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.

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.

The novel notion of Edge Computing Power Networks (ECPN) has recently been proposed to provide highly flexible matching strategies among edge servers and user devices to facilitate seamless computing power and network architectures. However, recent work about ECPN is still in its infancy, and almost all work has ignored the tidal phenomenon of computing power requests from mobile user devices, including temporal characteristic of the quantity and spatial characteristic of the distribution in different periods of one day, which leads to the energy waste of idle edge servers for always keeping active. To deal with this problem, we propose the ECPN model in this article, taking into account the tidal phenomenon of mobile user devices to develop energy-efficient computing power matching strategies. Specifically, we formulate the optimization problem that encompasses computing power allocation and task matching for user devices as well as dynamic on-off for edge servers. Our main objective is to maximize the quality of service (QoS) for user devices while minimizing the energy consumption for task execution with respect to resource limitations and task requirements. To solve the formulated problem efficiently, we propose a distributed algorithm based on matching theory to determine the optimal computing power allocation, task matching and on-off strategies. Extensive numerical results show that the proposed scheme can reduce energy consumption while ensuring the QoS.

The sixth-generation (6 G) networks aim to achieve ubiquitous intelligent connectivity while ensuring extremely low latency, reducing energy consumption, and enhancing privacy protection. Mobile edge computing (MEC) offers an effective solution to reduce latency and energy consumption by leveraging resources near end devices for task offloading. However, MEC faces significant challenges in meeting the requirements of 6 G networks, including limited computational resources, high mobility, and strict data privacy demands. Efficiently allocating edge resources while preserving privacy has become a critical issue for realizing the objectives of 6 G networks. In this paper, we propose a privacy-preserving edge computing power network (EdgeCPN) model that jointly leverages the computing resources of edge computing nodes and protects sensitive computing power information through differential privacy methods. In addition, we propose a task matching problem that aims to minimize the privacy-budget-weighted energy consumption while ensuring privacy protection and meeting task requirements. We propose a dynamic graph-based multiagent reinforcement learning (MADRL) algorithm to find the optimal strategy for task matching and computing resource allocation with privacy protection. The results show that our proposed task matching model with energy and privacy tradeoffs can minimize the energy consumption in the matching process while ensuring privacy, and the algorithm can find the optimal strategy for task matching efficiently.

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.

Federated Learning (FL) is an emerging distributed learning architecture that allows multiple agents to share knowledge in machine learning. However, in the scenario with mobile agents, the mobility of agents significantly affects the learning performance. Besides, frequent exchange of local models could result in high communication overhead. In this paper, we propose a Mobility-aware Federated Reinforcement Learning (MFRL) framework. In MFRL, we model the influences of agent mobility on communication quality and data correlation, and devise a mobility-aware node selection algorithm, so as to accelerate the training procedure and improve the learning performance, taking learning quality, wireless channel quality, and data correlation of agents into consideration. A knowledge distillation (KD) based model compression method is integrated into the MFRL to reduce the communication overhead as well as accelerate the inference process. Finally, taking deep reinforcement learning (DRL) based collision avoidance of intelligent vehicles as a study case, the effectiveness of MFRL is verified. Numerical results demonstrate that the proposed MFRL can accelerate the training process and improve the learning performance.

In the beyond 5G/6G era, aerial edge computing (AEC) is expected to be used as significant components in Internet of Things. AEC brings flexible deployment with Line-of-Sight communication links for task offloading and computing services. In this article, we present a comprehensive survey of the AEC technology. First, we introduce a three-layer architecture of AEC, which includes the satellite, unmanned aircraft vehicles, and ground terminals. Second, we illustrate challenges in AEC, and summarize recent studies in terms of AEC performance metrics that include energy efficiency, latency, and operation cost to address challenges in AEC. Further, we introduce the advanced technologies applied in AEC management, e.g., artificial intelligence (AI) and the distributed optimization. Finally, the applications and the open issues with AEC are summarized and classified in the study.

