关键词： Mobile edge computing  

摘要： Edge Computing (EC) extends computing functionalities from remote cloud data centers to the proximity of end-user devices at the network edges, thereby reducing application response time. However, existing solutions face challenges in scalability, efficient resource management, and near real-time adaptation in dynamic environments. In this paper, we jointly investigate the application placement and load distribution challenges in an EC-enabled mobile network. We propose a hierarchical distributed Limited Look-Ahead Control (LLC) approach that mitigates centralized control bottlenecks by breaking down the overall decision problem into a series of local problems solved cooperatively through a two-tier architecture. The global controller processes system-wide information and sets local constraints, while local controllers make autonomous decisions coordinated through mutual information exchange. Utilizing LLC allows for anticipation and adaptation to load fluctuations. Even though the results indicate that the trade-off between system performance and scalable decisions depends on how the overall problem is decomposed, our distributed solution significantly reduces the controller's execution time compared to a centralized approach. © 2024 Elsevier B.V.

关键词： Mobile edge computing   STAR-RIS   NOMA   User mobility   DDPG   Dueling DQN  

摘要： Reconfigurable intelligent surface (RIS) is expected to be able to significantly reduce task processing delay and energy consumptions of mobile users (MUs) in mobile edge computing (MEC) by intelligently adjusting its reflecting elements' phase-shifts and amplitudes. Nevertheless, both the passive and active RISs have the disadvantage of only reflecting the received signals, which means that the transmitters and receivers must be located on the same side of the RIS. This maybe unrealistic due to the movement of MUs. Simultaneously transmitting and reflecting (STAR) RIS, which can simultaneously transmit and reflect incident signals to achieve full-area coverage, has been recognized as a revolutionary technique to solve the above-mentioned problem. For the STAR-RIS-aided non-orthogonal multiple access (NOMA) communication MEC, we first formulate an optimization problem to minimize the sum of weighted delay and energy consumptions of all MUs which can move randomly at low speeds. Then, under the practical coupled phase-shift model of STAR-RIS, we propose hybrid deep reinforcement learning (DRL) scheme, in which we determine the amplitudes and phase-shifts of STAR-RIS, task offloading decisions of MUs, and computation resource allocations of MEC servers by using the deep deterministic policy gradient (DDPG) and Dueling deep Q learning (DQN). Finally, we validate and evaluate the performances of our proposed scheme through extensive simulations, which show that our proposed scheme outperforms the existing baseline schemes and its performance can indeed be improved due to the use of STAR-RIS.

关键词： Mobile edge computing   Handover authentication   Authenticated key exchange   Ratcheted key exchange  

摘要： Authenticated Key Exchange (AKE) has been playing a significant role in ensuring communication security. However, in some Multi-access Edge Computing (MEC) scenarios where a moving end-node switchedly connects to a sequence of edge-nodes, it is costly in terms of time and computing resources to repeatedly run AKE protocols between the end-node and each edge-node. Moreover, the cloud needs to be involved to assist the authentication between them, which goes against MEC's purpose of bringing cloud services from cloud to closer to end-user. To address the above problems, this paper proposes anew type of AKE, named as Handover Authenticated Key Exchange (HAKE). In HAKE, an earlier AKE procedure handovers authentication materials and some parameters to its temporally next AKE procedure, thereby saving resources and reducing the participation of remote cloud. Following the framework of HAKE, we propose a concrete HAKE protocol based on Elliptic Curve Diffie-Hellman (ECDH) key exchange and ratcheted key exchange. Then we verify its security via Burrows-Abadi-Needham (BAN) logic and the Automated Validation of Internet Security Protocols and Applications (AVISPA) tool. Finally, we evaluate and test its performance. The results show that the HAKE protocol achieves security goals and reduces communication and computation costs compared to similar protocols.

