies often overlook the latent privacy challenges caused by the UAV and extra data transmissions. To address this issue while ensuring efficient split learning, we propose a novel privacy-preserving split learning architecture. Based on this architecture, we present an improved pipeline scheme to synchronize the training and communicating period between the UAV and the IoT device. Then, in the context of privacy preservation, we formulate an optimization model to minimize the system energy consumption by jointly optimizing model split points, UAV service slot allocation, and flight trajectories. Based on Block Coordinate Descent (BCD) and Successive Convex Approximation (SCA), we put forward HOTSS algorithm to find the optimized solution of this model. Simulation results show the fluctuating characteristic of energy consumption changed with the increase of the privacy preservation requirement, and show our approach can reduce overall system energy consumption by an average of 6.7% compared to the benchmark scheme.

Urban air mobility (UAM) relies on developing aerospace industries, where safe aviation and efficient communication are critical features of aircraft. However, it is challenging for aircraft to sustain efficient air-ground communication in urban circumstances. Without continuous air-ground communication, aircraft may experience course deviation and safety accidents. To address these problems, a reconfigurable intelligent surface (RIS)-aided trajectory optimization scheme is proposed enabling efficient air-ground communication and safe aviation in UAM with a layered airspace structure. This article first devises a dual-plane RIS communication scheme for layered airspace. It fully engages the omnidirectional and directional signal attributes to reduce the transmission delay of the air-ground communication. Based on the dual-plane RIS configuration, we jointly develop the intra- and interlayer trajectory scheme to optimize communication and safe aviation. In the intralayer trajectory optimization, we propose a dual-time-scale flight scheme to improve communication capacity and horizontal flight safety. Meanwhile, we propose a safe layer-switching method to ensure collision avoidance during vertical flight in the interlayer trajectory optimization. The communication load of the proposed scheme can be improved 40% and the time of safe separation restoration can be lessened 66% compared with the benchmarks in the layered airspace.

Unmanned Aerial Vehicle (UAV)-powered 5G/6G networks integrated with rechargeable wireless sensor networks (RWSNs) offer promising solutions for extending system lifetime, collecting data, and providing computing services and power to sensor nodes (SNs). UAVs offer significant advantages, including exceptional mobility, cost-effective deployment, and the ability to be easily reprogrammed for a wide range of missions. However, the limited onboard power capacity of UAVs, coupled with the lack of dynamic and intelligent charging station (CS) management and inefficient path planning, can lead to SN failure in dynamic mobile environments. To address these challenges, we propose an energy-efficient laser-charged UAV (LCU)-enabled RWSN environment, wherein UAVs, powered by laser beams from ground-based stations, provide services, collect data, and transfer energy to SNs. We formulate a joint optimization problem involving power allocation, dynamic charging strategy (DCS), and path planning to minimize task completion time and sensor node death time. Given the NP-hard nature of the problem, we employ a stochastic game model based on a Markov decision process (MDP) for its solution. To solve this problem, we propose a deep reinforcement learning (DRL) based algorithm that enables real-time charging scheduling decisions while optimizing network performance. We introduce a multi-agent double deep Q-network (MA-DDQN) model to determine the optimal trajectories for all UAVs in large and complex environments. Simulation results demonstrate that the MA-DDQN approach outperforms state-of-the-art techniques, showing significant improvements in terms of average delay, energy consumption, and task completion time.

Unmanned Aerial Vehicles-based Multiple Targets Tracking (UAV-MTT) has been mainstream in serving mission-critical scenarios for public safety, such as hit-and-run tracking and border patrol. Nonetheless, it is challenging to implement high-efficiency UAV topology control due to the variable moving speeds of targets and the limited sensing and communication resources of UAVs. To address the problem, we propose a terminal-edge cooperative Digital Twin (DT) framework for real-time and accurate MTT. Based on the DT technology, we achieve joint optimization of local and global UAV topologies to track targets with diverse speeds. Explicitly, we construct time-spatial DT models based on temporal and spatial information of targets and UAVs. The DT models can instruct UAVs to dynamically adjust position relations among one-hop neighbors for local topology optimization using our proposed Time Spatial Graph Learning based DT (TSGL-DT) algorithm. UAVs can use the optimization results to invite feasible neighbors to track low-speed moving targets. Our DT models can also allocate feasible UAVs to connect suitable local topologies for global topology optimization. It can achieve cooperative MTT to track high-speed moving targets. The experiment results demonstrate that our solution reduces the MTT latency by 41.2% while improving the successful tracking ratio delivery ratio by 15.6% on average compared to state-of-the-art benchmarks.

UAV swarms offer substantial opportunities for Search and Rescue (SAR) applications. Confronted with numerous concurrent sensing tasks in complicated environment, resource-scarce UAV networks need a dynamic, task-driven deployment and resource configuration strategy for multi-UAV swarm coordination to ensure the efficient execution of sensing tasks. This paper introduces a Digital Twin (DT)-based collaboration architecture for resource management in UAV swarms, connecting realistic task crowdsourcing and virtual traffic flow scheduling to achieve a complementary multi-UAV swarm allocation. We propose an intelligent dynamic task crowdsourcing scheme that manages the swarm scale and membership configuration of multiple UAV swarms based on theoretical evaluation results. The architecture constructs DTs of UAV swarms and shifts the scheduling of traffic flow paths to the virtual world, thereby sidestepping the overhead of routing configuration and network reorganisation. With the aid of a traffic flow allocation algorithm based on Stochastic Network Calculus (SNC), the virtual swarm pre-schedules traffic flows and assesses end-to-end delay theoretically, so as to achieve a collaborative deployment of sensing, computational, and communication resources within the swarm. The simulation results substantiate that our architecture can uphold a 90% achievement ratio for task requirements while keeping UAV costs comparable to other algorithms.

Urban Air Mobility (UAM), powered by flying cars, is poised to revolutionize urban transportation by expanding vehicle travel from the ground to the air. This advancement promises to alleviate congestion and enable faster commutes. However, the fast travel speeds mean vehicles will encounter vastly different environments during a single journey. As a result, onboard learning systems need access to extensive environmental data, leading to high costs in data collection and training. These demands conflict with the limited in-vehicle computing and battery resources. Fortunately, learning model sharing offers a solution. Well-trained local Deep Learning (DL) models can be shared with other vehicles, reducing the need for redundant data collection and training. However, this sharing process relies heavily on efficient vehicular communications in UAM. To address these challenges, this paper leverages the multi-hop Reconfigurable Intelligent Surface (RIS) technology to improve DL model sharing between distant flying cars. We also employ knowledge distillation to reduce the size of the shared DL models and enable efficient integration of non-identical models at the receiver. Our approach enhances model sharing and onboard learning performance for cars entering new environments. Simulation results show that our scheme improves the total reward by 85% compared to benchmark methods.

With the development of cyber-physical systems, Digital Twins (DT)-powered network autonomy is emerging to embrace the fifth-generation industrial revolution. In this context, Unmanned Aerial Vehicles (UAVs)-based low-altitude networks are expected to be the engines that drive industrial development. As an attractive industry application, UAVs-based intelligent logistics has been widely investigated to achieve a fully automated distribution manner without the aid of a workforce. However, it is difficult to perform real-time DT implementations due to limited computing resources and the high mobility of UAVs. To address the mentioned problems, we propose a Versatile DT (VerDT) framework operating at the edge. It can enable a double DT cooperation manner with a resource scheduling model and a path planning model for real-time and accurate logistics distributions. The resource scheduling model can implement the integration of computing and communication resources among UAVs for feasible cooperative distribution decisions. With the decisions, the path planning model can imitate to derive positions and velocities of UAVs for low-latency distribution performance with energy saving. Experiment results demonstrate the efficiency of our VerDT framework. Compared to state-of-the-art logistics distribution solutions, our solution reduces the distribution latency by 63.9% while improving the successful distribution ratio by 10.9%.

The terahertz spectrum has emerged as a solution to alleviate the exhaustion of low-frequency resources, and meet the high-speed, large bandwidth transmission requirements of applications such as vehicular entertainment. However, the limited transmitting power of terahertz modules underscores the importance of researching high energy-efficient green communication schemes. The inherently low gain of terahertz phased arrays and Intelligent Reflecting Surfaces (IRS) fails to meet the energy efficiency demands, while economical high-gain directional antennas face challenges in achieving beam alignment within rapidly changing mobile environments. This paper addresses the challenges of utilizing high-gain directional antennas for terahertz communication in high-speed mobile scenarios. Unlike traditional network solutions that place antennas near the service area, our proposed green communication method positions the antennas at locations distant from the service area. This strategy leverages distance to expand the projection of narrow beams on the plane, thereby increasing the beam coverage area. Furthermore, through performance analysis, we demonstrate that our method not only offers a cost-effective advantage but also surpasses conventional method that employ state-of-the-art IRS or phased arrays in terms of energy efficiency. We also analyze the signal obstruction issues within the proposed scheme, calculate the coverage probability, and validate the derived expressions through simulations.

Urban Air Mobility (UAM) expands roads from the ground to the near-ground space, envisioned as a revolution for congestion alleviation and fast commutes. However, UAM is still in its infancy, encountering the safety flying challenge: high environmental perception requirements for Autonomous Air Vehicles (AAVs) conflict with limited onboard computing and poor communication quality. The primary purpose of this paper is attempted to facilitate the perception of AAVs by exploring collaborative learning through learning model propagation and integration. Specifically, we leverage well-trained perception learning models of local AAVs to boost the onboard training of new entrants by Knowledge Distillation (KD) technology. Therefore, a knowledge distillation-based model propagation protocol is proposed to obtain the well-trained learning models of nearby AAVs. This protocol also takes transmission errors between AAVs into account. Moreover, we develop an error repair scheme to reduce the upper bound delay of model propagation. Simulation results verify the efficiency of the proposed scheme to improve the AAV onboard learning performance whenever it encounters new environments.

Burdened by persistent traffic congestion, urban transportation is in a pressing need of more effective traffic guidance schemes. Existing traffic guidance approaches fall short in optimizing benefits, primarily due to their exclusive reliance on current road conditions for decision making and the prevalence of driving egoism within traditional patterns. Autonomous vehicles (AVs), liberated from human control and enhanced by the Internet of Vehicles and edge computing, provide new possibilities for traffic guidance. Nevertheless, it is tough to precisely determine the pertinent information to convey and establish an effective cooperative guidance mechanism in the face of the substantial number of AVs. This article proposes a social value orientation (SVO)-based cooperation mechanism for AVs, through which the driving routes are jointly determined by individual driving demands, local road network conditions, and global benefits. We design a digital twin-based Edge-to-Cloud traffic guidance architecture, leveraging real-time AV decisions and micro-driving characteristics for forthcoming road condition estimation. The hierarchical Edge-to-Cloud structure efficiently mitigates communication and computation overheads in traffic guidance by distributing tasks across different regions. Finally, an innovative method based on inverse reinforcement learning is proposed to address the challenge of adapting guidance policies in response to varying traffic densities and distributions. The simulation results show a 59.1% improvement in the travel achievement ratio under heavy road traffic load, with no significant change in the detour ratio. It indicates an enhanced system driving efficiency, while still safeguarding the individual benefits of AVs.

Urban air mobility (UAM) expands vehicles from the ground to the near-ground space, envisioned as a revolution for transportation systems. Comprehensive scene perception is the foundation for autonomous aerial driving. However, UAM encounters the intelligent perception challenge: high-perception learning requirements conflict with the limited sensors and computing chips of flying cars. To overcome the challenge, federated learning (FL) and other collaborative learning have been proposed. It enables resource-limited devices to conduct onboard deep learning (DL) collaboratively. But traditional FL relies on a central integrator for DL model aggregation, which is difficult to deploy in dynamic UAM environments. The fully decentralized learning schemes may be the intuitive solution while the convergence of decentralized learning cannot be guaranteed. Accordingly, this article explores reconfigurable intelligent surfaces (RISs)-empowered decentralized FL (DFL), taking account of topological attributes to facilitate the DFL performance with convergence guarantee. Several DFL topological criteria are proposed for optimizing the transmission delay and convergence rate. Subsequently, we innovatively leverage the RIS link construction and deconstruction ability to remold the current network based on the proposed topological criteria. This article rethinks the functions of RIS from the perspective of the network layer. Furthermore, a deep deterministic policy gradient-based RIS phase shift control algorithm is developed to reshape the communication network. Simulation experiments are conducted over MobileNet-based multiview learning to verify the efficiency of the DFL framework.

