关键词： Backscatter communications   mobile edge computing   energy consumption   resource allocation  

摘要： In this paper, we study the energy-efficient resource allocation for a backscatter-assisted wireless powered mobile edge computing (MEC) network. Considering the binary offloading mode and the limited computation capacity of the MEC server, we propose to minimize the MEC server's energy consumption by jointly optimizing the binary offloading decision of each IoT node, the computing frequencies and time at the MEC server and IoT nodes, the backscatter time and reflection coefficients of IoT nodes, as well as the MEC server's transmit power and time. It is formulated into a non-convex mixed integer programming problem. To solve this problem, we first obtain the optimal computing time and frequency for the MEC server and the optimal computing time of each IoT node based on the proof by contradiction and the monotonicity of the objective function, then we decouple the resulting simplified problem into two subproblems and transform each subproblem into a convex one. On this basis, we propose a low-complexity iterative algorithm to solve the simplified problem by solving the two convex subproblems alternately. Simulation results verify the fast convergence of the proposed iterative algorithm and its superiority over the baseline schemes in terms of the MEC server's energy consumption.

关键词： IRS   mobile edge computing   reinforcement learning   deep deterministic policy gradient  

摘要： By employing powerful edge servers for data processing, mobile edge computing (MEC) has been recognized as a promising technology to support emerging computation-intensive applications. Besides, non-orthogonal multiple access (NOMA)-aided MEC system can further enhance the spectral efficiency with massive tasks offloading. However, with more dynamic devices brought online and the uncontrollable stochastic channel environment, it is even desirable to deploy appealing technique, i.e., intelligent reflecting surfaces (IRS), in the MEC system to flexibly tune the communication environment and improve the system energy efficiency. In this paper, we investigate the joint offloading, communication and computation resource allocation for the IRS-assisted NOMA MEC system. We first formulate a mixed integer energy efficiency maximization problem with system queue stability constraint. We then propose the Lyapunov-function-based Mixed Integer Deep Deterministic Policy Gradient (LMIDDPG) algorithm which is based on the centralized reinforcement learning (RL) framework. To be specific, we design the mixed integer action space mapping which contains both continuous mapping and integer mapping. Moreover, the award function is defined as the upper-bound of the Lyapunov drift-plus-penalty function. To enable end devices (EDs) to choose actions independently at the execution stage, we further propose the Heterogeneous Multi-agent LMIDDPG (HMA-LMIDDPG) algorithm based on distributed RL framework with homogeneous EDs and heterogeneous base station (BS) as heterogeneous multi-agent. Numerical results show that our proposed algorithms can achieve superior energy efficiency performance to the benchmark algorithms while maintaining the queue stability. Specially, the distributed structure HMA-LMIDDPG can acquire more energy efficiency gain than the centralized structure LMIDDPG.

关键词： ant colony algorithm   mobile edge computing   resource scheduling   sapce information network   task decomposition  

摘要： With the development of space information network (SIN), new network applications are emerging. Satellites are not only used for storage and transmission but also gradually used for calculation and analysis, so the demand for resources is increasing. But satellite resources are still limited. Mobile edge computing (MEC) is considered an effective technique to reduce the pressure on satellite resources. To solve the problem of task execution delay caused by limited satellite resources, we designed Space Mobile Edge Computing Network (SMECN) architecture. According to this architecture, we propose a resource scheduling method. First, we decompose the user tasks in SMECN, so that the tasks can be assigned to different servers. An improved ant colony resource scheduling algorithm for SMECN is proposed. The heuristic factors and pheromones of the ant colony algorithm are improved through time and resource constraints, and the roulette algorithm is applied to route selection to avoid falling into the local optimum. We propose a dynamic scheduling algorithm to improve the contract network protocol to cope with the dynamic changes of the SIN and dynamically adjust the task execution to improve the service capability of the SIN. The simulation results show that when the number of tasks reaches 200, the algorithm proposed in this paper takes 17.52% less execution time than the Min-Min algorithm, uses 9.58% less resources than the PSO algorithm, and achieves a resource allocation rate of 91.65%. Finally, introducing dynamic scheduling algorithms can effectively reduce task execution time and improve task availability.

