The rapid advancement of 5G and 6G technologies has introduced various innovative applications, such as autonomous driving and augmented reality, which significantly increase network traffic and computational demands, particularly in densely populated areas. This study focuses on the utilization of unmanned aerial vehicles (UAVs) for Mobile Edge Computing (MEC) to address these challenges. Specifically, we explore the joint optimization of base station (BS) selection, computing resource allocation and UAV trajectory to minimize system delays and energy consumption in scenarios where computational requirements are related to user movement. A deep reinforcement learning-based approach, UM-DDPG, is proposed to optimize task offloading strategies and UAV trajectories. With our simulation platform, the results show that our method has been effective in reducing the system cost, including both delay and energy consumption, compared to traditional methods.

As 6G technology continues to evolve, future communication systems demand extremely low latency, improved energy efficiency, and support for massive device connectivity. To address these demands, the integration of Space-Air-Ground Integrated Networks (SAGIN) with Mobile Edge Computing (MEC) has emerged as a compelling strategy. This integration leverages edge nodes deployed on low Earth orbit (LEOs) satellites, unmanned aerial vehicles (UAVs), and terrestrial small base stations (SBSs) to deliver distributed computational resources. Nevertheless, due to the inherent heterogeneity and limited capabilities of these edge devices, reliably offloading computing tasks from ground user equipments (UEs) to appropriate edge nodes—whether satellite-based, aerial, terrestrial, or via local execution—remains a significant challenge. In this paper, we design a metadata-driven intelligent offload prediction and global resource optimization framework for centipede games, the metadata only contains the key information of the computing task, but not the actual task data itself. Then we envision a 6G smart city network architecture with complex computing scenarios, formulate the problem of minimizing the global average delay and energy consumption as a Markov Decision Process (MDP) by combining with Bellman’s optimization equations, and propose an asynchronous metadata driven centipede game method (AMDCG) based on deep reinforcement learning (DRL). Simulation results show that the AMDCG method significantly reduces the system offloading overhead in terms of latency and energy consumption compared to other benchmark algorithms.

Distributed microgrids are being deployed into our power grids to form large-scale multimicrogrid systems for utilizing growing renewable energy sources. An effective energy management strategy is fundamental to balancing energy supply and demand alongside maintaining the stability of multimicrogrid. In this article, we propose a scalable, privacy-preserving, distributed energy management approach (SPDEM) for multimicrogrid. Specifically, we first formulate the energy management problem in multimicrogrid as a decentralized partially observable Markov decision process (Dec-POMDP). Next, we develop an intelligent energy management algorithm using mean-field multiagent recurrent reinforcement learning to efficiently solve the Dec-POMDP. This approach incorporates a novel fingerprint-based importance sampling technique to address the obsolete experiences induced by mean field approximation. Extensive experiments on real-world datasets demonstrate that SPDEM can make effective energy management decisions under variable renewable energy generation and load demand. Comparisons with five typical baselines illustrate the superb performance of SPDEM in cost reduction and scalability enhancement.

With the rapid development of the Internet of Things (IoT), 6G space-air-ground integrated networks (SAGIN) have emerged to provide key solutions for wide-area coverage, data management and resource allocation. This paper presents an efficient data offloading and resource allocation strategy for 6G SAGIN assisted by Unmanned Aerial Vehicle (UAV) and Low Earth Orbit (LEO) satellite. In this paper, our goal is to minimize the total system cost, which is the weighted sum of latency and energy consumption, under UAV storage capacity constraint. Due to the complexity of optimization problem, we decompose the original problem into two sub-problems: the computation resource allocation sub-problem and the data offloading and transmission power allocation sub-problem. To address the first sub-problem, we demonstrate its convex optimization nature and propose the Newton Interior-Point Method (IPM)-based Computation resource Allocation (NICA) algorithm. For the second sub-problem, we introduce the Genetic Algorithm-based UAV Data Offloading and transmission Power Allocation (GADOPA) algorithm. Different offloading methods are provided for different types of data (i.e., computational and collected data) to meet their differentiated needs. Through simulation experiments, we demonstrate the effectiveness of our proposed methods.

As the requirements of wireless communication networks change, the space-air-ground integrated network (SAGIN) architecture will be the primary goal of future communication networks. However, due to the pressure of user devices (UDs) to process compute-intensive tasks and the limited power of aircraft, this remains a challenge of reducing energy consumption in SAGIN system. Multi-access edge computing (MEC) provides a promising solution by offloading tasks to edge nodes with more computing resources. In this paper, we study a multi-node cooperative task offloading problem, where user devices generate computing tasks that can be processed locally, offloaded to multiple unmanned aerial vehicles (UAVs) or a satellite. The aim is to minimize the total energy consumption of the system while maintaining the task latency constraints by allocating the optimal mmWave band bandwidth, and jointly optimizing UAV trajectories, offloading decisions making, and computing resource allocation. Since the proposed problem optimization is NP-hard, the proposed Deep Reinforcement learning based Cooperative Task Offloading (DRCTO) algorithm, which leverage the proximal policy optimization method, can accelerate the learning process of searching for the optimal solution. Numerical results indicate that the proposed DRCTO algorithm performs better than other comparable algorithms and significantly reduces the total system energy consumption.

In the context of integrated aerial-terrestrial networks, the use of unmanned aerial vehicles (UAVs) as aerial base stations (ABS) to provide additional edge computing and communication capabilities to ground users (GDs) is envisioned as a promising solution. Meanwhile, with the rapid development of 6G networks, there has been an explosive growth in GD demand for videos. Edge caching has been proposed to reduce redundant video transmission in the network and minimize GD waiting latency. In this paper, we investigate a video adaptive caching solution in UAV-assisted aerial-ground networks. Our objective is to minimize GD waiting latency by jointly optimizing caching decisions for both UAVs and base stations (BSs), as well as the trajectories of the UAVs. Due to the complexity of the proposed problem, which is difficult to solve by traditional optimisation methods, and the highly dynamic nature of video requests, we propose a reinforcement learning algorithm based on proximal policy optimisation and a long short-term memory neural network (PPO-L) to solve the problem. We use a real-world YouTube video request dataset as our simulation experiment dataset. Through a large number of simulation experiments and performance comparisons with other algorithms, we demonstrate the superiority of the proposed algorithm. The simulation results demonstrate that the proposed algorithm significantly improves performance in system experiments compared to other algorithms.

