关键词： Optimization   Servers   Reflection   Heuristic algorithms   Energy consumption   Vehicle dynamics   Resource management   Reconfigurable intelligent surfaces   Wireless communication   NOMA   Mobile edge computing   non-orthogonal multiple access   simultaneously transmitting and reflecting reconfigurable intelligent surface   multi-agent deep reinforcement learning  

摘要： This paper explores a system that integrates non-orthogonal multiple access (NOMA) with mobile edge computing (MEC), supported by a simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) in energy splitting (ES) mode. We aim to address the challenge of minimizing energy consumption over the long-term by modeling it as a non-convex optimization problem. To simplify the resolution of this complex issue, we introduce a decomposition approach using a multi-agent deep reinforcement learning (DRL) technique. Specifically, we decompose the problem into the sub-problems of computational resource and power allocation, and phase shifting, amplitude coefficient, offloading ratio, and time optimization. Based on theoretical derivations, we determine the optimal computational resource and power allocation for the given phase shifting, amplitude coefficient, offloading ratio, and time. Then, we propose a multi-agent twin delayed deep deterministic policy gradient (TD3) algorithm to jointly optimize phase shifting, amplitude coefficient, offloading ratio, and time for each time slot. Simulation outcomes demonstrate that the decomposition-based multi-agent TD3 (DB-MATD3) algorithm achieves faster convergence compared to conventional centralized DRL approaches. Compared with other existing methods, the proposed task offloading strategy, which employs the ES-based STAR-RIS, achieves energy savings of 28.7% to 40.8%.

关键词： Cellular Networks   6 G Mobile Communication   Distributed Databases   Computer Security   Next Generation Networking   Data Security   Network Systems   Network Architecture   Cellular Architecture   Named Data Networking   Scalable   Functional Networks   Cellular Networks   Data Security   Modular Design   Control Plane   Signaling Overhead   Virtual Network Functions   Network Slicing   Commercial Off The Shelf   Data Integration   Proof Of Concept   Data Center   Application Programming Interface   Traditional Network   Edge Computing   Core Network   Data Packets   Service Chain   Mobile Edge Computing   Caching   User Equipment   Point To Point Links   Public Key Infrastructure   Digital Signature   Representational State Transfer   Dynamic Scaling   Direct Route  

摘要： This article proposes adopting named data networking (NDN) as a foundation for future cellular networks, including 6G, to shift the focus from connection-based to data-centric communication. Securing data at the network layer, NDN reduces control plane signaling overhead and enables secure distributed deployments.

关键词： Industrial Internet of Things (IIoT)   Digital twin   Mobile Edge Computing   Age of information   Task offloading  

摘要： Mobile Edge Computing (MEC) is a significant technology employed in the development of the Industrial Internet of Things (IIoT) as it allows the collection and processing of high volumes of data at the network edge to support industrial processes and improve operational efficiency and productivity. However, despite significant advances in MEC capabilities, the stringent latency requirement that may occur in computation-intensive tasks may affect the freshness of status information. Therefore, there are practical challenges in scheduling the tasks associated with computational efficiency in local computation and remote computation. In this context, we propose an Age of Information (AoI)-based scheduler to determine where to execute computational tasks in order to continuously track state data updates, where the AoI metric measures the time elapsed from the generation of the computation task at the source to the latest received update at the destination. The contributions of this paper are threefold: First, we propose a digital twin-enabled AoI-based scheduler model that collects real-time data from IIoT nodes and predicts the best task assignment in terms of local computation and remote computation. The digital twin environment allows monitoring of the state changes of the real physical assets over time and optimizes the scheduling strategy. Second, we formulate the average AoI problem with the M/M/1 queueing model and propose a genetic algorithm-based scheduler to minimize AoI and task completion time to efficiently schedule the computation tasks between IIoT devices and MEC servers. Third, we compare the performance of our digital twin-enabled model with the traditional strategies and make a significant contribution to IIoT edge network management by analyzing AoI, task completion time and MEC server utilization.

