Our work proposes a comprehensive solution for predicting Metaverse network traffic, addressing the growing demand for intelligent resource management in eXtended Reality (XR) services. We first introduce a state-of-the-art testbed capturing a real-world dataset of virtual reality (VR), augmented reality (AR), and mixed reality (MR) traffic, made openly available for further research. To enhance prediction accuracy, we then propose a novel view-frame (VF) algorithm that accurately identifies video frames from traffic while ensuring privacy compliance, and we develop a Transformer-based progressive error-learning algorithm, referred to as ResLearn for Metaverse traffic prediction. ResLearn significantly improves time-series predictions by using fully connected neural networks to reduce errors, particularly during peak traffic, outperforming prior work by 99%. Our contributions offer Internet service providers (ISPs) robust tools for real-time network management to satisfy Quality of Service (QoS) and enhance user experience in the Metaverse.

In this paper, we design an exclusive Metaverse network traffic classifier, named Discern-XR, to help Internet service providers (ISP) and router manufacturers enhance the quality of Metaverse services. Leveraging segmented learning, the Frame Vector Representation (FVR) algorithm and Frame Identification Algorithm (FIA) are proposed to extract critical frame-related statistics from raw network data having only four application-level features. A novel Augmentation, Aggregation, and Retention Online Training (A2R-OT) algorithm is proposed to find an accurate classification model through online training methodology. In addition, we contribute to the real-world Metaverse dataset comprising virtual reality (VR) games, VR video, VR chat, augmented reality (AR), and mixed reality (MR) traffic, providing a comprehensive benchmark. Discern-XR outperforms state-of-the-art classifiers by 7 % while improving training efficiency and reducing false-negative rates. Our work advances Metaverse network traffic classification by standing as the state-of-the-art solution.

In this article, we propose a novel volumetric video caching and rendering approach for an edge-assisted extended reality (XR) system to enhance user Quality of Experience (QoE). Particularly, user QoE consists of visual quality and quality variation. Different quality of volumetric videos are required to be cached, rendered, and delivered to XR devices for different viewing distances within a time latency. Given the limited caching, computing, and communication resources on the edge server, we formulate a long-term user QoE maximization problem to jointly optimize video caching and rendering by considering user locations and viewing distances. To solve this problem, we first design an online optimization algorithm in which caching decisions are obtained using a regularization technique. We then develop a low-complexity binary search algorithm to determine optimal rendering quality. Extensive simulations are conducted to demonstrate that our proposed approach outperforms benchmark schemes by an average 46% improvement in terms of long-term user QoE.

In this paper, we propose a digital twin (DT)-based user-centric approach for processing sensing data in an integrated sensing and communication (ISAC) system. The considered scenario involves an ISAC device with a lightweight deep neural network (DNN) and a mobile edge computing (MEC) server with a large DNN. After collecting sensing data, the ISAC device either processes the data locally or uploads them to the server for higher-accuracy data processing. To cope with data drifts, the server updates the lightweight DNN when necessary, referred to as continual learning. Our objective is to minimize the long-term average computation cost of the MEC server by jointly optimizing two decisions, i.e., sensing data offloading and sensing data selection for the DNN update. A DT of the ISAC device is constructed to predict the impact of potential decisions on the long-term computation cost of the server, based on which the decisions are made with closed-form formulas. Experiments on executing DNN-based human motion recognition tasks are conducted to demonstrate the outstanding performance of the proposed DT-based approach in computation cost minimization.

This paper introduces a real-time collaborative sensing scheme for wireless sensor networks in time-varying environments. The objective is to maximize the sensors' performance by effectively allocating communication resources for data sharing. Specifically, we utilize digital twins (DTs) to characterize dynamic collaborative sensing demands for each sensor through data-driven methods. Building on the DT design, we propose a resource allocation scheme to optimize the communication resources allocated at each stage of collaborative sensing and determine the most effective collaborative sensing policy. By profiling sensors using DTs, the network controller can effectively coordinate the sensors without exhaustively exploring all collaborative sensing policies. Numerical results demonstrate the effectiveness of our proposed scheme in optimizing the sensing performance for all sensors.

