关键词： Task analysis   Resource management   Delays   Servers   Edge computing   Computer architecture   Reinforcement learning   Mobile edge computing   delay-constrained   constrained reinforcement learning   application-specific design  

摘要： We design a joint radio and computational resource allocation policy for a multi-user mobile edge computing system, such that the expected power consumption is minimized while satisfying long-term delay constraints. The problem is formulated as a constrained Markov decision process (CMDP) that is efficiently solved by the proposed constrained reinforcement learning (CRL) algorithm, called successive convex programming based policy optimization (SCPPO). SCPPO solves a convex objective/feasibility surrogate problem at each update and it can provably converge to a Karush-Kuhn-Tucker (KKT) point of the original CMDP problem almost surely under some mild conditions. Moreover, SCPPO adopts an application-specific policy architecture and employs a data-efficient estimation strategy that can reuse old experiences, such that SCPPO can realize fast learning with low computational complexity.

关键词： Servers   Energy consumption   Edge computing   Resource management   Unmanned aerial vehicles   Trajectory   Wireless communication   Computation efficiency   mobile edge computing   trajectory optimization   unmanned aerial vehicle  

摘要： With the high altitude and flexible mobility, unmanned aerial vehicle (UAV) assisted mobile edge computing (MEC) is becoming a promising technology to cope with the computation-intensive and latency-critical task in prospective Internet of Things. In this paper, we propose a novel MEC system with several ground servers at access points and one aerial server carried by UAV. To balance the vital metrics of the MEC system, computation bits and energy consumption, we aim to maximize the weighted computation efficiency of the system, subject to the constraints on communication and computation resources, minimum computation requirement and UAV's mobility. To this end, a joint optimization problem with the goal of weighted computation efficiency maximization is formulated. First, we analyze the problem and transform it into an equivalent tractable form. Then, we solve the challenging non-convex problem by jointly optimizing the computation task assignment, time slot partition, transmission bandwidth and CPU frequency allocation, transmit power allocation, and UAV's trajectory, based on the Dinkelbach's method, Lagrange duality and successive convex approximation technique. Furthermore, we propose an alternative computation efficiency maximization algorithm, followed by the convergence and complexity analysis. Finally, numerical simulations show that our proposed algorithm significantly improves the computation efficiency compared to benchmark schemes. It is also validated that the proposed algorithm effectively obtains a good tradeoff between the computation task bits and energy consumption of the system.

关键词： message transmission   mobile edge computing   secure messages   task offloading   vehicle communication  

摘要： With the rapid development of intelligent transportation, vehicle terminals generate a large number of data messages that need to be processed in real time, and the required computing and storage resources far exceed the load capacity of vehicle terminals. Mobile edge computing enables data resources to be processed near device terminals, and provides low-latency and high-reliability computing services to meet the power and service quality requirements of terminal devices. Therefore, in order to achieve better data resource management, this paper introduces mobile edge computing technology, and mainly researches secure message transmission optimization algorithms based on mobile edge computing. Firstly, we prioritize secure messages through the analytic hierarchy process. This can guarantee that the most urgent messages get the highest transmission level. Secondly, we establish an optimal task offloading model of delay and energy loss by assigning different weight factors to delay and energy loss. The Lagrangian relaxation method is used to transform the nonconvex problem into a convex problem. We use greedy algorithm to solve the main problem. Finally, the vehicle transmits secure messages through the topology of the local network within its defined communication range. Performance evaluation results show that the scheme not only reduces the redundant transmission of messages, but also improves the performance of end-to-end delay and message deliver success ratio of secure messages.

关键词： Optimization strategy   Computation offloading   Reinforcement learning   Mobile edge computing   Smart collaborative  

摘要： With the arrival of the 5th generation mobile networks (5 G) era, the data needed by mobile devices (MDs) is explosively growing. High-consumption, low-latency applications are huge challenges for resource-constrained Internet of things (IoT) devices. Mobile edge computing overcomes the limitations of computing resources on MDs by offloading tasks generated by MDs and assigning them to nearby MEC servers. Therefore, mobile edge computing (MEC) becomes important. This paper presents a task offloading strategy for the multi-device multi-server system. To meet the task requirements of different MDs, we formulate an overhead minimization problem to optimize the delay and energy consumption of the system. We propose the Double Deep Q Network (Double-DQN) algorithm to perform location selection strategies for tasks generated on the mobile devices and allocate respective computing resources. Simulation results show that the algorithm can allocate resources reasonably and reduce the overhead of the entire system.

关键词： Task analysis   NOMA   Wireless communication   Servers   Resource management   5G mobile communication   Minimization   Network slicing   NOMA   mobile edge computing   latency minimization   optimization   energy efficiency  

摘要： With the rapid growth of traffic in current wireless networks, the management of heterogeneous and time-critical services with limited computing and communication resources has become a significant challenge. Edge computing (EC) is one of the key techniques in enabling 5G networks to reduce the latency of services while the network slicing (NS) technique aims to provide users with tailored services by taking advantage of virtualization of a physical network. Besides, non-orthogonal multiple access (NOMA), one of the key enablers of 5G networks, enables more users to share wireless resources. We propose to leverage these techniques to minimize the total latency of the computing tasks with energy constraints. The combination of NS with NOMA for EC can reduce the unnecessary allocation of resources and consequently enhance both the energy and spectral efficiencies of the system. We first propose a binary scheme for offloading computing tasks and formulate an optimization problem to minimize the total latency of completing the tasks. We then transform the optimization problem to facilitate a near-optimal solution. We also introduce a heuristic algorithm to reduce the computational complexity of solving the optimization problem. Finally, extensive simulation results have demonstrated the effectiveness of our proposed algorithm.