关键词： Trust   Internet of Things   mobile edge computing   machine learning   online learning  

摘要： Mobile Edge Computing (MEC)-based Internet of Things (IoT) systems generate trust information in a real-time and distributed manner. Predicting trustworthiness of IoT services in such an MEC environment requires new prediction strategies that cater for the aforementioned characteristics of trust information. More importantly, it is imperative to investigate how the real-time trust information could be effectively integrated into trust prediction strategies in order to capture the ever-evolving nature of trustworthiness of IoT services. In turn, such a strategy allows IoT service consumers to derive more relevant and accurate trust-based decisions. To that end, our work models trust prediction in MEC-based IoT systems as an online regularized finite-sum problem in a distributed MEC environment with a given MEC topology. We then adopt the Online Alternating Direction Method (OADM) to effectively train trust prediction models in parallel over the distributed MEC environment. OADM allows splitting the aforementioned finite-sum problem into multiple sub-problems that correspond to different local MEC environments. These sub-problems can then be solved iteratively within each local MEC environment by using the local trust data therein. This can avoid the movement of data across the core networks of mobile network providers. Experiments on real-world and synthetic datasets demonstrate the effectiveness and scalability of the proposed method.

关键词： distributed computing paradigms   higher education   learners   mobile technologies   mobile edge computing   mobile learning  

摘要： This article aims to design a novel edge-centric hierarchical cloud architecture to optimize mobile learning (m-learning) performance during learners executing computation-intensive learning applications. This research adopts the potential of distributed computing paradigms, that is, mobile edge, for improving the effectiveness of m-learning performance in higher education. Edge computing enables computing at the network's edge and effectively avoids latencies while processing learners' computational requests. The envisioned architecture was designed on the ETSI MEC ISG protocols and deployed on the university mobile cloud infrastructure. Additionally, a use case was designed, focusing the edge computing's latency-avoiding ability, and executing it in a real-time environment involving sixteen students from an academic course. The execution results validated the architecture's contribution, such as tasks executed in the local server, optimized learner privacy, reduced latencies, instant access, lowered bandwidth consumption, and continued tasks' execution despite the failure of smart nodes. The result influences user acceptance and attracts designers to extend the architecture base focusing on machine learning algorithms (for learning analytics) and blockchain (to prevent malicious attacks) to improve the effectiveness of learning management system performance.

关键词： Energy minimization   hybrid multiple access   mobile edge computing   network slicing   partial offloading   resource allocation  

摘要： Meeting huge traffic demand with resource constraints imposes a significant challenge for future generation wireless networks. In this work, we propose to utilize limited resources in a dense mobile edge computing (MEC) network to compute user equipment (UE) tasks through a novel hybrid multiple access (HYMA) scheme that employs both non-orthogonal multiple access (NOMA) and orthogonal multiple access (OMA). The main purpose of using HYMA is to reduce the co-channel interference incurred in NOMA by selectively deploying OMA while maintaining the required signal-to-interference ratio. We adopt partial offloading of computing tasks in the MEC network. We also employ network slicing to efficiently utilize the resources of the MEC network to meet different types of application requirements. We prioritize NOMA to increase spectral efficiency as well as energy efficiency. We first formulate a mixed integer nonlinear programming (MINLP) optimization problem to minimize the total energy consumption for both local computing and wireless transmission of the MEC network and propose an algorithm consisting of three parts (UE association, computing resource allocation, and wireless resource and uplink transmission power allocation) to solve the MINLP problem with less computational complexity. We demonstrate the viability of our solution via extensive simulations.

