关键词： Clustering   Routing   Information centric networking   Mobile Edge Computing   Wireless sensor networks   Metaheuristics  

摘要： In present days, the utilization of mobile edge computing (MEC) and Internet of Things (IoT) in mobile networks offers a bottleneck in the evolving technological requirements. Wireless Sensors Network (WSN) become an important component of the IoT and is the major source of big data. In IoT enabled WSN, a massive amount of data collection generated from a resource-limited network is a tedious process, posing several challenging issues. Traditional networking protocols offer unfeasible mechanisms for large-scaled networks and might be applied to IoT platform without any modifications. Information-Centric Networking (ICN) is a revolutionary archetype which that can resolve those big data gathering challenges. Employing the ICN architecture for resource-limited WSN enabled IoT networks may additionally enhance the data access mechanism, reliability challenges in case of a mobility event, and maximum delay under multihop communication. In this view, this paper proposes an IoT enabled cluster based routing (CBR) protocol for information centric wireless sensor networks (ICWSN), named CBR-ICWSN. The proposed model undergoes a black widow optimization (BWO) based clustering technique to select the optimal set of cluster heads (CHs) effectively. Besides, the CBR-ICWSN technique involves an oppositional artificial bee colony (OABC) based routing process for optimal selection of paths. A series of simulations take place to verify the performance of the CBR-ICWSN technique and the results are examined under several aspects. The experimental outcome of the CBR-ICWSN technique has outperformed the compared methods interms of network lifetime and energy efficiency.

关键词： mobile edge computing   task offloading   delay sensitive   Markov decision process   deep reinforcement learning  

摘要： With the internet developing rapidly, mobile edge computing (MEC) has been proposed to offer computational capabilities to tackle the high latency caused by innumerable data and applications. Due to limited computing resources, the innovation of computation offloading technology for an MEC system remains challenging, and can lead to transmission delays and energy consumption. This paper focuses on a task-offloading scheme for an MEC-based system where each mobile device is an independent agent and responsible for making a schedule based on delay-sensitive tasks. Nevertheless, the time-varying network dynamics and the heterogeneous features of real-time data tasks make it difficult to find an optimal solution for task offloading. Existing centralized-based or distributed-based algorithms require huge computational resources for complex problems. To address the above problem, we design a novel deep reinforcement learning (DRL)-based approach by using a parameterized indexed value function for value estimation. Additionally, the task-offloading problem is simulated as a Markov decision process (MDP) and our aim is to reduce the total delay of data processing. Experimental results have shown that our algorithm significantly promotes the users' offloading performance over traditional methods.

关键词： Games   Servers   Delays   Wireless communication   Rendering (computer graphics)   Approximation algorithms   Virtual reality   Mobile edge computing   resource allocation   virtual reality   multiplayer interactive game  

摘要： Wireless multiplayer interactive virtual reality (VR) game has the high computing workload of VR and unpredictable interaction among players, which brings severe challenges to the design of wireless communication systems. In this paper, we propose a wireless multiplayer interactive VR game transmission framework based on mobile edge computing (MEC) that is able to model the interaction among players and compute the post-processing procedures at the MEC server or the mobile VR device. In the framework, the absolute delay of each player is used to avoid VR vertigo and the inter-player delay among players is used to model the fairness of the interactive game. Aiming to minimize the average inter-player delay, we optimize the computing resource allocation of the MEC server, the wireless bandwidth allocation and the post-processing decision policy subject to the constraints of the absolute delay requirements, the local energy limits of players, the total bandwidth limit and the computing resources limit. To tackle the non-convex problem efficiently, we design an iterative algorithm based on the NESTT-G algorithm which iteratively optimizes the truncated first-order Taylor approximation of the objective. Numerical results demonstrate the proposed algorithm can reduce the average inter-player delay significantly with lower complexity, and also reveal the impact of different parameters and the channel state conditions on the post-processing decision and edge resource allocation.

