关键词： Task analysis   Eavesdropping   Reinforcement learning   Cloud computing   Internet of Things   Mobile handsets   Edge computing   Deep reinforcement learning   Internet of Things   mobile edge computing   task offloading   unmanned aerial vehicles  

摘要： In this work, an intelligent mobile edge computing (MEC) network is studied for Internet of Things (IoT) in the presence of eavesdropping environments, where there are multiple users who can offload their confidential tasks to the computational access point (CAP) for the assistance of computation. One unmanned aerial vehicle (UAV) attacker exists in the system and it can listen to the confidential data transmission from the users to the CAP. We optimize the system design of the intelligent MEC network, by adaptively allocating the offloading ratio and wireless bandwidth, to reduce the linearly weighted cost of the latency as well as energy consumption (EnC). Specifically, starting from the deep reinforcement learning, we devise a deep Q-network (DQN) network to adjust the offloading ratio and transmission bandwidth, which can help calculate the computational tasks and suppress the eavesdropping from the UAV efficiently. We finally provide some simulation results to validate the proposed offloading strategy. In particular, the proposed offloading strategy can achieve a much lower cost compared to the conventional ones, in the terms of latency and EnC.

关键词： Wireless communication   Base stations   Network slicing   Simulation   Quality of service   Dynamic scheduling   User experience   Deep deterministic policy gradient   mobile communication   mobile edge computing   network slicing   resource allocation  

摘要： The development of multi-industry compatibility and the coexistence of multiple services and multiple functional communication networks will cause rapid growth in mobile communication system traffic. Users will have increasingly strict requirements for quality of service (QoS), e.g., a high rate, low latency, and low energy consumption. To address these problems, it is helpful to combine network slicing and mobile edge computing (MEC) to provide customized networks while reducing the service processing time. Due to the uncertainty of user requests and the environment, reasonable resource allocation is always particularly challenging. A novel dynamic resource allocation scheme for MEC slice systems, which formulates resource allocation and computation offloading issues as an optimization problem subject to the latency and rate, is proposed. Based on the dynamics of the slice requirements, quantity, and service time, the proposed problem is converted to a Markov decision process (MDP), and a state, action, and reward function are proposed. By exploiting the deep deterministic policy gradient (DDPG) algorithm, the wireless resources and computing resources are configured dynamically according to the requirements of different types of slices to maximize the revenue of the network operator. The simulation results demonstrate the influence of the slice arrival rate and total resources on the allocation policy. Compared with other schemes, the proposed scheme can provide a more effective performance when resources are scarce.

关键词： Mobile edge computing  

摘要： Multi-access/mobile edge computing (MEC) is a structural design for enabling cloud computing platform at the edge of mobile network, so as to reduce network congestion, improves fast response, and optimization of mobile resources to compute complex applications. MEC provides a distributed computing environment for its applications. In MEC, all the computational reserves will be brought to the base location of the mobile networks. It has the ability to store and process the contents at physical proximity to the mobile subscribers to assist delay sensitivity and contextaware applications. Generally, the packet forwarding and filtering will be operated by the traditional edge network. At present, applications’ online computa-tions and storage are done at remote servers, and those servers are placed far away from the users. New technologies are emerged to shift all the available resources to a edge from a cloud. Radio access network (RAN) could able to do the computational off-loading to the edge of the mobile network. So, it is mandatory to have a load balancer at the edge to distribute all the job to all the available processors without any congestion or delay. A routing specifies how to route the traffic between each origin-destination pair across a network. The traffic sharing is applied in a routing and allocating process to enhance the survivability of a network. The experimental results show the proposed algorithm works better than the existing dynamic load balancing algorithms. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021.

关键词： Internet of Things   cognitive radio   mobile edge computing   MEC services   spectrum management   energy consumption   mobility   analysis process   decision making  

摘要： The integration of cognitive radio (CR) in Internet of Things (IoT) is an effective step into the smart technology world. The capability of CR can effectively solve spectrum-related issues for IoT applications, but this association is still a big challenge that has led to a new research dimension of CR-based IoT. To this extend, in this paper the authors propose a novel distributed spectrum management approach based on mobile edge computing (MEC) technology in cooperative environment that enables CRIoT devices to share the unutilized spectrum efficiently. The simulation results show that the proposed solution achieves good performance in terms of spectrum access/sharing and maintains a balance energy consumption of CRIoT users within lower latency.

关键词： Mobile edge computing  

摘要： In this paper, we study an RF energy harvesting mobile edge computing network based on a SIMO/MISO system and NOMA schemes over Nakagami-m fading. Specifically, a multi-antenna user harvests RF energy from a power station by using a selection combining/maximal ratio combining scheme and offload its tasks to two MEC servers through downlink NOMA by employing transmit antenna selection/maximal ratio transmission scheme. Accordingly, we investigate the performance of six schemes, namely SC-TAS1, SC-TAS1, MRC-TAS1, MRC-TAS2, SC-MRT, and MRC-MRT, for this considered system. To evaluate the performance, exact closed- form expressions of successful computation probability are derived. We further propose the optimal algorithm to find the best parameter sets to achieve the best performance. Moreover, the impacts of the network parameters on the system performance for these schemes are investigated. Finally, the simulation results are also provided to verify the accuracy of our analysis. Copyright © 2021 Dac-Binh Ha et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution license, which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited. doi:10.4108/eai.28-4-2021.169425