With the continuous development of integrated satellite ground networks, edge servers are laid out on low orbit earth (LEO) satellites to provide seamless services for some remote areas. In order to further improve service quality for mobile devices, the collaborative work between edge and cloud has received widespread attention. In this article, we consider the scenario of insufficient base station (BS) coverage in remote areas and investigate a hybrid model of computing offloading and resource allocation for edge cloud collaborative computing, in which edge servers with limited resources collaborate with the cloud by purchasing cloud computing resources. In this way, cloud server (CS) and edge servers separately price their computing and storage resources to stimulate each other to participate in cooperation and maximize their respective utility. We jointly optimize the size of offloading tasks, offloading strategies for devices and resource pricing issues for edge servers and CS based on comprehensive consideration of price cost, energy cost and latency limitation. A multi-level Stackelberg game is formulated among the CS (leader), BSs (followers) and satellites (followers) in level I-II, the BSs (leaders), satellites (leaders) and IoT devices (followers) in level II-III. Furthermore, we prove the existence and uniqueness of Stackelberg equilibrium (SE) in Stackelberg game. The SERI algorithm is proposed and simulations is conducted to show that SERI algorithm has good convergence performance and better entity utility.

As an edge cache device, the unmanned aerial vehicle (UAV) auxiliary edge network provides a wider coverage for mobile edge computing (MEC), and its flexibility and low delay bring great convenience to user equipments (UEs). To fully utilize the characteristics of UAVs, we need to consider the deployment trajectory and associated UEs of UAV. For computational intensive tasks, proper caching decision and offloading decision also help reduce delay. In this paper, we jointly optimize UE-UAV association, UAV deployment, caching decision, and offloading decision to minimize service delay. Considering the service rate of UAVs, we use the M/M/1 queues to model the calculation process. The problem of minimizing delay is formulated as a multi-objective optimization problem. We decompose the objective into three sub problems and use corresponding algorithms to solve them, namely the successive convex approximation (SCA) method, binary particle swarm optimization (BPSO) algorithm, and convex function difference (DC) method. The simulation results show that our algorithm is better than the other three baseline algorithms in minimizing service delay, and verify the delay effect of different edge storage capabilities in our optimization.

Unmanned aerial vehicles (UAVs) play a crucial role in mobile edge computing (MEC). On one hand, UAVs can serve as relay nodes, being flexibly deployed in various complex areas to provide network coverage services for users in remote regions. On the other hand, UAVs can carry edge servers, enabling them to approach terminal devices more conveniently and enhance the efficiency of task computation. However, in the face of a large number of computing tasks, the load balancing, network performance and computational resource management of the network are facing serious challenges due to the differences in task density caused by the irregular movement of ground users (GUs), as well as the limitations of the UAV’s computational capability and coverage. In order to address the above challenges, in this paper, We propose a multi-UAV collaborative MEC system. Our goal is to offload tasks as efficiently as possible with full and reasonable utilisation of computational resources, and to evenly distribute computational resources to meet the needs of varying levels of task intensity in a region through rational deployment of UAV locations and collaboration among UAVs. Specifically, in our proposed system framework, GUs can offload tasks to their associated UAVs and further decide whether or not to offload tasks to collaborative UAV or remote Base station (BS) based on the load of computational resources. We study the problem of location deployment of multiple UAVs for better offloading and resource allocation to tasks. Then, we model the task offloading and resource allocation process as a Markov decision process (MDP) focusing on minimising the average user cost of the system and propose a deep reinforcement learning (DRL)-based multi-uav collaborative computation offloading and resource allocation (DMCOA) algorithm. It has been shown through extensive simulation experiments that the algorithm can effectively reduce the average user cost of the MEC system.

The paradigm of unmanned aerial vehicles (UAV)-assisted mobile edge computing (MEC) has emerged as an effective scheme for handling intensive tasks in heterogeneous networks. In this work, we consider a user-equipment-rich city network scenario. Due to the limited user equipments (UEs) resources and base station (BS) coverage, we utilise multiple-UAV-assisted UEs and partial offloading to handle the tasks. Meanwhile, considering the impact of task diversity on the quality of service (QoS) of the system, we design an integrated scheme that combines improved clustering techniques and deep reinforcement learning (DRL) for dynamic resource scheduling. Firstly, a random forest-based clustering algorithm (RFCA) is used to cluster UEs according to the service requirements (SR) of tasks, as a way to reduce the complexity of task processing and user association. Then a DRL-based computational offloading and bandwidth allocation algorithm (DCOBA) is used to improve the QoS by jointly optimising UAV-user associations, offloading ratios, and bandwidth allocation to reduce the system latency and energy consumption. Finally, experimental simulation data shows that our scheme can better optimise the Qos compared to traditional schemes.

In order to cope with the increasing data traffic, we try to enable Intelligent Reflecting Surface (IRS) interference elimination in Device-to-Device (D2D) communication networks to improve the Signal Interference Noise Ratio (SINR). We build the system model and divide the original problem into two subproblems: IRS reflection parameter adjustment and IRS allocation. We use the the Cross Entropy Global Optimization Method (CEGOM) to solve the first subproblem. For the second subproblem, in order to ensure the fairness of user rates and avoid user starvation, we propose a heuristic algorithm based on the Max-Min Fairness Method (MMFM) to solve the problem. Simulation results demonstrate the superiority of the proposed algorithms, which improves Jain's fairness index by 70%, 72% and 13% and reduces the blocking probability by 92%, 90% and 82%, respectively, when compared to the random, shortest distance, and traditional MMFM strategies.

