关键词： Task analysis   Resource management   Servers   Computational modeling   Bandwidth   Vectors   Uncertainty   Intelligent reflecting surface (IRS)   mobile edge computing   partial offloading   online ridge regression   multi-armed bandit  

摘要： In the ever-evolving realm of wireless communications, the integration of intelligent reflecting surface (IRS) and mobile edge computing for task offloading has ignited extensive curiosity. However, previous research primarily concentrated on task offloading assuming that a user equipment (UE) possesses the system channel information and the computing resources of base stations (BSs). Acknowledging that UEs typically lack access to such system information, and in pursuit of equilibrium in computing loads between UEs and BSs, we present a bilateral online task allocation approach grounded in partial offloading to minimize task completion latency. Specifically, addressing uncertainties in channel information and available computing resources at BSs, we employ the online ridge regression method on the UE side to continuously adjust the task allocation proportion for offloading to BSs. On the IRS side, we formulate the BS selection as a multi-armed bandit problem, proposing an online learning algorithm based on Thompson sampling to determine the set of BSs for edge computing while managing the task allocation among the selected BSs. Simulation results demonstrate the superior performance of our algorithm.

关键词： Mobile edge computing   Computation offloading   Deep Neural Networks   Supervised learning   Unsupervised learning   Reinforcement learning  

摘要： Mobile Edge Computing (MEC) is a modern paradigm that involves moving computing and storage resources closer to the network edge, reducing latency, and enabling innovative, delay-sensitive applications. Within MEC, computation offloading refers to the process of transferring computationally intensive tasks or processes from mobile devices to edge servers, optimizing the performance of mobile applications. Traditional numerical optimization methods for computation offloading often necessitate numerous iterations to attain optimal solutions. In this paper, we provide a tutorial on how Deep Neural Networks (DNNs) resolve the challenges of computation offloading. The article explores various applications of DNNs in computation offloading, encompassing channel estimation, caching, AR and VR applications, resource allocation, mode selection, unmanned aerial vehicles (UAVs), and vehicle management. We present a comprehensive taxonomy that categorizes these applications, and offer an overview of existing schemes, comparing their effectiveness. Additionally, we outline the open research issues that can be addressed through the application of DNNs in MEC offloading. We also highlight specific challenges related to DNN utilization in computation offloading. In conclusion, we affirm that DNNs are widely acknowledged as invaluable tools for optimizing computation offloading in MEC.

关键词： Servers   Data integrity   Image edge detection   Computational efficiency   Heuristic algorithms   Maintenance engineering   Quality of service   Edge data integrity   mobile edge computing   peredge one-round   signature aggregation  

摘要： Mobile edge computing allows for high-performance and low-latency applications by delegating computation and data processing tasks to edge servers. However, ensuring the integrity of cached data on these servers can be challenging due to their limited resources. Current designs often use a per-edge multi-round approach, which necessitates multiple communication rounds between each edge server and the application vendor (AppVend). This approach results in high communication and computational costs, as well as the stragglers effect during batch verification. To address these inefficiencies, we propose a Hierarchical Signature Aggregation for Edge Data Integrity (HSA-EDI) verification design. Our design adopts a novel per-edge one-round approach, which significantly reduce the number of communication rounds to one for each edge server, while mitigating the impact of stragglers. Furthermore, it remarkably reduces computational costs through a hierarchical aggregation mechanism. This mechanism supports intra-edge signature aggregation at the edge server level, followed by inter-edge aggregation at the AppVend, which enhances overall efficiency. We then conduct a theoretical analysis of HSA-EDI's correctness, security, and communication, computation, and storage efficiency. Experimental results validate its superior performance over state-of-the-art designs.