Aerial edge computing (AEC) networks, which employ multiple unmanned aerial vehicles (UAVs) as mobile edge computing servers, have emerged as a promising solution to provide computation offloading services, especially in scenarios where the coverage of existing infrastructures is limited for wireless networks. Recently, there has been a growing focus on leveraging UAVs with diverse computing capabilities to enhance the performance of AEC networks through cooperative computing. However, heterogeneity and cooperation introduce a higher degree of coupling between trajectory planning and computing offloading strategy for AEC networks. In particular, the joint decision-making in an AEC network need to balance minimizing the distance between UAVs and access users and enabling collaborative offloading. And this must be done while considering the time-varying computation requirements and the long-term impact on system performance. To address the aforementioned challenges, we formulate an optimization problem to design a joint dynamic cooperative computation offloading and trajectory optimization scheme for the AEC network. The complexity arises from the problem’s nature as a mixed-integer nonlinear program. To tackle this challenge, we propose a multi-agent deep reinforcement learning algorithm based on QMIX. We leverage both theoretical analysis and an action branching architecture to reduce the complexity of our proposed deep reinforcement algorithm. Simulation results demonstrate a substantial performance improvement over the benchmarks, affirming the effectiveness of our complexity reduction approach.

The coexistence of sixth-generation (6G) and other wireless networks in the large-scale environment is challenged by the problem of the efficient sharing of the unlicensed spectrum band, as well as the fair and reliable access of heterogeneous users. Blockchain with the advantages of trustworthiness and decentralization is supposed to be a potential solution. An intelligent two-layer blockchain framework is developed, consisting of an upper layer administrator blockchain (UAB) and multiple lower layer spectrum allocation blockchains (LSB). The LSB is an adjustable local blockchain to form a fast and lightweight consensus on spectrum allocation, and the UAB controls the adaptation for regional dynamic Quality-of-Service requirements. Moreover, the theoretical model is proposed to analyze the effect of different communication delays of wireless blockchain networks on the block confirmation delay of the directed acyclic graph (DAG) blockchain. Considering the wireless blockchain node must compete for scarce spectrum resources to broadcast transactions, we analyze the optimal spectrum allocation ratio for an LSB through mathematical modeling. Furthermore, the deep Q network (DQN) is used by UAB to form a consensus on the adjustment strategy for the blockchain model of LSB. Test results demonstrate that the proposed intelligent blockchain model can significantly improve spectrum efficiency and adapt to the dynamic environment.

With the development of the future wireless communication technology and the Internet of Things (IoT), the digital twin (DT) system has become a new enabler for high-efficiency sensing in industrial applications. However, traditional DT designers may encounter a challenging situation for highly dynamic mobile entities in large-scale unmanned aerial vehicle (UAV) application scenarios. It has a direct influence on accurate and real-time sensing. To address the issue, we propose a hierarchical DT-enhanced cooperative sensing architecture. We proposed an intelligent DT model acquisition algorithm for real-time DT model construction. The accuracy of DT models is improved through our proposed model aggregation algorithm for accurate cooperative sensing. In addition, we propose a model transfer algorithm to perform a real-time cooperative sensing manner. We demonstrate the effectiveness of the proposed architecture using a multitarget tracking case study. The results show that our solution provides an accurate and real-time mobile sensing performance in the case study, with up to 90% sensing accuracy, under an acceptable system latency, compared to the traditional centralized and distributed DT manners.

With the development of communication networks and Artificial Intelligence (AI) technologies, Digital Twin (DT) now emerges to support various applications such as engineering, monitoring, controlling, healthcare and the optimization of cyber-physical systems. There is an increasing demand to create DTs that can represent physical entities for improving operational efficiency. A conventional DT consists of monitoring, imitation, and feedback control. However, conventional DTs cannot ensure efficient real-time imitation due to the high dynamics of physical systems such as UAV-based target tracking scenario. To address this issue, we propose a federated DT framework to support the imitation of mobile systems. It can guarantee real-time and accurate imitations under the prerequisite of comprehensive information acquired by a cooperative collection algorithm with the aid of UAVs. The framework can rapidly aggregate local DT models using an attention-based mechanism to improve mobile imitation accuracy. Additionally, we propose a multimodal-based DT inspection algorithm that can correct the postures of UAVs affected by winds for reliable imitations. We implement the framework in Gazebo. Our system simulations demonstrate the efficiency of the proposed federated DT framework. Our solution can reduce the imitation latency by an average of 68.4%, meanwhile, can improve the imitation accuracy by 16.4% on average when compared to traditional centralized and distributed imitation schemes.

In sixth-generation (6G) networks, unmanned aerial vehicles (UAVs) are expected to be widely used as aerial base stations (ABS) due to their adaptability, low deployment costs, and ultra-low latency responses. However, UAVs consume large amounts of power to collect data from multiple sensor nodes (SNs). This can limit their flight time and transmission efficiency, resulting in delays and low information freshness. In this paper, we present a multi-access edge computing (MEC)-integrated UAV-assisted wireless sensor network (WSN) with a laser technology-based energy harvesting (EH) system that makes the UAV act as a flying energy charger to address these issues. This work aims to minimize the age of information (AoI) and improve energy efficiency by jointly optimizing the UAV trajectories, EH, task scheduling, and data offloading. The joint optimization problem is formulated as a Markov decision process (MDP) and then transformed into a stochastic game model to handle the complexity and dynamics of the environment. We adopt a multi-agent deep Q-network (MADQN) algorithm to solve the formulated optimization problem. With the MADQN algorithm, UAVs can determine the best data collection and EH decisions to minimize their energy consumption and efficiently collect data from multiple SNs, leading to reduced AoI and improved energy efficiency. Compared to the benchmark algorithms such as deep deterministic policy gradient (DDPG), Dueling DQN, asynchronous advantage actor-critic (A3C) and Greedy, the MADQN algorithm has a lower average AoI and improves energy efficiency by 95.5%, 89.9%, 78.02% and 65.52% respectively.

The development of the intelligent Internet of Things has facilitated the adoption of high-efficiency Multiple Targets Tracking (MTT) in many civil security applications. However, existing MTT technologies cannot offer full capability in accurate and real-time MTT for civil security. Many attractive applications in the next-generation wireless network, like Unmanned Aerial Vehicle (UAV) swarm, are envisioned to be exploited for enhanced MTT with the advantage of flexibility. Nonetheless, highly dynamic moving targets impose some new challenges. UAVs cannot always perform expected cooperative tracking in conventional architectures as well. To address these problems, we design a tiered Digital Twin-assisted tracking framework in this paper, which leverages multi-grained imitation for real-time and accurate MTT. We imitate a coarse-grained MTT to ensure a high successful tracking ratio. We then design a fine-grained imitation with a reaction-diffusion mechanism to explore the feasible cooperators based on trajectory prediction. Hardware-in-the-loop simulations demonstrate that our tiered framework can reduce 66.7% of the system latency overhead compared to the conventional DDPG benchmark while improving the successful tracking ratio by 30.6%.

With the development of Artificial Intelligence (AI) technology, diverse Internet of Things (IoT) devices digesting abundant data have been exploited to meet more application requirements. In this regard, Unmanned Aerial Vehicle-based Multiple Targets Tracking (UAV-MTT) applications have been paid attention to processing a vast amount of sensing information for accurate and consecutive MTT. However, this application exposes imperative computing requirements on resource-limited UAVs. Edge computing can provide extra resources to alleviate the computing pressure for high-efficiency tracking decisions. Nonetheless, it is challenging to dynamically allocate UAVs for optimal association with time-varying target trajectories. To address the mentioned problems, we propose a terminal-edge cooperative tracking framework with a cross-layer resource cooperation method. In this design, we propose an auction-based cooperative game algorithm to implement highly accurate trajectory prediction. We then propose a graph learning-based tracking algorithm to adaptively manage the dynamic UAV topology for consecutive MTT. Simulation results demonstrate that our algorithm improves 70% prediction accuracy compared to other benchmarks while saving 40% energy consumption.

Machine learning is an essential technology providing ubiquitous intelligence in Internet of Things (IoT). However, the model training in machine learning demands tremendous computing resource, bringing heavy burden to the IoT devices. Meanwhile, in the Proof-of-Work (PoW)-based blockchains, miners have to devote large amount of computing resource to compete for generating valid blocks, which is frequently disputed for tremendous computing resource waste. To address this dilemma, we propose an Evolved-PoW (E-PoW) consensus that can integrate the matrix computations in machine learning into the process of blockchain mining. The integrated architecture, the elaborated schemes of transferring matrix computations from machine learning to blockchain mining, and the reward adjustment scheme to affect the activity of the miners are, respectively, designed for E-PoW in detail. E-PoW can keep the advantages of PoW in blockchain and simultaneously salvage the computing power of the miners for the model training in machine learning. We conduct experiments to verify the availability and effect of E-PoW. The experimental results show that E-PoW can salvage by up to 80% computing power from pure blockchain mining for parallel model training in machine learning.

Multirobot system for manufacturing is an Industry Internet of Things (IIoT) paradigm with significant operational cost savings and productivity improvement, where unmanned aerial vehicles (UAVs) are employed to control and implement collaborative productions without human intervention. This mission-critical system relies on 3-dimension (3-D) scene recognition to improve operation accuracy in the production line and autonomous piloting. However, implementing 3-D point cloud learning, such as Pointnet, is challenging due to limited sensing and computing resources equipped with UAVs. Therefore, we propose a digital twin (DT) empowered knowledge distillation (KD) method to generate several lightweight learning models and select the optimal model to deploy on UAVs. With a digital replica of the UAVs preserved at the edge server, the DT system controls the model-sharing network topology and learning model structure to improve recognition accuracy further. Moreover, we employ network calculus to formulate and solve the model sharing configuration problem toward minimal resource consumption, as well as convergence. Simulation experiments are conducted over a popular point cloud data set to evaluate the proposed scheme. Experiment results show that the proposed model-sharing scheme outperforms the individual model in terms of computing resource consumption and recognition accuracy.

Cellular vehicle-to-vehicle (V2V) communications can support advanced cooperative driving applications such as vehicle platooning and extended sensing. As the safety critical applications require ultra-low communication latency and deterministic service guarantee, it is vital to characterize the latency upper bound of cellular V2V communications. However, the contention-based Medium Access Control (MAC) and dynamic vehicular network topology brings many challenges to model the upper bound of cellular V2V communication latency and assess the link capability for quality of service (QoS) guarantee. In this paper, we are motivated to reduce the research gap by modelling the latency upper bound of cellular V2V with network calculus. Based on the theoretical model, the probability distribution of the delay upper bound can be obtained under the given task features and environment conditions. Moreover, we propose an intelligent scheme to reduce upper bound of end-to-end latency in vehicular platoon scenario by adaptively adjusting the V2V communication parameters. In the proposed scheme, a deep reinforcement learning model is trained and implemented to control the time slot selection probability and the number of time slots in each frame. The proposed approaches and the V2V latency upper bound are evaluated by simulation experiments. Simulation results indicate that our network calculus based analytical approach is effective in terms of the latency upper bound estimations. In addition, with fast iterative convergence, the proposed intelligent scheme can significantly reduce the latency by about 80% compared with the conventional V2V communication protocols.

Various interconnected Internet of Things (IoT) devices have emerged, led by the intelligence of the IoT, to realize exceptional interaction with the physical world. In this context, UAV swarm-enabled Multiple Targets Tracking (UAV-MTT), which can sense and track mobile targets for many applications such as hit-and-run, is an appealing topic. Unfortunately, UAVs cannot implement real-time MTT based on the traditional centralized pattern due to the complicated road network environment. It is also challenging to realize low-overhead UAV swarm cooperation in a distributed architecture for the real-time MTT. To address the problem, we propose a cyber-twin-based distributed tracking algorithm to update and optimize a trained digital model for real-time MTT. We then design a distributed cooperative tracking framework to promote MTT performance. In the design, both short-distance and long-distance distributed tracking cooperation manners are firstly realized with low energy consumption in communication by integrating resources of sensing and communication. Resource integration promotes target sensing efficiency with a highly successful tracking ratio as well. Theoretical derivation proves our algorithmic convergence. Hardware-in-the-loop simulation results demonstrate that our proposed algorithm can remarkably save 65.7% energy consumption in communication compared to other benchmarks while efficiently promoting 20.0% sensing performance.

