关键词： Mobile edge computing   ultra-dense network   contextual bandit learning   Lyapunov optimization  

摘要： Integrating mobile edge computing (MEC) in ultra-dense network (UDN) is a key enabler to meet the service demand by allowing smart devices to perform uninterrupted task offloading via densely deployed MEC servers. In most cases, the smart devices randomly move around the whole network. Consequently, the popular "MEC-centralized decision" offloading approach could be inapplicable, as joint decision-making among multiple MEC servers becomes difficult due to time synchronization and information exchange overhead. In this paper, we take a user-centric approach to minimize a long-term delay for a given task duration under a price budget constraint. To address this problem, we develop a novel contextual sleeping bandit learning (CSBL) algorithm, which integrates contextual information and sleeping characteristic to accelerate the learning convergence and leverage Lyapunov optimization to deal with the price budget constraint. Furthermore, we extend to a multiple offloading scenario where multiple MEC servers can be selected in each offloading round and propose a CSBL-multiple (CSBL-M) algorithm to address the exponential increase of the offloading selections. For both CSBL and CSBL-M, we derive the upper bounds of learning regret and provide rigorous proofs that they asymptotically approach the Oracle algorithm within bounded deviations for finite task duration. Simulation results illustrate that our proposed algorithms significantly outperform existing algorithms in both single offloading and multiple offloading scenario.

关键词： Artificial neural network   deep learning   Internet of Vehicles   Internet of Things   information-centric networking   mobile edge computing  

摘要： With the rapid advancement of Internet of Things (IoT) communication technologies, the Internet of Vehicles (IoV) has gained significant attention for providing the real-time exchange of emergency traffic information among vehicles and Road Side Units (RSU) to improve ultimate driving experiences and road safety. Information-Centric Networking (ICN) has emerged as a novel networking architecture that shifts the communication model from Internet protocol (IP) based host-centric to content-centric architecture. ICN provides support to push and pull-based messages for efficient content dissemination and retrieval by aiming at content names rather than IP addresses. The Mobile Edge Computing (MEC) paradigm facilitates proximity-based real-time traffic applications and services, reducing the content retrieval latency from the core network without the excessive broadcast overhead. Deep Learning (DL) techniques have been tremendously successful in detecting the severity of real-time traffic data. The integration of DL based ANN model for edge-based ICN-IoV brings real-time traffic prediction, content caching, and forwarding of push-based messages closer to the target area. Furthermore, the deployment of mobile edge servers at critical network positions enhances the availability and responsiveness of the name-based content in the ICN paradigm. In this paper, we propose Mobile Edge-based Emergency Messages Dissemination Scheme (MEMDS) to deliver push-based messages delivery at the event-reported geographical location. We also propose a hybrid DL-based Artificial Neural Network (ANN) and MEMDS model to detect and predict the severity of the safety application under real traces from different cities based on specific parameters. The simulation results demonstrate that the proposed scheme significantly improves the data delivery ratio, average delay, hop count, content retrieval delay, and network overhead than DCN and flooding techniques. Secondly, the proposed hybrid model su

关键词： Mobile edge computing  

摘要： In view of the problem of long migration time and low availability of VM (virtual machine) migration in MD (mobile devices) in the MEC (mobile edge computing) environment, this paper designs a MD2N (Mobile Device to Network) model. This model no longer uploads requests to the cloud center, but centers on the edge server, scheduling and selecting migration services through the communication between the servers. And, a virtual machine migration strategy based on Markov decision and greedy algorithm is proposed. Model the edge server based on its geographic location, imitating the traditional cellular network method, and then the improved greedy algorithm is used to dynamically select the best edge device to migrate VM. Simulation and comparison experiments show that the proposed scheme has small migration fluctuations, stable migration status, short migration time, low average time delay, and high virtual machine migration availability after migration. © 2023 Xiaoxue Ma et al.