Aerial edge networks that leverage Unmanned Aerial Vehicles (UAVs) to provide task processing and data caching in infrastructure-less areas have emerged as a prominent paradigm in the upcoming 6G era. However, the endurance of UAV flight generally suffers from constrained onboard battery capacity. Thus, energy efficiency turns out to be a key issue for implementing aerial edge networks. In this paper, we focus on green mobile edge computing of rechargeable UAVs, and propose an aerial edge network aided by distributed charging services. In this network, an energy minimization problem that jointly considers aerial edge resource allocation, UAV trajectory planning and battery charging scheduling as well as task delay constraint is formulated. Due to the non-convexity and the coupled optimization variables of the problem, we adopt a deep reinforcement learning approach to design an intelligent iterative algorithm to obtain energy efficient aerial edge strategies, and demonstrate its convergence. Numerical simulations are conducted to verify the effectiveness of our proposed scheme as compared to two benchmark schemes.

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 rapid proliferation of smart vehicles along with the advent of powerful applications bring stringent requirements on massive content delivery. Although vehicular edge caching can facilitate delay-bounded content transmission, constrained storage capacity and limited serving range of an individual cache server as well as highly dynamic topology of vehicular networks may degrade the efficiency of content delivery. To address the problem, in this article, we propose a social-aware vehicular edge caching mechanism that dynamically orchestrates the cache capability of roadside units (RSUs) and smart vehicles according to user preference similarity and service availability. Furthermore, catering to the complexity and variability of vehicular social characteristics, we leverage the digital twin technology to map the edge caching system into virtual space, which facilitates constructing the social relation model. Based on the social model, a new concept of vehicular cache cloud is developed to incorporate the correlation of content storing between multiple cache-enabled vehicles in diverse traffic environments. Then, we propose deep learning empowered optimal caching schemes, jointly considering the social model construction, cache cloud formation, and cache resource allocation. We evaluate the proposed schemes based on real traffic data. Numerical results demonstrate that our edge caching schemes have great advantages in optimizing caching utility.

Technological advancements of urban informatics and vehicular intelligence have enabled connected smart vehicles as pervasive edge computing platforms for a plethora of powerful applications. However, varies types of smart vehicles with distinct capacities, diverse applications with different resource demands as well as unpredictive vehicular topology, pose significant challenges on realizing efficient edge computing services. To cope with these challenges, we incorporate digital twin technology and artificial intelligence into the design of a vehicular edge computing network. It centrally exploits potential edge service matching through evaluating cooperation gains in a mirrored edge computing system, while distributively scheduling computation task offloading and edge resource allocation in an multiagent deep reinforcement learning approach. We further propose a coordination graph driven vehicular task offloading scheme, which minimizes offloading costs through efficiently integrating service matching exploitation and intelligent offloading scheduling in both digital twin and physical networks. Numerical results based on real urban traffic datasets demonstrate the efficiency of our proposed schemes.

Connected Automated Vehicle (CAV) is a promising paradigm for achieving safe and intelligent transportation systems. In CAV scenario, massive raw sensor data needs to be shared among vehicles under strict latency and high data rate constraints, which poses critical challenges on existing low data rate vehicular communication on 5.9 GHz band. To address these challenges, we propose a millimeter wave (mmWave) enabled CAV network with high capacity to support the raw sensor data sharing among vehicles. A joint vehicle association and power allocation (JVAPA) algorithm is proposed to maximize date rate while minimize energy consumption for CAVs. The one-to-many vehicle association problem is formulated as a swap matching model, and the stable matching states for CAVs and BSs association are achieved. The non-convex power allocation problem is transformed into a convex problem using the first order Taylor expansion, and the optimal power allocation results are achieved. Numerical results verify that the proposed JVAPA algorithm can significantly improve the energy efficiency compared with the benchmark algorithms.