With the emergence of the Internet of Things (IoT), mobile edge computing (MEC) effectively reduces task delay of multiple applications. The limitations of computing and storage capabilities, as well as the complex dynamic network environments, make efficient edge service computing stressful. To address this challenge, digital twin (DT) technology is a promising solution that bridges the virtual and physical worlds by creating digital representations of physical objects. DT technology can model the behaviors of physical entities through virtual mirroring and assist physical networks in making optimal network strategies. In this paper, we combine DT technology with MEC networks to develop a utility-based task offloading and resource allocation scheme, aiming to maximize the quality of experience (QoE) of user equipments (UEs) and the utility of base stations (BSs). Specifically, we construct a hierarchical Stackelberg game model, study the interaction between BSs and UEs, and prove that the UE layer is an exact potential game with Nash equilibrium (NE). To achieve Stackelberg equilibrium (SE), we propose a game-based hierarchical interaction algorithm (GHIA) and analyze it. The experimental results show that GHIA has good convergence, and the utility performance of participants is better comparing to other algorithms.

With the explosive growth of compute-intensive and time-sensitive applications, edge service caching has been recognized as an effective solution. In this paper, we study a two-tier unmanned Aerial vehicle(UAV) system supporting service caching, where the lower-tier UAV acts as an airborne base station and the upper-tier UAV acts as a small cloud server. In order to improve quality of service for users and increase UAV endurance time, we propose a two time-scale decision framework to jointly optimize system latency and energy consumption. The framework consists of a short time scale and a long time scale, where the lower-tier UAV cache placement and ground user task offloading decisions are updated in the short time scale (i.e., each system time slot t). In the long time scale$\mathbf{T}$, the cache placement and flight trajectory decisions of the upper-tier UAV are updated. We then propose a two-tier decision-making algorithm based on deep deterministic policy gradient (DDPG) to solve the problem, and use the long and short-term memory neural networks(LSTM) algorithm to predict the service content information requested by the ground user. Simulation experimental results show that the proposed algorithm can significantly reduce the system latency while maintaining low energy consumption compared to other schemes.

To optimize short video data dissemination in edge networks, we propose a novel 3-layered network model. Based on the model, we investigate the optimal edge caching node selection problem, which is proved NP-hard. We also analyzed the upper and lower bounds of the data dissemination range in our proposed model. We developed the graph-attention-based cache propagation for degree calculation (GACPD) to predict the dissemination scale of short video data for each caching node, and the graph embedding and GAT-based caching node selection (GEG) algorithm to select the optimal caching nodes. We implement the GEG algorithm and evaluate its performance on both real and simulated data sets. It is found that GEG can reduce the backbone network traffic by 30% to 50%, as well as with 18% to 30% network bandwidth utilization improvement, compared with the existing ICS and CDA algorithms.

Electric Vehicle-assisted Multi-access Edge Computing (EV-MEC) is a promising paradigm where EVs share their computation resources at the network edge to perform intensive computing tasks while charging. In EV-MEC, a fundamental problem is to jointly decide the charging power of EVs and computation task allocation to EVs, for meeting both the diverse charging demands of EVs and stringent performance requirements of heterogeneous tasks. To address this challenge, we propose a new joint charging scheduling and computation offloading scheme (OCEAN) for EV-MEC. Specifically, we formulate a cooperative two-timescale optimization problem to minimize the charging load and its variance subject to the performance requirements of computation tasks. We then decompose this sophisticated optimization problem into two sub-problems: charging scheduling and computation offloading. For the former, we develop a novel safe deep reinforcement learning (DRL) algorithm, and theoretically prove the feasibility of learned charging scheduling policy. For the latter, we reformulate it as an integer non-linear programming problem to derive the optimal offloading decisions. Extensive experimental results demonstrate that OCEAN can achieve similar performances as the optimal strategy and realize up to 24% improvement in charging load variance over three state-of-the-art algorithms while satisfying the charging demands of all EVs.

With the successful commercialization of 5G technology and the accelerated research process of 6G technology, smart cities are entering the 3.0 era. In 6G smart cities, Multi-access Edge Computing (MEC) can provide computing support for a large number of computation-intensive applications. However, the randomness of the wireless network environment and the mobility of nodes make designing the best offloading schemes is challenging. In this article, we investigate the dynamic offloading optimization problem of base station (BS) selection and computational resource allocation for mobile users (MUs). We first envision a MEC-enabled 6G Smart City Network architecture, then formulate the minimizing average system user cost problem as a Markov Decision Process (MDP), and propose a deep reinforcement learning-based offloading optimization and resource allocation algorithm (DOORA). Numerical results illustrate that DOORA scheme significantly outperforms the benchmarks and can remarkably improves the quality of experience (QoE) of MUs.

With the gradual development of unmanned aerial vehicles (UAV) related disciplines such as mechanics, electronics and wireless communication. UAV technology is an important direction for the future development of sixth generation mobile communications (6G) technology. When the BS computing capacity is insufficient or unavailable, UAV-aided 6G mobile edge computing (MEC) is considered a practical solution to maintain the balance between the number of computing tasks and the number of base stations (BS). Firstly, we consider the transmission power setting and design a prioritization mechanism to determine the link selection. Secondly, we construct energy consumption models for user devices (UDs), BS and UAVs separately. Finally, we propose UAV-aided MEC Genetic Algorithm (UMGA) based on genetic algorithm (GA) to minimize the overall energy consumption of the UAV-aided MEC system. Simulation results show that the system energy consumption of our proposed scheme is lower than the system energy consumption of other baseline schemes. Compared with the three baseline algorithms, the performance of our algorithm is improved by 660%, 300% and 41.2%, respectively.