Device-edge collaboration on deep neural network (DNN) inference is a promising approach to efficiently utilizing network resources for supporting Artificial Intelligence of Things (AIoT) applications. In this article, we propose a novel digital twin (DT)-assisted approach to device-edge collaboration on DNN inference that determines whether and when to stop local inference at a device and upload the intermediate results to complete the inference on an edge server. Instead of determining the collaboration for each DNN inference task only upon its generation, multi-step decision making is performed during the on-device inference to adapt to the dynamic computing workload status at the device and the edge server. To enhance the adaptivity, a DT is constructed to evaluate all potential offloading decisions for each DNN inference task, which provides augmented training data for a machine learning-assisted decision-making algorithm. Then, another DT is constructed to estimate the inference status at the device to avoid frequently fetching the status information from the device, thus reducing the signaling overhead. We also derive necessary conditions for optimal offloading decisions to reduce the offloading decision space. Simulation results demonstrate the outstanding performance of our DT-assisted approach in terms of balancing the tradeoff among inference accuracy, delay, and energy consumption.

Timely and energy-efficient task offloading and computing can be challenging in mobile edge computing (MEC) networks when the communication links between devices and edge servers are unreliable. In this paper, we apply multiple aerial intelligent reflective surfaces (AIRSs) to assist devices in offloading computing tasks to the edge server in a timely and reliable manner in the MEC network with poor offloading environments. To evaluate the timeliness of offloading and computing, we derive the evolution process of age-of-information (AoI) under the random arrival of the computing tasks. The association between devices and AIRSs, offloading order of computing tasks, design of IRS phase shift, and allocation of communication and computing resources are jointly optimized to minimize the average AoI and system energy consumption given computing requirements. To solve the formulated minimization problem, we propose an efficient problem-solving framework to cope with the challenge of variable coupling. Firstly, we derive a closed-form optimal IRS phase shift to provide a reliable offloading environment. Then, we optimize the association between devices and AIRSs while reducing the offloading complexity and balancing the number of devices associated with each AIRS. Finally, we develop a low-complexity task offloading and resource allocation algorithm based on convex optimization to attain a good enough solution. Simulation results indicate the proposed solution outperforms benchmarks in timeliness and energy saving.

This article introduces an on-demand collaborative sensing scheme for industrial Internet of Things (IIoT) sensors in time-varying sensing environments, aiming to optimize the sensing performance by effectively allocating communication resources for sensory data sharing. Particularly, we propose a novel digital twins (DTs)-empowered resource allocation solution to facilitate scalable and flexible collaborative sensing. First, DTs create mathematical models using real-time network data to characterize the dynamic resource demands in collaborative sensing. Second, the performance of mathematical models in DTs is evaluated through data-driven methods. Building on our DT design, we propose a joint collaborative sensing and DT management scheme to optimize the resource allocation for sensory data sharing and DT operation. Furthermore, we develop a DT evaluation method featuring a variational autoencoder to evaluate the accuracy of DTs and enable closed-loop DT-based resource allocation. Numerical results demonstrate the effectiveness of our proposed collaborative sensing scheme in optimizing the sensing performance for all sensors.

Edge-device collaboration has the potential to facilitate compute-intensive device pose tracking for resource-constrained mobile augmented reality (MAR) devices. In this article, we devise a 3-D map management scheme for edge-assisted MAR, wherein an edge server constructs and updates a 3-D map of the physical environment by using the camera frames uploaded from an MAR device, to support local device pose tracking. Our objective is to minimize the uncertainty of device pose tracking by periodically selecting a proper set of uploaded camera frames and updating the 3-D map. To cope with the dynamics of the uplink data rate and the user’s pose, we formulate a Bayes-adaptive Markov decision process problem and propose a digital twin (DT)-based approach to solve the problem. First, a DT is designed as a data model to capture the time-varying uplink data rate, thereby supporting 3-D map management. Second, utilizing extensive generated data provided by the DT, a model-based reinforcement learning algorithm is developed to manage the 3-D map while adapting to these dynamics. Numerical results demonstrate that the designed DT outperforms Markov models in accurately capturing the time-varying uplink data rate, and our devised DT-based 3-D map management scheme surpasses benchmark schemes in reducing device pose tracking uncertainty.

