关键词： Mobile edge computing  

摘要： The daily functioning of civilization depends heavily on transportation. In most cities, a sizable section of the working class consistently attends both employment and education. There are many different things you can do when commuting, such as unwinding, eating out, and other things. The most popular means of transportation in North Cyprus, particularly in developing cities, is island transportation, which includes the usage of both private cars and commercial vehicles. The advent of edge computing, which offers the opportunity to connect potent processing servers next to the mobile device, is a significant step toward improving user experience and reducing resource use. Mobile Edge Computing is the next trustworthy approach for how mobile devices consume communications and computing. Offloading computation is a key component developing mobile edge computing, which enables devices to get around clustering techniques' limitations and get around computing, storage, and energy constraints. However, computation offloading is not always the best strategy to use;making choosing unloading is a critical step that requires consideration of numerous factors. For instance, shifting the high-resource node to an edge server and granting similar capabilities to the low-resource nodes would delegate heavy duties to the external unit inside the network. The evaluations' results were noteworthy and substantial. Problems involving the vehicles of institutions and organizations can be resolved using the suggested solution to the school bus routing issue. We also test the impact of network latency on the delivery of a particular result using an Edge Computing simulator. © 2023,International Journal of Interactive Mobile Technologies. All Rights Reserved.

关键词： Internet of Things (IoT)   Cloud Computing   Mobile Edge Computing   Brain Tumor   Cardiac Diseases   Healthcare systems  

摘要： Event intelligence for early disease detection is highly demanded in the current era and it requires reliable technology -oriented applications. Trusted emerging technologies play a vital role in modern healthcare systems for early diagnoses of different medical conditions because it helps to speed up the treatment process. Despite the enhancement of current healthcare systems, robust diagnosis of different types of diseases for intra-patients (outside of hospital settings) is still considered a difficult task. However, the continuous evolution of trusted technologies in health sectors narrates the reboot process which could upgrade the healthcare service provision as the trusted next-generation health unit. In order to assist healthcare providers to carry out early diseases detection for intra-patient clients, we designed this systematic review. We extracted 40 studies from the databases i.e. IEEE Xplore, Springer, Science direct and Scopus, from March 2016 and February 2023, and we formulated our research questions based on these studies. Subsequently, we rectified these studies using two filtration schemes namely, inclusion-omission policy and quality assessment, and as a result, we obtained 19 studies that successfully mapped our defined research *** found that these 19 studies highlighted the different trusted architectures of the internet of things, mobile cloud computing, and machine learning, which are significantly beneficial to diagnose medical conditions for intra-patient clients such as neurological diseases, cardiac malfunctions, and other common diseases.

关键词： Mobile edge computing   task offloading   resource allocation   genetic algorithm   deep learning  

摘要： With the rapid development of 5G and the Internet of Things (IoT), the traditional cloud computing architecture struggle to support the booming computation-intensive and latency-sensitive applications. Mobile edge computing (MEC) has emerged as a solution which enables abundant IoT tasks to be offloaded to edge services. However, task offloading and resource allocation remain challenges in MEC framework. In this paper, we add the total number of offloaded tasks to the optimization objective and apply algorithm called Deep Learning Trained by Genetic Algorithm (DL-GA) to maximize the value function, which is defined as a weighted sum of energy consumption, latency, and the number of offloaded tasks. First, we use GA to optimize the task offloading scheme and store the states and labels of scenario. Each state consists of five parameters: the IDs of all tasks generated in this scenario, the cost of each task, whether the task is offloaded, bandwidth occupied by offloaded task and remaining bandwidth of edge server. The labels are the tasks that are currently selected for offloading. Then, these states and labels will be used to train neural network. Finally, the trained neural network can quickly give optimization solutions. Simulation results show that DL-GA can execute 75 to 450 times faster than GA without losing much optimization power. At the same time, DL-GA has stronger optimization capability compared to Deep Q-Learning Network (DQN).