With the popularity of computation-intensive applications, tremendous computing pressure has been placed on the network edges. In order to effectively mitigate the computing pressure on network edges, an edge-cloud cooperative scheme is proposed. We consider mobile devices (MDs) can offload computation tasks to base stations (BSs) and cache content on cloud server (CS). Meanwhile, the overloaded BSs can offload tasks to CS for processing. CS and BSs are required to price their own resources or services, such as computing resources, storage resources, and computing services. Taking into account the economic factor, delay constraint and energy consumption cost, the problem of task offloading, content caching and resource pricing are jointly optimized to maximize the utility of all participants in the network. The three-stage Stackelberg game is established to investigate the interaction among CS, BSs and MDs. The existence of Stackelberg equilibrium (SE) is proved in three stages. Additionally, the Three-stage Equilibrium strategy algorithm based on Backward Induction (TEBI) is proposed to obtain the equilibrium strategies. The experimental results illustrate that TEBI algorithm can reach SE and converge rapidly, the cooperation pattern can shorten the task processing delay and improve the utility of participants.

With the rapid development of Internet of Things (IoT) and 5G wireless communication technology, a large amount of data is generated at the edge of the network. The combination of mobile edge computing (MEC) and federated learning has become a key technology to improve performance and protect users' privacy data in mobile networks. The selection of nodes for Hierarchical Federated Learning (HFL) affects the quality of model training. In this paper, we investigate the optimization problem of node selection accuracy in HFL. In order to improve the quality of model training, we design an algorithm of node selection based on reputation (NSRA). In NSRA, the edge server selects the node with high reputation prediction value to participate in the model training, and the node selects the neighbor node with high transmission capacity to cooperate. D2D communication is adopted for node cooperation. Through extensive simulations, it is verified the performance of NSRA. The mutual trust between nodes is enhanced, so the ideal prediction effect is achieved. We also observe that compared with RSA, the accuracy is improved by 11.48% and 19.38% in MNIST and CIFAR-10, respectively.

Nowadays, the surge of user data traffic has brought great challenges to the computing and energy capacity of mobile terminals (MTs). Mobile edge computing (MEC) technology is reckoned to be an efficient method to alleviate this problem. It can transfer tasks to MEC server and improve quality of service (QoS). In case of network failure, unmanned aerial vehicle (UAV) is deployed as a data transmission hub connecting MEC server to restore the network. In this article, we consider a UAV transmission hub (UTH) to communicate with the macro base station (MBS). MTs can offload tasks to MBS for processing through UTH, and the MEC server in MBS allocates computing resources to MTs. We raise a computing offloading and resource allocation decision scheme based on deep deterministic policy gradient (DDPG). The scheme considers the continuous generation of dynamic tasks, and the optimization objectives is to minimize the long-term average system cost. The simulation experiment datas verify the performance of DDPG-Based offloading and resource allocation decision scheme. It can validly optimize the average system cost in a random dynamic environment.

Computation offloading is a hot research topic in mobile edge computing (MEC). Computation offloading among multiedge nodes in heterogeneous networks can help reduce offloading cost. In addition, the unmanned aerial vehicles (UAVs) play a key role in MEC, where UAVs in the air communicate with ground base stations to improve the network performance. However, limited channel resources can lead to the increase of transmission delay and the decline of communication quality. Effective channel selection mechanisms can help address those issues by improving transmission rate and ensuring communication quality. In this paper, we study channel selection during communication between multiple UAVs and base stations in an MEC system with heterogeneous networks. To maximize the transmission rate of each UAV user, we formulate a channel selection problem and model it as a noncooperative game. Then, we prove the existence of Nash equilibrium (NE). In addition, we design a multiple UAV-enabled transmission channel selection (UTCS) algorithm to obtain the equilibrium strategy profile of all the UAV users. Experimental results validate that UTCS algorithm can converge after a finite number of iterations and it outperforms random transmission algorithm (RTA) and sequential transmission algorithm (STA).

With the development of 5G communication technologies and smart mobile devices, various computation-intensive and delay-sensitive tasks continue to increase. The combination of Mobile Edge Computing (MEC) and Ultra-Dense Networks (UDN) increases the network capacity and improves the computing capability of mobile devices, which effectively meets the transmission and computing demands of tasks. However, the ultra-dense deployment of network infrastructures causes energy shortage and channel interference, making it challenging to reduce the system cost. In this paper, we investigate the task offloading and resources scheduling problem in UDN with MEC. In order to minimize the total system cost including delay and energy consumption in the intensive deployment environment of edge servers and base stations (BSs) simultaneously, we design the strategy of task offloading, BS selection and resources scheduling of mobile devices. Because of the complex coupling of decision variables, the original problem is decomposed into two sub-problems. We propose Newton-IPM based Computing Resource Allocation (NICRA) algorithm and Genetic Algorithm based BS Selection and Resources Scheduling (GABSRS) algorithm to solve these two sub-problems, respectively. Then, we prove the number of iterations can be reduced effectively by the GABSRS algorithm while reaching the optimal solution through mathematical analysis. Through experiments analysis, the effectiveness of the GABSRS algorithm is validated.

The emerging Vehicle to Vehicle (V2V) technology supports the direct communication about road condition information between vehicles, which improves the communication efficiency and driving safety. Network slicing technology can provide network isolation for different users, which can reduce channel interference and save the cost of network resources. However, due to different resource demands and task requirements of each user, how to reasonably allocate resources and reduce the usage cost of resources is still a challenge. In this paper, we study the cost of V2V users to complete the transmission task in the scenario of shared channel with eMBB mobile cellular network users in Radio Access Network (RAN). In addition, we consider the bandwidth resource occupancy rate and the required resource size, and develop a dynamic resource pricing model to ensure the quality of user experience. Under the condition of ensuring the basic rate of cellular users, we divide the cost minimization problem of V2V users transmission tasks into two subproblems: control of transmission power and allocation of slicing bandwidth resources. Then, we propose an Evolutionary Strategy-based Bandwidth Allocation (ESBA) algorithm to complete the transmission tasks within the tolerable delay, which can avoid local minima. Simulation results show that ESBA algorithm has good convergence, and reduce the cost of V2V users transmission tasks effectively.

