关键词： blockchain   data access and sharing   data security   mobile edge computing   privacy   smart contract  

摘要： Mobile edge computing (MEC) integrates mobile and edge computing technologies to provide efficient computing services with low latency. It includes several Internet of Things (IoT) and edge devices that process the user data at the network's edge. The architectural characteristic of MEC supports many internet-based services, which attract more number of users, including attackers. The safety and privacy of the MEC environment, especially user information is a significant concern. A lightweight accessing and sharing protocol is required because edge devices are resource constraints. This paper addresses this issue by proposing a blockchain-enabled security management framework for MEC environments. This approach provides another level of security and includes blockchain security features like temper resistance, immutable, transparent, traceable, and distributed ledger in the MEC environment. The framework guarantees secure data storage in the MEC environment. The contributions of this paper are twofold: (1) We propose a blockchain-enabled security management framework for MEC environments that address the security and privacy concerns, and (2) we demonstrate through simulations that the framework has high performance and is suitable for resource-constrained MEC devices. In addition, a smart contract-based access and sharing mechanism is proposed. Our research uses a combination of theoretical analysis and simulation experiments to demonstrate that the proposed framework offers high security, low latency, legitimate access, high throughput, and low operations cost.

关键词： Reliability   Servers   Measurement   Cloud computing   Clustering algorithms   Low latency communication   Costs   Network function virtualization   mobile edge computing   latency   reliability  

摘要： Mobile edge computing allows efficient cloud servicing by building small-scale cloud infrastructures at the network edge close to end users. The cloud servers implement virtual network functions in a flexible manner without dedicated middleboxes. In such a network design, latency and reliability are two key performance metrics which are not always aligned and a tradeoff can appear between them. In this paper, we identify three key factors that impact the existence of a tradeoff such as the number of services, the number of functions the services require and the existence of redundancy in the implementation of functions. Our main contribution is indicating exactly whether a tradeoff can exist in each combination of these three factors. In case a tradeoff exists, we find guarantees on the performance in a non-optimized metric of an optimal solution to the other metric. We also study the tradeoff when each service should be served in a particular order. We also propose a function placement method that jointly optimizes both latency and reliability. Lastly, we evaluate our analysis in an experimental setting.

关键词： mobile edge computing   directed acyclic graphs   deep reinforcement learning   soft actor-critic   Markov decision process   task offloading  

摘要： Multi-access edge computing (MEC), based on hierarchical cloud computing, offers abundant resources to support the next-generation Internet of Things network. However, several critical challenges, including offloading methods, network dynamics, resource diversity, and server decision-making, remain open. Regarding offloading, most conventional approaches have neglected or oversimplified multi-MEC server scenarios, fixating on single-MEC instances. This myopic focus fails to adapt to computational offloading during MEC server overload, rendering such methods sub-optimal for real-world MEC deployments. To address this deficiency, we propose a solution that employs a deep reinforcement learning-based soft actor-critic (SAC) approach to compute offloading and facilitate MEC server decision-making in multi-user, multi-MEC server environments. Numerical experiments were conducted to evaluate the performance of our proposed solution. The results demonstrate that our approach significantly reduces latency, enhances energy efficiency, and achieves rapid and stable convergence, thereby highlighting the algorithm's superior performance over existing methods.

关键词： Blockchain   Classification   Mobile edge computing   Meta-heuristics   Henry gas algorithm   Resource allocation  

摘要： This paper investigates a wireless blockchain network with mobile edge computing in which Internet of Things (IoT) devices can behave as blockchain users (BUs). This blockchain network's ultimate goal is to increase the overall profits of all BUs. Because not all BUs join in the mining process, using traditional swarm and evolution algorithms to solve this problem results in a high level of redundancy in the search space. To solve this problem, a modified chaotic Henry single gas solubility optimization algorithm, called CHSGSO, has been proposed. In CHSGSO, the allocation of resources to BUs who decide to engage in mining as an individual is encoded. This results in a different size for each individual in the entire population, which leads to the elimination of unnecessary search space regions. Because the individual size equals the number of participating BUs, we devise an adaptive strategy to fine-tune each individual size. In addition, a chaotic map was incorporated into the original Henry gas solubility optimization to improve resource allocation and accelerate the convergence rate. Extensive experiments on a set of instances were carried out to validate the superiority of the proposed CHSGSO. Its efficiency is demonstrated by comparing it to four well-known meta-heuristic algorithms.

