关键词： Energy harvesting   Mobile edge computing   Energy transmission   Wireless power transmission  

摘要： Mobile edge computing (MEC) can provide rich computing services near mobile terminals, making it possible for computing intensive tasks to be performed at the edge, however, when renewable energy is the main power supply method for MEC servers, due to the unpredictability of renewable energy, the server will generate additional processing delays due to low energy. In this paper, in order to solve this problem, we incorporate renewable energy into the mobile edge computing, and use wireless power transmission technology to realize energy transmission between MEC servers. By optimizing offloading and wireless resource allocation, the total delay of the system is minimized. The main difficulty of this paper is the combination of unloading decision and its strong coupling with wireless resource allocation. In order to solve this problem, we use Lagrange multiplier method to get the optimal allocation of computing resources, and proposes an effective offloading exclusion algorithm (OEA) to determine the optimal offloading decision. Finally, the experimental results show that compared with other comparison schemes, the proposed scheme can effectively improve the performance of MEC and reduce the time consumption of the system.

关键词： Mobile edge computing   Federated learning   Systems and statistical heterogeneity   Convergence speed   Adaptive workload assignment  

摘要： With rapid advancement of Internet of Things (IoT) and social networking applications generating large amounts of data at or close to the network edge, Mobile Edge Computing (MEC) has naturally been proposed to bring model training closer to where data is produced. However, there still exists privacy concern since typical MEC frameworks need to transfer sensitive data resources from data collection end devices/clients to MEC server. So the concept of Federated Learning (FL) has been introduced which supports privacy-preserved collaborative machine learning involving multiple clients coordinated by the MEC server without centralizing the private data. Unfortunately, FL is prone to multiple challenges: 1) systems heterogeneity between clients causes straggler issue, and 2) statistical heterogeneity between clients brings about objective inconsistency problem, both of which may lead to a significant slow-down in the convergence speed in heterogeneous MEC environment. In this paper, we propose a novel framework, FedAda (Federated Adaptive Training), that incorporates systems capabilities and data characteristics of the clients to adaptively assign appropriate workload to each client. The key idea is that instead of running a fixed number of local training iterations as in Federated Averaging (FedAvg), our algorithm adopts an adaptive workload assignment strategy by minimizing the runtime gap between clients and maximizing convergence gain in heterogeneous MEC environment. Moreover, we design a light mechanism extending FedAda to accelerate the convergence speed by further fine-tuning the workload assignment based on the global convergence status in each communication round. We evaluate FedAda on CIFAR-10 dataset to explore the performance of the algorithm in the simulated heterogeneous MEC environment. Experimental results show that FedAda is able to assign appropriate amount of workload to each client and substantially reduces the convergence time by up to 49.5% compar

关键词： Mobile edge computing   Computing offloading   Usage pattern   Privacy protection   Lyapunov optimization  

摘要： In Mobile Edge Computing (MEC), attackers can speculate and mine sensitive user information by eavesdropping wireless channel status and offloading usage pattern, leading to user privacy leakage. To solve this problem, this paper proposes a Privacy-preserving and Energy-efficient Offloading Algorithm (PEOA) based on Lyapunov optimization. In this method, a continuous Markov process offloading model with a buffer queue strategy is built first. Then the amount of privacy of offloading usage pattern in wireless channel is defined. Finally, by introducing the Lyapunov optimization, the problem of minimum average energy consumption in continuous state transition process with privacy constraints in the infinite time domain is transformed into the minimum value problem of each timeslot, which reduces the complexity of algorithms and helps obtain the optimal solution while maintaining low energy consumption. The experimental results show that, compared with other methods, PEOA can maintain the amount of privacy accumulation in the system near zero, while sustaining low average energy consumption costs. This makes it difficult for attackers to infer sensitive user information through offloading usage patterns, thus effectively protecting user privacy and safety.

关键词： Mobile edge computing  

摘要： Mobile edge computing (MEC) uses multiple mobiles to compute several complex tasks that are unable to compute on a single device. Taking advantage of all abundant mobile resources and making a mobile cloud from them will be useful. This study aims to propose and implement a novel framework to cover challenges raised by application execution on resource-constrained devices. The purpose is to overcome the waste of resources in MEC and provide time efficiency to the data packets that are sent and received between offloaded and offloading devices. The main task of MEC is to offload tasks by first selecting resources and then allocating tasks to selected resources. A multiple linear regression algorithm is used for the selection of compatible devices. Particle swarm optimization techniques are used as a benchmark technique to design an algorithm for optimized resource allocation. Multiple mobile devices acted as a major component for making edge clouds. The study finds that response time of processing tasks is reduced, ineffectual resources become beneficial, increase in demand for mobile devices, and usage of mobile resources as a replacement for mobile edge cloud servers. Abundant resource usage of two or more edge clouds, that is, interedge resource usage is the originality of this research, while others are using intraedge resources only.

关键词： Mobile edge computing  

摘要： The sixth-generation (6G) network will shift its focus to supporting everything including various machine-type devices (MTDs) in an everyone-centric manner. To ubiquitously cover the MTDs working in rural and disastrous areas, satellite communications become indispensable, while mobile edge computing (MEC) also plays an increasingly crucial role. Their sophisticated integration enables wide-area edge intelligence which promises to facilitate globally-distributed customized services. In this article, we present typical use cases of integrated satellite-MEC networks and discuss the main challenges therein. Inspired by the protein structure and the systematic engineering methodology, we propose three minimal integrating structures, based on which a complex integrated satellite-MEC network can be treated as their extension and combination. We discuss the unique characteristics and key problems of each minimal structure. Accordingly, we establish an on-demand network orchestration framework to enrich the hierarchy of network management, which further leads to a process-oriented network optimization method. On that basis, a case study is utilized to showcase the benefits of on-demand network orchestration and process-oriented network optimization. Finally, we outline potential research issues to envision a more intelligent, more secure, and greener integrated network. © 2022, CC BY.