关键词： Reconfigurable intelligent surfaces   Servers   Energy consumption   Resource management   Optimization   Computational modeling   Training   Stochastic processes   Rician channels   Multi-access edge computing   Mobile edge computing   rotatable reconfigurable intelligent surface   orientation   soft actor-critic   user mobility  

摘要： The rotatable reconfigurable intelligent surface (RIS) can enhance mobile edge computing (MEC) performance by optimizing its orientation to improve the gain of received and transmitted signals. This correspondence investigates a rotatable RIS-assisted MEC system, aimed at minimizing energy consumption for multiple moving user equipment (UEs) through the joint design of RIS orientation, discrete phase shift, computation resource allocation, transmitting power and task offloading strategies. Considering the mobility of UEs, this problem is formulated as a sequential decision-making across multiple time slots. To address this challenge, a soft actor-critic (SAC)-based algorithm is proposed to optimize RIS orientation, phase shift and task offloading strategies, while computation resource allocation and transmitting power are determined based on the actions. Numerical results demonstrate that the proposed scheme exhibits superior convergence and performance compared to benchmarks. Additionally, the rotatable RIS scheme reduces total energy consumption by up to 47.3% compared to the fixed RIS, enhancing MEC system performance.

关键词： Cloud computing   Mobile edge computing   Service placement   Service allocation   Optimization  

摘要： Mobile edge computing (MEC) is a new networking pattern that deploys resources layer by layer to yield high-quality services. The MEC reduces the response time, energy consumption, process time, and bandwidth usage while enhancing privacy, security, and communications in real-life applications. Determining the most optimal resources from the edge, fog, and cloud layers for the requested services by the users under different quality-of-service constraints is one of the most critical challenges in MEC networks, known as the service placement problem (SPP). An optimal service placement algorithm can increase the benefits of service providers and users' satisfaction. Consequently, a novel metaheuristic-based resource allocation algorithm named ERSGWO in presented in the current paper. In the ERSGWO, the reptile search algorithm (RSA) and gray wolf optimizer (GWO) are hybridized through an innovative complementary strategy. The RSA is enhanced in the ERSGWO by increasing the number of leaders. The outcome hybrid algorithm is then discretized using an intermediate phase. Likewise, a neighborhood search procedure with two new movement strategies is introduced in the ERSGWO to augment exploration and exploitation capabilities. Besides, the local departure ability is enriched through a mechanism that detects and reinitializes the trapped search agents in local optimums. Eventually, 12 MEC environments with different specifications are designed, and the ERSGWO is applied to them. The results are compared with the ARO, AOA, FOX, GWO, MRFO, RSA, SCA, STOA, TSA, APO, and EHO algorithms numerically and visually. The experimental results reveal that the ERSGWO outperformed its competitors in 97.73% of test cases.

关键词： Delays   NOMA   Uplink   Resource management   Protocols   Energy consumption   Optimization   Servers   Reliability   Performance evaluation   Rate-splitting multiple access (RSMA)   mobile edge computing   full offloading   distributed  

摘要： In this work, we introduce multi-access edge computing (MEC) with distributed uplink rate-splitting multiple access (RSMA)-assisted, which has been shown to increase the achievable capacity region compared to other distributed uplink counterparts, including non-orthogonal multiple access (NOMA). It is noteworthy that the integration of distributed uplink multiple access and MEC has not been investigated in the existing literature, while distributed uplink RSMA can also be seen as a generalization of distributed uplink NOMA. In more detail, considering a system with two remote radio heads (RRHs) and two users, where the users have the ability to offload their data to either RRH, we aim to minimize the delay for the full offloading scenario. To this end, both the power allocation coefficient and the number of bits offloaded to each RRH are optimized. Simulations show that the proposed scheme outperforms both OMA-assisted MEC and NOMA-assisted MEC.

关键词： UAV communication   Aerial inspection   Trajectory design   Mobile edge computing   Deep reinforcement learning  

摘要： This paper investigates an Unmanned Aerial Vehicle (UAV)-based detection system deployed in an urban environment. Cellular network-connected UAVs collect data from multiple inspection points scattered in urban buildings and upload the data to a ground base station (GBS). Our goal is to minimize the energy consumption of the UAVs while accomplishing the data uploading task by designing the UAV inspection sequence, UAV path planning, and UAV correlation rate. To solve this intractable non-convex problem, we propose the EEGA-TD3 algorithm. First, an adaptive genetic algorithm is proposed to obtain the optimal inspection sequence by considering the energy consumption and throughput of the UAV and the transmission task. Subsequently, we utilize the dual-delay deep deterministic policy gradient (TD3) algorithm to optimize the UAV flight trajectory. The scheme is able to realize continuous control and guide the UAV to dynamically adjust its flight strategy according to the amount of data. Simulation results show that the proposed algorithm is able to flexibly select the trajectory scheme that accomplishes the data transmission task and saves energy compared to the traditional algorithm.