关键词： Mobile edge computing   Smart factory   Task caching   Task offloading  

摘要： In the application scenario of a smart factory, the computing tasks of the smart devices are intensive, and these tasks are sensitive to delay. The smart devices need to offload their tasks to an edge server for execution. However, the computing resources of edge servers are limited, and a workload imbalance will tend to arise among multiple edge servers. To solve these problems, we propose a joint optimization scheme of task caching and offloading based on task scenario awareness, which decouples the joint optimization problem into two subproblems: task caching and task offloading. These two subproblems are solved using a task-scenario-aware bilateral auction algorithm, which enables an edge server to adapt its task caching strategy to the task scenario of the smart devices. Experimental results show that this paper's scheme reduces the task execution time by up to 21.5% compared to existing schemes and has better load balancing effects.

关键词： Mobile edge computing   unmanned aerial vehicle   computation offloading   deep reinforcement learning   digital twin  

摘要： With the high flexibility of supporting resource-intensive and time-sensitive applications, unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) is proposed as an innovational paradigm to support the mobile users (MUs). As a promising technology, digital twin (DT) is capable of timely mapping the physical entities to virtual models, and reflecting the MEC network state in real-time. In this paper, we first propose an MEC network with multiple movable UAVs and one DT-empowered ground base station to enhance the MEC service for MUs. Considering the limited energy resource of both MUs and UAVs, we formulate an online problem of resource scheduling to minimize the weighted energy consumption of them. To tackle the difficulty of the combinational problem, we formulate it as a Markov decision process (MDP) with multiple types of agents. Since the proposed MDP has huge state space and action space, we propose a deep reinforcement learning approach based on multi-agent proximal policy optimization (MAPPO) with Beta distribution and attention mechanism to pursue the optimal computation offloading policy. Numerical results show that our proposed scheme is able to efficiently reduce the energy consumption and outperforms the benchmarks in performance, convergence speed and utilization of resources.

关键词： Energy consumption minimization   Intelligent reflecting surfaces   Latency   Mathematical optimization   Mobile edge computing   Resource allocation  

摘要： The rapid evolution of communication systems towards the next generation has led to an increased deployment of Internet of Things (IoT) devices for various real-time applications. However, these devices often face limitations in terms of processing power and battery life, which can hinder overall system performance. Additionally, applications such as augmented reality and surveillance require intensive computations within tight timeframes. This research focuses on investigating a mobile edge computing (MEC) network empowered by unmanned aerial vehicle intelligent reflecting surfaces (UAV-IRS) to enhance the computational energy efficiency of the system through optimized resource allocation. The MEC infrastructure incorporates the energy transfer circuit (ETC) and edge server (ES), co-located with the intelligent access point (AP). To eliminate interference between energy transfer and data transmission, a time-division multiple access method is utilized. In the first phase, the ETC wirelessly transfers power to low-power IoT devices, which efficiently harvest and store the received energy in their batteries. In the second phase, IoT devices employ the stored energy for local computing or offloading tasks. Furthermore, the presence of tall buildings may obstruct communication routes, impacting system functionality. To address these challenges, we propose an optimization framework that simultaneously considers time, power, phase shift design, and local computational resources. This joint optimization problem is non-convex and non-linear, making it NP-hard. To tackle this complexity, we decompose the problem into subproblems and solve them iteratively using a convex optimization toolbox like CVX. Through simulations, we demonstrate that our proposed optimization framework significantly improves 40:7% system performance compared to alternative approaches. (c) 2023 The Authors. Published by Elsevier B.V. on behalf of King Saud University. This is an open access article un