Emerging technologies, such as mobile-edge computing (MEC) and next-generation communications are crucial for enabling rapid development and deployment of the Internet of Things (IoT). With the increasing scale of IoT networks, how to optimize the network and allocate the limited resources to provide high-quality services remains a major concern. The existing work in this direction mainly relies on models that are of less practical value for resource-limited IoT networks, and can hardly simulate the dynamic systems in real time. In this article, we integrate digital twins with edge networks and propose the digital twin edge networks (DITENs) to fill the gap between physical edge networks and digital systems. Then, we propose a blockchain-empowered federated learning scheme to strengthen communication security and data privacy protection in DITEN. Furthermore, to improve the efficiency of the integrated scheme, we propose an asynchronous aggregation scheme and use digital twin empowered reinforcement learning to schedule relaying users and allocate spectrum resources. Theoretical analysis and numerical results confirm that the proposed scheme can considerably enhance both communication efficiency and data security for IoT applications.

Digital twin network (DTN) is an emerging network that utilizes digital twin (DT) technology to create the virtual twins of physical objects. DTN realizes co-evolution between physical and virtual spaces through DT modeling, communication, computing, data processing technologies. In this article, we present a comprehensive survey of DTN to explore the potentiality of DT. First, we elaborate key features and definitions of DTN. Next, the key technologies and the technical challenges in DTN are discussed. Furthermore, we depict the typical application scenarios, such as manufacturing, aviation, healthcare, 6G networks, intelligent transportation systems, and urban intelligence in smart cities. Finally, the new trends and open research issues related to DTN are pointed out.

The rapid development of industrial Internet of Things (IIoT) requires industrial production towards digitalization to improve network efficiency. Digital Twin is a promising technology to empower the digital transformation of IIoT by creating virtual models of physical objects. However, the provision of network efficiency in IIoT is very challenging due to resource-constrained devices, stochastic tasks, and resources heterogeneity. Distributed resources in IIoT networks can be efficiently exploited through computation offloading to reduce energy consumption while enhancing data processing efficiency. In this article, we first propose a new paradigm digital twin network to build network topology and the stochastic task arrival model in IIoT systems. Then, we formulate the stochastic computation offloading and resource allocation problem to minimize the long-term energy efficiency. As the formulated problem is a stochastic programming problem, we leverage Lyapunov optimization technique to transform the original problem into a deterministic per-time slot problem. Finally, we present asynchronous actor-critic algorithm to find the optimal stochastic computation offloading policy. Illustrative results demonstrate that our proposed scheme is able to significantly outperforms the benchmarks.

Emerging technologies, such as digital twins and 6th generation (6G) mobile networks, have accelerated the realization of edge intelligence in industrial Internet of Things (IIoT). The integration of digital twin and 6G bridges the physical system with digital space and enables robust instant wireless connectivity. With increasing concerns on data privacy, federated learning has been regarded as a promising solution for deploying distributed data processing and learning in wireless networks. However, unreliable communication channels, limited resources, and lack of trust among users hinder the effective application of federated learning in IIoT. In this article, we introduce the digital twin wireless networks (DTWN) by incorporating digital twins into wireless networks, to migrate real-time data processing and computation to the edge plane. Then, we propose a blockchain empowered federated learning framework running in the DTWN for collaborative computing, which improves the reliability and security of the system and enhances data privacy. Moreover, to balance the learning accuracy and time cost of the proposed scheme, we formulate an optimization problem for edge association by jointly considering digital twin association, training data batch size, and bandwidth allocation. We exploit multiagent reinforcement learning to find an optimal solution to the problem. Numerical results on real-world dataset show that the proposed scheme yields improved efficiency and reduced cost compared to benchmark learning methods.

The rapid development of artificial intelligence and 5G paradigm, opens up new possibilities for emerging applications in industrial Internet of Things (IIoT). However, the large amount of data, the limited resources of Internet of Things devices, and the increasing concerns of data privacy, are major obstacles to improve the quality of services in IIoT. In this article, we propose the digital twin edge networks (DITENs) by incorporating digital twin into edge networks to fill the gap between physical systems and digital spaces. We further leverage the federated learning to construct digital twin models of IoT devices based on their running data. Moreover, to mitigate the communication overhead, we propose an asynchronous model update scheme and formulate the federated learning scheme as an optimization problem. We further decompose the problem and solve the subproblems based on the deep neural network model. Numerical results show that our proposed federated learning scheme for DITEN improves the communication efficiency and reduces the transmission energy cost.