With the development of Internet of Things (IoT) and network technologies, traditional networks cannot cope with the growth of network traffic and the changes in service requirements. The 5-th Generation Mobile Communication (5G) technology improves network transmission performance. In the communication network, 5G combines Software Defined Network (SDN) and Network Function Virtualization (NFV), through the deployment of end-to-end network slicing, to meet the challenge of differentiated service requirements in the complex environment. In the mobile network, users need to choose appropriate slices for access. Its performance is related to the quality of service and determines the efficiency of system resources utilization. We research the problem of slice re-access and slice resource scheduling caused by user mobility in 5G network slicing architecture and propose a slice access mechanism based on maximum throughput. A slice access selection algorithm based on genetic algorithm (GA) is proposed. Related simulations and comparative tests are carried out to prove the effectiveness and superiority of the algorithm.

In recent years, driven by the commercialization of the 6th Generation Communication Technology (6G), an increasing number of 6G devices connected to mobile networks produces computation-intensive tasks such as ultra-high-resolution video streaming, inter-active visual reality (VR) gaming, augmented reality (AR). However, the computing capacity and the capacity of battery of the 6G devices are limited. The technology of computation offloading would offload the tasks from the IoT devices to the edge network in the scenario of mobile edge computing (MEC). Not only can solve the shortage of mobile user device in energy effciency, but also deal with the tasks in low latency. IoT devices can offload computing tasks or execute them locally to finish the work. In order to find the optimal allocation rate of local computing tasks and offloading tasks, a resource allocation policy gradient (RAPG) based DDPG is considered. Finally we analyze the performance of RAPG by contrasts with different resource allocation algorithms. Numerial simulation results showed that the RAPG can achieve the best allocate rate between the BS and local, also can reduce the overall system delay of task combination with minimum energy consumption.

With the rapid development of 5G technologies, Internet-of-Vehicles (IoV) has become a promising and important research hotspot. The high-speed mobility of vehicles brings great challenges for services with low delay and high stability requirements. To address these challenges, this paper takes the relative movement between vehicles into account and analyzes the mobility in detail based on probability distribution. We propose a proactive caching and multi-link cooperative communication scheme to cope with mobility. According to the driving and content request information of vehicle users, the requested content is cached in the road side units (RSUs) and neighboring vehicles in advance. Furthermore, the optimal bandwidth is allocated for each communication link in order to improve the stability of vehicle communication and data transmission efficiency. We propose a Deep Reinforcement Learning-based Proactive Caching and Bandwidth Allocation Algorithm (DPCBA) by considering the high-dimensional continuity of the state and action space. The extensive simulation results demonstrate that our DPCBA scheme can effectively improve the Quality-of-Experience (QoE) of vehicle users in various situations, and outperforms traditional benchmark algorithms.

Offloading tasks from computing intensive mobile devices (MDs) to neighboring edge servers in the form of incentive mechanism can effectively reduce latency and increase utility in mobile edge computing (MEC). In this paper, we design an auction mechanism for a MEC system, and the system consists of multiple MDs and one service provider (SP). As the auctioneer, SP receives the bidding information from MDs, making resource allocation strategies by optimizing social welfare. An exact algorithm to solve social welfare maximization problem and a perturbation-based randomized allocation algorithm to achieve (1 - α) optimal social welfare approximation rate are proposed. Furthermore, we prove that the truthful random auction mechanism can achieves the auction properties, including individual rationality, incentive compatibility, weakly budget balance and computational efficiency in theory. Finally, simulation results show the effectiveness of the auction mechanism.

Summary The proliferation of mobile phones and webcams has led to an exponential increase in video data. One of the key technologies of video surveillance systems is Person Re‐identification (Re‐ID). The Re‐ID is used to identify whether the target pedestrian is the same person, and through scene matching, cross‐field tracking and track prediction of suspected pedestrians can be achieved. The edge computing has become the first choice for video analysis and processing, because of shorter response time and more efficient processing. In this paper, we propose a deep square similarity learning (DSSL), which considers the difference correlation, first‐order correlation, and two‐order correlation of image pairs. The training data automatically adjusts the network parameters and the weights of the three correlations to minimize the loss of the training set. Moreover, we conducted experiments on the challenging Re‐ID databases CuHK03 and Male1501. Compared with algorithm IDLA and DHSL, the first recognition rate is increased by 18% and 40%, respectively, in CuHK03, and 22% and 80% in Male1501. Then, we propose an online deep square similarity learning (ODSSL) algorithm to solve problem of data updating after the model is established by DSSL strategy. Meanwhile, ODSSL shows shorter update time and more efficient processing.

With proliferation of computation-intensive Internet of Things (IoT) applications, the limited capacity of end devices can deteriorate service performance. To address this issue, computation tasks can be offloaded to the Mobile Edge Computing (MEC) for processing. However, it consumes considerable energy to transmit and process these tasks. In this paper, we study the energy efficient task offloading in MEC. Specifically, we formulate it as a stochastic optimization problem, with the objective of minimizing the energy consumption of task offloading while guaranteeing the average queue length. Solving this offloading optimization problem faces many technical challenges due to the uncertainty and dynamics of wireless channel state and task arrival process, and the large scale of solution space. To tackle these challenges, we apply stochastic optimization techniques to transform the original stochastic problem into a deterministic optimization problem, and propose an energy efficient dynamic offloading algorithm called EEDOA. EEDOA can be implemented in an online manner to make the task offloading decisions with polynomial time complexity. Theoretical analysis is provided to demonstrate that EEDOA can approximate the minimal transmission energy consumption while still bounding the queue length. Experiment results are presented which show the EEDOA’s effectiveness.