关键词： cooperative offloading   decision-making   intelligent computing   mobile edge computing   resource allocation  

摘要： The multiuser cooperative offloading mobile edge computing (COMEC) system has attracted much attention because it can realize delay-sensitive tasks. However, in the coupling optimization of offloading decision and resource allocation, the existing numerical optimization algorithms are difficult to obtain high-quality optimization solutions. In this paper, we propose a differential dynamic gradient descent (DDGD) optimization algorithm to solve the above optimization problems. DDGD algorithm decomposes the constrained NP-hard optimization problem into two network layers and integrates the constraint function into a larger end-to-end training network. These two-layer networks encode the dependencies and optimization constraints between parameter hidden states, which cannot be captured by a numerical optimization model or a full connection layer neural network. Because the ring learning and self-repeating learning architecture are adopted and the information is stored in the differential dynamics network, the proposed algorithm can achieve better and more intelligent decision-making in searching the solution trajectory without setting accurate parameters in advance and reduce the complexity of the network. We show that compared with the baseline method, the DDGD method has superior optimization performance in the energy consumption optimization of COMEC.

关键词： degradable system   microservice   mobile edge computing   performability   resource allocation  

摘要： This paper explores resource allocation strategy in the Baidu Over The Edge system to enable mobile edge computing (MEC) datacenters to effectively support cloud-native services downstream to the network edge. There are many challenges to this issue. First, MEC datacenters are resource-constrained to fully meet resource demands. Second, previous works regard the resource requirements of each service as an indivisible unit, resulting in idle MEC resources, even if the resources can meet the demands of some microservices decoupled by the service. Third, they are confined to optimize the allocation for a single slot, failing to adapt to the dynamic demands. To improve resource utilization, we propose performability-aware resource allocation (PARA), a PARA on the edges for cloud-native services. It takes microservices as the unit of resource allocation and allows services to perform with degraded services when only part of microservices' demands are met. It also considers dependency among microservices, dynamic resource requirements, and resource supply characteristics of MEC and cloud. Performability is a unified performance-reliability measure for evaluating such degradable systems. To maximize the long-term overall performability, we model the resource optimization problem and then develop an online greedy heuristic algorithm. The algorithm predicts services' resource demands and then adapts the online allocation. The experimental results show that PARA reduces the reallocation overhead by 47.7%-53.6%, and improves the long-term overall performability by 23.14%-43.25% of existing state-of-the-art works.

关键词： Mobile edge computing   software decomposition   federated learning   distributed computing  

摘要： Deep Learning (DL) is an essential technology formodern intelligent sensor network and interactive multimedia applications, having problems with user data privacy when training on a central cloud. While Federated Learning (FL) motivates to preserve user privacy, it also causes new problems of lower user terminal usability and training efficiency, which caused substantial energy consumption. This article proposes a novel energy-efficient and privacy-aware decomposition framework to improve user-side FL efficiency under pre-defined privacy requirements with the assistance of Mobile Edge Computing (MEC) and Software Decomposition. It takes the propagation of each neural layer as the migrating unit and considers the tradeoff relationship between privacy and efficiency. We also propose an online scheduling algorithm to optimize the framework's training performance. Furthermore, we summarize eight privacy-sensitive information classes onwhich existing privacy attacks base and design configurable privacy preservation mechanisms for each class. Simulations and experiments prove the effectiveness of our framework and algorithm in FL efficiency improvement and the effects of different privacy constraints on the overall training efficiency.

关键词： Servers   Quality of service   Power demand   Delays   Planning   Task analysis   Minimization   Mobile edge computing   quality of service   power efficiency   resource optimization   integer gaining and sharing knowledge (IGSK)   workload-based threshold control  

摘要： Mobile edge computing (MEC) allows mobile devices to offload computationally intensive tasks to nearby base stations equipped with computational resources. MEC also provides a solution for supporting low latency responses. However, MEC increases the system power consumption by adding extra computational units. It is important to carefully plan computational resources to achieve a balance between quality of service (QoS) and power consumption. Moreover, the MEC system control should shut down unused resources when traffic rates fall to further decrease the power consumption. In this letter, we investigate a workload-based core power state control for cooperative MEC systems and a heterogeneous resource planning method. We also formulate a joint resource planning and workload-threshold optimization problem for power consumption minimization. The problem is solved using a hybrid method using metaheuristic and heuristic algorithms. The problem is first solved for an equivalent uniform system using integer gaining and sharing knowledge (IGSK). The resources are then redistributed over the base stations using a heuristic that considers the effect of power state control on core distribution. The results show that the iterative algorithm can accomplish a satisfactory QoS with the same number of resources obtained by other approaches or less. Additionally, threshold control reduced the system power consumption by 19.6%.