Cooperative autonomous driving has emerged as an appealing paradigm to expand the perception range of vehicles and improve driving safety by sharing local sensing data and driving intentions. However, the constrained communication resource and unstable link quality seriously restrict the coordination and reliability of driving decisions. The distributed consensus mechanism is a potential approach to address the problem. This paper proposes a fast and efficient vehicular consensus framework to improve the coordination and reliability of driving decisions in delay-sensitive applications. We first design a Raft empowered two-hop consensus mechanism with dynamic negotiation. Moreover, we theoretically analyze the performance of the mechanism in terms of successful consensus ratio, latency, and link quality by leveraging Jensen's inequality and binomial distribution. In addition, an adaptive joint design algorithm for consensus process and communication is put forward to minimize the consensus delay while satisfying the requirements of vehicular resources and coordination degree. Simulation results demonstrate that our proposed scheme can improve the reliability of critical decisions by 15.4% compared with existing approaches.

In this paper, we investigate energy-efficient computation offloading and trajectory planning for aerial edge networks, wherein multiple Unmanned Aerial Vehicles (UAVs) cooperate with each other to provide computing service to the ground users. In particular, we formulate the joint optimization of computation offloading, channel allocation, power control, resource allocation, and trajectory planning as a multi-agent deep reinforcement learning (MA-DRL) problem to minimize the system energy consumption while guaranteeing the latency re-quirements of computation tasks. Then, we propose a distributed algorithm to enable UAV s to independently make action decisions based on their local observations, still collaboratively learn their policies for system performance. Numerical results demonstrate that our proposed algorithm can effectively plan the trajectory, reduce the system energy consumption, and improve the latency requirements satisfaction ratio.

Driven by the development of the smart Internet of Things (IoT), unmanned aerial vehicle (UAV) swarms have been widely applied to implement diverse sensing tasks for many IoT applications. By integrating resources of UAVs, a UAV swarm can collaboratively collect and process massive image data for fast system response. However, it is difficult to reduce the computing delay caused by overlapping sensing operations and insufficient computing resources. In addition, transmission delays among UAVs may be intensified due to limited bandwidth resources. To address the mentioned challenges, we propose a UAV-swarm-based hierarchical network architecture to jointly schedule sensing, computing, and communication resources. Specifically, multiple computing groups that are formed by UAVs execute image processing in a pipeline manner to improve computing resource utilization. In order to reduce task execution time, we formulate a nonlinear integer optimization problem for the coordination of heterogeneous resources. A multiagent reinforcement learning (MARL)-based algorithm is designed to find the optimal joint resource allocation strategy under sensing accuracy constraints. Simulation results demonstrate that our algorithm reduces the task execution time while significantly improving the computing resource utilization.

The rapidly growing number of Internet-of-Things (IoT) devices poses new challenges for spectrum management in future wireless communication networks. It is critical to achieve efficient and dynamic spectrum management in the sixth-generation (6G) wireless communication networks era. To tackle the challenges of managing a large-scale IoT network with heterogeneous devices, we propose a directed acyclic graph (DAG) blockchain-enhanced user-autonomy spectrum sharing model. As the proposed consensus rule is closely related to system utility, the swarm intelligence of users gradually reaches the point of convergence in the process of blockchain consensus. We analyze the effect of the tip selection method of the DAG blockchain on spectrum allocation utility. A dynamic tip selection method is proposed to enhance the global utility, which is related to the spectrum supply–demand. In addition, the ring signature technique is utilized to realize privacy protection during the sharing process. Simulation indicates that the proposed tip selection method achieves a 10% enhancement in terms of the global utility. Furthermore, significant reductions in administrative expense and reliability improvement are demonstrated by simulation results. The stability of the tip number in the proposed model has been proved theoretically, which is also validated by simulation experiments.

Edge computing has emerged as a prospective paradigm to meet ever-increasing computation demands in mobile target tracking wireless sensor networks (MTT-WSNs). This paradigm can offload time-sensitive tasks to sink nodes to improve computing efficiency. Nevertheless, it is intractable to execute dynamic and critical missions in the MTT-WSN network due to static property. Besides, the network cannot ensure consecutive tracking with limited energy. To address the problems, this article proposes a new hierarchical tracking structure based on the edge intelligence (EI) technology. The structure can integrate the computing resource of both mobile nodes and edge servers to provide high-efficient computing for real-time tracking. Based on the proposed structure, we propose a long-term dynamic resource allocation algorithm to obtain the optimal resource scheduling solution for accurate and consecutive tracking. Simulation results demonstrate that our algorithm outperforms the deep${Q}$-learning over 14.5% in terms of systematic energy consumption. It can also obtain a significant enhancement in tracking accuracy compared with the noncooperative scheme.

With the advantages of easy deployment and flexible usage, unmanned aerial vehicle (UAV) has advanced the multitarget tracking (MTT) applications. The UAV-MTT system has great potentials to execute dull, dangerous, and critical missions for frontier defense and security. A key challenge in UAV-MTT is how to coordinate multiple UAVs to track diverse invading targets accurately and consecutively. In this article, we propose a UAV swarm-based cooperative tracking architecture to systematically improve the UAV tracking performance. We design an intelligent UAV swarm-based cooperative algorithm for consecutive target tracking and physical collision avoidance. Moreover, we design an efficient cooperative algorithm to predict the trajectory of invading targets accurately. Our simulation results demonstrate that the swarm behaviors stay stable in realistic scenarios with perturbing obstacles. Compared with state-of-the-art solutions, such as the matched deep$Q$-network, our algorithms can increase tracking accuracy by 60%, reduce tracking delay by 23%, and achieve physical collision-avoidance during the tracking process.

The emergence of Intelligent Connected Vehicles (ICVs) shows great potential for future intelligent traffic systems, enhancing both traffic safety and road efficiency. However, the ICVs relying on data driven perception and driving models face many challenges, such as the lack of comprehensive knowledge to deal with complicated driving context. In this paper, we investigate cooperative knowledge sharing for ICVs. We propose a secure and efficient blockchain based knowledge sharing framework, wherein a distributed learning based scheme is utilized to enhance the efficiency of knowledge sharing and a directed acyclic graph (DAG) system is designed to guarantee the security of shared learning models. To cater for the time-intense demand of highly dynamic vehicular networks, a lightweight DAG is designed to reduce the operation latency in terms of fast consensus and authentication. Moreover, to further enhance model accuracy as well as minimizing bandwidth consumption, an adaptive asynchronous distributed learning (ADL) based scheme is proposed for model uploading and downloading. Experiment results show that the DAG based framework is lightweight and secure, which reduces both chosen and confirmation delay as well as resisting malicious attacks. In addition, the proposed adaptive ADL scheme enhances driving safety related performance compared to several existing algorithms.

Energy saving for mobile devices is considered to be one of prospective benefits of mobile edge computing (MEC) networks, where computation-intensive tasks can be offloaded from the mobile devices to their associated MEC servers for execution. Extra energy consumption for data migration should therefore be less than the energy consumption for local execution. However, in multi-cell MEC-assisted networks, due to both the presence of co-channel interference and the latency requirement of each offloading task, power control is tightly coupled with computation offloading, which becomes an obstacle to achieve the aim of energy saving. In this paper, we develop an analytic model to decouple power control and computation resource allocation from each other, in which the transmission power can be considered as a solution to a set of linear equations with a coefficient matrix depending on the computation resource budget. Based on this analytic foundation, we show that with a fixed offloading decision, the joint power control and computation resource allocation problem is invex, which ensures that every KKT (Karush–Kuhn–Tucker) stationary point of the problem must be a global minimizer. Moreover, we deduce a criterion for energy-efficient offloading decision making from the partial derivative of the total energy consumption of mobile devices with respect to the computation resource budget. Finally, we propose a heuristic algorithms to jointly optimizing power and computation resource allocation, and offloading decision. The numerical results demonstrate the optimality and efficiency of our proposed algorithm.

Knowledge sharing in IoV shows great potential for future vehicular networks. Vehicles, platoons and even traffic infrastructures can exchange the driving experiences or sensing data to facilitate intelligent transportation applications such as autodriving and traffic analysis. However, it is challenging for vehicular knowledge-sharing systems to address the issues brought by information security and vehicular mobility. Although blockchain technology shows defensibility in dealing with trust issues, it is difficult to be applied in large-scale vehicular networks due to the computation consumption of mining process and frequent synchronization of ledger. In this paper, we propose a directed acyclic graph (DAG) enabled knowledge-sharing framework in which vehicular knowledge is encapsulated as a site in the DAG. A new tip selection algorithm (TSA) and a fast authentication scheme for cross-regional vehicles are designed to reduce computation and storage expenditure. Simulation results show that the proposed DAG framework can achieve a higher knowledge sharing quality and lower authentication latency compared with traditional DAG systems.

Vehicle platooning is one of the advanced driving applications expected to be supported by the 5G vehicle to everything (V2X) communications. It holds great potentials on improving road efficiency, driving safety and fuel efficiency. Apart from the organization and internal communication of the platoons, real-time prediction of surrounding road users (such as vehicles and cyclists) is another critical issue. While artificial intelligence (AI) is receiving increasing interests on its application to trajectory prediction, there is a potential problem that the pre-trained neural network models may not well fit the current driving environment and needs online fine-tuning to maintain an acceptable high prediction accuracy. In this paper, we propose a digital twin based real-time trajectory prediction scheme for platoons of connected intelligent vehicles. In this scheme the head vehicle of a platoon senses the surrounding vehicles. A LSTM neural network is applied for real-time trajectory prediction with the sensing outcomes. The head vehicle controls the offloading of the trajectory data and maintains a digital twin to optimize the update of LSTM model. In the digital twin a Deep-Q Learning (DQN) algorithm is utilized for adaptive fine tuning of the LSTM model, to ensure the prediction accuracy and minimize the consumption of communication and computing resources. A real-world dataset is developed from the KITTI datasets for simulations. The simulation results show that the proposed trajectory prediction scheme can maintain a prediction accuracy for safe platooning and reduce the delay of updating the neural networks by up to 40%.

Research on the sixth-generation cellular networks (6G) is gaining huge momentum to achieve ubiquitous wireless connectivity. Connected autonomous vehicles (CAVs) is a critical vertical application for 6G, holding great potentials of improving road safety, road and energy efficiency. However, the stringent service requirements of CAV applications on reliability, latency, and high speed communications will present big challenges to 6G networks. New channel access algorithms and intelligent control schemes for connected vehicles are needed for 6G-supported CAV. In this article, we investigated 6G-supported cooperative driving, which is an advanced driving mode through information sharing and driving coordination. First, we quantify the delay upper bounds of 6G vehicle-to-vehicle (V2V) communications with hybrid communication and channel access technologies. A deep learning neural network is developed and trained for the fast computation of the delay bounds in real-time operations. Then, an intelligent strategy is designed to control the intervehicle distance for cooperative autonomous driving. Furthermore, we propose a Markov chain-based algorithm to predict the parameters of the system states, and also a safe distance mapping method to enable smooth vehicular speed changes. The proposed algorithms are implemented in the AirSim autonomous driving platform. Simulation results show that the proposed algorithms are effective and robust with safe and stable cooperative autonomous driving, which greatly improve the road safety, capacity, and efficiency.