As an emerging computing paradigm, mobile edge computing (MEC) can improve users’ service experience by provisioning the cloud resources close to the mobile devices. With MEC, computation-intensive tasks can be processed on the MEC servers, which can greatly decrease the mobile devices’ energy consumption and prolong their battery lifetime. However, the highly dynamic task arrival and wireless channel states pose great challenges on the computation task allocation in MEC. This paper jointly investigates the task allocation and CPU-cycle frequency, to achieve the minimum energy consumption while guaranteeing that the queue length is upper bounded. We formulate it as a stochastic optimization problem, and with the aid of stochastic optimization methods, we decouple the original problem into two deterministic optimization subproblems. An online Task Offloading and Frequency Scaling for Energy Efficiency (TOFFEE) algorithm is proposed to obtain the optimal solutions of these subproblems concurrently. TOFFEE can obtain the close-to-optimal energy consumption while bounding the applications’ queue length. Performance evaluation is conducted which verifies TOFFEE’s effectiveness. Experiment results indicate that TOFFEE can decrease the energy consumption by about 15 percent compared with the RLE algorithm, and by about 38 percent compared with the RME algorithm.

Nowadays, driven by the rapid development of smart mobile equipments and 5G network technologies, the application scenarios of Internet of Things (IoT) technology are becoming increasingly widespread. The integration of IoT and industrial manufacturing systems forms the industrial IoT (IIoT). Because of the limitation of resources, such as the computation unit and battery capacity in the IIoT equipments (IIEs), computation-intensive tasks need to be executed in the mobile edge computing (MEC) server. However, the dynamics and continuity of task generation lead to a severe challenge to the management of limited resources in IIoT. In this article, we investigate the dynamic resource management problem of joint power control and computing resource allocation for MEC in IIoT. In order to minimize the long-term average delay of the tasks, the original problem is transformed into a Markov decision process (MDP). Considering the dynamics and continuity of task generation, we propose a deep reinforcement learning-based dynamic resource management (DDRM) algorithm to solve the formulated MDP problem. Our DDRM algorithm exploits the deep deterministic policy gradient and can deal with the high-dimensional continuity of the action and state spaces. Extensive simulation results demonstrate that the DDRM can reduce the long-term average delay of the tasks effectively.

Driven by Internet of Things (IoT) and 5G communication technologies, the paradigm of mobile computing has changed from centralized mobile cloud computing to distributed mobile edge computing (MEC). Narrowing the gap between high quality of service (QoS) requirements and limited computing resources, and improving the utilization of computing resources between IoT devices and edge servers have become key issues. In this paper, we formulate a stochastic optimization problem involving dynamic offloading and resource scheduling between the local devices, base station (BS) and the back-end cloud. The goal is to minimize the consumption of energy and computing resources in the MEC system with energy harvesting (EH) devices, while meeting the QoS requirements of IoT devices. In order to solve this stochastic optimization problem, we convert it into a deterministic optimization problem, and propose an online dynamic offloading and resource scheduling algorithm (DORS) based on Lyapunov optimization theory. It is proved that the DORS algorithm can effectively balance the relationship between scheduling cost and MEC system’s performance. The comparison experiments show the effectiveness of the DORS algorithm in reducing the energy consumption.

The emerging fifth generation (5G) wireless technologies support services with huge heterogeneous requirements. Network slicing technology can compose multiple logical networks and allocate wireless resources according to the needs of each user, which can reduce the cost of hardware and network resources. Nevertheless, considering how systems containing different types of users reduce the cost of resources remains challenging. In this paper, we study the system cost of two types of user groups requesting resource blocks (RBs) at the radio access network (RAN), which are the enhanced mobile broadband (eMBB) cellular user group and the cellular vehicle to everything (C-V2X) user group. In order to improve the rational utilization, we make dynamic resource pricing according to the needs of users. Then, we propose a Cooperative Resource Sharing (CRS) Algorithm, which makes two user groups jointly purchase and share resources. The simulation results show that the strategy used in this algorithm can effectively reduce the unit price of RB and minimize the total cost of the system.

Summary Narrow Band Internet of Things (NB‐IoT) is a cellular‐based low power wide area network (LPWAN) radio technology, which can provide highly reliable services and wide coverage for IoT devices. Software defined networking (SDN) as an emerging network architecture can realize flexible resource allocation and network management. We introduce SDN into NB‐IoT and investigate the traffic modeling and performance evaluation of SDN‐based NB‐IoT access network. To evaluate the network performance in different environments, we introduce the Beta/D/1, Uniform/D/1, and M/D/1 queuing models, respectively. The proposed queuing models are suitable for different scenarios, in which NB‐IoT devices access the network in a highly synchronized, unsynchronized, or stochastic manner. We use the general solution to the G/G/1 and the M/G/1 queuing model to solve the proposed modeling problems. Through simulations, we investigate the influence of different network parameters. The analysis and simulation results can be used in the SDN controller to dynamically allocate resources and make network management decisions to satisfy different performance requirements of NB‐IoT applications.

With the popularity of computationally intensive applications, more and more computing resources are required. Mobile edge computing (MEC) is widely applied as an effective method to meet the increasing computing demands. In a relatively stable state, MEC can provide computing services with low latency and energy consumption. However, in special cases such as communication traffic, the unmanned aerial vehicle (UAV), by taking advantage of its mobility and flexibility, can assist the edge server to cope with the challenge of instantaneous computing surge. In this study, the authors consider a UAV‐enabled edge computing system. In addition to delay and energy consumption, the authors also consider computing resources costs in the offloading model. Besides, in order to minimise the computing cost of each mobile user (MU), they apply the non‐cooperative game method to model the channel and computing resources competition among MUs. Then, the authors prove that the proposed game is an ordinal potential game and the existence of Nash equilibrium in the game. The authors propose the UAV‐enabled computation offloading (UECO) algorithm to obtain the equilibrium strategy. Finally, the authors show that the UECO algorithm can quickly converge through iterative experiments, and it can achieve lower computing cost through comparative experiments.