关键词： C-RAN   deep reinforcement learning   DRL   mobile edge computing   Q-learning   resource allocation   task offloading  

摘要： Mobile applications often demand computationally heavy resources to attain high quality, on the other hand, running all programs on a single mobile device still consumes a lot of energy and generates a lot of delay. By breaking a single operation into distinct components, partial computational offloading may be intelligently investigated in mobile edge computing (MEC) to minimise energy consumption and service latency of mobile device. Some of the components run on the mobile device itself, while the rest are sent to a mobile edge server. Current task offloading systems, primarily focus on average-based performance indicators, failing to satisfy the deadline constraints. This paper offers a deep reinforcement learning (DRL) empowered energy efficient task offloading method, to optimise the reward under task deadline constraints. Simulation results show that the proposed technique can efficiently transfer traffic from cloud-radio access networks to next-generation node B, while also saving energy by turning off underutilised baseband processing units.

关键词： Computational offloading   machine learning   mobile edge computing   optimization  

摘要： The progress in computational offloading is heavily pushing the development of the modern Information and Communications Technology domain. The growth in resource-constrained Internet of Things devices demands the development of new computational offloading strategies to be sustainably integrated in beyond 5G networks. One of the solutions to said demand is enabling Mobile Edge Computing (MEC) powered by advanced methods of Machine Learning (ML). This paper proposes the application of ML-powered computational offloading strategy in a wireless cellular network by applying the traditional fundamental Travelling Salesman Problem (TSP) on computational offloading location selection. The main specificity of the proposed approach is the use of imagery data. Thus, the paper executes a literature review to identify existing strategies. It further proposes a novel method utilizing the location-like imagery data to identify the most suitable computational location by executing the search for an identified route between locations using the proposed Deep Learning (DL) model. The model was evaluated and achieved MAE - 1,575, MSA - 10,119,205, R-2 - 0.98 on the testing dataset, which outperforms or is comparable with other well-known architectures. Moreover, the training time is proven to be 2-10 times faster. Interestingly, the MAE values are relatively low compared to the target values that should be predicted (despite rather high MSE results), which is confirmed by the almost perfect R-2 value. It is concluded that the proposed neural network can predict the target values, and this solution can be applied to real-world tasks.

关键词： Mobile edge computing   unmanned aerial vehicles   resource allocation   blockchain   multi-task offloading   matching mechanism  

摘要： Mobile edge computing (MEC) is a technology deployed at the edge of mobile networks to enhance computation capabilities and reduce transmission distances. It has been extensively researched in the context of both the internet of things (IoT) and 5G communication. Recently, unmanned aerial vehicles (UAVs) have been integrated into MEC networks to create a novel architecture that utilizes line-of-sight (LOS) transmission links. In this architecture, UAVs act as relay nodes to facilitate the offloading of computing tasks from UAVs to edge computing stations (ECSs). However, the problem of creating an incentive system that guarantees the confidentiality and integrity of communication while simultaneously making it easier for UAVs and ECSs to coordinate a variety of activities remains unsolved. Consequently, this paper proposes a blockchain-based architecture for UAV-assisted MEC networks that addresses the aforementioned issues of security and privacy and investigates the problem of multi-task matching based on this architecture. A joint optimization problem is explicitly proposed to maximize both task completion rates and social welfare. Consequently, the formulated problem is decomposed into two subproblems: 1) The double auction problem, which aims to maximize societal utility and determines the winning pairs and trading price;and 2) The auction losers matching problem, which aims to increase task completion rates. Then, to identify the appropriate matching pairings and establish the trading price, a satisfaction breakeven-based double auction (SBDA) method is suggested. Sequentially, two auction losers' second selection schemes, the shortest distance (SD) and the largest difference (LD) scheme are proposed to realize second matching to enhance the task completion rate. Finally, numerical simulations are given to show the effectiveness of the proposed mechanism. Particularly, the SBDA+LD mechanism has the best system utility and overall income compared with other sch

关键词： Mobile edge computing   Deep Q Network Algorithm   Latency Optimized   Computation Offloading   5G  

摘要： The future Internet of Things (IoT) era is anticipated to support computation-intensive and time-critical applications using edge computing for mobile (MEC), which is regarded as promising technique. However, the transmitting uplink performance will be highly impacted by the hostile wireless channel, the low bandwidth, and the low transmission power of IoT devices. Using edge computing for mobile (MEC) to offload tasks becomes a crucial technology to reduce service latency for computation-intensive applications and reduce the computational workloads of mobile devices. Under the restrictions of computation latency and cloud computing capacity, our goal is to reduce the overall energy consumption of all users, including transmission energy and local computation energy. In this article, the Deep Q Network Algorithm (DQNA) to deal with the data rates with respect to the user base in different time slots of 5G NOMA network. The DQNA is optimized by considering more number of cell structures like 2, 4, 6 and 8. Therefore, the DQNA provides the optimal distribution of power among all 3 users in the 5G network, which gives the increased data rates. The existing various power distribution algorithms like frequent pattern (FP), weighted least squares mean error weighted least squares mean error (WLSME), and Random Power and Maximal Power allocation are used to justify the proposed DQNA technique. The proposed technique which gives 81.6% more the data rates when increased the cell structure to 8. Thus 25% more in comparison to other algorithms like FP, WLSME Random Power and Maximal Power allocation.