关键词： Online learning   content caching   mobile edge computing   context-awareness   trust   edge intelligence  

摘要： Content caching is widely recognized a promising functionality to improve service performance in mobile edge computing (MEC). In the big data era, there are massive heterogeneous contents collected by the mobile devices, belonging to different users with specific context (e.g., hobby, environment, age, etc). However, local content caching without content popularity and context information in advance is not accurate enough. Especially, multiple large-scale contents cached in the local database bring high pressure to the process of content selection. Hence, to handle these important issues, we propose a context-aware distributed online learning algorithm for efficient content caching according to a novel tree-based and contextual multi-arm bandit theory for collaborative MEC in this paper. To guarantee the trustworthy collaboration, we introduce a trust evaluation factor to find reliable neighboring ENs. Moreover, our system extracts contextual information from users into the context space and builds up a content cover tree to maximize caching hit rates to satisfy users' demands. Our simulation results based on a real-world dataset indicate that our proposal can achieve a balance between caching hit rates and time cost, and have a sublinear bound of cumulative regret. This verifies its superior caching-hits performance gain compared to the other related algorithms.

关键词： Edge/fog computation offloading   Drone video analytics   Mobile edge computing   Learning-based scheme   Data processing in fog computing  

摘要： Multi-Unmanned Aerial Vehicle (UAV) systems with high-resolution cameras have been found useful for operations such as smart city and disaster management. These systems feature Flying Ad-Hoc Networks (FANETs) that connect the computation edge with UAVs and a Ground Control Station (GCS) through air-to-ground wireless network links. Leveraging the edge/fog computation resources effectively with energy-latency-awareness, and handling intermittent failures of FANETs are the major challenges in supporting video processing applications. In this paper, we propose a novel "DroneCOCoNet" framework for drone video analytics that coordinates intelligent processing of large video datasets using edge computation offloading and performs network protocol selection based on resource-awareness. We present two edge computation offloading approaches, i.e., heuristic-based and reinforcement learning-based approaches. These approaches provide intelligent task sharing and co-ordination for dynamic offloading decision-making among UAVs. Our scheme handles the problem of computation offloading tasks in two separate ways: (i) heuristic decision-making process, and (ii) Markov decision process;wherein we aim to minimize the total computation costs as well as latency in the edge/fog resources while minimizing video processing times to meet application requirements. Our experimental results show that our heuristic-based offloading decision-making scheme enables lower scheduling time and energy consumption for low drone-to-ground server ratios. In comparison, our dynamic reinforcement learning-based decision-making approach increases the accuracy and saves overall time periodically. Notably, these results also hold in various other multi-UAV scenarios involving largely different numbers of detected objects in e.g., smart farming, transportation traffic flow monitoring and disaster response. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Mobile edge computing   Backhaul link   Offload   Secure transmission   Energy consumption  

摘要： Mobile edge computing can provide short-range cloud computing capability for the mobile users, which is considered to be a promising technology in 5G communication. The mobile users offload some computing tasks to the edge server through the wireless backhaul link, which can reduce the energy consumption and the time latency. Meanwhile, due to the open characteristics of the wireless channel, the offloading tasks through the backhaul link may face the risk of eavesdropping. Therefore, the secure transmission based on physical layer security for the offloading tasks to the edge server is considered. The optimization problem of minimizing the energy consumption for the vehicular stations (VSs) in mobile edge computing-assisted high-speed railway communication system is studied in this paper. The energy consumption of the mobile users is generated by executing the local computing task and by transmitting the partial offloading task to the edge server. In this paper, a novel joint iterative optimization algorithm is proposed. By jointly optimizing the task scheduling, the task offloading and the transmission power, the energy consumption of all VSs is minimized under the constraint of the time latency. Numerical simulation results verify the effectiveness of the proposed algorithm.

关键词： Mobile edge computing  

摘要： This paper addresses join wireless and computing resource allocation in mobile edge computing (MEC) systems with several access points and with the possibility that users connect to many access points, and utilize the computation capability of many servers at the same time. The problem of sum transmission energy minimization under response time constraints is considered. It is proved, that the optimization problem is non-convex. The complexity of optimization of a part of the system parameters is investigated, and based on these results an Iterative Resource Allocation procedure is proposed, that converges to a local optimum. The performance of the joint resource allocation is evaluated by comparing it to lower and upper bounds defined by less or more flexible multi-cell MEC architectures. The results show that the free selection of the access point is crucial for good system performance. © 2021, CC0.

关键词： decision making   energy‐   awareness   mobile edge computing   tasks migration   UAV delivery system  

摘要： Nowadays, unmanned aerial vehicles (UAVs) are widely used in many smart systems such as smart logistics, smart agriculture, and environmental monitoring systems. However, the limited computing capability and restricted battery lifetime of existing UAVs could significantly impact the quality of service (QoS) of UAV-based smart systems and the quality of experience (QoE) of end users. Recently, Mobile Edge Computing (MEC) which provisions computing resources close to the mobile end devices has become a promising solution. However, since high-speed UAV often flies through the signal range of the different edge nodes, the interruption of services in the MEC-based UAV delivery system is a critical issue. A challenging question is when and how to perform dynamic task migration among the edge nodes to ensure service continuity. In this paper, we investigate the task migration issue for multiple UAVs in the MEC-based UAV delivery system. Specifically, we propose an energy-aware decision-making strategy for the dynamic task migration named GAD to optimize the UAV energy consumption. Given the real-time system status and QoS constraints, and through a dynamic two-tier decision-making mechanism, GAD can efficiently make the task migration decision from four candidate decisions, viz. No Migration, Data Migration Only, Cold Migration, and Live Migration. Experimental results based on a real-world scenario show that our strategy can well outperform other baseline strategies in various metrics including the flying distances and the energy consumption of UAVs.