关键词： Edge caching   Mobile edge computing   Social-aware   User behavior   Social relationship   User preference   User mobility   Literature review  

摘要： In recent years, we have witnessed the rapid development of the 5G technology, the computing capabilities of smartphones, and the use of these technologies and types of smart devices by users in everyday life. Along with the rapid growth of these technologies, new architectures at mobile edge caching (MEC) are also evolving. One of the aspects of smartphone's popularity is using social networks and sharing content between users of these networks. Generating various types of content on social networks is impressive and has caused huge data traffic in the network. Therefore, providing edge caching mechanisms in terms of social relationships and behavioral characteristics of users to reduce network traffic and delays in accessing popular content should be considered. As far as we know, no review paper has been provided in this context. In this paper, we intend to express the literature on the subject and then review the social-aware edge caching mechanisms. Then, we consider users' social and behavioral characteristics that are effective in caching strategies. Furthermore, we provide a taxonomy of social-aware edge caching approaches consisting of game theory-based, machine learning-based, model-based, and heuristic-based approaches. Finally, this study concludes by outlining some of the challenges ahead and research future directions.

关键词： Mobile Edge Computing   Digital Twin   Service migration   Digital Twin edge network   User mobility   Resource allocation  

摘要： Mobile Edge Computing (MEC) is emerging as one of the key technologies to process massive amount of data at the edge of the network for upcoming 6G networks. In the paradigm of MEC, users can offload the computation -intensive and delay-sensitive tasks to the edge servers, since the capabilities of User Equipment (UE) cannot be not sufficient to meet different quality of service (QoS) requirements. However, due to the limited coverage of edge servers, users can move between multiple edge servers. In such cases, service migration is complicated which arises the challenge of where and when to migrate the service dynamically to maintain acceptable QoS. In addition, high uncertainty of the user mobility and different QoS requirements of the services are challenging. Instead of assigning each user to the nearest edge server, we propose a Digital Twin (DT)-assisted service migration approach to minimize the task completion time. The contributions in this paper are threefold: First, a DT edge network architecture is presented that uses the real-time and historical data to determine service migration strategy. Second, the DT classifies the computing tasks as delay-sensitive task and delay -insensitive tasks to meet the QoS requirements. Third, the DT predicts the mobility of users in the future based on the Hidden Markov Model (HMM) and studies the impact of reducing the cost of service migration. Thus, we propose an optimization algorithm to find optimal resource allocation at each edge server. The simulation results shows that our DT-assisted service migration model can achieve less delay and service migration rate compared with the traditional methods.

关键词： mobile edge computing   mobile servers   task assignment   energy use balancing  

摘要： Mobile edge computing has been an important computing paradigm for providing delay-sensitive and computation-intensive services to mobile users. In this paper, we study the problem of the joint optimization of task assignment and energy management in a mobile-server-assisted edge computing network, where mobile servers can provide assisted task offloading services on behalf of the fixed servers at the network edge. The design objective is to minimize the system delay. As far as we know, our paper presents the first work that improves the quality of service of the whole system from a long-term aspect by prolonging the operational time of assisted mobile servers. We formulate the system delay minimization problem as a mixed-integer programming (MIP) problem. Due to the NP-hardness of this problem, we propose a dynamic energy criticality avoidance-based delay minimization ant colony algorithm (EACO), which strives for a balance between delay minimization for offloaded tasks and operational time maximization for mobile servers. We present a detailed algorithm design and deduce its computational complexity. We conduct extensive simulations, and the results demonstrate the high performance of the proposed algorithm compared to the benchmark algorithms.

关键词： Unmanned aerial vehicle   Mobile edge computing   Internet of Things   Matching theory  

摘要： Unmanned aircraft vehicles (UAVs)-enabled mobile edge computing (MEC) can enable Internet of Things devices (IoTD) to offload computing tasks to them. Considering this, we study how multiple aerial service providers (ASPs) compete with each other to provide edge computing services to multiple ground network operators (GNOs). An ASP owning multiple UAVs aims to achieve the maximum profit from providing MEC service to the GNOs, while a GNO operating multiple IoTDs aims to seek the computing service of a certain ASP to meet its performance requirements. To this end, we first quantify the conflicting interests of the ASPs and GNOs by using different profit functions. Then, the UAV scheduling and resource allocation is formulated as a multi-objective optimization problem. To address this problem, we first solve the UAV trajectory planning and resource allocation problem between one ASP and one GNO by using the Lagrange relaxation and successive convex optimization (SCA) methods. Based on the obtained results, the GNOs and ASPs are then associated in the framework based on the matching theory, which results in a weak Pareto optimality. Simulation results show that the proposed method achieves the considerable performance.