关键词： Internet of things   Mobile edge computing   Cloud   Optimization   Fog computing   Task scheduling  

摘要： Due to the evolution of Internet of Things (IoT) paradigm, the number of devices is growing day by day which arises the stringent requirements for various communications. The standard cloud computing model is not capable of efficiently hosting IoT tasks due to the high latency associated with it. Hence, this work utilizes the mobile edge computing and fog concept to process the input tasks independent of the cloud layer. Generally, the high computation costs and energy consumption resulted in the applications such as power-hungry and computation-intensive, which become massive challenges for an IoT device. Motivated by the previous research, we mainly plan to investigate the resource allocation issues that arise in the MEC-enabled IoT-Fog-cloud architecture. We propose a Poplar Optimization algorithm (POA) based on Attention 1DCNN-LSTM architecture (POA-A1DCNN-LSTM) for improving the total utility of the MEC servers by optimizing the energy consumption and task delay. Initially, the POA algorithm is implemented for cluster head (optimal fog node) selection using the node degree, node distance, and residual energy. Next, the A1DCNN-LSTM architecture is employed for task offloading by selecting the fog node with minimal task length and processing delay. The performance of the proposed method is validated by average latency, user satisfaction, network lifetime, energy consumption, average time delay, and normalized system utility. The experimentation results revealed that the proposed method attained better effectiveness in different metrics by achieving 1.5 ms, 0.65, 60 s, 0.45 j, 385 ms, and 0.98 compared to state-of-the-art *** experimentation outcomes demonstrate the effectiveness of the proposed POA-A1DCNN-LSTM architecture to decrease task completion delay and offer effective task scheduling as well as resource allocation performances in the MEC-enabled IoT-Fog-cloud network.

关键词： deep reinforcement learning   digital twin   Internet of Things   mobile edge computing  

摘要： Limited by battery and computing re-sources,the computing-intensive tasks generated by Internet of Things(IoT)devices cannot be processed all by *** edge computing(MEC)is a suitable solution for this problem,and the gener-ated tasks can be offloaded from IoT devices to *** this paper,we study the problem of dynamic task offloading for digital twin-empowered *** twin techniques are applied to provide information of environment and share the training data of agent de-ployed on IoT *** formulate the task offload-ing problem with the goal of maximizing the energy efficiency and the workload balance among the ***,we reformulate the problem as an MDP problem and design DRL-based energy efficient task offloading(DEETO)algorithm to solve *** experi-ments are carried out which show the superiority of our DEETO algorithm in improving energy efficiency and balancing the workload.

关键词： Mobile edge computing   D2D communication   parallel task offloading   content caching   multi-objective optimization  

摘要： In device to device (D2D) aided mobile edge computing (MEC) networks, by implementing content caching and D2D links, the edge server and nearby mobile devices can provide task offloading platforms. For parallel tasks, proper decisions on content caching and task offloading help reduce delay and energy consumption. However, what is often ignored in the previous works is the joint optimization of parallel task offloading and content caching. In this paper, we aim to find optimal content caching and parallel task offloading strategies, so as to minimize task delay and energy consumption. The minimization problem is formulated as a multi-objective optimization problem, concerning both content caching and parallel task offloading. The content caching is formulated as an integer knapsack problem (IKP). To solve the IKP problem, an enhanced Binary Particle Swarm Optimization algorithm is proposed. The parallel task offloading problem is formulated as a constrained multi-objective optimization problem, an improved multi-objective bat algorithm is proposed to address the problem. Experimental results show that our algorithm can decrease delay and energy cost by at most 45% and 56%, respectively. In addition, the parallel task offloading ratio remains over 91% even with large number of mobile devices (MDs).