关键词： satellite edge computing   mobile edge computing   task offloading   data-dependent constraint   multi-objective optimization  

摘要： By enabling a satellite network with edge computing capabilities, satellite edge computing(SEC) provides users with a full range of computing service. In this paper, we construct a multi-objective optimization model for task offloading with data-dependent constraints in an SEC network and aim to achieve optimal tradeoffs among energy consumption, cost, and makespan. However, dependency constraints between tasks may lead to unexpected computational delays and even task failures in an SEC network. To solve this, we proposed a Petri-net-based constraint amending method with polynomial complexity and generated offloading results satisfying our constraints. For the multiple optimization objectives, a strengthened dominance relation sort was established to balance the convergence and diversity of nondominated solutions. Based on these, we designed a multi-objective wolf pack search (MOWPS) algorithm. A series of adaptive mechanisms was employed for avoiding additional computational overhead, and a Lamarckian-learning-based multi-neighborhood search prevents MOWPS from becoming trapped in the local optimum. Extensive computational experiments demonstrate the outperformance of MOWPS for solving task offloading with data-dependent constraints in an SEC network.

关键词： Mobile edge computing   microservice   Internet of Vehicles   service placement  

摘要： The integration of Mobile Edge Computing (MEC) and microservice architecture drives the implementation of the sustainable Internet of Vehicles (IoV). The microservice architecture enables the decomposition of a service into multiple independent, fine-grained microservices working independently. With MEC, microservices can be placed on Edge Service Providers (ESPs) dynamically, responding quickly and reducing service latency and resource consumption. However, the burgeoning of IoV leads to high computation and resource overheads, making service resource requirements an imminent issue. What's more, due to the limited computation power of ESPs, they can only host a few services. Therefore, ESPs should judiciously decide which services to host. In this paper, we propose a Microservice-oriented Service Placement (MOSP) mechanism for MEC-enabled IoV to shorten service latency, reduce high resource consumption levels and guarantee long-term sustainability. Specifically, we formulate the service placement as an integer linear programming program, where service placement decisions are collaboratively optimized among ESPs, aiming to address spatial demand coupling, service heterogeneity, and decentralized coordination in MEC systems. MOSP comprises an upper layer to map the service requests to ESPs and a lower layer to adjust the service placement of ESPs. Evaluation results show that the microservice-oriented service deployment mechanism offers dramatic improvements in terms of resource savings, latency reduction, and service speed.

关键词： Mobile edge computing   battery saving   wireless power transfer   full duplex   Stackelberg game  

摘要： Mobile edge computing (MEC) is a promising paradigm to handle the mismatch between computation-intensive applications and resource-limited devices. Nevertheless, as most Internet of Things (IoT) terminals are battery-limited, the computation gain of MEC may be compromised due to insufficient battery energy for task offloading. Wireless power transfer (WPT) and full duplex (FD) communications are economical charging and transmission methods for battery-limited IoT terminals. However, when integrating wireless power transfer and FD into MEC, the incentive problem should be jointly addressed with task offloading, because the WPT facilities and their powered IoT nodes belong to different service operators. In this paper, we investigate the efficiency of WPT from the perspective of battery saving, and propose an efficient wireless powered task offloading and incentive mechanism in FD MEC-enabled cellular IoT networks. The battery saving efficiency, which addresses both the total cost of WPT and saved energy of battery, is proposed as the performance metric. By adopting this metric as the utility function of the network operator (NO), the task offloading and incentive problem are jointly formulated as a Stackelberg game. We then propose an efficient alternating direction iteration-based algorithm to solve its equilibrium efficiently. Simulation results demonstrate the benefits of our algorithm in battery saving by comparisons with utility oriented benchmarks. Moreover, it reveals the tradeoff between the utility of NO and battery saving, which verifies the positive effects of FD communications and WPT in improving the efficiency of battery saving.