Internet of Vehicles (IoVs) is highly characterized by collaborative environment data sensing, computing and processing. Emerging Big Data and Artificial Intelligence (AI) technologies show significant advantages and efficiency for knowledge sharing among intelligent vehicles. However, it is challenging to guarantee the security and privacy of knowledge during the sharing process. Moreover, conventional AI-based algorithms cannot work properly in distributed vehicular networks. In this paper, a hierarchical blockchain framework and a hierarchical federated learning algorithm are proposed for knowledge sharing, by which vehicles learn environmental data through machine learning methods and share the learning knowledge with each others. The proposed hierarchical blockchain framework is feasible for the large scale vehicular networks. The hierarchical federated learning algorithm is designed to meet the distributed pattern and privacy requirement of IoVs. Knowledge sharing is then modeled as a trading market process to stimulate sharing behaviours, and the trading process is formulated as a multi-leader and multi-player game. Simulation results show that the proposed hierarchical algorithm can improve the sharing efficiency and learning quality. Furthermore, the blockchain-enabled framework is able to deal with certain malicious attacks effectively.

With the rapid development of autonomous driving technologies, it becomes difficult to reconcile the conflict between ever-increasing demands for high process rate in the intelligent automotive tasks and resource-constrained on-board processors. Fortunately, vehicular edge computing (VEC) has been proposed to meet the pressing resource demands. Due to the delay-sensitive traits of automotive tasks, only a heterogeneous vehicular network with multiple access technologies may be able to handle these demanding challenges. In this article, we propose an intelligent task offloading framework in heterogeneous vehicular networks with three Vehicle-to-Everything (V2X) communication technologies, namely Dedicated Short Range Communication (DSRC), cellular-based V2X (C-V2X) communication, and millimeter wave (mmWave) communication. Based on stochastic network calculus, this article firstly derives the delay upper bounds of different offloading technologies with certain failure probabilities. Moreover, we propose a federated Q-learning method that optimally utilizes the available resources to minimize the communication/computing budgets and the offloading failure probabilities. Simulation results indicate that our proposed algorithm can significantly outperform the existing algorithms in terms of resource cost and offloading failure probability.

Advanced sensor technology and emerging Internet of Vehicles (IoV) have significantly accelerated the realization of autonomous driving. In operating an autonomous vehicle, traffic information needs to be collected and processed under strict delay constraints. But an individual vehicle equipped with few types of sensors and limited computation resources could not achieve precise environmental awareness and real-time information processing. Vehicular cooperative sensing and edge computing are promising approaches to address these problems. However, various types of sensing tasks and heterogeneous smart vehicles with different computing power make the cooperation between vehicles a complicated problem. To cope this problem, we form multiple vehicles into platoons, and design a novel cooperative sensing architecture. Moreover, we fully exploit unoccupied computation resources of smart vehicles, and propose a vehicular edge serving scheme, which jointly schedules cooperative sensing and task offloading. Numerical results demonstrate that our proposed scheme outperforms traditional approaches with lower delay costs.

With the increasing requirement of spectral efficiency in 6G mobile networks, the Citizens Broadband Radio Service (CBRS) proposed by the Federal Communications Commission (FCC) is considered as the potential dynamic spectrum sharing solution. Traditional CBRS suffers high administrative expense and privacy risk. In addition, conventional consensus methods in blockchain consume excessive computing power or lack well-founded consensus standard. In this paper, we propose a distributed CBRS-Blockchain model with a specialized consensus method, which is able to reduce administrative expense of dynamic access system. Based on the ring signature techniques, a privacy protection method is proposed. Furthermore, we design a new consensus method named as proof-of-strategy, which combines with the process of spectrum allocation. The proposed method not only provides well-founded consensus mechanism, but also prevents spectrum allocation system from the event of single point failure. Simulation results show the divergent privacy protection for legal users and malicious users, as well as the system utility under the proposed consensus method.

With the rapid growth of the network applications, more services with diverse QoS requirements have emerged. Efficient routing technique plays a vital role in supporting the diversified serveries in dynamically changing wireless multi-hop networks. To this end, we propose the reinforcement learning empowered QoS-aware adaptive Q-routing (RL-QAQ) algorithm, so as to provide discriminated transmission for different services with various QoS requirements as well as reduce the delivery delay and routing overhead. In the proposed RL-QAQ algorithm, an adaptive probability is devised to optimize the exploration strategy to reduce the overhead of acquiring the network status. Besides, the QoS-aware reward function and Q-tables for the different services are devised to support multi-QoS transmission requirements. Simulation results demonstrate that the proposed RL-QAQ algorithm can adaptively adjust the routing policy according to the varying network environment to meet the transmission requirements of different services with low delivery delay and routing overhead.

Intelligent Transportation System has emerged as a promising paradigm providing efficient traffic management while enabling innovative transport services. The implementation of ITS always demands intensive computation processing under strict delay constraints. Machine Learning empowered Mobile Edge Computing (MEC), which brings intelligent computing service to the proximity of smart vehicles, is a potential approach to meet the processing demands. However, directly offloading and calculating these computation tasks in MEC servers may seriously impair the privacy of end users. To address this problem, we leverage federated learning in MEC empowered internet of vehicles to protect task data privacy. Moreover, we propose optimized learning cooperation schemes, which adaptively take smart vehicles and road side units to act as learning agents, and significantly reduce the learning costs in task execution. Numerical results demonstrate the effectiveness of our schemes.

Mobile Edge Computing (MEC) can significantly promote the development of Internet of Vehicles (IoV) for providing a low-latency and high-reliability environment. Nevertheless, a huge amount of sensor data or computing requirements generated by massive vehicles in adjacent area may be duplicated. In order to realize the efficient diffusion of information, we propose a hierarchical end-edge framework with the aid of deep collaboration among data communication, computation offloading and content caching to minimize network overheads. Specially, duplicated perceived data and computation results are cached in advance to decrease repeated data uploading and duplicated computation in offloading process. In addition, the problem is formulated as a mixed integer non-linear programming (MINLP) problem, and the deep deterministic policy gradient (DDPG)-based resource allocation scheme is utilized to obtain a sub-optimal solution with low computation complexity. Performance evaluation demonstrates that the proposed scheme can significantly reduce network overheads compared with other benchmark methods.

In this article, we propose a cooperative edge caching scheme, a new paradigm to jointly optimize the content placement and content delivery in the vehicular edge computing and networks, with the aid of the flexible trilateral cooperations among a macro-cell station, roadside units, and smart vehicles. We formulate the joint optimization problem as a double time-scale Markov decision process (DTS-MDP), based on the fact that the time-scale of content timeliness changes less frequently as compared to the vehicle mobility and network states during the content delivery process. At the beginning of the large time-scale, the content placement/updating decision can be obtained according to the content popularity, vehicle driving paths, and resource availability. On the small time-scale, the joint vehicle scheduling and bandwidth allocation scheme is designed to minimize the content access cost while satisfying the constraint on content delivery latency. To solve the long-term mixed integer linear programming (LT-MILP) problem, we propose a nature-inspired method based on the deep deterministic policy gradient (DDPG) framework to obtain a suboptimal solution with a low computation complexity. The simulation results demonstrate that the proposed cooperative caching system can reduce the system cost, as well as the content delivery latency, and improve content hit ratio, as compared to the noncooperative and random edge caching schemes.

The integration of Mobile-edge Computing (MEC) and Wireless Energy Transfer (WET) has been recognized as a promising technique to enhance computation capability and to prolong battery lifetime of resource-constrained wireless devices in the Internet of Things (IoT) era. However, it is challenging to jointly schedule energy, radio, and computational resources for coordinating heterogeneous performance requirements in wireless powered MEC systems. To fill this gap, this paper investigates the fundamental tradeoff between Energy Efficiency (EE) and delay in a multi-user wireless powered MEC system. Considering the random channel conditions and task arrivals, we formulate a stochastic optimization problem to study the EE-delay tradeoff, which optimizes network EE subject to network stability, maximum central processing unit frequency, peak transmission power, available communication resource, and energy causality constraints. Further, we propose the online computation offloading and resource allocation algorithm by transforming the original problem into a series of deterministic optimization problems in each time block based on Lyapunov optimization theory. In addition, theoretical analysis shows that the algorithm achieves the EE-delay tradeoff as [O(1/V), O(V)] and introduces a control parameter V to balance the EE-delay performance. Numerical results verify the theoretical analysis and reveal the impact of various parameters to the system performance.

Computation offloading can be widely used in Internet of Things (IoT), since most IoT devices do not have enough resource for complex computing. According to the target of computing results, computation offloading can be divided into source driven pattern and destination driven pattern. Currently, most studies focus on the former, where the results should be returned to the task source after computing. Another widely existing pattern, i.e., destination driven computation offloading, where the results should be transmitted to some other node instead of the task source, is rarely noticed. In this paper we propose a scheme of Destination Driven Step-by-step Computation Offloading along the Route (DDSCOR), which optimizes the delay from the source node releasing the task to the results arriving the destination node. Specifically, the proposed scheme combines the data transmission and computation offloading together, dividing the computing task into several procedures and offloading them to appropriate nodes along the data transmission route. Simulation results show that the proposed scheme can approximately achieve the optimal offloading- transmission combined route, and tremendously reduce the overall latency in resource limited IoT.

The emerging blockchain technology provides a new paradigm for maintaining data integrity and unforgeability in a distributed manner. However conventional public blockchain systems suffer large consensus latency thus cannot be well applied to Internet of Vehicles (IoV) with the high mobility of vehicles and low latency requirement. In addition, not all messages in IoV should be stored in a global ledger. In this paper, we propose a novel Hierarchical Blockchain (HB) which divides the system into two layers and each layer maintains an exclusive ledger. Sensing information of vehicles are recorded in differnet layer according to its influence scope. Furthermore, based on the transactions recorded in the hierarchical blockchain, we design the proactive file duplicate caching scheme considering not only popularities but influence scopes for the enhancement of overall system performance. Simulation results shows the superiority of the proposed architecture compared with conventional vehicular systems, in terms of failure rate, latency and system utility.

Distributed machine learning (DML) is widely used in resource-constrained networks where the intelligent decision is needed, since no single node can work out the accurate results from massive dataset within an acceptable time complexity. However, this will inevitably expose more potential targets to adversaries compared with non-distributed environment, especially when the dataset is periodically sent to distributed nodes for updating the decision model, which should be adapted to changing environments and requirements. In order to prevent the dataset from manipulation, we put forward a novel scheme, which depends on partial training results, to detect and correct manipulated train samples for DML. Based on variable training loops, the proposed scheme utilizes a cross-learning mechanism and a manipulated data detection algorithm to ensure the data security and the learning model correctness in an unsafe environment. Furthermore, a mathematical model is established, in order to find the optimal training loops. Simulation experiments show the mathematical model matches simulation results well, in which the optimal training loops achieve the best performance. Moreover, based on the optimal training loops, the proposed data manipulation avoidance schemes can significantly improve the accuracy of the final trained model.

Mobile-edge computing (MEC) and non-orthogonal multiple access (NOMA) has been envisioned as two promising technologies in the future wireless networks. In this paper, we concentrate on the joint computation offloading and result downloading strategy for a NOMA-assisted MEC system, where uplink/downlink NOMA is used for computation task offloading or result downloading, respectively. The energy consumption minimization problem is formulated with joint optimization of time assignment, power control, CPU frequency, and computation offloading scheme. By exploiting block coordinate descent (BCD) method, we develop a joint communication and computation resource allocation algorithm to address the original nonconvex problem. Specifically, the optimal solution is obtained in closed form. Furthermore, extensive numerical results are provided to demonstrate the effectiveness of the NOMA-assisted MEC system, when compared to the OMA-based scheme.

This paper proposes a new device-to-device edge computing and networks (D2D-ECN) framework which facilitates low-latency execution of real-time Internet-of Things applications through computation offloading with minimal overhead. Our framework accounts for key challenges of D2D-ECN in terms of the efficiency of the resource management and the resulting security concerns caused by lacking trustworthy between task owners and resource providers. In particular, we propose to use a blockchain-empowered framework for implementing resource trading and task assigment as the smart contracts. However, the existing Proof-of-Work (PoW) is impractical for the resource-constrained IoT devices due to high computational complexity of the mining process. Thus, we present a reputation-based consensus mechanism called proof-of-reputation (PoR), where the device with the highest reputation score is responsible for packaging the resource transactions and reputation records in the blockchain. Furthermore, we evaluate the reputation score of each device according to the current computation performance and history reputation. Security, feasibility analysis and numerical results show that our proposed computation offloading scheme can be deployed in the decentralized D2D-ECN system safely and effectively.