关键词： Computation   energy efficiency   Internet of Things   mobile edge computing   offloading   resource allocation   UAVs  

摘要： With the emergence of computation-intensive and delay-sensitive applications, such as face recognition, virtual reality, augmented reality, and Internet of Things (IoT) devices;Mobile Edge Computing (MEC) allows the IoT devices to offload their heavy computation tasks to nearby edge cloud network rather than to compute the tasks locally. Therefore, it helps to reduce the energy consumption and execution delay in the ground mobile users. Flying Unmanned Aerial Vehicles (UAVs) integrated with the MEC server play a key role in 5G and future wireless communication networks to provide spatial coverage and further computational services to the small, battery-powered and energy-constrained devices. The UAV-enabled MEC (U-MEC) system has flexible mobility and more computational capability compared to the terrestrial MEC network. They support line-of-sight (LoS) links with the users offloading their tasks to the UAVs. Hence, users can transmit more data without interference by mitigating small-scale fading and shadowing effects. UAVs resources and flight time are very limited due to size, weight, and power (SWaP) constraints. Therefore, energy-aware communication and computation resources are allocated in order to minimize energy consumption. In this paper, a brief survey on U-MEC networks is presented. It includes the brief introduction regarding UAVs and MEC technology. The basic terminologies and architectures used in U-MEC networks are also defined. Moreover, mobile edge computation offloading working, different access schemes used during computation offloading technique are explained. Resources that are needed to be optimized in U-MEC systems are depicted with different optimization problem, and solution types. Furthermore, to guide future work in this area of research, future research directions are outlined. At the end, challenges and open issues in this domain are also summarized.

ISBN: (数字)9783030698935 ISBN: (纸本)9783030698928

关键词： 5G mobile communication   Device-to-device communication   Cellular networks   Network slicing   3G mobile communication   Business   Cloud computing   5G cellular networks   D2D communication   mobile edge computing   network slicing  

摘要： With the tremendous demand for connectivity anywhere and anytime, existing network architectures should be modified. To cope with the challenges that arise due to the increasing flood of devices/users and a diverse range of application requirements, new technologies and concepts must be integrated to enable their benefits. Service providers and business companies are looking for new areas of research to enhance overall system performance. This article gives a detailed survey about the recent 5G technologies, the solutions they provide, and the effect caused by their addition to current cellular networks. It is based on the three most important 5G concepts: Device to Device (D2D), Network Slicing (NS), and Mobile Edge Computing (MEC). This study proposes to design the future 5G networks by the integration of all three technologies. It is believed that spectrum efficiency, energy efficiency, and overall throughput will be greatly improved by using D2D. The system delay and computational load will be reduced as tasks will be handled by edge routers located at the base stations. Thus offloading the core network and the system capital expenses and operational expenses will be reduced significantly by slicing the network.

关键词： Bandwidth   Cloud computing   Throughput   Mobile handsets   Heuristic algorithms   Computational modeling   Partitioning algorithms   Mobile edge computing   computation partitioning   resource allocation  

摘要： Mobile edge computation partitioning is an effective technique to improve the applications performance on the mobile devices by selectively offloading some computations from the devices to the nearby edge cloud. Most previous works focus on the computation partitioning for a single user. Recent works begin to study the computation partitioning in a multiple user environment in which a number of users compete for the constrained computation resources on the edge cloud. However, these works neglect the fact that the users normally also share the network resources to access the edge cloud, and thus the allocation of bandwidth to the users significantly affects the overall performance of the users. In this paper, we study network aware mobile edge computation partitioning in multi-user environments, i.e., to decide for each user which parts of the application should be offloaded onto the edge cloud, and which others should be executed locally, and meanwhile to allocate the access bandwidth among the users, such that the average application performance of the users is maximized. This problem is new in that we consider the competition among users for the network bandwidth as well as the computation resources in a multi-user environment. With a set of novel models and formulations, we transform the problem into the classic Multi-class Multi-dimensional Knapsack Problem, and develop an effective algorithm, namely Performance Function Matrix based Heuristic (PFM-H), to solve it. We further consider the user mobility and design effective online algorithms that could be easily deployed in practical systems. Comprehensive trace driven simulations show that our proposed algorithm outperforms the benchmark algorithms significantly in the average application performance.

关键词： task scheduling   crowd sensing   MEC   mobile edge computing   task offloading   task shifting   Lyapunov optimisation  

摘要： Crowd sensing effectively solves the dilemma of massive data collection faced by most data-driven applications. Recently, mobile edge computing (MEC) is proposed to extend the frontier of cloud to the network edge so that it is quite suitable to integrate MEC with current crowd sensing systems. In this paper, we focus on the basic problem of task scheduling in such systems. The problem discussed here has some unique challenges, e.g., edge devices are not dedicated to perform sensing tasks, task scheduling on edge devices and edge servers are highly coupled, and it is hard to achieve long-term objectives. To this end, we first present a workflow framework that captures the unique execution logic of sensing tasks. Then we propose a staged scheme to decouple the original scheduling problem. Moreover, we leverage Lyapunov optimisation technique to achieve long-term objective. The experiment results verify the effectiveness and efficiency of our proposed algorithm.