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.

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.

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.

Empowered with urban informatics, transportation industry has witnessed a paradigm shift. These developments lead to the need of content processing and sharing between vehicles under strict delay constraints. Mobile edge services can help meet these demands through computation offloading and edge caching empowered transmission, while cache-enabled smart vehicles may also work as carriers for content dispatch. However, diverse capacities of edge servers and smart vehicles, as well as unpredictable vehicle routes, make efficient content distribution a challenge. To cope with this challenge, in this article we develop a social-aware nobile edge computing and caching mechanism by exploiting the relation between vehicles and roadside units. By leveraging a deep reinforcement learning approach, we propose optimal content processing and caching schemes that maximize the dispatch utility in an urban environment with diverse vehicular social characteristics. Numerical results based on real urban traffic datasets demonstrate the efficiency of our proposed schemes.

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.

Fog computing is a promising solution for new emerging applications requiring intensive computation resources and low latency. Devices at the edge of network can share idle resources and collaboratively accomplish the computing tasks in fog computing. Thus, task publishers have heterogeneous options when offloading computing tasks considering the quality of transmission links, energy consumption and other hardware constraints of fog nodes. To incentivize these devices to participate in computation offloading, effective incentive mechanisms are needed. In this paper, utilizing the framework of contract theory, we formulate the negotiation between task publisher and fog nodes as an optimization problem. The optimal contract is the Nash equilibrium solution achieved by task publisher and fog nodes. Simulation results show that an optimal contract can maximize the utility of task publisher meanwhile guarantee the individual rationality and incentive compatibility of fog nodes. Therefore, edge devices can be incentivized effectively to involve in the computation offloading.

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.

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.

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.

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.

In Long Term Evolution (LTE) systems, the evolved Node B (eNodeB) performs link adaptation to improve the system performance on the basis of channel quality information estimated and fedback by the user equipments (UEs). However, the channel estimation error (CEE) can not be avoided due to hardware constraint. To make up the performance loss caused by CEE, two types of link adaptation algorithms are proposed: weighted average (WA) algorithm based on three different parameters and an enhanced adaptive time window (ATW) algorithm. Unlike pervious work, this paper not only presents the specific process of all the algorithms but also takes the theoretical derivation of performance comparison into consideration. Moreover, simulation results are demonstrated using an LTE system-level simulator, which show that the WA algorithm based on channel estimation value can effectively mitigate CEE and achieve a significant gain. Subsequently, a better system performance can be obtained using ATW algorithm by adjusting the window size dynamically.

One of the most important features of the evolving Fourth Generation (4G) wireless communication system is heterogeneous wireless access in which users could connect to several wireless access networks simultaneously. The new feature brings new challenges for the bandwidth allocation (BA) among heterogeneous networks. A non-cooperative bandwidth allocation game (NCBAG) algorithm for heterogeneous wireless networks is proposed in this paper. A BA problem is modeled as a non-cooperative game, and formulated to maximize the total utility of different networks. The existence of Nash equilibrium is verified for the proposed game model. The utility functions are developed for different applications to avoid assigning too much bandwidth to single user. Simulation results show that our proposed scheme can not only achieve high utility, but also reduce the blocking probability within a few steps of iteration.

This paper propose an iterative inter-cell interference coordination strategy to meet the requirements of the next generation mobile communication systems, which are higher data rate, broader coverage, especially more stringent demand on cell edge spectral efficiency. Sector cooperation is introduced in MU MIMO (multi-user multiple input multiple output) systems. In the aspect of user pairing, both the system capacity and fairness among users are taken into consideration through pairing center and edge users. As for the inter-cell interference coordination, iterative sector cooperation on selecting pre-coders from code books is implemented, in order to avoid inter-cell interference and improve cell edge performance. Theoretical analysis and simulation results demonstrate that the proposed strategy can significantly improve cell edge throughput while guaranteeing the performance of average cell spectrum efficiency.