In this paper, we explore data poisoning attacks and their defenses in local differential privacy (LDP)-based crowdsensing systems. First, we construct data poisoning attacks launched by corrupted workers to subvert crowdsensing results by tampering information reported. Specifically, the attacks are formulated as a bi-level optimization problem where attackers strive to conceal their malicious behavior by delicately exploiting noise perturbation introduced by LDP protocols. In this way, the attacks can not be detected, even with the weight-based truth discovery methods. Due to the NP-hard nature of the bi-level problem, we decompose it into upper-level and lower-level sub-problems and employ the augmented Lagrangian method to iteratively solve them, ultimately identifying optimal attack strategies. Second, we propose corresponding countermeasures to defend against the attacks. The countermeasures are formulated as a minimization problem, with the objective of minimizing disruptions caused by attacks through the identification and removal of corrupted workers from crowdsensing systems. To solve the problem, we utilize a differential evolution algorithm instead of gradient-based methods since the objective function of the problem is not differentiable. Extensive experiments on real-world datasets are conducted to evaluate the performance of the proposed attacks and defenses. The evaluation results demonstrate that LDP perturbation indeed facilitates the success of data poisoning attacks, and the proposed defenses can accurately distinguish malicious behaviors disguised.

In this paper, we propose a digital twin (DT)-assisted cloud-edge collaborative transcoding scheme to enhance user satisfaction in live streaming. We first present a DT-assisted transcoding workload estimation (TWE) model for the cloud-edge collaborative transcoding. Particularly, two DTs are constructed for emulating the cloud-edge collaborative transcoding process by analyzing spatial-temporal information of individual videos and transcoding configurations of transcoding queues, respectively. Two light-weight Bayesian neural networks are adopted to fit the TWE models in DTs, respectively. Moreover, we formulate a transcoding-path selection problem to maximize long-term user satisfaction within an average service delay threshold, taking the dynamics of video arrivals and video requests into account. The problem is transformed into a standard Markov decision process by using the Lyapunov optimization, which is further solved by a deep reinforcement learning algorithm. Simulation results based on the real-world dataset demonstrate that the proposed scheme can effectively enhance user satisfaction compared with benchmark schemes.

In this paper, we investigate joint caching and computing resource reservation for supporting location-aware augmented reality (AR) applications in an edge-assisted two-tier radio access network. We aim at minimizing the caching and computing resource consumption while satisfying the AR service delay requirement. Specifically, to capture the spatio-temporal AR service dynamics, the resource consumption minimization problem is formulated as a long-term stochastic optimization problem. Due to the time-varying service demands and tightly coupled multi-resource reservation decisions, we propose a novel resource reservation algorithm based on the Lyapunov optimization technique to solve the problem. We first transform the original long-term problem into multiple one-shot optimization problems, each of which is then solved by our designed iterative algorithm in an online manner. Simulation results demonstrate that the proposed algorithm can significantly reduce the overall resource consumption compared to benchmark algorithms.

In this paper, we propose collaborative beamforming (CB) in unmanned aerial vehicle (UAV)-assisted communication networks to improve transmission data rate with minimum energy consumption. Specifically, CB allows a group of UAVs forming a virtual element antenna array (VEAA) and transmitting data collaboratively in a synchronous manner through a high-gain mainlobe (ML) beam. The goal is to optimize the deployment locations of UAVs in the VEAA and excitation current weights for performing CB transmissions considering the energy cost for UAV deployment. Accordingly, we formulate an Energy-Efficient Communication Multi-objective Optimization Problem (EECMOP) to jointly maximize the transmission rate and minimize the maximum sidelobe level (SLL) as well as UAV energy consumption. Then, we propose an Enhanced Multi Objective Ant Lion Optimizer (EMOALO) algorithm which incorporates a chaos theory to develop chaotic initialization and adjustable mode operators for solving the problem. Simulation results demonstrate the effectiveness of the EMOALO algorithm in improving energy efficiency for UAV-assisted communication networks.

Aerial intelligent reconfigurable surface (IRS) is a promising technology to enhance channel quality in data delivery. In this article, we study an aerial IRS deployment problem to enable timely and reliable data delivery in a remote Internet of Things (IoT) scenario, in which an IRS mounted on an unmanned aerial vehicle (UAV) is adopted as a mobile relay to assist devices in uploading data to the base station (BS). The objective is to minimize the average Age of Information (AoI) of the data received by the BS over time by jointly determining the aerial IRS deployment position and phase shift, transmit power of devices, and data uploading time. Under the requirements of peak AoI (PAoI) and communication reliability, we formulate an average AoI minimization problem. Since the nonlinear relations among optimization variables make the formulated problem nonconvex and intractable to solve, we propose a block coordinate descent (BCD)-based iterative algorithm which decomposes the formulated problem into several subproblems. The variables are optimized in each subproblem individually in an alternately iterative manner to attain a near-optimal solution. Simulation results demonstrate the superiority of the proposed algorithm in improving the information freshness compared with the benchmark schemes.