To satisfy the increasingly complex demands of the Internet of Things (IoT) applications, multiclouds are a promising solution that can provide scalable, various, and abundant resources. However, in multiclouds, each cloud has its specific virtual machine (VM) type and pricing scheme. In addition, the request arrival, network bandwidth, and VM's price all vary with time and are hardly predicted. In such cases, the request scheduling and resource provisioning (RSRP) for cost efficiency becomes a highly challenging work. In this article, to capture the dynamics in the multiclouds environment, we formulate a stochastic optimization problem where the aim is to minimize the system cost and guarantee the IoT applications' queueing delay. By applying stochastic optimization theory, the original problem is transformed into a deterministic optimization problem in each slot, and then the deterministic problem is further decomposed into three independent subproblems. An online RSRP algorithm is devised to obtain these subproblems' optimal solutions. Mathematical analysis shows that RSRP can approach the optimal system cost while bounding the queueing delay, and make an arbitrary tradeoff between system cost and queueing delay as well. Moreover, trace-driven simulation results show the effectiveness of RRSP.

Mobile edge computing (MEC) has recently become a promising paradigm to meet the increasing computing requirement of mobile devices, and hybrid energy supply has been considered as an effective approach for saving the energy consumption of the MEC system and making it environmentally friendly. In particular, the joint task scheduling and energy management (TSEM) scheme plays a crucial role in reaping the benefits of MEC with hybrid energy supply. In this article, we focus on jointly optimizing the TSEM decisions to maximize the utility of the MEC system which accounts for both the computation throughput and the fairness among different cells, by formulating a stochastic optimization problem subject to the constraints of queue stability and energy budget. We transform the formulated problem into a deterministic problem and then decouple it into four independent subproblems, which can be solved in a distributed manner without future system statistical information. An online TSEM algorithm is developed to derive the optimal solutions to these subproblems. Mathematical analysis shows that TSEM can achieve a close-to-optimal system utility and realize the utility-queue tradeoff. The experimental results validate the advantages of TSEM in improving the system utility and stabilizing the queue length.

The enhanced Mobile Broadband (eMBB) and ultra-Reliable Low Latency Communications (URLLC) are two main scenarios of 5G networks. There is an obvious difference in service requirements between the two different scenarios. When multiple heterogeneous services coexist in the network, it is important to explore optimal resource scheduling and allocation strategies. In this paper, we study the Quality of Service (QoS) optimization problem in eMBB and URLLC coexisting scenario. Considering the services' characteristics of heterogeneity and dynamics, we first introduce the flexible numerology structure which defines a set of flexible transmission time interval (TTI) to satisfy different QoS requirements of heterogeneous services, and then, we formulate a Markov decision process (MDP)-based dynamic resource optimization problem with the flexibilities of time and frequency domains. Next, we prove this optimization problem to be NP-hard and propose an innovative joint scheduling strategy DRSA based on flexible numerology and deep reinforcement learning method to allocate dynamic resources. Through experiments, the flexible numerology significantly outperforms the non-flexible ones. Comparison experiments with Sequence, Greedy and Random strategies show that the average throughput of DRSA is 7.1%, 14.8% and 23.9% higher than them, and DRSA can reduce URLLC services' loss rate by 43.7%, 28.6% and 53.8%.

With the rapid growth of Internet of Things (IoT), network slicing is regarded as an important technology to support the multi-users' needs for 5G mobile network. Network slicing allows network operators to provide services to different users, which can improve the rational utilization of network and hardware resources. In order to ensure the quality of service and build low-cost network infrastructure services, it is a challenging problem to find an appropriate resource allocation mechanism. In this paper, we discuss resource allocation in 5G radio access network (RAN). Considering the real-time resource request of the slice user, we propose a semi-Markov decision system model, which enables the virtual network provider to effectively satisfy the different user demands in real time. Then, we propose a resource slicing algorithm based on deep reinforcement learning (RS-DRL), which aims to improve the long-term benefits of virtual network providers and the utilization of slicing resources. We evaluate the performance of the RS-DRL through evaluations and comparisons. The results show that the proposed RS-DRL algorithm can effectively improve the performance and achieve the long-term benefits quickly.

Limited capacities in computation and battery of Internet of things (IoT) devices are two main bottlenecks for quality of service. Emerging mobile edge computing (MEC) and radio frequency based wireless power transfer (WPT) can help alleviate the issues. Incorporating WPT into MEC, IoT devices can get sustainable energy supply by WPT, and offload computation tasks to MEC to improve the computing ability. In this paper, we jointly consider the radio resource and task allocation for the wireless powered MEC system. To capture the high dynamics in task arrival and wireless network, a stochastic optimization problem which minimizes the energy consumption while guaranteeing queue stability is formulated. By exploiting the stochastic optimization theory, we transform the original problem into a deterministic optimization problem. A radio resource and task allocation (RRTA) algorithm is designed to acquire the optimal solutions of this problem. Theoretical analysis shows that RRTA can achieve arbitrary tradeoff between the energy consumption and queue length. Moreover, the close-to-optimal energy consumption can be reached by RRTA while bounding the queueing length. Experiment results reveal that RRTA can effectively decrease the energy consumption and maintain a small queue length.