关键词： Mobile edge computing  

摘要： Cellular networks equipped with mobile edge computing (MEC) servers can be beneficial for unmanned aerial vehicles (UAVs) with limited onboard computation power and battery life-time. In this paper, we compare energy consumption of a UAV connected to cellular MEC servers in various possible scenarios such as onboard/MEC processing or parallel computation. Using detailed 3GPP-based modelings, we provide quantitative understanding of the most energy efficient approach and its relation with communication technologies, computation factors, and mobility parameters. Our findings show that, across the different frequencies from sub-6GHz to THz bands, the mmWave cellular MEC network is more energy efficient than UAV’s onborad processing for a broader range of network densities. Secondly, while the UAV propulsion power consumption is non-decreasing function of velocity, the UAV movement cost yet can be optimized to further provide remarkable energy savings as compared to hovering. Finally, our results show that the most energy efficient approach can be obtained if mobility of UAV is combined with efficient parallel onboard-MEC processing. © 2022, CC BY.

关键词： Mobile edge computing  

摘要： Next-generation wireless communication networks are expected to support massive connectivity with high data rate, low power consumption, and computational latency. However, it can significantly enhance the existing network complexity, which results in high latency. To ease this situation, mobile edge cloud and massive multiple input and multiple output (MIMO) have recently emerged as the effective solutions. Mobile edge cloud has the ability to overcome the constraints of low power and finite computational resources in next-generation communication systems by allowing devices to offload their extensive computation to maximize the computation rate. On the other hand, MIMO can enhance network spectral efficiency by using large number of antenna elements. The integration of mobile edge cloud with massive MIMO also helps to increase the energy efficiency of the devices;as a result, more bits are computed with minimal energy consumption. In this work, a mathematical model is formulated by considering the devices' energy constraint, which is nonconvex in nature. Following that, to overcome this, we transformed the original optimization problem using the first approximation method and solved the partial offloading schemes. Results reveal that the proposed scheme outperforms the others by considering computational rate as a performance matrix.

关键词： Mobile edge computing  

摘要： The high computational complexity and high energy consumption of artificial intelligence (AI) algorithms hinder their application in augmented reality (AR) systems. This paper considers the scene of completing video-based AI inference tasks in the mobile edge computing (MEC) system. We use multiply-andaccumulate operations (MACs) for problem analysis and optimize delay and energy consumption under accuracy constraints. To solve this problem, we first assume that offloading policy is known and decouple the problem into two subproblems. After solving these two subproblems, we propose an iterative-based scheduling algorithm to obtain the optimal offloading policy. We also experimentally discuss the relationship between delay, energy consumption, and inference accuracy. Copyright © 2022, The Authors. All rights reserved.

关键词： Data offloading   mobile edge computing   security   machine learning   artificial intelligence   XGBoost   salp swarm algorithm  

摘要： Mobile edge computing(MEC)provides effective cloud services and functionality at the edge device,to improve the quality of service(QoS)of end users by offloading the high computation ***,the introduction of deep learning(DL)and hardware technologies paves amethod in detecting the current traffic status,data offloading,and cyberattacks in *** study introduces an artificial intelligence with metaheuristic based data offloading technique for Secure MEC(AIMDO-SMEC)*** proposed AIMDO-SMEC technique incorporates an effective traffic prediction module using Siamese Neural Networks(SNN)to determine the traffic status in the MEC ***,an adaptive sampling cross entropy(ASCE)technique is utilized for data offloading in MEC ***,the modified salp swarm algorithm(MSSA)with extreme gradient boosting(XGBoost)technique was implemented to identification and classification of cyberattack that exist in the MEC *** examining the enhanced outcomes of the AIMDO-SMEC technique,a comprehensive experimental analysis is carried out and the results demonstrated the enhanced outcomes of the AIMDOSMEC technique with the minimal completion time of tasks(CTT)of 0.680.

关键词： mobile edge computing   energy harvesting   device-mobility   partial computation offloading   resource allocation   deep reinforcement learning  

摘要： Mobile edge computing(MEC)emerges as a paradigm to free mobile devices(MDs)from increasingly dense computing workloads in 6G *** quality of computing experience can be greatly improved by offloading computing tasks from MDs to MEC *** energy harvested by energy harvesting equipments(EHQs)is considered as a promising power supply for users to process and offload *** this paper,we apply the uniform mobility model of MDs to derive a more realistic wireless channel model in a multi-user MEC system with batteries as EHQs to harvest and storage *** investigate an optimization problem of the weighted sum of delay cost and energy cost of MDs in the MEC *** propose an effective joint partial computation offloading and resource allocation(CORA)algorithm which is based on deep reinforcement learning(DRL)to obtain the optimal scheduling without prior knowledge of task arrival,renewable energy arrival as well as channel *** simulation results verify the efficiency of the proposed algorithm,which undoubtedly minimizes the cost of MDs compared with other benchmarks.