The advances in artificial intelligence (AI) and edge computing enable edge intelligence to support pervasive intelligent Internet of Things (IoT) applications in the future wireless networks. We focus on deep neural network (DNN)-based classification tasks, and investigate how to improve the confidence level and delay performance of DNN inference via device-edge collaboration. We first develop a stochastic cumulative DNN inference scheme that aggregates multiple random DNN inference results and generates a cumulative DNN inference result with improved confidence level. Then, based on a computation-efficient DNN model deployment strategy with shared computation between a locally deployed fast DNN model and a full DNN model partitioned between the device and edge, a closed-loop adaptive device-edge collaboration scheme is developed to support cumulative DNN inference for multiple devices. We adaptively determine how to offload DNN inference computation to the edge and how to allocate transmission and edge-computing resources among multiple devices, for Quality-of-Service (QoS) satisfaction in terms of both confidence level and inference delay with resource and energy efficiency. A reinforcement learning (RL) approach is used for adaptive offloading decision, which relies on a resource allocation solution for reward calculation. Simulation results demonstrate the effectiveness of the adaptive device-edge collaboration scheme for cumulative DNN inference, in terms of confidence level improvement, delay violation minimization, network resource efficiency, and device energy efficiency.

Split learning (SL) is a collaborative learning framework, which can train an artificial intelligence (AI) model between a device and an edge server by splitting the AI model into a device-side model and a server-side model at a cut layer. The existing SL approach conducts the training process sequentially across devices, which incurs significant training latency especially when the number of devices is large. In this paper, we design a novel SL scheme to reduce the training latency, namedCluster-basedParallelSL(CPSL) which conducts model training in a “first-parallel-then-sequential” manner. Specifically, the CPSL is to partition devices into several clusters, parallelly train device-side models in each cluster and aggregate them, and then sequentially train the whole AI model across clusters, thereby parallelizing the training process and reducing training latency. Furthermore, we propose a resource management algorithm to minimize the training latency of CPSL considering device heterogeneity and network dynamics in wireless networks. This is achieved by stochastically optimizing the cut layer selection, device clustering, and radio spectrum allocation. The proposed two-timescale algorithm can jointly make the cut layer selection decision in a large timescale and device clustering and radio spectrum allocation decisions in a small timescale. Extensive simulation results on non-independent and identically distributed data demonstrate that the proposed solution can greatly reduce the training latency as compared with the existing SL benchmarks, while adapting to network dynamics.

In this paper, we investigate digital twin (DT)-assisted adaptive deep neural network (DNN) inference in the Industrial Internet of Things (IIoT). We consider a scenario that an edge server has a full-size DNN for high-accuracy inference, while an IIoT device has a lightweight DNN for fast on-device inference. The IIoT device generates computing tasks, such as object recognition, to be processed by DNN. For each task, a local controller at the network edge determines whether or not to offload the task to the edge server before it enters each layer of the lightweight DNN. The objective is to find the task offloading point that maximizes a utility including delay, inference accuracy, and on-device energy consumption. To achieve this objective, we propose an online DT-assisted task offloading scheme, which exploits DTs to capture the task processing status at the IIoT device and the workload at the edge server. Simulation results demonstrate the excellent performance of the proposed adaptive DT-assisted DNN inference on delay, inference accuracy, and on-device energy consumption.

In this paper, we present a digital twin (DT)-assisted adaptive video streaming scheme to enhance personalized quality-of-experience (PQoE). Since PQoE models are user-specific and time-varying, existing schemes based on universal and time-invariant PQoE models may suffer from performance degradation. To address this issue, we first propose a DT-assisted PQoE model construction method to obtain accurate user-specific PQoE models. Specifically, user DTs (UDTs) are respectively constructed for individual users, which can acquire and utilize users' data to accurately tune PQoE model parameters in real time. Next, given the obtained PQoE models, we formulate a resource management problem to maximize the overall long-term PQoE by taking the dynamics of users' locations, video requests, and buffer statuses into account. To solve this problem, a deep reinforcement learning algorithm is developed to jointly determine segment version selection, and communication and computing resource allocation. Simulation results on the real-world dataset demonstrate that the proposed scheme can effectively enhance PQoE compared with benchmark schemes.

