Energy-efficient task offloading strategy in mobile edge computing for resource-intensive mobile applications

Mobile Edge Computing (MEC) has been considered a promising solution that can address capacity and performance challenges in legacy systems such as Mobile Cloud Computing (MCC). In particular, such challenges include intolerable delay, congestion in the core network, insufficient Quality of Experience (QoE), high cost of resource utility, such as energy and bandwidth. The aforementioned challenges originate from limited resources in mobile devices, the multi-hop connection between end-users and the cloud, high pressure from computation-intensive and delay-critical applications. Considering the limited resource setting at the MEC, improving the efficiency of task offloading in terms of both energy and delay in MEC applications is an important and urgent problem to be solved. In this paper, the key objective is to propose a task offloading scheme that minimizes the overall energy consumption along with satisfying capacity and delay requirements. Thus, we propose a MEC-assisted energy-efficient task offloading scheme that leverages the cooperative MEC framework. To achieve energy efficiency, we propose a novel hybrid approach established based on Particle Swarm Optimization (PSO) and Grey Wolf Optimizer (GWO) to solve the optimization problem. The proposed approach considers efficient resource allocation such as sub-carriers, power, and bandwidth for offloading to guarantee minimum energy consumption. The simulation results demonstrate that the proposed strategy is computational-efficient compared to benchmark methods. Moreover, it improves energy utilization, energy gain, response delay, and offloading utility.     

Resource scheduling in mobile edge computing using improved ant colony algorithm for space information network

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.     

Joint offloading strategy based on quantum particle swarm optimization for MEC-enabled vehicular networks

With the development of the mobile communication technology, a wide variety of envisioned intelligent transportation systems have emerged and put forward more stringent requirements for vehicular communications. Most of computation-intensive and power-hungry applications result in a large amount of energy consumption and computation costs, which bring great challenges to the on-board system. It is necessary to exploit traffic offloading and scheduling in vehicular networks to ensure the Quality of Experience (QoE). In this paper, a joint offloading strategy based on quantum particle swarm optimization for the Mobile Edge Computing (MEC) enabled vehicular networks is presented. To minimize the delay cost and energy consumption, a task execution optimization model is formulated to assign the task to the available service nodes, which includes the service vehicles and the nearby Road Side Units (RSUs). For the task offloading process via Vehicle to Vehicle (V2V) communication, a vehicle selection algorithm is introduced to obtain an optimal offloading decision sequence. Next, an improved quantum particle swarm optimization algorithm for joint offloading is proposed to optimize the task delay and energy consumption. To maintain the diversity of the population, the crossover operator is introduced to exchange information among individuals. Besides, the crossover probability is defined to improve the search ability and convergence speed of the algorithm. Meanwhile, an adaptive shrinkage expansion factor is designed to improve the local search accuracy in the later iterations. Simulation results show that the proposed joint offloading strategy can effectively reduce the system overhead and the task completion delay under different system parameters.     

An architecture for digital hate content reduction with mobile edge computing

Mobile devices with social media applications are the prevalent user equipment to generate and consume digital hate content. The objective of this paper is to propose a mobile edge computing architecture for regulating and reducing hate content at the user's level. In this regard, the profiling of hate content is obtained from the results of multiple studies by quantitative and qualitative analyses. Profiling resulted in different categories of hate content caused by gender, religion, race, and disability. Based on this information, an architectural framework is developed to regulate and reduce hate content at the user's level in the mobile computing environment. The proposed architecture will be a novel idea to reduce hate content generation and its impact.     

Mobility-driven user-centric AP clustering in mobile edge computing-based ultra-dense networks

ultra-Dense Network (UDN) has been envisioned as a promising technology to provide high-quality wireless connectivity in dense urban areas, in which the density of Access Points (APs) is increased up to the point where it is comparable with or surpasses the density of active mobile users. In order to mitigate inter-AP interference and improve spectrum efficiency, APs in UDNs are usually clustered into multiple groups to serve different mobile users, respectively. However, as the number of APs increases, the computational capability within an AP group has become the bottleneck of AP clustering. In this paper, we first propose a novel UDN architecture based on Mobile Edge Computing (MEC), in which each MEC server is associated with a user-centric AP cluster to act as a mobile agent. In addition, in the context of MEC-based UDN, we leverage mobility prediction techniques to achieve a dynamic AP clustering scheme, in which the cluster structure can automatically adapt to the dynamic distribution of user traffic in a specific area. Simulation results show that the proposed scheme can highly increase the average user throughput compared with the baseline algorithm using max-SINR user association and equal bandwidth allocation, while it guarantees at the same time low transmission delay.     