Interest-based communities, where users may have diversified interests and consequently cause communities overlapping, exist extensively in social networks. However, this will inevitably introduce more opportunities for malware spreading, whose propagation model is fundamentally different from that in currently widely-studied contact-based social networks. To address this issue, we firstly investigate the basic differences between interest-based communities and contact-based social networks. Then, the problem is formulated, and a model for malware propagation in interest-based overlapping communities is put forward. The proposed model fully considers the characters of such environment and reveals the malware spreading rules in randomly overlapping interest communities. Moreover, the model is transformed into a lightweight computational complexity mode so as to be easily utilized in practice. Finally, our model is verified with a simulation which is based on real-world dataset from Youtube interest communities, showing the theoretical results match the simulation results very well.

Internet-of-Vehicles (IoV) has the potentials of enhancing automatic driving in various transportation environment. However, there is very little investigation on quantifying the potential influence of automatic driving applications with the road efficiency in IoV. This paper studies the connection of safety distance to the road congestion under different IoV resource conditions. We propose an elastic wave equation model to reveal the relation between safety distance and road congestion. It can be found that the propagation speed of road congestion is largely affected by the safety distance. To recoup the efficient road safety and alleviate road congestion, an optimization problem is formulated with cooperative communication and computing via platoons that aims to minimize the total safety distance. Since the optimization is a complicated 0-1 programming problem, we propose a practical resource allocation algorithm and solve the problem through Lagrangian relaxation. Simulation experiments show that the proposed algorithm leads to near-optimal results with low complexity but no overhead of vehicular information exchange.

Road accidents and traffic congestion are two critical problems for global transport systems. Connected vehicles (CV) and automated vehicles (AV) are among the most heavily researched and promising automotive technologies to reduce road accidents and improve road efficiency. However, both AV and CV technologies have inherent shortcomings, for example, line of sight sensing limitation of AV sensors and the dependency of high penetration rate for CVs. In this paper we present a cooperative connected intelligent vehicles (CAV) framework. It is motivated by the observation that vehicles are increasingly intelligent with various levels of autonomous functionalities. The vehicles intelligence is boosted by more sensing and computing resources. These sensor and computing resources of CAV vehicles and the transport infrastructure could be shared and exploited. With resource sharing and cooperation CAVs can have comprehensive perception of driving environments, and novel cooperative applications can be developed to improve road safety and efficiency (RSE). The key feature of the cooperative CAV system is the cooperation within and across the key players in the road transport systems and across system layers. For example, the various levels of cooperation include cooperative sensing, cooperative RSE applications and cooperation among the vehicles and among the vehicles and infrastructure. We will present the potentials that could be brought by cooperative CAV, the roadmap for research and development, the preliminary research results and open issues.

Current approaches to information‐centric network (ICN) implementation employ the edge storage of network infrastructures to minimise the latency of information retrieval. However, the edge storage of infrastructures is finite. Distributed content caching assisted by mobile devices becomes a potential solution to content publishing services. Nevertheless, under the conventional half‐duplex‐based ICN, extra memory is occupied and the delay is increased. Fortunately, full duplex (FD) communications can improve the spectrum efficiency of wireless networks, and reduce the access delay by more flexible content caching. In this study, the authors propose a novel FD‐based ICN (FD‐ICN) framework, where content caching is not only implemented in network infrastructures but also in mobile devices. Content cached by mobile devices can be delivered in a FD amplify and forward way without extra BS memory occupation and delay. To maximise the network utility of the FD‐ICN, they formulate the caching strategy and FD radio resource allocation as a joint optimisation problem. A joint caching strategy and FD radio resource allocation algorithm are proposed. Simulation results show their FD‐ICN framework can achieve superior system utility compared to the conventional ICN. Moreover, the proposed algorithm exhibits higher utility with similar converge rate compared to the heuristic algorithm.

The Internet of Things (IoT) platform has played a significant role in improving road transport safety and efficiency by ubiquitously connecting intelligent vehicles through wireless communications. Such an IoT paradigm however, brings in considerable strain on limited spectrum resources due to the need of continuous communication and monitoring. Cognitive radio (CR) is a potential approach to alleviate the spectrum scarcity problem through opportunistic exploitation of the underutilized spectrum. However, highly dynamic topology and time-varying spectrum states in CR-based vehicular networks introduce quite a few challenges to be addressed. Moreover, a variety of vehicular communication modes, such as vehicle-to-infrastructure and vehicle-to-vehicle, as well as data QoS requirements pose critical issues on efficient transmission scheduling. Based on this motivation, in this paper, we adopt a deep Q -learning approach for designing an optimal data transmission scheduling scheme in cognitive vehicular networks to minimize transmission costs while also fully utilizing various communication modes and resources. Furthermore, we investigate the characteristics of communication modes and spectrum resources chosen by vehicles in different network states, and propose an efficient learning algorithm for obtaining the optimal scheduling strategies. Numerical results are presented to illustrate the performance of the proposed scheduling schemes.

Led by industrialization of smart cities, numerous interconnected mobile devices, and novel applications have emerged in the urban environment, providing great opportunities to realize industrial automation. In this context, autonomous driving is an attractive issue, which leverages large amounts of sensory information for smart navigation while posing intensive computation demands on resource constrained vehicles. Mobile edge computing (MEC) is a potential solution to alleviate the heavy burden on the devices. However, varying states of multiple edge servers as well as a variety of vehicular offloading modes make efficient task offloading a challenge. To cope with this challenge, we adopt a deep Q-learning approach for designing optimal offloading schemes, jointly considering selection of target server and determination of data transmission mode. Furthermore, we propose an efficient redundant offloading algorithm to improve task offloading reliability in the case of vehicular data transmission failure. We evaluate the proposed schemes based on real traffic data. Results indicate that our offloading schemes have great advantages in optimizing system utilities and improving offloading reliability.

Conventional cryptographic method faces enormous issues in future internet of things (IoT) networks such as key distribution and computational complexity. Physical layer security has been concerned as a potential technology for IoT networks. In this paper, we investigate the secrecy performance of a distributed antenna IoT network with full-duplex (FD) wireless power transfer. Under the composite wireless channel assumption, we establish the model for receiving signal to interference plus noise ratio (SINR) of both hybrid distributed antennas and the eavesdropper. The closed-form expressions for secrecy capacity of the IoT network is derived with the consideration of impact from both jammers and the eavesdropper. Furthermore, the secrecy outage probability in terms of target secrecy data rate is derived. Simulation results show both secrecy capacity and secrecy outage probability under different network configurations, which evince the effect of the distributed antenna number, the self-interference cancellation factor, and the target secrecy rate on the secrecy performance.

We study a distributed coordination function (DCF) aided WLAN system, where the user stations (STAs) are capable of harvesting energy either from the AP's downlink data transmission or from the AP's dedicated downlink energy transfer. The energy harvested by the STAs is then exploited for powering their own uplink data transmission. Based on the classic carrier-sense-multiple-access with collision avoidance (CSMA/CA) protocol, a pair of enhanced versions are proposed for the sake of incorporating the active energy request of the STAs and the dedicated energy transfer of the AP. The first one is the energy-backoff-interval aided CSMA/CA (EBI-CSMA/CA) protocol. The other is the energy-packet aided CSMA/CA protocol (EP-CSMA/CA). According to our simulation results, both of the protocols are capable of substantially increasing the uplink throughput, when compared to their counterpart without the dedicated energy transfer. Furthermore, the EP-CSMA/CA protocol is more energy-efficient than the EBI-CSMA/CA protocol, since it does not require frequent control signalling exchange for the energy requests and responses.

This paper studies the sustainable resource allocation for energy harvesting (EH) powered cloud radio access network (C-RAN), where EH powered remote radio units (RRUs) cooperatively transmit wireless energy and information to the energy-constrained mobile devices. To investigate network resource allocation problem, firstly, a general system utility optimization framework is proposed for the design of coordinated beamforming and power splitting algorithm based on Lyapunov optimization theory. The randomness of channel conditions and energy arrivals are considered in the framework. Secondly, an online algorithm is designed to maximize the system utility subject to energy sustainable constraints at the RRUs, signal-to-interference-plus-noise (SINR) and energy harvesting requirements of mobile devices. Specifically, there is no requirements of prior distribution knowledge about channel condition or energy arrival in the proposed online algorithm. Finally, performance analysis demonstrate that the proposed algorithm can achieve close-to-optimal system utility. In addition, extensive simulation results are provided to validate the theoretical analysis and to evaluate the performance of the proposed algorithm.

This paper investigates energy-efficient resource allocation for cooperative wireless powered cellular networks (WPCNs), where the cellular and device-to-device (D2D) users first harvest energy from the HAP and then the D2D user consumes a portion of power to help the cell-edge cellular user relay the data in exchange for some time from cellular user for D2D communications. Under the proposed cooperation scheme, we formulate an energy efficiency (EE) maximization problem. The energy beamforming vector, time assignment and power allocation are jointly optimized under the transmission rate requirements and available energy constraints of both D2D and cellular users. Based on the fractional programming theory and semi-definite relaxation (SDR) method, we transform the originally non-convex EE maximization problem into a standard convex problem. This allows us to design efficient resource allocation algorithm for achieving optimal solution. Extensive simulation results are provided to show the convergence rate of the proposed iterative algorithm and to demonstrate the EE improved by the proposed system than that of two baseline systems.

In this poster, we envision the promising cooperation between autonomous drones and driverless cars. We discuss potential applications and opportunities enabled by this cooperation.

Due to their environment friendliness, electric vehicles (EVs) are anticipated to form a considerable fraction of vehicles for transportation in smart cities. It is essential to design an electricity charging scheme that takes the utilities of both the charging stations and the EVs into consideration. However, the self-interested nature of the EVs together with the information asymmetry between the energy demand and supply sides makes the design a significant challenge. In this paper, we propose a queuing network-based model to characterize the charging process of the multiple EVs in a renewable energy-aided charging station. Based on the model, we adopt a contract theoretic approach to design an optimal charging policy in an information asymmetry scenario. Furthermore, we propose the new contract-based charging rate assignment and admission control schemes that maximize the utility of the charging station under certain charging constraints. To derive the optimal contract, we present a two-step iterative algorithm and prove its convergence. We evaluate the proposed schemes based on the IEEE 69-bus distribution test system. Results indicate that the contract-based charging schemes can effectively benefit both the charging stations and the EVs and concurrently improve the load level of the smart grid.

Prolonging battery lifetime, enhancing computation capability and improving spectral efficiency have been the key design challenges in Internet of Things (IoT) era. This paper provides a novel solution to jointly optimize the allocation of the communication, computing and energy resources in IoT, with the aid of some advanced wireless communication technologies including Wireless Energy Transfer (WET), Mobile-Edge Computing (MEC) and Full-Duplex (FD). Specifically, the Hybrid Access-Point (HAP) (integrated with a MEC server) operates in FD mode to simultaneously broadcast energy and receive computation tasks to/from the mobile devices in the same band. Each mobile relies on the harvested energy to accomplish computation tasks by locally executing or (partial) offloading to the HAP. We concentrate on max-min energy efficiency optimization problem (MMEP) with the joint the optimization of the transmission power at the HAP, computation energy consumption and offloaded bits at each mobile device, time slots for energy transfer and computation offloading. We study the cases with perfect and imperfect self-interference cancellation at the HAP. To solve the non-convex MMEP, we apply the fractional programming theory and Block Coordinate Descent (BCD) method to design the algorithms with low complexity. Numerical results demonstrate that the proposed solutions outperform the baseline scheme in terms of the worst-case mobile EE. Moreover, the proposed algorithms can converge to the optimal solution through a few iterations.