This paper presents a novel approach for computing resource management of edge servers in vehicular networks based on digital twins and artificial intelligence (AI). Specifically, we construct two-tier digital twins tailored for vehicular networks to capture networking-related features of vehicles and edge servers. By exploiting such features, we propose a two-stage computing resource allocation scheme. First, the central controller periodically generates reference policies for real-time computing resource allocation according to the network dynamics and service demands captured by digital twins of edge servers. Second, computing resources of the edge servers are allocated in real time to individual vehicles via low-complexity matching-based allocation that complies with the reference policies. By leveraging digital twins, the proposed scheme can adapt to dynamic service demands and vehicle mobility in a scalable manner. Simulation results demonstrate that the proposed digital twin-driven scheme enables the vehicular network to support more computing tasks than benchmark schemes.

The rechargeable battery of a plug-in electric vehicle (PEV) endows the PEV with dual roles in the power grid as power load and mobile energy storage (MES). Owing to the technical advancement of autonomous driving, private PEVs that are parked most of the day can be used as private MESs (PMESs) to autonomously deliver energy for overloaded charging stations (CSs). In this paper, we investigate an energy compensation problem where PMESs are scheduled to deliver energy to overloaded CSs so that the energy balance can be achieved while the energy delivery time can be minimized. Based on the time-variant CS operation status and traffic conditions, we propose a pricing-based scheduling scheme that considers both PMES navigation and incentive price design. First, to navigate PMESs in the energy-capacitated transportation system, a minimum-cost flow problem is formulated to minimize the energy delivery time. Then, the incentive price is determined to encourage PMESs to follow the optimal navigation results for energy delivery. Simulations are conducted based on the traffic data of California highway to validate the effectiveness of the proposed scheduling scheme.

In the second part of this article, we develop a centralized packet transmission scheduling scheme to pair with the protocol designed in Part I and complete our medium access control (MAC) design for machine-type communications in the industrial Internet of Things. For the networking scenario, fine-grained scheduling that attends to each device becomes necessary, given stringent Quality-of-Service (QoS) requirements and diversified service types, but prohibitively complex for a large number of devices. To address this challenge, we propose a scheduling solution in two steps. First, we develop algorithms for device assignment based on the analytical results from Part I, when parameters of the proposed protocol are given. Then, we train a deep neural network for assisting in the determination of the protocol parameters. The two-step approach ensures the accuracy and granularity necessary for satisfying the QoS requirements and avoids excessive complexity from handling a large number of devices. Integrating the distributed coordination in the protocol design from Part I and the centralized scheduling from this part, the proposed MAC protocol achieves high performance, demonstrated through extensive simulations. For example, the results show that the proposed MAC can support 1000 devices under an aggregated traffic load of 3000 packets per second with a single channel and achieve <; 0.5 ms average delay and <; 1% average collision probability among 50 high priority devices.

In this two-part paper, we propose a novel medium access control (MAC) protocol for machine-type communications in the Industrial Internet of Things. The considered use case features a limited geographical area and a massive number of devices with sporadic data traffic and different priority types. We target supporting the devices while satisfying their Quality-of-Service (QoS) requirements with a single access point and a single channel, which necessitates a customized design that can significantly improve the MAC performance. In Part I of this paper, we present the MAC protocol that comprises a new slot structure, corresponding channel access procedure, and mechanisms for supporting high device density and providing differentiated QoS. A key idea behind this protocol is sensing-based distributed coordination for significantly improving channel utilization. To characterize the proposed protocol, we analyze its delay performance based on the packet arrival rates of devices. The analytical results provide insights and lay the groundwork for the fine-grained scheduling with QoS guarantee as presented in Part II.