关键词： Mobile edge computing   Industrial internet of things   Multiobjective optimization   Task offloading   Evolutionary multiobjective multitasking  

摘要： In mobile edge computing(MEC) scenarios, offloading computation-intensive and delay-critical tasks to nearby mobile edge servers with abundant computing resources can reduce the time delay(TD) of tasks and energy consumption(EC) of terminal devices, thus improving application performance and user experience. In order to reduce the TD of tasks and the EC of factory terminal devices(FTDs), MEC is introduced into the Industrial Internet of Things(IIoT). Because TD and EC are conflicting objectives, most existing research models the task offloading problem(TOP) as a multiobjective optimization problem to optimize both TD and EC. However, as the number of FTDs and tasks in the MEC network increases, the scale and computational cost of the TOP increases, and it is very challenging to obtain the optimal task offloading scheme through multiobjective optimization. Multitasking optimization can make use of knowledge transfer between related tasks to promote the solving efficiency of each task. Based on this, we model the TOP in IIoT as a multiobjective TOP (MTOP). A cheap task that is highly similar to MTOP is constructed, and a novel evolutionary multiobjective multitasking framework is developed, which uses the positive knowledge transfer between tasks to optimize TD and EC in the network. Then, an effective evolutionary multiobjective multitasking algorithm is proposed, which includes multi-functional knowledge transfer strategy(MFKT) and adaptive cheap task update strategy(ACTU). MFKT uses decision variables with different functions in the cheap tasks for positive knowledge transfer, so as to improve the performance of MTOP. ACTU dynamically updates the cheap task to maintain knowledge transfer between tasks. Indifferent test instances, the proposed algorithm is compared with the existing multiobjective and multitasking algorithms. The experimental results show that the proposed algorithm is more competitive in terms of TD and EC, it can guarantee the service quality of t

关键词： Cooperation   Edge server deployment   Mobile edge computing   Utility optimization  

摘要： In our exploration of Mobile Edge Computing (MEC) systems, we address the critical challenge of edge server deployment, aiming to enhance application responsiveness through optimized server placement and cooperation. Our study diverges from traditional approaches that prioritize server location, instead highlighting the untapped potential of server collaboration for sharing computing resources. This cooperative strategy not only boosts resource utilization and trims response times but also intricately complicates deployment strategies. We introduce an innovative Collaboration-Based Server Deployment (CBSD) algorithm that stands out by facilitating cooperative communication between edge servers via Base Stations (BSs), even under stringent resource constraints. This algorithm employs a dual-phase approach: initially utilizing a non-collaborative Gradient algorithm for resource allocation among cooperative regions, followed by a strategic distribution of resources based on regional demand. Our comprehensive simulations show that our proposed methodology improves system utility and throughput by 35% and 25%, respectively, while robustness reaches 90% compared to the baseline. These results represent improvement in managing limited edge resources effectively.

关键词： Mobile edge computing   Internet of vehicles   Deep reinforcement learning   Content caching   Task offloading  

摘要： Considering the dynamic variability of the vehicular edge environment and the limited edge servers resources, this paper proposes a joint task caching and computation offloading scheme based on deep reinforcement learning (DRL). Considering that the motion trajectories of different vehicles overlap and their task requests may be the same, this paper designs a vehicle-edge-cloud computing framework to fully use the cache resources of vehicles, edge servers, and clouds to reduce task processing delays and energy consumption. Secondly, this paper adopts partial offloading and collaboration between edge servers to make full use of the computational resources of vehicles, edge servers and clouds, avoiding the waste of resources and reducing the burden of vehicles and edge servers. In addition, this paper proposes a DRL-based task offloading scheme to obtain better task caching and offloading strategies. The simulation results show that the scheme proposed in this article performs better compared to other schemes and effectively reduces the latency and energy consumption of task processing.

关键词： application deployment   genetic algorithm   IoT   microservices   mobile edge computing  

摘要： With the increasing popularity of cloud native and DevOps, container technology has become widely used in combination with microservices. However, deploying container-based microservices in distributed edge-cloud infrastructure requires complex selection strategies to ensure high-quality service for users. Existing container orchestration tools lack flexibility in selecting the best deployment location based on user cost budgets and are insufficient in providing personalized deployment solutions. This paper proposes a genetic algorithm-based Internet of Things (IoT) application deployment and selection strategy for personalized cost budgets. The application deployment problem is defined as an optimization problem that minimizes user service latency under cost constraints, which is an NP-hard problem. The genetic algorithm is introduced to solve this problem effectively and improve deployment efficiency. Comparative results show that the proposed algorithm outperforms four baseline algorithms, including time-greedy, cost-greedy, random, and PSO, using real datasets and some synthetic datasets. The proposed algorithm provides personalized deployment solutions for edge-cloud infrastructure. It supports flexibly selecting the best microservice deployment location in hybrid cloud-edge infrastructure according to the user's cost budget, with personalized deployment requirements. The application deployment problem is defined as an optimization problem that minimizes user service latency under cost constraints and could be solved by the genetic algorithm. image

