关键词： Mobile edge computing   Distributed computing   DNN offloading   Interleaved offloading   Edge computing   Computational offloading  

摘要： Deep Neural Networks (DNNs) are increasingly used on the edge in mobile and other resource-constrained devices for inference tasks ranging from object detection and image recognition to video processing. Many of these tasks have low latency requirements that cannot be satisfied if they are processed locally due to their high computational complexity. Offloading computation to the edge and cloud offers a way to alleviate this computational latency. Doing so introduces communication delays, which makes offloading a balancing act between the benefits of reduced processing time and the communication delays incurred. Existing algorithms for DNN offloading based on DNN partitioning are optimised for handling successive tasks on a single remote server, and perform sub-optimally when tasks are interleaved, or with multiple servers, or when privacy concerns require local processing of certain DNN layers. A viable alternative are generic computational offloading algorithms (GOAs), which can break down DNN tasks into their components and perform fine-grained offloading. We perform a simulation-based comparison of traditional GOAs with various levels of proactivity and offloading constraints and a naive DNN partitioning approach. We identify key requirements for offloading algorithms in the ability to create processing chains between several remote servers in order to reduce communication overheads, and the ability to prioritise already running tasks. The results confirm the expectations about the shortcomings of DNN partitioning, and show that GOA algorithms can provide a significant performance improvement.

关键词： mobile edge computing   privacy protection   federated learning   differential privacy   differential evolutionary  

摘要： Data sharing and analyzing among different devices in mobile edge computing is valuable for social innovation and development. The limitation to the achievement of this goal is the data privacy risk. Therefore, existing studies mainly focus on enhancing the data privacy-protection capability. On the one hand, direct data leakage is avoided through federated learning by converting raw data into model parameters for transmission. On the other hand, the security of federated learning is further strengthened by privacy-protection techniques to defend against inference attack. However, privacy-protection techniques may reduce the training accuracy of the data while improving the security. Particularly, trading off data security and accuracy is a major challenge in dynamic mobile edge computing scenarios. To address this issue, we propose a federated-learning-based privacy-protection scheme, FLPP. Then, we build a layered adaptive differential privacy model to dynamically adjust the privacy-protection level in different situations. Finally, we design a differential evolutionary algorithm to derive the most suitable privacy-protection policy for achieving the optimal overall performance. The simulation results show that FLPP has an advantage of 8 similar to 34% in overall performance. This demonstrates that our scheme can enable data to be shared securely and accurately.

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： 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： Mobile edge computing   Resource allocation   Non-orthogonal multiple access   Unmanned aerial vehicle   Task offloading  

摘要： To address the challenges in the era of artificial intelligence, this paper investigates how computing offloading techniques can be utilized to improve the system performance in cellular connected unmanned aerial vehicle (UAV) networks with limited resources using non-orthogonal multiple access (NOMA) and mobile edge computing techniques. Energy consumed by the UAV in performing tasks and the time required to complete tasks are minimized by joint optimal task offloading decision, sub-channel allocation, and computing resource allocation while satisfying the user's quality of service requirements. We consider this problem as a nonlinear programming problem with integer and non-integer variables and solve it by iteratively updating the resource allocation and offloading decision. Specifically, given a fixed offloading decision, a many-to-one matching model is first applied for sub-channel allocation, then a detailed allocation of computing resource is performed by optimizing the total system overhead, and are solved by utilizing the matching algorithm and the Lagrange dual method, respectively. Then, according to the scenarios we have been given for resource allocation, we design a multi-objective task offloading algorithm to update the offloading decision. Simulation experiments demonstrate the feasibility of the scheme, which shows a significant reduction in both energy consumption and latency compared to the benchmark scheme.

关键词： Mobile edge computing   HTTP-based video streaming   Adaptive streaming   Quality of experience   Streaming media  