关键词： Mobile edge computing   D2D   mmWave   latency minimization  

摘要： Mobile edge computing (MEC) and millimeter wave (mmWave) communications are capable of significantly reducing the network's delay and enhancing its capacity. In this paper we investigate a mmWave and device-to-device (D2D) assisted MEC system, in which user A carries out some computational tasks and shares the results with user B with the aid of a base station (BS). We assume partial offloading model and the task can be partitioned into two portions: the first part is computed locally at user A, while the second part is transmitted to the BS and computed by the MEC server. The computational results are then sent to user B through a D2D link and via the link from the BS to user B, respectively. To support computation offloading, both the users and the BS are equipped with multiple antennas and employ analog and digital (A/D) hybrid beamforming. Moreover, we propose a novel two-timescale joint hybrid beamforming and task allocation algorithm to reduce the system latency whilst cut down the required signaling overhead. Specifically, the high-dimensional analog beamforming matrices are updated in a frame-based manner based on the channel state information (CSI) samples, where each frame consists of a number of time slots, while the low-dimensional digital beamforming matrices and the offloading ratio are optimized more frequently relied on the low-dimensional effective channel matrices in each time slot. A stochastic successive convex approximation (SSCA) based algorithm is developed to design the long-term analog beamforming matrices. As for the short-term variables, the digital beamforming matrices are optimized relying on the innovative penalty-concave convex procedure (penalty-CCCP) for handling the mmWave non-linear transmit power constraint, and the offloading ratio can be obtained via the derived closed-form solution. Simulation results verify the effectiveness of the proposed algorithm by comparing the benchmarks.

关键词： Task analysis   Array signal processing   Wireless communication   Downlink   Uplink   Program processors   Optimization   Wireless power transfer   intelligent reflecting surface   mobile edge computing  

摘要： This letter studies the joint active and passive beamforming in intelligent reflecting surface (IRS)-enhanced wireless powered mobile edge computing (MEC) networks, in which a hybrid access point (HAP) is deployed to charge multiple wireless devices (WDs) and assist in computation of the offloaded tasks from the WDs. An IRS is deployed to enhance the downlink wireless power transfer (WPT) and the uplink tasks offloading (TO). To maximize the systems' sum computation rate, an optimization problem is formulated by jointly optimizing the time assignment of WPT and TO, the active beamforming at the HAP, the transmit power and computing frequency of the WDs, and the passive beamforming at the IRS. To tackle the non-convex problem, we present an efficient algorithm by utilizing the alternating optimization (AO) and semidefinite relaxation (SDR) techniques. Simulation results show that by deploying IRS in the system, a significant computation rate gain can be achieved. Especially, when the IRS is closer to the WDs or the HAP, the much higher computation rate can be reached.

关键词： Deep reinforcement learning   Integrated trust mechanism   Mobile edge computing   Task offloading  

摘要： As a new computing model, mobile edge computing (MEC) is designed to better deal with various forms of service requests, such as computing intensity and delay sensitivity, in the era of big data and the Internet of Things (IoT). However, the development of MEC is still in its infancy, and many issues need to be further investigated. One of the key issues that needs to be addressed in MEC is rational task offloading. Due to the dynamic, real time and complex nature of the MEC environment, the security and reliability of edge data are becoming increasingly important. Based on the above problems, we construct a task offloading integrated trust evaluation mechanism and, combined with the double deep Q-network (DDQN) algorithm in deep reinforcement learning (DRL), propose a novel task offloading algorithm, named DDTMOA. Simulation results show that the DDTMOA algorithm can effectively reduce the average task response time and total system energy consumption while ensuring task offloading performance compared to other classical algorithms. (C) 2022 Elsevier Inc. All rights reserved.