Similar physical entity matching strategy for mobile edge search

In recent years, a large number of intelligent sensing devices have been deployed in the physical world, which brings great difficulties to the existing entity search. With the increase of the number of intelligent sensing devices, the accuracy of the search system in querying the entities to match the user’s request is reduced, and the delay of entity search is increased. We use the mobile edge technology to alleviate this problem by processing user requests on the edge side and propose a similar physical entity matching strategy for the mobile edge search. First, the raw data collected by the sensor is lightly weighted and expressed to reduce the storage overhead of the observed data. Furthermore, a physical entity matching degree estimation method is proposed, in which the similarity between the sensor and the given sensor in the network is estimated, and the matching search of the user request is performed according to the similarity. Simulation results show that the proposed method can effectively reduce the data storage overhead and improve the precision of the sensor search system.     

Users' experience matter: Delay sensitivity-aware computation offloading in mobile edge computing

As a promising computing paradigm, Mobile Edge Computing (MEC) provides communication and computing capability at the edge of the network to address the concerns of massive computation requirements, constrained battery capacity and limited bandwidth of the Internet of Things (IoT) systems. Most existing works on mobile edge task ignores the delay sensitivities, which may lead to the degraded utility of computation offloading and dissatisfied users. In this paper, we study the delay sensitivity-aware computation offloading by jointly considering both user's tolerance towards delay of task execution and the network status under computation and communication constraints. Specifically, we use a specific multi-user and multi-server MEC system to define the latency sensitivity of task offloading based on the analysis of delay distribution of task categories. Then, we propose a scoring mechanism to evaluate the sensitivity-dependent utility of task execution and devise a Centralized Iterative Redirection Offloading (CIRO) algorithm to collect all information in the MEC system. By starting with an initial offloading strategy, the CIRO algorithm enables IoT devices to cooperate and iteratively redirect task offloading decisions to optimize the offloading strategy until it converges. Extensive simulation results show that our method can significantly improve the utility of computation offloading in MEC systems and has lower time complexity than existing algorithms.     

An intelligent task offloading algorithm (iTOA) for UAV edge computing network

Unmanned Aerial Vehicle (UAV) has emerged as a promising technology for the support of human activities, such as target tracking, disaster rescue, and surveillance. However, these tasks require a large computation load of image or video processing, which imposes enormous pressure on the UAV computation platform. To solve this issue, in this work, we propose an intelligent Task Offloading Algorithm (iTOA) for UAV edge computing network. Compared with existing methods, iTOA is able to perceive the network’s environment intelligently to decide the offloading action based on deep Monte Calor Tree Search (MCTS), the core algorithm of Alpha Go. MCTS will simulate the offloading decision trajectories to acquire the best decision by maximizing the reward, such as lowest latency or power consumption. To accelerate the search convergence of MCTS, we also proposed a splitting Deep Neural Network (sDNN) to supply the prior probability for MCTS. The sDNN is trained by a self-supervised learning manager. Here, the training data set is obtained from iTOA itself as its own teacher. Compared with game theory and greedy search-based methods, the proposed iTOA improves service latency performance by 33% and 60%, respectively.     