关键词： Streaming media   Servers   Bit rate   Quality of experience   Backhaul networks   Bandwidth   Adaptive systems   Mobile edge computing   adaptive bitrate streaming   reinforcement learning   content caching   content popularity  

摘要： Mobile Edge Computing (MEC) has become an important concept in modern video communication and broadcasting scenarios to address varied user expectations in an ever-evolving network environment. It has been observed that by preventing redundant access to the Origin Server through backhaul links, caching popular video content in MEC servers minimizes network congestion. Designing an efficient caching mechanism in the MEC server is challenging. To maintain a decent QoE (Quality of Experience) for end-users, we must consider diverse parameters like content popularity, network conditions, etc. In this work, we have proposed a QoE-aware Adaptive BitRate (ABR) caching mechanism at the MEC server using Reinforcement Learning (RL). The proposed model predicts the content popularity of the video and the most preferred video quality for the end-users of a Base Station. In this work, an efficient caching mechanism is devised in the MEC server to provide a decent QoE among the end-users. The primary goal of our RL-based framework is to increase the cache hit rate and reduce the backhaul load while maintaining a satisfactory QoE. Experimental results demonstrate that the proposed model, which emphasizes on video quality, video quality switching, and cache hit rate, outperforms state-of-the-art caching algorithms in terms of the overall QoE reward.

关键词： Internet of things   edge computing environment   deep learning   safety warning   intelligent router   long short-term memory network   anomaly detection  

摘要： Aiming at the problems of poor real-time processing and low security performance caused by the massive information of the Internet of things, a security early warning model of the Internet of things based on deep learning in edge computing environment is proposed. Firstly, the system architecture of the Internet of things is designed by using the edge computing technology, in which the intelligent router is used to obtain the network stub and send it to the nearest edge computing node for anomaly detection. Then, the attention mechanism is used to improve the long short-term memory network (LSTM), and the multidimensional LSTM model is constructed. At the same time, it is used to analyze the combined network data. Finally, according to the set threshold value, judge whether there is abnormal behavior in the network, and give early warning in time to take security defense measures. The experimental analysis of the proposed model based on NS2 simulation platform shows that its early warning success rate and time are 95.2% and 20.6ms, respectively, and it can detect and defend various network attacks well, with high security performance.

关键词： Emergency logistics   Resource scheduling   Cloud computing   Emergency supplier distribution   Mobile edge computing   Vehicle  

摘要： In dealing with emergency logistics, it is essential to use regional and central distribution hubs efficiently, and local and international sources of supply for automotive relief supplies are analyzed in this research. Resource scheduling algorithms employed by service providers to supply and assign resources in an environment are collectively referred to as resource scheduling. Cloud computing uses computational resources pooled and made available across a network, including information storage (cloud) and processing capabilities, requiring user participation in its deployment, operation, and maintenance. The challenging characteristics of such emergency logistics resource scheduling are inconsistencies in tracking, empty miles, and delivery delays. Hence, Emergency Supplier Distribution Mobile Edge Computing (ESD-MEC) research has been designed to improve emergency logistics resource scheduling algorithms in cloud computing. With the abovementioned requirements, ESD may address the distributed scheduling issue of vehicle emergency logistics resources. In particular, the MEC employs a specialized negotiating system to manage the scheduling of resources in impacted regions using an agent-based approach to ESD management in light of the need to do so. The proposed technology helps the decision-maker schedule resources in a dynamic environment and addresses supply demands. An ESD-MEC effectively predicts the rise in emergency logistics resources with faster vehicle strategies in cloud computing. The research concludes that ESD-MEC effectively indicates emergency logistics resource scheduling in cloud computing. The experimental analysis of vehicle logistics outperforms the method in terms of performance, accuracy, prediction ratio, mean square error, and efficiency ratio.

关键词： Computation offloading   knowledge-defined networking   mobile edge computing   deep reinforcement learning  

摘要： With the proliferation of devices connected to the Internet of Things (IoT), the complexity of network management has increased. To intelligently manage large-scale networks, we propose a Knowledge-Defined Edge Computing Networks (KDECN) architecture. Edge Nodes (ENs) deployed in the KDECN architecture are responsible for collecting and preprocessing the relevant information uploaded by User Devices (UDs), and provide computation resources for UDs. Futhermore, since multiple UDs share system computation resources, one computing decision will affect the subsequent decision-making of other UDs. Thus, accurately predicting the demands for UD task requests is a key challenge to maximize long-term execution utility. To this end, we deploy the LSTM-based Task Request Demand Prediction (TRDP) method on the management plane of KDECN architecture to predict the task request quantity of UDs in each future time slot. In order to maximize long-term execution utility of the system, we propose a Deep Reinforcement Learning (DRL)-based Long-term Computation Offloading and computation Resource Allocation (L-CORA) algorithm. Specifically, the proposed L-CORA algorithm makes computing decisions based on the prediction of the offloading task quantity and the personalized demands of UDs to ensure the long-term quality of computing service. Extensive experiments with Shanghai real-world datasets to prove that the KDECN-based L-CORA algorithm effectively improves the average utility of the system.

