关键词： Wireless body area networks   Deep reinforcement learning   Offloading policy   Mobile edge computing  

摘要： Wireless body area network (WBAN) is widely adopted in healthcare services, providing remote real-time and continuous healthcare monitoring. With the massive increase of detective sensor data, WBAN is largely restricted by limited storage and computation capacity, resulting in severely decreased efficiency and reliability. Mobile edge computing (MEC) technique can be combined with WBAN to resolve this issue. This paper studies the joint optimization problem of computational offloading and resource allocation (JCORA) in MEC for healthcare service scenarios. We formulate JCORA as a Markov decision process and propose a deep deterministic policy gradient-based WBAN offloading strategy (DDPG-WOS) to optimize time delay and energy consumption in interfered transmission channels. This scheme employs MEC to mitigate the computation pressure on a single WBAN and increase the transmission ability. Further, DDPG-WOS optimizes the offloading strategy-making process by considering the channel condition, transmission quality, computation ability and energy consumption. Simulation results verify the effectiveness of the proposed optimization schema in reducing energy consumption and computation latency and increasing the utility of WBAN compared to two competitive solutions.

关键词： Mobile edge computing  

摘要： This paper designs a helper-assisted resource allocation strategy in non-orthogonal multiple access (NOMA)enabled mobile edge computing (MEC) systems, in order to guarantee the quality of service (QoS) of the energy/delay-sensitive user equipments (UEs). To achieve a tradeoff between the energy consumption and the delay, we introduce a novel performance metric, called energy-delay tradeoff, which is defined as the weighted sum of energy consumption and delay. The joint optimization of user association, resource block (RB) assignment, power allocation, task assignment, and computation resource allocation is formulated as a mixed-integer nonlinear programming problem with the aim of minimizing the maximal energy-delay tradeoff. Due to the non-convexity of the formulated problem with coupled and 0-1 variables, this problem cannot be directly solved with polynomial complexity. To tackle this challenge, we first decouple the formulated problem into a power allocation, task assignment and computation resource allocation (PATACRA) subproblem. Then, with the solution obtained from the PATACRA subproblem, we equivalently reformulate the original problem as a discrete user association and RB assignment (DUARA) problem. For the PATACRA subproblem, an iterative parametric convex approximation (IPCA) algorithm is proposed. Then, based on the solution obtained from the PATACRA subproblem, we first model the DUARA problem as a four-sided matching problem, and then propose a low-complexity four-sided UE-RB-helper-server matching (FS-URHSM) algorithm. Theoretical analysis demonstrates that the proposed algorithms are guaranteed to converge to stable solutions with polynomial complexity. Finally, simulation results are provided to show the superior performance of our proposed algorithm in terms of the energy consumption and the delay. © 2023, CC BY.

关键词： Alternating direction method of multipliers   D2D-assisted computation offloading   Dynamic pricing   Mobile edge computing   Social relationships  

摘要： The development of collaborative computing architecture in mobile edge computing provides an effective solution for the task processing of mobile devices with low computing capability, which can make up for computation resource and physical size restrictions of mobile devices and improve the performance of mobile communication networks in the future. In this paper, we focus on network congestion caused by increased load in hotspots. In particular, we consider the integrated network architecture of mobile edge computing (MEC) and device-to-device (D2D) communication to reduce the sum overhead of all task request users (TRUs). Accordingly, the computing offloading decision, wireless resource allocation strategy, computing resource allocation strategy, and assistant selection decision are optimized to minimize the sum overhead of the energy consumption and the cost of purchasing computing resource of all TRUs. The formulated overhead minimization problem is a mixed integer nonlinear programming problem. In order to tackle this problem, an algorithm based on the distance and the transaction price is first proposed to determine the target assistant user (TAU) of each TRU. Second, for a given TAU selection strategy, the original problem is further transformed into a convex problem and decomposed to make it more tractable. Finally, a distributed algorithm based on the alternating direction method of multipliers (ADMM) is adopted to solve the optimization problem. The simulation results prove the effectiveness of the proposed scheme in reducing user overhead.

关键词： mobile edge computing   dynamic service migration   delay   network connection   deep reinforcement learning  

摘要： In mobile edge computing, service migration can not only reduce the access latency but also reduce the network costs for users. However, due to bandwidth bottleneck, migration costs should also be considered during service migration. In this way, the trade-off between benefits of service migration and total service costs is very important for the cloud service providers. In this paper, we propose an efficient dynamic service migration algorithm named SMDQN, which is based on reinforcement learning. We consider each mobile application service can be hosted on one or more edge nodes and each edge node has limited resources. SMDQN takes total delay and migration costs into consideration. And to reduce the size of Markov decision process space, we devise the deep reinforcement learning algorithm to make a fast decision. We implement the algorithm and test the performance and stability of it. The simulation result shows that it can minimize the service costs and adapt well to different mobile access patterns.

关键词： Mobile edge computing   Literature similarity checking   Hash index   Lightweight  

摘要： With the advent of information age, mobile devices have become one of the major convenient equipment that aids people's daily office activities such as academic research, one of whose major tasks is to check the repetition rate or similarity among different English literatures. Traditional literature similarity checking solutions in cloud paradigm often call for intensive computational cost and long waiting time. To tackle this issue, in this paper, we modify the tra-ditional literature similarity checking solution in cloud paradigm to make it suitable for the light-weight mobile edge environment. Furthermore, we put forward a lightweight similarity checking approach SCMEC for English literatures in mobile edge computing environment. To validate the advantages of SCMEC, we have designed massive experiments on a dataset. The reported experimental results show that SCMEC can deliver a satisfactory similarity checking result of literatures compared to other existing approaches.