In this paper, we investigate a radio access network (RAN) slicing problem for Internet of vehicles (IoV) services with different quality of service (QoS) requirements, in which multiple logically-isolated slices are constructed on a common roadside network infrastructure. A dynamic RAN slicing framework is presented to dynamically allocate radio spectrum and computing resource, and distribute computation workloads for the slices. To obtain an optimal RAN slicing policy for accommodating the spatial-temporal dynamics of vehicle traffic density, we first formulate a constrained RAN slicing problem with the objective to minimize long-term system cost. This problem cannot be directly solved by traditional reinforcement learning (RL) algorithms due to complicatedcoupled constraintsamong decisions. Therefore, we decouple the problem into a resource allocation subproblem and a workload distribution subproblem, and propose atwo-layer constrainedRL algorithm, namedResourceAllocation andWorkload diStribution (RAWS) to solve them. Specifically, anouter layerfirst makes the resource allocation decision via an RL algorithm, and then aninner layermakes the workload distribution decision via an optimization subroutine. Extensive trace-driven simulations show that the RAWS effectively reduces the system cost while satisfying QoS requirements with a high probability, as compared with benchmarks.

As an important application scenario of the industrial Internet of things, the Internet of Vehicles can significantly improve road safety, improve traffic management efficiency, and improve people's travel experience. Due to the high dynamics of the Internet of vehicles environment, the traditional resource optimization technologies cannot meet the requirements of the Internet of vehicles for dynamic communication, computing and storage resources optimization management, and artificial intelligence algorithms can adaptively obtain dynamic resource allocation schemes through self-learning. Therefore, adopting artificial intelligence techniques to optimize the dynamic resource of the Internet of Vehicles is the research focus of this article. In this article, we first model the Internet of Vehicles resource allocation problem as a semi-Markov decision process that introduces a resource reservation strategy and a secondary resource allocation mechanism. Then, the reinforcement learning algorithm is used to solve the model. Thereafter, it theoretically analyzes the joint optimization of computing and communication resources, models it as a hierarchical architecture, and uses hierarchical reinforcement learning to obtain the optimal system resource allocation plan. Finally, the results of simulation experiments show that the dynamic resource allocation scheme of the Internet of vehicles based on the reinforcement learning in this article greatly improve resource utilization and user quality of experience with guaranteeing system quality of service compared with the traditional greedy algorithm.

Mobile edge computing (MEC) has been recognized as a promising technology to support various emerging services in vehicular networks. With MEC, vehicle users can offload their computation-intensive applications (e.g., intelligent path planning and safety applications) to edge computing servers located at roadside units. In this paper, an efficient computing offloading and server collaboration approach is proposed to reduce computing service delay and improve service reliability for vehicle users. Task partition is adopted, whereby the computation load offloaded by a vehicle can be divided and distributed to multiple edge servers. By the proposed approach, the computation delay can be reduced by parallel computing, and the failure in computing results delivery can also be alleviated via cooperation among edges. The offloading and computing decision-making is formulated as a long-term planning problem, and a deep reinforcement learning technique, i.e., deep deterministic policy gradient, is adopted to achieve the optimal solution of the complex stochastic nonlinear integer optimization problem. Simulation results show that our collaborative computing approach can adapt to different service environments and outperform the greedy offloading approach.

Plug-in Electric Vehicles (PEVs) keep on penetrating the automobile market. However, uncoordinated PEV charging can impair the reliability of power grid. In this paper, an interesting problem of PEV charging power allocation is investigated, in which both power distribution and transportation constraints are considered. A novel approach for PEV charging management based on optimal power flow (OPF) analysis is proposed to optimize PEV charging energy in a power distribution system. Firstly, spatial and temporal PEV demand scheduling is introduced to maximize PEV charging service capacity while considering the maximum traveling distance of PEVs. Secondly, to ensure the scalability of the OPF analysis, a distributed optimization technique, i.e., proximal Jacobian alternating direction multiplier method, is applied to attain the optimal power allocation in a decentralized manner. The resulting PEV charging service capacity in the power distribution system is improved without violating power distribution and transportation constraints. Furthermore, kernel density estimation method is adopted to identify the PEV range anxiety constraint without the PEV battery information. Simulation results are presented to validate the effectiveness of our approach with high PEV penetration.

