关键词： Mobile edge computing   load balance   mobility-aware task offloading   reinforcement learning  

摘要： Mobile edge computing is a promising computing paradigm enabling mobile devices to offload computation-intensive tasks to nearby edge servers. However, within small-cell networks, the user mobilities can result in uneven spatio-temporal loads, which have not been well studied by considering adaptive load balancing, thus limiting the system performance. Motivated by the data analytics and observations on a real-world user association dataset in a large-scale WiFi system, in this paper, we investigate the mobility-aware online task offloading problem with adaptive load balancing to minimize the total computation costs. However, the problem is intractable directly without prior knowledge of future user mobility behaviors and spatio-temporal computation loads of edge servers. To tackle this challenge, we transform and decompose the original task offloading optimization problem into two sub-problems, i.e., task offloading control (ToC) and server grouping (SeG). Then, we devise an online control scheme, named MOTO (i.e., Mobility-aware Online Task Offloading), which consists of two components, i.e., Long Short Term Memory based algorithm and Dueling Double DQN based algorithm, to efficiently solve the ToC and SeG sub-problems, respectively. Extensive trace-driven experiments are carried out and the results demonstrate the effectiveness of MOTO in reducing computational costs of mobile devices and achieving load balancing when compared to the state-of-the-art benchmarks.

关键词： Industry 5.0   Mobile edge computing   Joint optimization   Computational energy efficiency   Partial offloading  

摘要： Smart devices in Industry 5.0, such as sensors and robots, are often limited by low battery life and finite computational resources, hindering their ability to perform complex tasks. By offloading computation-intensive tasks to Mobile Edge Cloud Computing (MEC) servers at the network's edge, businesses can achieve real-time data processing and analysis, reducing communication latency, quicker response times, and improved system reliability. This work presents an integrated framework for MEC and Industry 5.0, aimed at enhancing the performance, efficiency, and flexibility of industrial processes. In particular, we propose a joint optimization problem that maximizes computational energy efficiency by optimally allocating resources, such as processing power and computational resources, as well as device association, in the most efficient manner possible. The problem is formulated as nonconvex/nonlinear, which is intractable and poses high complexity. To solve this challenging problem, we first transform and decouple the original optimization problem into a series of subproblems using the block coordinate descent method. Then, we iteratively obtain an efficient solution using convex optimization methods. In addition, our work sheds light on the fundamental trade-off between local computation and partial offloading schemes. The results show that for small data size requirements, the performance is comparable among different schemes. However, as data size increases, our proposed hybrid scheme, which includes a partial offloading scheme, outperforms others, highlighting the effectiveness of the proposed joint optimization scheme.

关键词： smart sustainable city   6G   non-terrestrial networks   vision-aided wireless communication   mobile edge computing   deep learning   integrated sensing and communication  

摘要： With the advancement of information and communication technologies (ICTs), the way we live and communicate with each other is changing rapidly. As urban environments continue to evolve, the smart sustainable city (SSC) has sparked considerable attention. We are hoping for a new era in which numerous devices and machines including vehicles, sensors, and robots are all connected to communicate, respond, and operate in real time. The next-generation communication system, the sixth generation (6G), is expected to play a crucial role in improving the efficiency of urban operations and services. In this paper, we first provide the recent trends and key features of standardization in the SSC. To make the future SSC, we highlight key candidate technologies of 6G such as non-terrestrial networks, advanced mobile edge computing, vision-aided wireless communication, artificial intelligence (AI)-based wireless communication, and integrated sensing and communication. We put forth the main technical challenges given to each prime technology along with the potential benefits to pave the way for 6G-enabled SSC. We further address how the potential benefits of prime technologies enable various urban practice cases for 6G-enabled SSC.