关键词： Mobile edge computing   resource reservation   resource brokerage   competitive analysis   online algorithm  

摘要： With the development of MEC services, MEC brokers will emerge to facilitate the purchase and management of resources for individual MEC users. Both data communication and computing resources offered by MEC service providers can be purchased by pay-as-you-go (PAYG) or reserved plans. Besides data and computing plans for each type of resource, we also consider combo plans specifically designed for MEC services covering both resources. In this paper, we propose a smart online aggregated reservation (SOAR) framework for MEC brokers to minimize their cost of reserving resources for multiple users without the knowledge of future demands. In our framework, a task aggregation algorithm is designed to aggregate the users' demands in each PAYG billing cycle to improve the plan utilization, and plan reservation algorithms are proposed to decide when to reserve which plans. The performance gap (competitive ratio) between SOAR and optimal solution which knows all future demands in advance, is analyzed and derived in closed-form. The performance gap is proved to be the minimum among all deterministic online algorithms. Trace-driven simulations verify the cost advantage of our SOAR framework, which can save nearly 40 percent of cost for users through the brokerage service.

关键词： Security   Quality of service   Forecasting   Real-time systems   Predictive models   Privacy   Servers   Mobile edge computing   joint training   independent learning   privacy-aware forecasting  

摘要： We propose a novel privacy-aware Quality of Service (QoS) forecasting approach in the mobile edge environment - Edge-PMAM (Edge QoS forecasting with Public Model and Attention Mechanism). Edge-PMAM can make real-time, accurate and personalized QoS forecasting on the premise of user privacy preservation. Edge-PMAM comprises a public model for privacy-aware QoS forecasting in an edge region and a private model for personalized QoS forecasting for an individual user. An attention mechanism atop Long Short-Term Memory and an automated edge region division solution are devised to enhance the prediction accuracy of the public and private models. We conduct a series of experiments based on public and self-collected data sets. The results demonstrate that our approach can effectively improve forecasting performance and protect user privacy.

关键词： Mobile edge computing   cooperative security   online scheduling   DDoS   prototype  

摘要： Mobile edge computing (MEC), extending computing services from cloud to edge, is recognized as one of key pillars to facilitate real-time services and tackle backhaul bottleneck. However, it is not economically efficient to attach intensive security appliances to every MEC node to defend application-level DDoS attacks and ensure the availability of services. Thus, we explore the elasticity of security defense among MEC nodes by proposing a COoperative DEfense (CODE) framework for MEC, referred to as CODE4MEC. CODE4MEC aims to adapt to traffic changes by coordinating container-carried defensive resources among cooperative MEC nodes in an automatic way. Towards this aim, we propose four control plane functions to enable a life-cycle management for CODE4MEC, namely, CODE triggering, scheduling, coordination and releasing. However, an effective CODE4MEC requires non-trivial algorithmic schemes, in particular for CODE scheduling and coordination functions. We thus design an online combinatorial auction mechanism for real-time CODE scheduling, and prove a tighter performance bound relative to prior arts. As for CODE coordination, a flow-based traffic and context information coordination scheme is proposed to enable classical defense schemes to work properly and efficiently. Finally, using a combination of real testbed and simulation evaluations, we validate the effectiveness of CODE4MEC.

关键词： Authentication   Security   Servers   Protocols   Internet of Things   Privacy   Impersonation attacks   Anonymous authentication key agreement (AKA)   mobile edge computing   ProVerif   random oracle model   semi-trusted server attacks  

摘要： In mobile edge computing, the computing tasks of IoT terminal devices with limited computing power often need to be offloaded to servers for processing. However, there are malicious attacks by adversaries and malicious behaviors of servers in the network, coupled with the use of insecure network channels for data information transmission. These factors seriously threaten the privacy and data security of terminal devices and users. Therefore, it is urgent to use a safe and efficient anonymous authentication key agreement mechanism to verify the legitimacy of the identities of computing participants and ensure the safe transmission of task data. Recently Jia et al. proposed an identity-based authentication scheme, which combines many advantages of previous work and is resistant to various attacks. However, we found that their scheme has security problems, such as offline key guessing attack, internal attack, and user anonymity problems. We classify them as semi-trusted server attacks. In order to solve these security problems, we propose an improved scheme to better realize the authentication function by using flexible and security-enhanced keys for terminal equipment (TE), while ensuring the anonymity of the TE through implicit ID. Furthermore, we provide formal security proof, formal security verification, and security analysis for the improved protocol. Compared with the previous scheme, the scheme has certain improvements in security and performance.

关键词： Resource management   Servers   Quality of service   Task analysis   Costs   Games   Quality of experience   Budget-constrained service allocation   lyapunov optimization method   mobile edge computing   non-cooperative game   nash equilibrium  

摘要： The service resource allocation strategy optimization problem has always been a hot issue in mobile edge computing (MEC). In this article, we formulate the problem as a long-term quality of service (QoS) improvement problem while satisfying the budget of MEC service provider (MSP). Since it is very unrealistic to accurately obtain the request information of user equipments (UEs) over a long time, we first transform the original problem into a series of real-time linear programing sub-problems by using Lyapunov optimization method, and propose a centralized algorithm to determine the resource allocation strategies. However, since the sub-problems are still NP-hard problems, it is a huge challenge to determine the strategies for all UEs with the centralized algorithm in a large scale MEC environment. Thus, we then formulate the sub-problem as an $N$N players non-cooperative game, prove that there exists a Nash equilibrium, and develop two iterative algorithms to find the Nash equilibrium while determining the strategies. Experimental results show that the algorithms can take into account QoS and budget of MSP at the same time, and perform better compared to five other common schemes.