关键词： Service offloading   Edge computing   Fog computing   Mobile edge computing   Cloud computing   Artificial intelligence  

摘要： The present and next-generation networks of service offloading present exciting issues. The traditional network optimisation techniques still require rigorous heuristic tuning to obtain a good result. Traditional methods employ data as an input and produce solutions that are close to ideal. These methods only operate with tiny networks and exhibit exponential computation times. Thus, we are driven to understand the behaviour of conventional optimisation methods while boosting service quality and meeting next-generation applications. The Optimization problem in Virtual Mobile Edge Computing using Twin Delayed Deep Deterministic Policy Gradient-based Intelligent Computation Offloading (TD3PG-ICO). Optimal Stopping Theory is used in virtual mobile edge computing for the best service offloading (OST). To support the use case, a service offloading protocol is also described. We use Software Defined Networking (SDN) and Network Function Virtualization (NFV) ideas to manage and virtualise network components. To deal with dense Internet of Things (IoT) networks, a TD3PG-OST-based offloading is therefore suggested. There are extensive analyses and comparisons with cutting-edge methods. Results demonstrate the effectiveness of the proposed methods in terms of networking, resource utilisation, and service offloading.

关键词： Mobile edge computing   Status update system   Age of information   Priority queueing model   Stochastic hybrid system  

摘要： Timely status updating in mobile edge computing (MEC) systems has recently gained the utmost interest in internet of things (IoT) networks, where status updates may need higher computations to be interpreted. Moreover, in real-life situations, the status update streams may also be of different priority classes according to their importance and timeliness constraints. The classical disciplines used for priority service differentiation, preemptive and non-preemptive disciplines, pose a dilemma of information freshness dissatisfaction for the whole priority network. This work proposes a hybrid preemptive/non-preemptive discipline under an M/M/1/2 priority queueing model to regulate the priority-based contention of the status update streams in MEC systems. For this hybrid discipline, a probabilistic discretionary rule for preemption is deployed to govern the server and buffer access independently, introducing distinct probability parameters to control the system performance. The stochastic hybrid system approach is utilized to analyze the average age of information (AoI) along with its higher moments for any number of classes. Then, a numerical study on a three-class network is conducted by evaluating the average AoI performance and the corresponding dispersion. The numerical observations underpin the significance of the hybrid-discipline parameters in ensuring the reliability of the whole priority network. Hence, four different approaches are introduced to demonstrate the setting of these parameters. Under these approaches, some outstanding features are manifested: exploiting the buffering resources efficiently, conserving the aggregate sensing power, and optimizing the whole network satisfaction. For this last feature, a near-optimal low-complex heuristic method is proposed.

关键词： satellite edge computing   earth observation satellite   task offloading   mobile edge computing  

摘要： Satellite edge computing (SEC) plays an increasing role in earth observation, due to its global coverage and low-latency computing service. In SEC, it is pivotal to offload diverse observation-data-processing tasks to the appropriate satellites. Nevertheless, due to the sparse intersatellite link (ISL) connections, it is hard to gather complete information from all satellites. Moreover, the dynamic arriving tasks will also influence the obtained offloading assignment. Therefore, one daunting challenge in SEC is achieving optimal offloading assignments with consideration of the dynamic delay-sensitive tasks. In this paper, we formulate task offloading in SEC with delay-sensitive tasks as a mixed-integer linear programming problem, aiming to minimize the weighted sum of deadline violations and energy consumption. Due to the limited ISLs, we propose a fully-decentralized method, called the PI-based task offloading (PITO) algorithm. The PITO operates on each satellite in parallel and only relies on local communication via ISLs. Tasks can be directly offloaded on board without depending on any central server. To further handle the dynamic arriving tasks, we propose a re-offloading mechanism based on the match-up strategy, which reduces the tasks involved and avoids unnecessary insertion attempts by pruning. Finally, extensive experiments demonstrate that PITO outperforms state-of-the-art algorithms when solving task offloading in SEC, and the proposed re-offloading mechanism is significantly more efficient than existing methods.