Nowadays, billions of Internet of Things (IoT) devices arise around us running complex and computation-intensive applications. Due to the limited resources of the IoT devices, it is appealing to offload the application tasks from IoT devices to the remote cloud data centers. However, offloading all the tasks to the cloud can put a significant burden on the network. One promising way to solve this issue is edge computing, where edge servers are provisioned at the network edge. In edge computing for IoT, as the task generating process is highly dynamic and the statistical information can hardly be obtained or precisely predicted, it is of great importance yet very challenging to effectively offload application tasks to achieve the tradeoff between offloading cost and performance. In this paper, we formulate the computation offloading as an optimization problem to minimize offloading cost while providing performance guarantees. Based on stochastic optimization, we propose a dynamic computation offloading algorithm (DCOA), which decomposes the optimization problem into a series of subproblems, and solves these subproblems concurrently in an online and distributed way. Theoretical analysis is presented which demonstrates that DCOA can achieve the tradeoff between offloading cost and performance. Experiments are also carried out to evaluate the effectiveness of DCOA.

Caching the popular files in mobile terminal equipments and transferring those files via Device-to-Device (D2D) communication technology can offload the traffic from the Base Station(BS) over edge computing wierless network. The user equipments(UE) are mobile and the D2D communication links may drop any instant. In addition, UEs are carried by people with social attributes. Considering those information about mobile terminals when we study the D2D caching content deployment strategy can improve the caching efficiency. In order to reduce the probability of requesting files from BS, we design an efficient social-aware caching content deployment strategy, which includes the community discovery mechanism, the caching nodes selection algorithm and the file caching probability determination algorithm. Simulation experiments are provided to prove that our D2D caching content deployment strategy can improve the traffic offload rate in edge computing wireless networks.

With the rapid development of the mobile Internet and the Internet of Things, the number of mobile communication services has grown rapidly. When multiple different types of networks cover the same region, it is important to decide which one users connect to, known as the network selection problem. In this paper, we explore the optimal network selection problem in 5G ultra-dense network. We consider several different types of transmission data such as session, media, background and interactive, which conclude different QoS requirements. And then, we formulate the network selection problem as an MDP model in ultra-dense system, and propose NS-MDP algorithm which aims to obtain best target network by calculating the benefits of the utility function. NS-MDP algorithm takes into account user data requirements, current system status, and network load conditions. Comparison experiments with Best-Rate, Random and Greedy_AHP strategies, show that NS-MDP algorithm's average throughput is 8.6%, 17.8% and 20.5% higher than them, and NS-MDP can reduce blocking rate by 33.3%, 17.6%, and 37.7%.

Nowadays, mobile services (applications) running on terminal devices are becoming more and more computation‐intensive. Offloading the service requests from terminal devices to cloud computing can be a good solution, but it would put a high burden on the network. Edge computing is an emerging technology to solve this problem, which places servers at the edge of the network. Dynamic scheduling of offloaded service requests in mobile edge computing systems is a key issue. It faces challenges due to the dynamic nature and uncertainty of service request patterns. In this article, we propose a Dynamic Service Request Scheduling (DSRS) algorithm, which makes request scheduling decisions to optimize scheduling cost while providing performance guarantees. The DSRS algorithm can be implemented in an online and distributed way. We present mathematical analysis which shows that the DSRS algorithm can achieve arbitrary tradeoff between scheduling cost and performance. Experiments are also carried out to show the effectiveness of the DSRS algorithm.

With the rapid growth of bandwidth-intensive applications, Carrier Aggregation (CA) has been introduced in Long Term Evolution-Advanced (LTE-A) Networks to provide higher data rates. In this paper, we investigate the joint resource block allocation and link adaptation problem with CA in LTE-A downlink. The problem is formulated as an Integer Programming problem aimed at maximizing the cell throughtput while guaranteeing Quality of Service (QoS) of each User Equipment (UE), in the form of the minimum transmission rate. We also consider the proportional fairness of radio allocation among UEs. Due to the NP-hardness of the problem, we propose an efficient algorithm QA-PFRA. QA-PFRA consists of two phases. In the first one we assign radio resource to UEs successively for QoS requirements according to UEs' priority, and in the second one we assign CCs to other UEs to maximize the cell weighted throughput. We develop a simulator to evaluate the performance of QA-PFRA. For comparison, we also implement GA algorithm and ERAA algorithm. It is found that the cell throughput obtained by QA-PFRA is about 70 higher than that obtained by ERAA, and QA-PFRA can also achieve higher throughput compared with GA when the UEs are sparsely distributed in the cell. Furthermore, we can observe that Jain's fairness index obtained by QA-PFRA is about 10 higher than that obtained by GA and 40 higher than that obtained by ERAA.

In this paper, we consider the Request Routing (RR) strategy in the CDN live streaming application. We show that to find an optimal RR strategy is correspond to a static optimization problem if the total number of clients is known in advance. However, this static approach is ineffective due to the difficulty in precisely estimating the number of clients off-line. We then develop the MPS scheduling algorithm to compute the multiphase RR strategy. Experimental study shows that our algorithm can generate a close-to-optimal strategy with respect to a wide range of the number of clients.

This paper concentrates on the performance analysis of channel contention in wireless ad hoc networks by using Stochastic Game Nets (SGN). We refine the definition of SGN so that it can be used to precisely capture the underlying details of a given system. A SGN model is developed to evaluate the channel utilization in a two-node wireless system. We quantify the system performance under equilibrium strategies. The findings of this paper are instructive for the design and deployment of wireless ad hoc networks.

With the development of Ethernet technology, Full Duplex Switched Ethernet shows great potential in hard real-time applications since its low cost and high bandwidth. In general, traditional Ethernet cannot provide deterministic service required for hard real-time applications. In this paper, we address a framework, which uses traffic shaper and SCED scheduling policy, to provide deterministic service in Full Duplex Switched Ethernet. Our analytical study shows that this framework can satisfy end-to-end delay requirement of the data flows. We also show that this framework can effectively allocate MUX resource and make adjusting the path of data flow easier.