摘要： The traditional client-based HTTP adaptation strategies do not explicitly coordinate between the clients, servers, and cellular networks. A lack of coordination leads to suboptimal user experience. In addition to optimizing Quality of Experience (QoE), other challenges in adapting HTTP adaptive streaming (HAS) to the cellular environment are overcoming unfair allocation of the video rate and inefficient utilization of the bandwidth under the high-dynamics cellular links. Furthermore, the majority of the adaptive strategies ignore important video content characteristics and HAS client information, such as segment duration, buffer size, and video duration, in the video quality selection process. In this paper, we present a content-aware hybrid multi-access edge computing (MEC)-assisted quality adaptation algorithm by taking advantage of the capabilities of edge cloud computing. The proposed algorithm exploits video content characteristics, HAS client settings, and application-layer information to jointly adapt the bitrates of multiple clients. We design separate strategies to optimize the performance of short and long duration videos. We then demonstrate the efficiency of our algorithm against client-based solutions as well as MEC-assisted algorithms. The proposed algorithm guarantees high QoE, equitably selects video rates for clients, and efficiently utilizes the bandwidth for both short and long duration videos. The results from our extensive experiments reveal that the proposed long video adaptation algorithm outperforms state-of-the-art algorithms, with improvements in average video rate, QoE, fairness, and bandwidth utilization of 0.4%-12.3%, 8%-65%, 3.3%-5.7%, and 60%-130%, respectively. Furthermore, when high bandwidth is available to competing clients, the proposed short video adaptation algorithm improves QoE by 11.1% compared to the long video adaptation algorithm.

关键词： Mobile edge computing   Task offloading   Genetic algorithm   VANETs  

摘要： With the advancement of information technology, Mobile Edge Computing (MEC) has been integrated into Vehicular Ad Hoc Networks (VANETs) to efficiently address the resource constraints of vehicle terminals. Within the realm of MEC, the effective utilization of wireless channels and edge server resources for task offloading to meet users' Quality of Service (QoS) requirements has become a significant research area. This paper presents a queuing model and proposes a Predictive Vehicle-to-Vehicle Task Offloading (PV2VO) strategy based on predictable MEC state. The PV2VO strategy employs Vehicle-to-Vehicle (V2V) communication for task data upload and computation result download, thus reducing result return delay. Selective offloading is performed based on the edge server's load status and waiting time, resulting in reduced waiting time for the edge server's computing tasks and meeting users' QoS requirements under high load conditions. By considering both the delay model and energy model, two preference factors are defined, and an objective function for local execution and offloading to the edge server is formulated. The genetic algorithm is then employed to determine the optimal local computing frequency, optimal transmission power, and the most suitable computation offloading approach. The proposed PV2VO strategy is simulated and compared with conventional strategies through experiments involving five different types of typical computing tasks. The comparison of the results validates the superiority of the PV2VO strategy.

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： many-objective evolutionary algorithm   many-objective optimization   mobile edge computing   virtual machine migration  

摘要： With the rapid development of big data, the explosive growth of data promotes the progress of the Internet of Things (IoT). Because it is hard for traditional cloud computing to meet vast computing tasks, scholars propose mobile edge computing (MEC) for the IoT. However, the mobility of users results in the instability of MEC performance. Besides, the conflict of interest between users and service providers needs to be balanced. To solve these problems, this paper constructs a virtual machine migration model based on many-objective optimization (MaOVMMM). In MaOVMMM, four objectives are considered simultaneously: communication expense, computing expense, delay, and energy consumption. A many-objective evolutionary algorithm with double population confrontation (MaOEA-DPC) is suggested to support the MaOVMMM that is proposed. First, the population confrontation strategy is designed to better simulate the relationship between users and service providers. Second, the dynamic probability integration selection strategy is used to ensure the evolution ability of the algorithm. Simulation results demonstrate the effectiveness and superiority of MaOEA-DPC when compared with other algorithms. This proposed approach can provide a superior virtual machine migration scheme for decision-makers.

关键词： Mobile edge computing  

摘要： Widely available healthcare services are now getting popular because of advancements in wearable sensing techniques and mobile edge computing. People’s health information is collected by edge devices such as smartphones and wearable bands for further analysis on servers, then send back suggestions and alerts for abnormal conditions. The recent emergence of federated learning allows users to train private data on local devices while updating models collaboratively. However, the heterogeneous distribution of the health condition data may lead to significant risks to model performance due to class imbalance. Meanwhile, as FL training is powered by sharing gradients only with the server, training data is almost inaccessible. The conventional solutions to class imbalance do not work for federated learning. In this work, we propose a new federated learning framework FedImT, dedicated to addressing the challenges of class imbalance in federated learning scenarios. FedImT contains an online scheme that can estimate the data composition during each round of aggregation, then introduces a self-attenuating iterative equivalent to track variations of multiple estimations and promptly tweak the balance of the loss computing for minority classes. Experiments demonstrate the effectiveness of FedImT in solving the imbalance problem without extra energy consumption and avoiding privacy risks. © 2023, CC BY.