Prolonging battery lifetime and enhancing computation capability have been the key challenges for designing the mobile devices in the Internet of Things (IoT) era. The investigation of Mobile-Edge Computing (MEC) with Wireless Energy Transfer (WET) is a promising solution to overcome such challenges. In this paper, we study the fundamental tradeoff between Energy Efficiency (EE) and delay in the multi-user wireless powered MEC systems. In order to tackle the randomness of channel conditions and task arrivals, we formulate a stochastic optimization problem to achieve the EE-delay tradeoff, which optimizes the network energy efficiency subject to the network stability, Central Processing Unit (CPU)-cycle frequency, peak transmission power, and energy causality constraints. Furthermore, we propose a joint computation allocation and resource management algorithm by transforming the original problem into a series of deterministic optimization problems in each time block based on Lyapunov optimization theory, whose convexity is further proved. Specifically, the proposed algorithm with low complexity requires no prior distribution knowledge of channel conditions and task arrivals. In addition, theoretical analysis shows that the algorithm achieves the EE-delay tradeoff as [O(1/V ),O(V )] and provides a control parameter V to balance the EE-delay performance. Numerical results verify the theoretical analysis and reveal the impacts of various parameters to the system performance.

Through the self interference cancellation, the emerging full duplex (FD) technology possesses the potential to double the link capacity of wireless cellular networks. However, under the simultaneous uplink and downlink transmission in the same band, the co-channel interference (CCI) remains in FD cellular networks is becoming a prominent obstacle for FD performance improvement. In this paper, a novel co-channel interference cancellation (CCIC) scheme is proposed to improve the spectrum efficiency by cancelling CCI in FD cellular networks. Based on the CCIC scheme, the upper bound of the instantaneous end-to-end equivalent link capacity in a FD cellular network is derived. Then we provide a new closed-form expression for the general successful transmission probability that captures the joint effect of CCI and residual self-interference (RSI). The accurate match between simulation and theoretical results can verify the derivation. Besides, the obtained results indicate that the successful transmission probability under CCIC scheme increases with the decline of the channel fading which significantly outperforms the conventional FD schemes.

This paper study a new paradigm of heterogeneous charging stations (CSs) for the excellent charging services of Electric Vehicles (EVs) in a dense urban environment. When a large number of EVs are driving on the roads, their travel demands and charging activities have significant impact on the charging management of multiple CSs including large-CSs (LCSs) and small-CSs (SCSs). The problem of charging scheduling can be formulated as a two-side Hospital/Residents (HR) matching game. In this matching game, we not only consider the economic interest of multiple CSs and charging experience of EVs but also utilize the natural advantages of SCSs to balance the traffic on the roads around LCSs. Furthermore, a two-stage scheme that is composed of distributed HR admission scheme and centralized HR reassignment scheme is proposed to reduce the computational complexity of the matching process. Simulation results indicate that the proposed algorithms can effectively offer the scheduling management among multiple CSs while providing good satisfaction to EV drivers.

This paper focuses on resource allocation in heterogeneous Ultra Dense small-cell Networks (UDNs), in which massive overlaid small cells are under the coverage of a macro cell. In UDN, both co-tier and cross-tier interference need to be taken into account. When increasing the deployment density of Small-cell Base Stations (SBSs) and the unreasonable energy usage, it results in serious interference and degrades the user's satisfaction, which encourages us to agree on maximizing the total system Energy Efficiency (EE). This problem is non-convex and cannot be solved within polynomial time. Therefore, an efficient hierarchical Resource Allocation (RA) algorithm with reduced computational complexity is proposed to decompose the EE optimization problem into two sub-optimization problems: Sub-Channel Allocation (SCA) process and Power Allocation (PA) process. To solve these two processes, thereafter, we employ a uniform pricing scheme to reduce the feedback overhead and build the PA problem into a two-stage Stackelberg game, where the Macro-cell Base Station (MBS) acts as a follower and SBSs are leaders. The obtained simulation results show that the proposed hierarchical RA algorithm and the two-stage game can achieve a higher EE and a lower computation complexity.

The increasing number of smart vehicles and their resource hungry applications pose new challenges in terms of computation and processing for providing reliable and efficient vehicular services. Mobile Edge Computing (MEC) is a new paradigm with potential to improve vehicular services through computation offloading in close proximity to mobile vehicles. However, in the road with dense traffic flow, the computation limitation of these MEC servers may endanger the quality of offloading service. To address the problem, we propose a hierarchical cloud-based Vehicular Edge Computing (VEC) offloading framework, where a backup computing server in the neighborhood is introduced to make up for the deficit computing resources of MEC servers. Based on this framework, we adopt a Stackelberg game theoretic approach to design an optimal multilevel offloading scheme, which maximizes the utilities of both the vehicles and the computing servers. Furthermore, to obtain the optimal offloading strategies, we present an iterative distributed algorithm and prove its convergence. Numerical results indicate that our proposed scheme greatly enhances the utility of the offloading service providers.

Summary Monitoring video of city surveillance camera plays an important role in public security and disaster relief, which is often used by first responder teams, such as firefighters and police officers. The first responders in emergency situations require consistent connection with one another and request the remote real‐time monitoring video for effective cooperation and coordination. However, the capacity and privacy of public wireless networks fail to satisfy the requirements in many emergency scenarios, which often leads to exceptionally high traffic loads and insecurity. Device‐to‐device (D2D) communications have been deemed a key solution for this problem, as responders can use D2D links for traffic offloading and secure communications. To investigate the D2D‐based solution for public safety video sharing, this paper focuses on the Focus Geographical Area (FGA) video consisting of multiple camera streams requiring higher bandwidth consumption than that of the traditional single‐camera stream, which attracts a large number of contents delivery requests in emergency situations. This paper develops a new Traffic Burden Switching Markovian (TBSM) model to evaluate the performance of transmitting real‐time FGA video in wireless networks with D2D communications. First, a novel D2D area model is introduced to characterize link‐switching in wireless D2D communication networks. Based on the proposed D2D area model, the state and transition matrix of TBSM model are then derived by jointly considering user mobility, link‐switching, and FGA video view‐switching. Thereafter, some key performance metrics including system offloading ratio, D2D link‐switching ratio, and view‐switching ratio are derived on the basis of coverage probability and ergodic rate. The performance results show the significant varying performance among D2D areas with different geographical locations in D2D enabled wireless networks, which is referred to as multi‐D2D‐area diversity. The excellent match between simulation and model results validates the accuracy of the TBSM model, which can be used to provide guidelines for the deployment and optimization of future wireless video networks with D2D communications.

With the conversion capability from radio frequency into electricity, Radio Frequency based Energy Harvesting (RF-EH) has appeared as a promising means to overcome the battery exhaustion problem of mobile devices. However, despite the great efforts in RF-EH communication techniques made recently, a big drawback that limits the application of RF-EH is the large propagation loss of radio signal energy. This paper attempts to address this problem through deploying multiple energy sources in the network based on cognitive techniques that can perceive and analyze user behaviors. Under a Mobile Social Network (MSN) scenario, we conduct energy source deployment in combination with the throwbox deployment problem, which aims at improving data forwarding efficiency. Our focus is on both data forwarding (DF) and energy harvesting (EH) efficiency. We propose three deployment schemes, i.e., D-deployment, E-deployment and T-deployment. With a continuous-time Markov chain based mobility model, we formulate these deployment schemes as three optimization problems, respectively. Simulation results indicate that the proposed schemes outperform an existing deployment scheme in terms of both DF efficiency and EH efficiency.

In the future wireless heterogeneous network, it is significantly critical to rationally and efficiently utilize network communication resource for satisfying the diversification and differentiation of user and service requirements. In this paper, we propose a user-centric wireless access and service matching scheme in heterogeneous networks with multiple radio access technologies (Multi-RATs). We model the interaction between user requirements and the quality of cloud services as the Markov decision process with Multi-RATs. The optimal matching scheme is derived by an iteration algorithm. To reduce time complexity, we further present a heuristic matching algorithm for determining whether the current stationary scheme is optimal in an off-line. Moreover, the difference between the current stationary scheme and the optimal matching scheme is derived by an upper bound. Performance evaluation demonstrates the achievable quality of experience by employing the two algorithms.

This paper studies the problem of optimal power allocation towards maximizing the throughput of point-to-point wireless communication systems with energy harvesting. A novel energy-transferring approach is proposed to analyze the throughput maximization problem with causality constraints, in which we study the transfer energy rather than the water level widely used in the existing literature. The proposed approach simplifies the power allocation as a linear function with respect to only two transfer energy variables, i.e., the energy transferred from the previous epoch and the energy transferred to the next epoch. Moreover, we prove that all the positive transfer energy variables can be determined by solving a set of linear equations with a special coefficient matrix derived from the KKT conditions for the dual problem. Based on the energy-transferring approach, we propose an iterative algorithm to obtain the optimal solution with a much lower complexity compared to those existing algorithms based on the directional water-filling structure results. Numerical studies verify the analytical results as well as the effectiveness of the proposed algorithm.

Demand response management (DRM) is envisaged to play a key role in the smart grid operation, which requires reliable communications between power providers and consumers. To reduce communication cost, the cognitive radio technique is proposed to transmit the meter data from consumers to the control center. Unlike most existing studies on cognitive enabled smart grid, where only individual spectrum sensing is considered, in this paper, both cooperative spectrum sensing and harvested renewable energy are incorporated. The tradeoff between spectrum sensing cost and the DRM management gain makes the optimization of cooperation scheme a challenging research problem. To solve the problem, we analytically study the degradation of communication reliability of the renewable energy supplied consumers who attend the cooperative sensing as well as the DRM gain obtained by the communication improvement. Further, the optimization problem of selecting the number of cooperative consumers is formulated. We prove that there exists a unique optimal cooperative number under some constraints and propose an efficient searching algorithm. Numerical results are presented to validate our theoretical analysis.

Due to the ever-increasing use of electric vehicles (EVs) and renewable energy sources, the charging station with renewable energy supply is made as one promising energy solution. Moreover, grouping vehicles into platoons could greatly improve road capacity and reduce energy consumption. These tendencies urgently demand a theoretical performance analysis framework of the EV platoons charging at renewable energy supplied stations. In this paper, we present such a framework based on a queuing network model. In this model, the fluctuation of the renewable energy, the uncertainty of the EV platoons arrival, the variation of the charging price, and the serving capacity limitation of the charging station are taken into account. The steady-state distribution of the queuing system have been presented based on the equilibrium equations. Then, various performance metrics of the charging system together with the charging gain of EVs have been proposed. The queuing network model, validated by the simulation results, can be used in the planning of practical charging stations and the charging scheduling of EV platoons.

Mobile social network (MSN) is a special kind of delay tolerant network that consists of mobile users with social characteristics. The existing social-aware data delivery algorithms usually ignore the energy cost of devices as well as the time-varying characteristic of user clustering in the vicinity of hotspots, which result in the degradation of the energy efficiency and the delay performance of data delivery in the MSN. This paper proposes an Energy Saving data Delivery (ESD) scheme, which can reduce energy consumption and data delivery delay. Moreover, the optimal number of data copies, the optimal set of destination hotspots and the route paths with the minimum energy cost are derived towards the highest energy efficiency for data delivery in a MSN. Simulation results indicate that the proposed ESD scheme outperforms the existing hotspotbased MSN schemes in terms of both energy cost and delay of data delivery. We also investigate the impact of the community similarity of users on the performance of data delivery.

The real-time streams play an increasingly important role in both traditional and emerging networks. Whereas, transmitting such streams requires vast channel resources, posing a huge challenge to the networks. Motivated by this problem, this paper considers a real-time stream system, where real-time messages with different importance should be transmitted through a burst erasure channel, and be decoded by the receiver within a fixed delay. We introduce the concepts of Symmetric Real-time (SR) stream and Asymmetric Real-time (AR) stream according to the stream characteristics. Different from the classical Shannon capacity, the channel transmission capacities are derived for SR and AR streams respectively, which can be used to guide the design of efficient real-time stream coding approaches. The capacity for SR streams is shown asymptotically achievable with a Proportional Time-invariant Intra-session Code (PTIC). Moreover, a Maximum Time-invariant Intra-session Code (MTIC) is presented for AR streams, which can also asymptotically achieve the capacity. Finally, the performances of PTIC and MTIC are verified by simulation experiments.