关键词： Video content placement   mobile edge computing   online optimization   multi-agent reinforcement learning  

摘要： In the traditional video streaming service paradigm, content providers typically provision the requested video content to viewers through a central content delivery network (CDN). However, remote viewers usually experience long video streaming delay due to uncertain wide area network delay, which severely affects the quality of experience. Multi-Access Edge Computing (MEC) offers a way to shorten the video streaming delay by building small-scale cloud infrastructures at the network edge, which are in close proximity to the viewers. In this paper, we present novel centralized and distributed algorithms for the video content placement problem in MEC. In the proposed centralized video content placement algorithm, we leverage the Lyapunov optimization technique to formulate the video content placement problem as a series of one-time-slot optimization problems and apply an Alternating Direction Method of Multipliers (ADMM)-based method to solve each of them. We further devise a distributed Multi-Agent Reinforcement Learning (MARL)-based method with value decomposition mechanism and parallelization policy update method to solve the video content placement problem. The value Decomposition mechanism deals with the credit assignment among multiple agents, which promotes the cooperative optimization of the global target and reduces the frequency of information exchange. The parallelization of policy network can speed up the convergence process. Simulation results verify the effectiveness and superiority of our proposed centralized and distributed algorithms in terms of performance.

关键词： Communication security   Mobile edge computing   Data offloading   Artificial intelligence   UAV communication  

摘要： To solve the problem that mobile users cannot handle all data tasks by themselves due to the huge amount of data at present, this paper proposes a four-layer model of efficient and secure multiuser multitask computing offload. Since the user needs to offload the data to the server for processing through the wireless communication link, and users have mobility, it is difficult to achieve dynamic updates of user status and data information based on traditional cloud computing data processing methods, and it is difficult to ensure security during data unloading. In this paper, we studied how to realize the efficient and safe transmission of mobile users' data tasks when deploying Mobile Edge Computing (MEC) servers with artificial intelligence to assist in data processing on Unmanned Aerial vehicles (UAVs). First of all, to ensure the efficient use of computing resources, we use compression algorithms to reduce transmission overhead. Secondly, to enhance the security of the data trans-mission process, we have designed a security layer in the system model, which mainly uses secure multiparty technology to encrypt sensitive data, and a data transmission algorithm based on artificial intelligence is designed to assure the efficient and secure transmission of data in the network. Finally, through the analysis of the simulation results, we can know that the proposed strategy in this study has better performance than other existing strategies.

关键词： Mobile edge computing   Virtual machine   I   O interference   Task scheduling   Mixed-integer nonlinear programming   Karush-Kuhn-Tucker  

摘要： Mobile edge computing (MEC) is expected to support the computation-intensive and delay-sensitive applications of mobile internet users. In this paper, we investigate the resource allocation of MEC with the effect of I/O interference among parallel virtual machines (VMs) while satisfying the quality of service (QoS) of tasks. Different from existing works, we propose a flexible task scheduling approach that combining parallel and sequential computing to minimize the computing energy consumption of MEC server. We formulate the task scheduling problem as a mixed-integer nonlinear programming (MINLP) and decompose it as a CPU resource allocation subproblem, a computing time slot subproblem, and a VM selection subproblem. We show the first subproblem is a convex problem and propose a CPU frequency allocation (CFA) algorithm based on the Karush-Kuhn-Tucker (KKT) conditions to obtain the optimal CPU frequency resource allocation. For the time slot allocation and VM selection subproblems, we propose the three step allocation (TSA) and urgency based adjusting (UBA) algorithms to obtain the near-optimal solutions, respectively. Simulation results show that compared with several time slot allocations and VM selections, the proposed TSA and UBA algorithms can save up to 21.7% and 95.8% of energy consumption, respectively.& COPY;2023 Elsevier B.V. All rights reserved.

关键词： Application placement   application reliability   approximation algorithm   integer programming   low-latency services   mobile edge computing   optimization  