关键词： Mobile edge computing   Blockchain   Computation offloading   Pricing strategy   Admission control  

摘要： The solution of proof-of-work in blockchain requires a large amount of resources;the lack of computing power on mobile devices limits the development of blockchain in mobile applications. In order to cope with the problem of insufficient resources on mobile devices, the combination of blockchain and Mobile Edge Computing has attracted much attention. In this paper, we consider an edge-enabled blockchain system containing two types of miners and a single Edge Service Provider (ESP). Different types of miners have different resource requirements and corresponding prices. Each miner makes a computation offloading decision based on the resource price set by ESP. Different miners possess varying resource requirements and correspond to distinct prices. Miners formulate task offloading decisions based on the prices set by ESP for different resource types. The ESP dynamically adjusts resource price based on miners' offloading requests and makes admission control according to resource usage in each time slot. The problem is a joint optimization problem of computation offloading, price strategy, and admission control, which is challenging to solve due to the interaction among three issues. In this paper, we model the problem as a sequential decision-making problem and use a dynamic programming algorithm to obtain the optimal solution. Through simulation experiments, our results demonstrate that the proposed algorithm can effectively improve the overall social utility.

关键词： Microservice architectures   Routing   Cloud computing   Optimization   Delays   Vehicle-to-everything   Servers   Training   Resource management   Containers   Artificial intelligence and machine learning   cloud computing services   mobile edge computing   microservice deployment   request routing  

摘要： The service modules of the traditional Mobile Edge Computing (MEC) are difficult to deploy, extend, and maintain in real networks because of the highly sophisticated systems. To promote the generalization, openness, and flexibility of the network edge environment, an increasing number of studies are exploring the integration of microservices with MEC. However, the existing work usually treats microservice deployment and request routing as two separate issues, ignoring the interaction between them. Therefore, this paper focuses on the joint optimization of microservice deployment and request routing in the multi-edge cloud scenarios. We establish a problem model for minimizing the average response latency, considering the transmission of requests across edge clouds. Then, in view of the complexity of the scene, this paper proposes a joint training strategy of microservice deployment and request routing based on deep reinforcement learning and Best Fit Decreasing algorithm. The algorithm takes the change of microservice deployment scheme as the action of the agent, introduces the Best Fit Decreasing algorithm to construct request routing based on the deployment scheme, and calculates rewards using the complete joint microservice deployment and request routing scheme for subsequent network training. Finally, experimental results show that the proposed algorithm can effectively reduce the response time delay and system running power compared with other algorithms.

关键词： Costs   Servers   Quality of service   Delays   Multi-access edge computing   Optimization   Dynamic scheduling   Resource management   Heuristic algorithms   Computational modeling   QoS-aware   dynamic service caching   service updating   mobile edge computing  

摘要： In the context of mobile edge computing, achieving dynamic service caching and updating to guarantee the QoS of users and reduce system costs is a challenging problem. However, existing research still has certain deficiencies in considering the dynamic behavior of users and the limited storage resources of edge servers. To address this problem, this paper investigates optimizing the service caching and updating problem within multi-stage and proposes a novel framework with three proposed strategies for the different stages to jointly optimize the delay and cost. At the initial service caching stage, we propose a basic caching strategy based on dynamic programming for the single-area scenario, taking into account the constraint of limited memory resources. To improve the caching strategy, we extend our consideration to the multiple-area scenario and design an improved algorithm based on tabu search. Given the dynamic behavior of users, we formulate the joint optimization problem as a Markov Decision Process (MDP) and design a service extension strategy based on reinforcement learning at the service updating decision-making stage and a replacement strategy taking both the distribution of service replications and service access frequency into account at the service updating replacement stage to guarantee the QoS of users. We effectively tackle the challenges arising from the dynamic behavior of users and limited storage resources. Through extensive comparative experiments, our approach outperforms traditional strategies by significantly reducing user latency and system cost.