关键词： Deep reinforcement learning   Density clustering   Ensemble learning   Mobile edge computing   Offloading decision  

摘要： Mobile edge computing offloads compute-intensive tasks generated on mobile wireless devices (WD) to edge servers (ES), which provides mobile users with low-latency computing services. Opportunistic computing offloading is effective to enhance computing performance in dynamic edge network environments; however, careless offloading of tasks to ESs can lead to WDs preempting network computing resources with limited bandwidth, thereby resulting in inefficient allocation of computing resources. To address these challenges, this paper proposes the density clustering and ensemble learning training–based deep reinforcement learning (DCEDRL) method for task offloading decision-making in mobile edge computing (MEC). Firstly, DCEDRL utilizes multiple deep neural networks to explore the environment. It trains multiple models using ensemble learning methods to obtain a combination of prediction results. Secondly, DCEDRL utilizes an optimized density clustering method to identify and classify computing tasks with similar characteristics to improve subsequent task scheduling and resource allocation efficiency. Finally, according to the stored priority information, DCEDRL utilizes the priority weight to resample the samples, adjust the sampling strategy in real time, and improve the adaptability and robustness of the system. Simulation results demonstrate that the proposed DCEDRL method can reduce the backlog of tasks by greater than over 21% compared to the baseline algorithms.

关键词： Authentication frameworks   Physical Unclonable Functions (PUF)   Communication security   Internet of Things   Mobile edge computing   Elliptic Curve Cryptography (ECC)   Scyther  

摘要： The Internet of Things (IoT) devices are becoming increasingly integral to daily life, with cloud computing platforms serving as essential hubs for managing and processing the vast data generated by distributed IoT devices and sensors. The advent of 6G-powered cloud services facilitates applications such as augmented reality, virtual reality, autonomous driving, and healthcare, all of which require rapid data processing. Mobile edge computing (MEC) extends cloud capabilities to the network’s edge, enabling large-scale, real-time data processing. However, this transition introduces security challenges due to the open nature of MEC infrastructures, which increases the risk of data breaches and privacy violations. To address these challenges, RAAF-MEC is proposed as an innovative authentication framework designed specifically for IoT-enabled MEC environments. The framework incorporates hash functions, PUF, ECC, and GIFT-COFB. GIFT-COFB, a lightweight encryption mechanism, and NIST finalist, ensures data authenticity and integrity. PUF technology, integrated on the MEC server side, dynamically derives secret keys, mitigating the risk of privileged insider attacks by eliminating the need to store keys in the MEC server’s database. This approach enhances security by preventing unauthorized access to sensitive key material. RAAF-MEC also supports single sign-on for seamless access across MEC servers. The effectiveness of RAAF-MEC has been validated through comprehensive formal and informal security assessments, as well as performance evaluations against existing authentication frameworks. Our results show that RAAF-MEC reduces computational costs by 27.3% to 52.12% and communication costs by 69.44% to 75%, while significantly enhancing security features.

关键词： Mobile edge computing   Vehicular Ad-hoc network   Sticky bacteria   Link stability   Routing  

摘要： With the rapid development of Telematics, the role of edge computing (Mobile Edge Computing, MEC) is becoming more and more significant. Mobile users can obtain massive computing and storage resources in a local way, thus effectively solving the congestion problem of the core network. However, in general urban edge network scenarios of VANET (Vehicular Ad-hoc Network), due to low node density and poor connectivity, there are few chances to encounter suitable forwarding nodes. In order to avoid the above situation and adapt to sparse scenarios, we propose new link-stabilized routing method & protocol based on sticky bacteria algorithm in this paper. The new idea and the significant findings of this proposed algorithm in enhancing the routing protocol is as follows: five factors affecting routing decision are identified: evaluation distance, deflection angle, number of neighboring nodes, rate difference, and the traffic of the road section at which it is located, and then these five factors are comprehensively evaluated by using the our designed Analytic Hierarchy Process (AHP) strategy, so as to determine the score of each node and the candidate routing paths;And the best routing path between two communicating nodes is found through the stronger global exploration capability of the sticky bacteria algorithm. Our experiments (that are based on our developed C++ programs) have proved that the method proposed in this paper has stronger link stability and the highest packet delivery rate. And the experiments and their results has enhanced our new method credibility in the field of ad hoc networks.