摘要： Mobile edge computing (MEC) provides a new distributed computing paradigm that overcomes the inability of cloud computing to offer low end-to-end latency. In a MEC environment, app vendors can deliver lower-latency services to mobile app users by placing applications on edge servers in close proximity to app users. From an app vendor's perspective, an optimal edge application placement strategy under a budget (k) constraint aims to place application instances on k edge servers within a specific area to maximize user coverage. However, edge servers may be subject to failure due to multiple reasons, e.g., hardware faults, software exceptions, cyber-attacks, etc. App users served by failed edge servers need to access applications from remote cloud servers if they cannot access any other edge servers. This impacts app users' quality of experience significantly. Thus, app vendors need to consider the reliability of the edge server network when choosing edge servers for placing their application instances. We make the first attempt in this paper to tackle this problem of joint optimization of coverage and reliability for edge application placement (EAP-CR). We formulate this problem as a constrained optimization problem and prove its NP-hardness theoretically. Then, we propose an optimal approach to find the optimal solutions with the integer programming technique, an approximation approach is also proposed to find approximate solutions for large-scale EAP-CR problems. We evaluate EAP-OPT and EAP-APX against three relevant approaches through experiments conducted on a widely-used real-world data set and a synthetic data set. The results demonstrate that our proposed approaches can solve the EAP-CR problem effectively and efficiently.

关键词： Task offloading   mobile edge computing   fog   deadline-constrained   modelling   performance analysis  

摘要： In Vehicular Fog Computing (VFC), the RSU-toVehicle (R2V) task offloading process is highly affected by undesirable yet sometimes inevitable events (e.g., buffer exhaustion, task HoL blocking and deadline expiry), the occurrence of which will notably alter the RSU's performance in terms of crucial Quality-of-Service (QoS) metrics such as the average system response time, the blocking and deadline expiry probabilities. This article proposes a novel R2V Deadline-Constrained Task Offload (R2V-DCTO) scheduling scheme with the objective of improving the RSU's performance in terms of the above-mentioned metrics;hence, filling an important literature gap. For this purpose, a stochastic modelling framework is established to capture theRSU's functional dynamics and assess its performance as it operates under R2V-DCTO. Extensive simulations are conducted to confirm the model's validity and accuracy and then compare R2V-DCTO's performance to that of the often adopted FIFO scheme. Results indicate that, on average, R2V-DCTOoutperforms FIFO by 33.4% in terms of the probability of deadline mismatch and by 83.3% in terms of mean system response time;those being critical QoS performance metrics.

关键词： Aerial data aggregator   Data aggregation   Data gathering   Internet of things   Mobile edge computing   Unmanned aerial vehicle  

摘要： In recent years, unmanned aerial vehicles (UAVs) have been used to extend the Internet of things (IoT) frame-work owing to their vast applications, monitoring and surveillance capability, ubiquity, and mobility. To support IoT requirements, UAVs must be capable of aggregating, processing, and transmitting data in real-time basis. As not only the number of IoT devices but also the amount of data to be collected is increased, data aggregation is of great importance. Recently, the UAV can also function as a mobile edge computing server in association with aerial data aggregation. This paper is the first to survey the various aspects and techniques of UAV-based aerial data aggregation for IoT networks. After addressing key design issues, we review the existing data aggregation techniques along with possible future direction. They are then compared with each other in terms of major operational features, performance characteristics, advantages, and limitations. Open issues and research chal-lenges are also discussed with possible solution approaches.

关键词： Autonomous aerial vehicles   Task analysis   Trajectory   Energy consumption   Wireless communication   Uncertainty   Relays   Mobile edge computing   unmanned aerial vehicle   channel uncertainty   information security  

摘要： This paper investigates the robust and secure task transmission and computation scheme in multi-antenna unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) networks, where the UAV is dual-function, i.e., aerial MEC and aerial relay. The channel uncertainty is considered during information offloading and downloading. An energy consumption minimization problem is formulated under some constraints including users' quality of service and information security requirements and the UAV's trajectory's causality, by jointly optimizing the CPU frequency, the offloading time, the beamforming vectors, the artificial noise and the trajectory of the UAV, as well as the CPU frequency, the offloading time and the transmit power of each user. To solve the non-convex problem, a reformulated problem is first derived by a series of convex reformation methods, i.e., semi-definite relaxation, S-Procedure and first-order approximation, and then, solved by a proposed successive convex approximation (SCA)-based algorithm. The convergence performance and computational complexity of the proposed algorithm are analyzed. Numerical results demonstrate that the proposed scheme outperforms existing benchmark schemes. Besides, the proposed SCA-based algorithm is superior to traditional alternative optimization-based algorithm.