Supported by the technical development of electric battery and charging facilities, plug-in electric vehicle (PEV) has the potential to be mobile energy storage (MES) for energy delivery from resourceful charging stations (RCSs) to limited-capacity charging stations (LCSs). In this paper, we study the problem of using on-road PEVs as MESs for energy compensation service to compensate charging station (CS) overload. A price-incentive scheme is proposed for power system operator (PSO) to stimulate on-road MESs fulfilling energy compensation tasks. The price-service interaction between the PSO and MESs is characterized as a one-leader, multiple-follower Stackelberg game. The PSO acts as a leader to schedule on-road MESs by posting service price and on-road MESs respond to the price by choosing their service amount. The existence and uniqueness of the Stackelberg equilibrium are validated, and an algorithm is developed to find the equilibrium. Simulation results show the effectiveness of the proposed scheme in utility optimization and overload mitigation.

As the number of Plug-in Electric Vehicles (PEVs) is increasing in recent years, there has been a growing interest in terms of improving the charging service for on-the-move PEVs. In this paper, a task time allocation and reward scheme for advertising PEV charging station information is proposed. PEVs passing by a charging station are assigned a period of time to spread the charging station information within an interest area. To stimulate PEVs behaving cooperatively, two incentive policies provided by the charging station are studied in the task time allocation: the pre-determined reward policy and the optimal reward policy. For the former one, a fairness task time allocation scheme is developed to maximize the utility of the recruited PEVs. For the latter one, a Stackelberg game based optimization problem is formulated to obtain the optimal reward according to the utility of the charging station. An optimization tool, Geometric Water-filling, is utilized to analyze both problems efficiently. Simulation results are provided to validate the optimality of proposed schemes.

Energy harvesting (EH) becomes more desirable to save the world's energy consumption. This paper investigates energy resource allocation problem for EH networks. We propose a resource allocation framework based on a Semi-Markov Decision Process (SMDP). The objective of the framework is to provide a solution for a throughput maximization problem in EH networks by maximizing the total long-term expected reward of the EH system. The system reward is derived by considering both the income and the cost of the EH wireless communications. The numerical results illustrate a significant expected reward performance gain over a Greedy scheme. Moreover, simulations illustrate that the proposed approach is efficient and provides important guidelines for network deployment and resource management in a green radio network with EH technology.

Incentives for distributed optimization are investigated in two types of scenarios in which network users have private valuations (objective functions). A network center aims at maximizing the sum of users' valuations in the first scenario or the sum of its own valuations in the second scenario. It is shown that nontrivial strategies can be found by a user so that it can improve its own utility by providing false information to the center without leading a distributed algorithm to diverge. It demonstrates that a dual variable based pricing mechanism in distributed optimization cannot guarantee truthful reporting. While truthful reporting can be realized by using the classic Groves mechanism in the first scenario, the possibility of incentivizing truthful reporting in the second scenario depending on whether the center is willing to consider the valuations of the users in addition to those of its own.

Due to the mobility and flexibility of the Electrical Vehicles (EV), the power allocation of EV loads conducts as a critical part of demand side management. Since the EV charging stations provide the essential access for mass EV loads into the power grid, we introduce an efficient and decentralized real-time EV power allocation scheme among the neighbouring charging stations. In this paper, firstly, EV load power is managed in parallel according to current base load power inside the bus, and the power fluctuation is minimized for the whole system via Proximal Jacobian Alternating Direction Method of Multipliers (ADMM) technique. Then, the EV units access the network by the random charging scheme which is defined by the power analysis results and the EV's characteristics. Facilitated by the proposed approach, the stability and efficiency of the whole system can be improved by the local optimization process with decentralized manner.

To enhance the reliability of the power grid, further processing of the power demand to achieve load balancing is regarded as a critical step in the context of smart grids with Internet of Things technology. In this paper, dynamic offline and online scheduling algorithms are proposed to minimize the power fluctuations by applying a geometric water-filling approach. For the offline approach, full information in the power demand is available, possibly by predicting from the power utilities. We present an exact approach in order to allocate the elastic loads based on the inelastic load's information considering the group-and node-power upper constraints. For the online approach, the reference level is computed dynamically using historical demand data to minimize the fluctuation in the grid, and the elastic loads can only be scheduled in the future time slots. Two dynamic algorithms are investigated to achieve load balancing in the power grid without influencing user experience by real-time reference level adjustment. Facilitated by the proposed methodologies, the power utilities can significantly reduce the cost of improving the power capacity, and the consumers are able to enjoy more stable electrical power.