In the next-generation wireless networks such as 5G network or very-high-throughput WLAN, the users (humans or machines) in human-to-human, human-to-machine and machine-to-machine communications usually have heterogenous demands and behaviors. In this case, it is very difficult to uniformly evaluate user Quality-of-Experience (QoE) by one or more traditional performance metrics (e.g. throughput, delay and interference level) due to the heterogeneity. Moreover, the diversity of terminal capacities further exacerbates the difficulty in QoE provisioning since parameter adjustment may be inapplicable to all the equipments. To solve the aforementioned problems, this paper introduces a new QoE evaluation metric: satisfaction, which is a measurable, comparable and general assessment to indicate the gap between the current QoE and the desire of a user. Based on this new metric, we model the channel contention of random access networks as a non-cooperative game, where the players (contention nodes) are altruistic but also care about their individual interests. Theoretical analysis and simulation experiments indicate that a reinforcement learning algorithm can help the proposed game to achieve an efficient Nash equilibrium, which maximizes individual satisfaction with proportional fairness.

In Mobile Social Networks (MSNs), data caching techniques are widely applied to enhance the performance of data delivery by using storage devices called throwboxes. A throwbox is usually placed at a particular place and acts as a stationary relay. When putting throwboxes into a network, the deployment and storage allocation of throwboxes are two fundamental problems. Although throwbox deployment has been studied, optimal storage allocation is often ignored in these approaches. In this paper, we investigate the two problems jointly. Contact strength between a user and a particular place is evaluated with the aid of the contact history of users. Moreover, a joint optimization model is established to calculate the optimal throwbox deployment and storage allocation. Simulation results show that the proposed scheme performs well in decreasing data loss incurred by storage saturation and improving the efficiency of data delivery.

Because of the space and time dynamics of moving vehicles, network connectivity is an important performance metric to affect packet delivery in Vehicular Ad Hoc Networks (VANETs). Grouping vehicles into platoons in VANETs can improve road safety, change the network connectivity, and even reduce channel access collisions. Unfortunately, network connectivity is often ignored in the design of exiting MAC protocols for VANETs. In this paper, we analyze the connectivity probability and present a connectivity-aware Medium Access Control (MAC) protocol for platoon-based VANETs. A multi-priority Markov model is presented to derive the relationship between the connectivity probability and the system saturated throughput. Based on variable traffic status and network connectivity, a multi-channel reservation scheme is adopted to dynamically adjust the length of the Control CHannel (CCH) interval and the Service CHannel (SCH) interval for the improvement of the system performance, in terms of network throughput and the priority packet transmission opportunities for platoons. As a result, some important observations to the design and analysis of such communication systems are provided.

The combination with Orthogonal Frequency Multiple Access (OFDMA) technology is an effective approach for improving the performance of random channel access in wireless broadband networks, such as LTE, WiMax and the next-generation WLAN. In this paper, we propose a mathematical model to analyze the delay performance of an OFDMA-random-access system under unsaturated load condition. The proposed model adopts a general distribution for the duration of each time slot. In this way, the model no longer depends on a certain protocol. On the other hand, our model supports a more general setting, that is, a terminal may simultaneously access multiple subchannels in the system. Under this general assumption, we investigate the system delay performance and the stability condition. Simulation results not only show the accuracy of the presented analysis, but also demonstrate that the analysis based on saturated load assumption may underestimate the actual performance of the OFDMA-random-access system.

With the aid of advanced Information and Communication Technologies (ICT), Vehicle-to-Grid (V2G) networks will play an important role in supporting and enhancing the distributed electricity supply in the next generation power grid-smart grid. In order to ensure stability of the power grid and satisfy the Quality of Energy (QoE) requirements of Electric Vehicles (EVs), this paper proposes a two-level group bidding mechanism for the electric energy trade between the grid and EVs. Trading information between the grid and EVs is exchanged through communication networks. EVs acted as mobile energy storage are organized to form an electricity feedback group by aggregators. The grid aims at minimizing the cost of given electricity demand while EVs expect to maximize their profits. A quantity based feedback electricity unit pricing scheme is proposed to incentivize the participation of EVs in V2G networks. Moreover, Vickrey-Clarke-Groves (VCG) auction-based algorithms are designed to implement our proposed mechanisms. Simulation results indicate that our mechanism is able to reduce the cost of the grid while offer EVs significant incentives to participate in the V2G power market.

It is a challenge to support multimedia services with high Quality of Service (QoS) requirements for upcoming 5G radio access techniques that has the characteristics of Heterogeneous network Architecture, Heterogeneous Terminals and Heterogeneous Spectrum (H3ATS). Multi-view Video (MVV) consisting of multiple video streams captured by close spaced cameras is increasingly popular, permitting changeable viewpoints by playing different streams. As those close spaced cameras will capture overlapping frames (OFs) and transmit OFs in multiple streams, when switching video streams from one to another, it is redundant to transmit and receive OFs in the latter stream for base stations and users, respectively. Moreover, since the data rate requirement of a MVV with multiple streams is much higher than that of the traditional video with single-stream, base stations will consume tremendous bandwidth if the number of playing MVVs increases. To eliminate OFs and offload traffic from base stations, we propose a new MVV stream architecture called Overlapping Reduced Multi-view Video Transmission (ORMVVT) and a new network architecture named Multicast Multi-Traffic Source (MMTS) for multicast small cell networks (MSCNs). Then, an optimization problem is formulated to minimize the data rate of small cells under QoS constraint. An Offloading Based Traffic Placement (OBTP) scheme is introduced to solve the optimization problem. Simulation results show that the proposed low-complexity OBTP is able to get a higher performance than the traditional schemes in terms of bandwidth saving.

In the context of Mobile Social Networks (MSNs), a type of wireless storage device called throwbox has emerged as a promising way to improve the efficiency of data delivery. Recent studies focus on the deployment of throwboxes to maximize data delivery opportunities. However, as a storage device , the storage usage of throwboxes has seldom been addressed by existing work. In this paper, the storage allocation of throwboxes is studied as two specific problems: (1) if throwboxes are fixed at particular places, how to allocate storage to the throwboxes; and (2) if throwboxes are deployable, how to conduct storage allocation in combination with throwbox deployment. Two optimization models are proposed to calculate the optimal storage allocation with a knowledge of the contact history of users. Real trace based simulations demonstrate that the proposed scheme is able to not only decrease data loss on throwboxes but also improve the efficiency of data delivery.

Vehicle-to-grid (V2G) system with efficient demand response management (DRM) is critical to solve the problem of supplying electricity by utilizing surplus electricity available at electric vehicles (EVs). An incentivized DRM approach is studied to reduce the system cost and maintain the system stability. EVs are motivated with dynamic pricing determined by the group-selling-based auction. In the proposed approach, a number of aggregators sit on the first-level auction responsible to communicate with a group of EVs. EVs as bidders consider quality of energy (QoE) requirements, and report interests and decisions on the bidding process coordinated by the associated aggregator. Auction winners are determined based on the bidding prices and the amount of electricity sold by the EV bidders. We investigate the impact of the proposed mechanism on the system performance with maximum feedback power constraints of aggregators. The designed mechanism is proven to have essential economic properties. Simulation results indicate that the proposed mechanism can reduce the system cost and offer EVs significant incentives to participate in the V2G DRM operation.

Multi-view video (MVV) consists of multiple video streams captured simultaneously by multiple closely spaced cameras and enables users to freely change their viewpoints by playing different video streams. Those close deployed cameras will capture overlapping frames (OFs) and then transmit OFs in multiple video streams. With the viewpoint change from one stream to another, redundant OFs in the latter stream can be useless for a user. Moreover, the redundant transmission will increase with the number of users and result in severe bandwidth waste for both base stations and users. In order to reduce the redundant transmission of OFs and increase energy efficiency (EE), a new architecture Overlapping Reduced Multi-view Video Transmission (ORMVVT), which is based on User dependent Multi-view video Streaming for Multi-users (UMSM), is proposed for downlink transmission of MVV in small cell networks (SCNs). Then, a novel resource allocation model is introduced to help users achieve different data rates according to their Quality of Service (QoS) requirements. Contrarily, for the simplicity of implementation, existing schemes can only support a single reception data rate for all users in a multicast group. Further, we formulate an optimization problem to maximize the EE with the QoS constraint of services. Finally, a suboptimal QoS-aware Energy-efficient Multicast Resource Allocation scheme (QEMRA) is proposed to reduce the computational complexity. Numerical results show that the proposed low-complexity QEMRA scheme is able to get a close-to-optimal performance under general fading distributions.

Neuro-spike communication is an important branch of molecular communications and has attracted much attention recently. Seminal works on the analyses of signal processing and channel models for the synaptic communication have recently been carried out. However, these works do not consider interference. In this paper, we propose an interference model for synaptic channels with particular focus on InterSymbol Interference (ISI) and Single-Input Single-Output (SISO) channel. We have investigated the overlapping between the two consecutively signals which are sent from a presynaptic terminal to a postsynaptic terminal and their interferences. Furthermore, important parameters of synaptic communication channel that are related to the ISI are also analyzed. The relationship between channel rate region and ISI is also studied.

Wireless networks which would connect vehicles via the Internet to a location based service, LBS, also would expose vehicles to online surveillance. In circumstances when spatial cloaking is not effective, such as when continuous precise location is required, LBSs may be designed so that users relay dummy queries through other vehicles to camouflage true locations. This paper introduces PARROTS, Position Altered Requests Relayed Over Time and Space, a privacy protocol which protects LBS users' location information from LBS administrators even (1) when the LBS requires continuous precise location data in a vehicular ad hoc network, (2) when LBS administrators collude with administrators of vehicular wireless access points (a.k.a. roadside units, or RSUs), and (3) when precise location data might be deanonymized using map databases linking vehicle positions with vehicle owners' home/work addresses and geographic coordinates. Defense against deanonymization requires concealment of endpoints, the effectiveness of which depends on the density of LBS users and the endpoint protection zone size. Simulations using realistic vehicle traffic mobility models varying endpoint protection zone sizes measure improvements in privacy protection.

Vehicular Ad Hoc Networks (VANETs) can provide safety and non-safety related applications and services to improve the passenger safety and comfort. Due to the space and time dynamics of moving vehicles, network connectivity is an important performance metric to indicate the quality of the network and the user's satisfaction. Grouping vehicles into platoons in the highway can improve road safety, reduce fuel consumption, and decrease traffic congestion. In this paper, we study the connectivity characteristic of platoon-based VANETs. The connectivity probabilities are analyzed for the Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communication scenarios. The relationships between the connectivity probability and the key parameters are investigated, including the traffic density, the coverage of the ordinary vehicle, the coverage of the platoon, the coverage of the Road Side Unit (RSU), the distance between two adjacent RSUs and the ratio of the platoon in the VANET. The results can help the transport system designer to control the traffic on the highway to satisfy the connectivity requirement. Analysis results show that the connectivity probability can be significantly improved when there are platoons in a network.

In Mobile Social Networks (MSNs), users with specific relationships are usually treated as a community for data sharing. However, the demand of data sharing among distributed strangers also exists. Those users that have the same interest but do not necessarily know or usually encounter each other can form a gossip community and share information. This paper proposes a data dissemination approach, i.e., the Gathering Point-aided Spreading (GPS) algorithm, which explores the encounter pattern of users and the aid of gathering points to facilitate data sharing in the gossip community. Based on the past encounter pattern, the GPS algorithm predicts the encounter probability among users and assigns the best users to carry the data for a wide spreading. Moreover, by storing a copy of data at the gathering points, GPS enables a further sharing of the data even the carriers leave the gathering points. With different utility functions, GPS can be modified into three versions (GPS-DR, GPS-DE and GPS-TR). Simulation experiments show that GPS outperforms SocialCast in both delivery ratio and delay in data sharing. In addition, among the three versions, GPS-DR and GPS-DE perform the best in terms of delivery ratio and delay respectively, while GPS-TR makes a tradeoff between them.

As a part of public transportation, taxi plays a very important role of passengers' travel from the airport to downtown. In order to exploring an efficient taxi dispatch mechanism and saving passenger waiting times, we develop an adaptive airport taxi dispatch system based on principal components analysis wavelet neural network (PCA-WNN). A series of new online short term time forecasting techniques are used to capture the relationship between taxi supply and demand. Then we proposed an adaptive feedback-based taxi dispatch algorithm for the effective response to the non-stationarity of taxi service under a changing environment. By using the real data of Beijing Capital International Airport within twenty weeks, our experiment demonstrated that this algorithm can predict accurately of the taxi service data and greatly improve the efficiency of taxi management.