温度感知的MapReduce节能任务调度策略

现有的FIFO、Fair、Capacity、LATE及Deadline Constraint等MapReduce任务调度器的主要区别在于队列与作业选择策略的不同,而任务选择策略基本相同,都是将数据的本地性(data-locality)作为选择的主要因素,忽略了对TaskTracker当前温度状态的考虑。实验表明,当TaskTracker处于高温状态时,一方面使CPU利用率变高,导致节点能耗增大,任务处理速度下降,导致任务完成时间增加;另一方面,易发的宕机现象将直接导致任务的失败,推测执行(speculative execution)机制容易使运行时任务被迫中止。继而提出温度感知的节能任务调度策略,将节点CPU温度纳入任务调度的决策信息,以避免少数高温任务执行节点对作业整体进度的影响。实验结果表明,算法能够避免任务分配到高温节点,从而有效地缩短作业完成时间,减小作业执行能耗,提高系统稳定性。     

Location privacy protection of maritime mobile terminals

Mobile Edge Computing (MEC) can support various high-reliability and low-delay applications in Maritime Networks (MNs). However, security risks in computing task offloading exist. In this study, the location privacy leakage risk of Maritime Mobile Terminals (MMTs) is quantified during task offloading and relevant Location Privacy Protection (LPP) schemes of MMT are considered under two kinds of task offloading scenarios. In single-MMT and single-time offloading scenario, a dynamic cache and spatial cloaking-based LPP (DS-CLP) algorithm is proposed; and under the multi-MMTs and multi-time offloading scenario, a pseudonym and alterable silent period-based LPP (PA-SLP) strategy is proposed. Simulation results show that the DS-CLP can save the response time and communication cost compared with traditional algorithms while protecting the MMT location privacy. Meanwhile, extending the alterable silent period, increasing the number of MMTs in the maritime area or improving the pseudonym update probability can enhance the LPP effect of MMTs in PA-SLP. Furthermore, the study results can be effectively applied to MNs with poor communication environments and relatively insufficient computing resources.     

车联网场景下移动边缘计算协作式资源分配策略

为了有效利用边缘云的计算资源,尽可能降低任务卸载时的平均等待时延,提出了一种满足边缘计算服务器容限阈值和任务卸载成功率约束条件下的多个边缘计算服务器相互协作的资源分配方案,通过单位时间总代价指标优化边缘计算服务器个数.将此方案建模为一个整数优化问题,之后设计了一种最小代价算法求解此优化问题,得到约束条件下的单位时间总代...     

An improved handover algorithm based on signal strength plus distance for interoperability in mobile cellular networks

In future generation mobile cellular systems, position location of mobile terminal is expected to be available. In this paper, we propose an initiation algorithm for intersystem handover based on the combination of position location of mobile terminal and the absolute signal strength thresholds. Global System for Mobile communication (GSM) and Universal Mobile Telecommunication Systems (UMTS) networks are considered for interworking. The proposed algorithm reduces the handover rate by around 50% and thus improves the network resource efficiency as compared to that based on signal strength thresholds only.     

Deep reinforcement learning-based joint task offloading and bandwidth allocation for multi-user mobile edge computing

The rapid growth of mobile internet services has yielded a variety of computation-intensive applications such as virtual/augmented reality. Mobile Edge Computing (MEC), which enables mobile terminals to offload computation tasks to servers located at the edge of the cellular networks, has been considered as an efficient approach to relieve the heavy computational burdens and realize an efficient computation offloading. Driven by the consequent requirement for proper resource allocations for computation offloading via MEC, in this paper, we propose a Deep-Q Network (DQN) based task offloading and resource allocation algorithm for the MEC. Specifically, we consider a MEC system in which every mobile terminal has multiple tasks offloaded to the edge server and design a joint task offloading decision and bandwidth allocation optimization to minimize the overall offloading cost in terms of energy cost, computation cost, and delay cost. Although the proposed optimization problem is a mixed integer nonlinear programming in nature, we exploit an emerging DQN technique to solve it. Extensive numerical results show that our proposed DQN-based approach can achieve the near-optimal performance.     

PSMECS: A provably secure ID-based communication in mobile edge computing

Human-centered systems play an important role in the modern world, for example, driverless car, autonomous and smart vehicles, drones, and robotics. The internet of things environment demands a faster real-time response depending on the applications processed in a particular duration. Mobile edge computing (MEC) allows a user to get a real-time response as compared with cloud computing (CC), although ensuring a number of security attributes in MEC environment remains challenging. In this article, a protocol is designed to achieve mutual authentication, anonymous communication, and security against traceability, as these are very crucial factors to ensure the security of data and user's privacy. Moreover, the proposed scheme ensures mutual authentication between a mobile user and an edge server along with the user's anonymity and untraceability. The proof of security and evaluation of performance of the scheme validates that it ensures security attributes and improves efficiency in terms of communication and computation overheads.     