关键词： The Internet of unmanned aerial vehicles   mobile edge computing   machine learning   computation offloading   intelligent decision making  

摘要： Unmanned Aerial Vehicles (UAVs) play an important role in the Internet of Things and form the paradigm of the Internet of UAVs, due to their characteristics of flexibility, mobility, and low costs. However, resource constraints such as dynamic wireless channels, limited battery capacities, and computation resources of UAVs make traditional methods inefficient in the Internet of UAVs. The thriving of Mobile Edge Computing (MEC) and Machine Learning (ML) is of great significance and is promising for real-time resource allocation, trajectory design, and intelligent decision making. This survey provides a comprehensive review of key technologies, applications, solutions, and challenges based on the integration of MEC and ML in the Internet of UAVs. First, key technologies of MEC and ML are presented. Then, their integration and major issues in the Internet of UAVs are presented. Furthermore, the applications of MEC and ML in the Internet of UAVs under urban, industrial, and emergency scenarios are discussed. After that, this survey summarizes the current solutions for MEC and ML in the Internet of UAVs based on the considered issues. Finally, some open problems and challenges are discussed.

关键词： Mobile edge computing   industrial internet of things   deep reinforcement learning   adaptive traffic matching  

摘要： Traffic matching for transmitting and computing is a pivotal limitation of data processing in mobile edge computing (MEC)-enabled Industrial Internet of Things (IIoT), which remains insufficiently addressed. This letter proposes a hybrid DRL-based adaptive traffic matching strategy to augment the data processing capability of IIoT. A multivariate model is proposed to maximize the long-term processed data volume by matching the traffic for wireless transmitting, backhaul transmitting, and edge computing in MEC-enabled IIoT, which takes into account minimum processed data volume and long-term energy constraints. A hybrid DRL-based method is proposed to solve the intractable problem, which combines operations research and a proposed twin delayed deep deterministic policy gradient (TD3) with the reward function engineering (RFE) method. The TD3 with RFE method introduces an enhanced reward function to alleviate sparse penalty predicament arising from the long-term constraint and incorporates prior knowledge (PK) to expedite convergence. The simulation validates that the proposed method attains processed data gains of 468.43%, 14.35%, 13.45%, 11.93%, and 4.65% over different benchmark methods, respectively.

关键词： Optical communication   Mobile edge computing   Channel allocation   Task scheduling   Machine learning   5G networks  

摘要： Edge computing enables even low-powered devices to move part of their processing work to the edges of the network, improving service quality for everyone. Though several studies have hinted that offloaded work's data volume might affect resource allocation, no comprehensive study has been undertaken on the issue. In this research, a new optical communication strategy for 5G networks is presented, one that makes use of a machine learning-based routing architecture for channel allocation and job scheduling. Within HDFS (Hadoop data file system) environment for Hadoop processing, the header controls the stimulated control. Edge fog networks communicate through optical tier networks, and resources are managed by a service reinforcement heterogeneous multi-objective architecture. In order to schedule jobs, a multi-node delay proximal strategy is used. This policy uses genetically competing objectives. Throughout the course of the experiment, data on things like throughput, delivery rate, efficiency, end-to-end latency, and collision probability will be collected and analyzed. By implementing the plan, we decreased the probability of a collision by 65%, increased efficiency by 95%, decreased latency to all nodes by 55%, increased throughput by 98%, and increased the delivery rate by 92%.

关键词： Task analysis   Servers   Federated learning   Training   Computational modeling   Data models   Computer architecture   continual learning   mobile edge computing  

摘要： Federated Learning (FL) in mobile edge computing (MEC) systems has recently been studied extensively. In ubiquitous environments, there are usually cross-edge devices that learn a series of tasks across multiple independent edge FL systems. Due to the differences in the scenarios and tasks of different FL systems, cross-edge devices will forget past tasks after learning new tasks, which is unacceptable for devices that pay system costs to participate in FL. Continual learning (CL) is a viable solution to this problem, which aims to train a model to learn a series of tasks without forgetting old knowledge. Currently, there is no work to investigate the problem of CL in a cross-edge FL scenario. In this paper, we propose Cross-FCL, a Cross-edge Federated Continual Learning framework. Specifically, it enables devices to retain the knowledge learned in the past when participating in new task training through a parameter decomposition based FCL model. Then various cross-edge strategies are introduced, including biased global aggregation and local optimization, to trade off memory and adaptation. We conducted experiments on a real-world dataset and other public datasets. Extensive experiments demonstrate that Cross-FCL achieves best accuracy on IID and highly non-IID tasks with a low storage cost compared to other baselines.