关键词： Mobile edge computing  

摘要： As a new cloud service mode, mobile edge computing distributes the traditional centralized deployment and management of cloud resources to the wireless access network. Mobile services can be processed for the sake of obtaining a good service experience and reduce the network load of the backtrip network. In order to effectively improve the end-to-end service delay of the flow in multi-clusters coexisting mobile edge computing (MEC) network, an improved virtual network function (VNF) service chain deployment algorithm based on Q-learning was proposed. Based on the planning model, a spatial-temporal optimization model of VNF service chain deployment was established by markov decision process, and an improved BPQ-learning algorithm was designed to solve the model. The method considered both the virtual deployment of MEC service chain in space dimension and the life cycle management of VNF in time dimension. The optimization of VNF deployment is realized. The possible network congestion is avoided by deployment service nodes in advance. Experimental results show that the proposed strategy can provide end-to-end services with lower latency and better experience for delaysensitive mobile services under different VNF service chain scale, service node scale, cluster number and logical connection relationship among the virtual network functions. © 2023, IAENG International Journal of Computer Science. All Rights Reserved.

关键词： Aerial computing   computation offloading   deep reinforcement learning   double deep Q-learning   mobile edge computing   multi-agent reinforcement learning   unmanned aerial vehicle  

摘要： The rapid development of the Internet of Things and wireless communication has resulted in the emergence of many latency-constrained and computation-intensive applications such as surveillance, virtual reality, and disaster monitoring. To satisfy the computational demand and reduce the prolonged transmission delay to the cloud, mobile edge computing (MEC) has evolved as a potential candidate that can improve task completion efficiency in a reliable fashion. Owing to its high mobile nature and ease of use, as promising candidates, unmanned aerial vehicles (UAVs) can be incorporated with MEC to support such computation-intensive and latency-critical applications. However, determining the ideal offloading decision for the UAV on basis of the task characteristics still remains a crucial challenge. In this paper, we investigate a surveillance application scenario of a hierarchical UAV swarm that includes an UAV-enabled MEC with a team of UAVs surveilling the area to be monitored. To determine the optimal offloading policy, we propose a deep reinforcement learning based computation offloading (DRLCO) scheme using double deep Q-learning, which minimizes the weighted sum cost by jointly considering task execution delay and energy consumption. A performance study shows that the proposed DRLCO technique significantly outperforms conventional schemes in terms of offloading cost, energy consumption, and task execution delay. The better convergence and effectiveness of the proposed method over conventional schemes are also demonstrated.

关键词： & nbsp   Generative adversarial network   5G   mobile edge computing   synthetic data generation   resource efficiency   performance evaluation  

摘要： Mobile Edge Computing (MEC) in 5G networks has emerged as a promising technology to enable efficient and low-latency services for mobile users. In this paper, we present a novel synthetic data generation approach tailored for evaluating MEC in 5G networks. Our methodology incorporates resource-efficient techniques to generate realistic synthetic datasets that capture the spatio-temporal patterns of mobile traffic and user behavior. By leveraging advanced modeling techniques, including multi-head attention and bidirectional LSTM, we accurately model the complex dependencies in the data while optimizing computational resources. The proposed synthetic data generator enables the creation of diverse datasets that closely resemble real-world scenarios, facilitating the evaluation of MEC performance and optimizing resource utilization. Through extensive experiments and evaluations, we demonstrate the effectiveness of our approach in enabling accurate assessments of MEC in 5G networks. Our work contributes to the field by providing a robust methodology for synthetic data generation specifically tailored for MEC evaluation, addressing the need for resource-efficient evaluation frameworks in the context of emerging technologies. The results of our study provide valuable insights for the design and optimization of MEC systems in real-world deployments.