智慧边缘计算安全综述

边缘计算将传统的云服务扩展到网络边缘,更贴近用户,适用于具有低时延需求的网络服务。随着边缘计算范式的兴起,其安全问题也得到越来越多的关注。首先介绍了边缘计算范式的基本概念、系统架构以及与其他计算范式的关系。然后分析了当前边缘计算中存在的安全威胁,并针对各种安全威胁探讨了相应的安全技术问题。最后对边缘计算安全技术中关键的入侵检测、访问控制、防御策略、密钥管理技术进行了分析,并提出了进一步研究方向。     

An efficient reliable broadcasting protocol for wireless mobile ad hoc networks

The mobile ad hoc network (MANET) has recently been recognized as an attractive network architecture for wireless communication. Reliable broadcast is an important operation in MANET (e.g., giving orders, searching routes, and notifying important signals). However, using a naive flooding to achieve reliable broadcasting may be very costly, causing a lot of contention, collision, and congestion, to which we refer as the broadcast storm problem. This paper proposes an efficient reliable broadcasting protocol by taking care of the potential broadcast storm problem that could occur in the medium-access level. Existing protocols are either unreliable, or reliable but based on a too costly approach. Our protocol differs from existing protocols by adopting a low-cost broadcast, which does not guarantee reliability, as a basic operation. The reliability is ensured by additional acknowledgement and handshaking. Simulation results do justify the efficiency of the proposed protocol.     

A distributed laxity-based priority scheduling scheme for time-sensitive traffic in mobile ad hoc networks

Characteristics of Mobile Ad hoc Networks such as shared broadcast channel, bandwidth and battery power limitations, highly dynamic topology, and location dependent errors, make provisioning of quality of service (QoS) in such networks very difficult. The Medium Access Control (MAC) layer plays a very important role as far as QoS is concerned. The MAC layer is responsible for selecting the next packet to be transmitted and the timing of its transmission. We have proposed a new MAC layer protocol that includes a laxity-based priority scheduling scheme and an associated back-off scheme, for supporting time-sensitive traffic. In the proposed scheduling scheme, we select the next packet to be transmitted, based on its priority value which takes into consideration the uniform laxity budget of the packet, the current packet delivery ratio of the flow to which the packet belongs, and the packet delivery ratio desired by the user. The back-off mechanism devised by us grants a node access to the channel, based on the rank of its highest priority packet in comparison to other such packets queued at nodes in the neighborhood of the current node. We have studied the performance of our protocol that combines a packet scheduling scheme and a channel access scheme through simulation experiments, and the simulation results show that our protocol exhibits a significant improvement in packet delivery ratio under bounded end-to-end delay requirements, compared to the existing 802.11 DCF and the Distributed Priority Scheduling scheme proposed recently in [ACM Wireless Networks Journal 8 (5) (2002) 455–466; Proceedings of ACM MOBICOM '01, July 2001, pp. 200–209].     

IFQ:一种新的ATM网络业务调度算法

本文提出了插空公平公队列（ＩＦＱ）调度算法。由于该方法考虑了ＡＴＭ网络中不同种类连接的特性,能充分利用网络资源。理论分析和仿真实验表明,ＩＦＱ算法能为Ｇ连接提供预约带宽保证和确定的时延上界,满足业务的实时传输要求。同时ＩＦＱ调度算法还具有连接独立性特点,能灵活地分配带宽资源。     

Research on intelligent computing offloading model based on reputation value in mobile edge computing

Aiming at the problem of high-latency,high-energy-consumption,and low-reliability mobile caused by computing-intensive and delay-sensitive emerging mobile applications in the explosive growth of IoT smart mobile terminals in the mobile edge computing environment,an offload decision-making model where delay and energy consumption were comprehensively included,and a computing resource game allocation model based on reputation that took into account was proposed,then improved particle swarm algorithm and the method of Lagrange multipliers were used respectively to solve models.Simulation results show that the proposed method can meet the service requirements of emerging intelligent applications for low latency,low energy consumption and high reliability,and effectively implement the overall optimized allocation of computing offload resources.