关键词： Mobile edge computing   video analysis   video stream offloading   location privacy   usage pattern privacy  

摘要： In Mobile Edge Computing (MEC), the collaboration between end devices and servers guarantees the low-latency and high-accuracy video stream analysis. However, such paradigm of video stream offloading poses a serious threat to the location privacy and the usage pattern privacy of end devices. The existing works offer strict privacy guarantee for users, but they do not take the features of video stream into consideration, thus leading to the relatively higher computation cost. To tackle this issue, we propose a personalized and differential privacy-aware video stream offloading scheme that supports users personalized and time-varying privacy requirements, provides corresponding differential privacy preservation, and generates minimal latency and energy cost. Specifically, we formulate an NP-hard optimization that jointly optimizes the video frame rate, frame resolution and offloading ratio to maximize the analysis accuracy of video stream and minimize the energy cost and the latency subject to the channel bandwidth, computing resources, and personalized and time-varying privacy requirements. Then, we design a online learning-based and personalized privacy-aware video stream offloading algorithm for the optimization problem and thereby obtain the optimal video stream offloading scheme. Last, the extensive experimental results validate the superior performance of the proposed scheme, compared to the three latest existing works.

关键词： mobile edge computing   data consistency verification   reputation model   homomorphic hash   incentive mechanism  

摘要： The emergence of mobile edge computing (MEC) has facilitated the development of data caching technology, which enables application vendors to cache frequently used data on the edge servers close to the user, thereby providing low-latency data access services. However, in an unstable MEC environment, the multi-replica data cached by different edge servers is prone to corruption, making it crucial to verify the consistency of multi-replica data on different edge servers. Although the existing research realizes data integrity verification based on the cooperation of multiple edge servers, the integrity proof generated by multiple copies of data is the same, which has low verification efficiency and is vulnerable to attacks such as replay and replace. To address the above issues, based on homomorphic hash and sampling algorithms, this paper proposes an efficient and lightweight multi-replica integrity verification algorithm, which has significantly less storage cost and computational cost and can resist forgery and replay and replace attacks. Based on the verification algorithm, this paper further proposes a multi-replica edge data integrity collaborative audit scheme EDI-C based on the reputation model. EDI-C realizes the efficient collaborative audit of multiple edge servers in a distributed discrete environment through an incentive mechanism to avoid the trust problem of both sides caused by centralized audit. Also, it supports batch auditing of multiple copies of original data files at the same time through parallel processing and data block auditing technology, which not only significantly improves the verification efficiency but also realizes the accurate location and repair of corrupted data at the data block level. Finally, the security analyses and performance evaluation show the security and practicability of EDI-C. Compared with the representative schemes, the results show that EDI-C can ensure the integrity verification of cached data more efficiently in an

关键词： Mobile edge computing  

摘要： Mobile edge computing (MEC) is increasingly being applied to task computation with the continuing increase of computing-intensive applications in vehicular networks. The MEC can offload the tasks generated by the vehicle to the MEC server to mitigate the computational constraints on the mobile vehicle. However, most task offloading algorithms can not cope with the dynamic distribution of vehicles in vehicular networks. Moreover, most task offloading algorithms do not consider vehicular networks using multipath transmission schemes. Therefore, it remains a challenge to implement efficient joint task offloading and resource allocation (JTORA) algorithms in multipath transmission vehicular networks. In this paper, we introduce a novel cooperative transmission network architecture in multipath transmission vehicular networks. The computational tasks generated by the vehicles can be offloaded to MEC servers deployed near the base station or to cloud servers via multipath transmission. We formulate the task offloading and resource allocation problem as a multi-objective optimization problem in a multipath transmission vehicular network. To achieve efficient task offloading and resource allocation under cooperative transmission network architecture, we propose a JTORA algorithm for multipath transmission vehicular networks based on deep reinforcement learning (DRL). JTORA converts the optimization problem into a DRL model and obtains a specific optimization policy through model training. Simulation results show that JTORA achieves lower average task delay and lower average task cost than other traditional heuristic algorithms. In this article, a joint task offloading and resource allocation (JTORA) algorithm is proposed for multipath transmission vehicular networks based on deep reinforcement learning (DRL). Simulation results show that JTORA achieves lower average task delay and lower average task cost than other traditional heuristic ***