ABSTRACT In this paper, a new channel allocation and re‐location scheme is proposed for cognitive radio users to efficiently utilize available spectrums. We also present a multiple‐dimension Markov analytical chain to evaluate the performance of this scheme. Both analytical results and simulation results demonstrate that the new scheme can enhance the radio system performance significantly in terms of blocking probability, dropping probability, and throughput of second users. The proposed scheme can work as a non‐server‐based channel allocation, which has practical values in real engineering design. Copyright ©2011 John Wiley & Sons, Ltd.

The emerging wireless vehicular communication technologies are intended to improve the safety of transportation system. This paper proposes a multi-hop to deliver Safety Packets (SPs) in Vehicular Ad Hoc Network (VANET). The proposed broadcast scheme, namely the Information Relevance Related Broadcast (IRRB) approach, introduces three related parameters of SPs, i.e., the Affecting Time (AT), the Affecting Distance (AD) and the Affecting Lane Set (ALS), to confine the dissemination of each SP within a limited area. IRRB also deploys the Distance-and-Lane-based Relay Selection (DLRS) strategy and the Distance-based Delay Time (DDT) strategy to improve the broadcast efficiency. Simulation results show that the IRRB scheme is able to reduce redundant packets while ensuring the delivery reliability of SPs.

Vehicular Ad Hoc Networks (VANETs) and their diverse applications experience growing interest in both academic and industry. Different types of traffic packets delivered through vehicle-to-vehicle and vehicle-to-infrastructure communications are intended to improve passenger safety and comfort. In this paper, we propose a multiple priority supported Medium Access Control (MAC) protocol for VANETs based on the time slotted p-persistent channel access mechanism. The protocol differentiates the services packets into multi-priority on the Control Channel (CCH). Theoretical analysis based on Markov model is presented to optimize the transmission probabilities of the packets with different priorities, as well as the adjustable intervals of the CCH and the Services Channels (SCHs). Both analytical results and simulation experiments show that the proposed MAC protocol is able to ensure the prioritized transmission of the safety packets and also achieve optimal system performance with respect to saturated throughput.

The mixture of users with different carrier aggregation (CA) capabilities presents new challenges to optimize the performance of the next generation wireless networks. This paper focuses on the joint resources allocation for OFDMA-based multi-carrier system. Distinguished from many existing methods, our approach deploys the joint dynamic spectrum resources assignment and adaptive power allocation technologies for the carrier-aggregated systems. A low complexity suboptimal algorithm, named as the joint CC, RB and power allocation (JCRPA) algorithm, is proposed in this paper. The algorithm combines the dynamic component carrier (CC) and resource block (RB) assignment, as well as adaptive power allocation iteratively. In contrast to the conventional static CC assignment, a novel suboptimal dynamic CC and RB assignment algorithm is designed to maximize network utility. Simulation results demonstrate that JCRPA is able to improve the system performance in terms of network utility, average throughput and fairness.

In recent years, governments, standardization bodies, automobile manufacturers, and academia are working together to develop vehicular ad hoc network (VANET)-based communication technologies. VANETs apply multiple channels, i.e., control channel (CCH) and service channels (SCHs), to provide open public road safety services and the improve comfort and efficiency of driving. Based on the latest standard draft IEEE 802.11p and IEEE 1609.4, this paper proposes a variable CCH interval (VCI) multichannel medium access control (MAC) scheme, which can dynamically adjust the length ratio between CCH and SCHs. The scheme also introduces a multichannel coordination mechanism to provide contention-free access of SCHs. Markov modeling is conducted to optimize the intervals based on the traffic condition. Theoretical analysis and simulation results show that the proposed scheme is able to help IEEE 1609.4 MAC significantly enhance the saturated throughput of SCHs and reduce the transmission delay of service packets while maintaining the prioritized transmission of critical safety information on CCH.

The mixture of users with different bandwidth capability presents new challenges to optimize the transmission performance of the next generation wireless networks. This paper focuses on resource allocation for pervasive wireless networks with component carrier (CC) aggregation. Distinguished from many existing methods that decompose the resource optimization problem into two sequence steps, i.e., CC scheduling and resource block (RB) assignment on each carrier, we develop a novel joint CC and RB allocation algorithm to maximize network utility, namely Minimizing System Utility Loss (MSUL) algorithm. Numerical results indicate that MSUL is able to improve the system performance in terms of network utility, average throughput and fairness compared with the algorithms optimizing CCs and RBs allocation separately.

Communication messages in Vehicular Ad-hoc Networks (VANETs) can be used to track movement of vehicles. In this paper, we address the problem of movement tracking and enhance location privacy without affecting security and safety of vehicles. By considering unique characteristics of VANETs, we firstly propose a synchronized pseudonym changing protocol based on the concept of forming groups among neighboring vehicles. Secondly, we analytically evaluate the anonymity and unlinkability of the proposed protocol. Finally, we do a series of simulations to evaluate the performance of our protocol in real VANET environments such as Manhattan and Urban. Simulation results show that our protocol is feasible and produces excellent performances. The main advantages of our protocol compared with the existing approaches include: 1) it makes larger anonymity set and higher entropy; 2) it reduces the tracking probability; 3) it can be used in both safety and non-safety communications; and 4) Vehicles need not suspend regular communication for changing pseudonyms.

The emerging wireless vehicular communication technology is intended to improve safety and comfort of transportation systems. Different types of traffic information could be delivered through vehicle-to-vehicle and vehicle-to-infrastructure communications. This paper proposes a Quality-of-Service (QoS) supported multi-channel MAC scheme for Vehicular Ad Hoc Networks (VANETs), which can adaptively tune the contention window for different services at each node, and dynamically adjust the intervals of the Control Channel (CCH) and the Service Channels (SCHs) working in multi-rate. Theoretical model is proposed to obtain the contention window and optimize the intervals based on traffic conditions. Analysis and simulation results show that the proposed MAC is able to help IEEE 1690.4 MAC support QoS services, while ensuring the high saturation throughput and the prioritized transmission of critical safety information.

Abstract The distinguishing properties of VehicularAd hocwireless Networks (VANETs) strongly challenge the design of Medium Access Control (MAC) protocols, which are responsible for the medium access coordination among active vehicles, as well as the accommodation of both driving safety applications and non‐safety applications. In this paper, we focus on a comprehensive survey of VANET MAC schemes by integrating various related issues and challenges. Our analysis not only deepens the understanding of MAC techniques in VANETs but also presents the key ideas and potential directions for future research in this area. In order to significantly improve the communication performance of VANETs, more research efforts on MAC techniques must be made for optimizing multichannel coordination and allocation approaches, enhancing the Quality of Service (QoS) capability, and combating the hidden terminal problem, broadcast storm problem and even ACK (acknowledgment) explosion problem. Copyright © 2009 John Wiley & Sons, Ltd.

Communications through road side units in Vehicular Ad hoc Networks (VANETs) can be used to track the location of vehicles, which makes serious threat on users' privacy. In this paper, we propose and evaluate a novel location privacy enhancement protocol for VANETs. Firstly, we propose an Anonymous Online Service Access (AOSA) protocol. Secondly, we analytically evaluate the anonymity and the unlinkability of the proposed protocol. Finally, a series of simulation studies are conducted to evaluate the performance of our protocol in the real VANET environments such as Manhattan and Urban scenarios. According to analytical evaluation and simulations, our protocol provides higher level of anonymity and location privacy for on-line service access applications. Simulation results further show that our protocol is feasible and produces better performance in real VANET environments by producing higher success ratio and smaller delay.

This paper focuses on the characterization of vehicle mobility in vehicular ad hoc networks (VANETs). The performance of vehicle mobility in terms of link available time and the number of inter-vehicle link changes for maintaining active links in VANET is analyzed using both the handover and random moving models. The theoretical analysis is verified by simulation experiments. The numerical results indicate that the vehicle random moving analytical model is able to provide a more accurate description of the complicated vehicle moving behavior than the conventional random way point mobility model, especially when vehicles are moving relatively fast.

Mobile ad hoc networks (MANETs) are designed for "a special purpose'', usually supporting some special functions, such as sensing environments, providing services. However, the traditional model of service accessing, such as the server/client model, can not be directly applied to MANETs, as the node, running the server, providing services, may be multi-hop distant, even mobile and thus unreachable from the client. This paper proposes a novel approach, by which a node can query other nodes by using the standard set of SQL (Structured Query Language) functions, which is the foundation of mobile databases. Analysis and simulation demonstrate that there is a tradeoff between the communication overhead and the query accuracy, which should be carefully weighed in the real network environment.

In order to combat the exposed terminal problem and the interference accumulation phenomenon in wireless channels, this paper proposed a new Asymmetrical Dual Busy Tones (ADBT) MAC protocol. By introducing BT-CTS/BTACK mechanism and the approximate algorithm of interfering nodes, ADBT MAC can solve the exposed terminal problem and the hidden terminal problem in the environment with large interference range. Moreover, the effect of the interference accumulation phenomenon can be alleviated by the power adjustment approach for the busy tones. The network performances of DCF, DBTMA, VPDBT and ADBT MAC protocols are compared via OPNET-based simulation experiments, which verify that ADBT is able to prevent packet collisions effectively and improve the channel utilization significantly.

This paper presents a novel k-hop compound metric based clustering (KCMBC) scheme, which uses the host connectivity and host mobility jointly to select cluster-heads. KCMBC is a fast convergent and load balancing clustering approach that is able to offer significant improvement on scalability for large-scale ad hoc networks. On the other hand, since host mobility has been taken into account in terms of the average link expiration time, the clusters constructed by KCMBC are more stable than many other schemes. Simulation results show that the clusters created by using the KCMBC approach retain modest but more uniform cluster size, and cluster-head life-time can be increased by KCMBC up to 50%. Moreover, the control overheads for cluster formation using the KCMBC scheme are kept relatively low if compared to other clustering schemes.

Two novel MAC schemes are proposed to solve the hidden terminal problem and the exposed problem in ad hoc networks with large interference range. With the aid of dual busy tones, one scheme is a simple enhancement to IEEE 802.11, and the other is to exactly signify the interference range by adjusting the transmission power of busy tones. Both of the two schemes are able to prevent the collisions of data/ACK packets. The numerical results show that the two proposed schemes are able to enhance the saturation throughput of IEEE 802.11 significantly. The proposed VPDBT MAC can balance the trade off between spatial reuse and collision avoidance.

This paper focuses on the design of a novel approach for dynamic k-hop clustering architecture, which is called k-hop compound metric based clustering (KCMBC). KCMBC is a fast convergent and load balancing clustering approach that is able to demonstrate significant improvement on the network performance, in terms of scalability and stability for large-scale ad hoc networks. On the other hand, since KCMBC has taken into account the host mobility, the clusters constructed by KCMBC are more stable than the other schemes. Simulation results show that the clusters created by using the KCMBC approach have modest but more uniform cluster size. Moreover, cluster-head duration can be increased by KCMBC significantly.

This paper presents a novel Call Admission Control (CAC) scheme which adopts the neural network approach, namely Minimal Resource Allocation Network (MRAN) and its extended version EMRAN. Though the current focus is on the Call Admission Control (CAC) for Asynchronous Transfer Mode (ATM) networks, the scheme is applicable to most high-speed networks. As there is a need for accurate estimation of the required bandwidth for different services, the proposed scheme can offer a simple design procedure and provide a better control in fulfilling the Quality of Service (QoS) requirements. MRAN and EMRAN are on-line learning algorithms to facilitate efficient admission control in different traffic environments. Simulation results show that the proposed CAC schemes are more efficient than the two conventional CAC approaches, the Peak Bandwidth Allocation scheme and the Cell Loss Ratio (CLR) upperbound formula scheme. The prediction precision and computational time of MRAN and EMRAN algorithms are also investigated. Both MRAN and EMRAN algorithms yield similar performance results, but the EMRAN algorithm has less computational load.