关键词： unmanned aerial vehicle   UAV   satisfaction   cooperative trajectory optimisation   mobile edge computing   MEC  

摘要： This paper investigates an unmanned aerial vehicle (UAV)-assisted mobile edge computing scenario, where multiple UAVs provide computational services for user devices (UDs) with different service priorities. Taking into account UDs' different service priorities as well as diverse computation-offloading demands, a satisfaction-model is proposed, which gives consideration to UDs' satisfaction degree toward the offloading services as well as the fairness of their offloading amount. We aim to maximise UDs' sum satisfaction by jointly optimising the offloading time allocation, computing time allocation, users' transmit power, as well as UAV trajectory. A non-convex optimisation problem is formulated and an effective solution framework is proposed, where the problem is decomposed into three sub-problems that are solved alternately and iteratively by applying successive convex approximation (SCA) technique. Numerical results show that the proposed scheme achieves higher satisfaction with lower standard deviation as compared to benchmark schemes.

关键词： emergency communication   mobile edge computing   swap matching algorithm   unmanned aerial vehicle  

摘要： This article studies mobile edge computing technologies enabled by unmanned aerial vehicles(UAVs) in disasters. First, considering that the ground servers may be damaged in emergency scenarios, we proposed an air-ground cooperation architecture based on ad-hoc UAV networks. We defined the system cost as the weighted sum of task delay and energy consumption because of different delay sensitivity and energy sensitivity tasks in emergency communication networks. Then, we formulated the system cost-minimization problem of task scheduling and multi-UAV deployments. To solve the proposed mixed integer nonlinear programming problem, we decomposed it to two sub-problems that were solved by proposing a swap matchingbased task scheduling sub-algorithm and a successive convex approximation-based multi-UAV deployment sub-algorithm. Accordingly, we propose a joint optimization algorithm by iterating the two sub-algorithms to obtain a low complexity sub-optimal solution. Finally, the simulation results show that(i) the proposed algorithm converges in several iterations, and(ii) compared with the benchmark algorithms, the proposed algorithm has better performance of reducing task delay and energy consumption and achieves a good tradeoff between them for diverse tasks.

关键词： DRL   Fusion System Design   Intelligent Video Surveillance   Internet of Things   Machine Learning   Mobile Edge Computing  

摘要： Currenlty, wireless communication that is successful in the Internet of Things (IoT) must be long-lasting and self-sustaining. The integration of machine learning (ML) techniques, including deep learning (DL), has enabled IoT networks to become highly effective and self-sufficient. DL models, such as enhanced DRL (EDRL), have been developed for intelligent video surveillance (IVS) applications. Combining multiple models and optimizing fusion scores can improve fusion system design and decision-making processes. These intelligent systems for information fusion have a wide range of potential applications, including in robotics and cloud environments. Fuzzy approaches and optimization algorithms can be used to improve data fusion in multimedia applications and e-systems. The camera sensor is developing algorithms for mobile edge computing (MEC) that use action-value techniques to instruct system actions through collaborative decision-making optimization. Combining IoT and deep learning technologies to improve the overall performance of apps is a difficult task. With this strategy, designers can increase security, performance, and accuracy by more than 97.24%, as per research observations. © 2023, American Scientific Publishing Group (ASPG). All rights reserved.

关键词： Mine 5G communication   mobile edge computing   deep learning   intelligent coal mine  

摘要： In the actual production of coal mines, the transmission needs of existing underground applications cannot be met due to a lack of strategies and customized equipment for underground 5G application scenarios, which causes increased underground data processing delay and low transmission efficiency. To solve the problem above, the mobile edge computing (MEC) technology based on the 5G wireless base station is studied, and underground 5G communication capability is improved by edge caching and dynamic resource allocation according to the actual situation of coal mines. The experimental result shows that under the premise of maintaining the rated power and transmit power of the existing base station, the average delay of executing tasks is 15 ms, which is 50% lower than the average delay of all local execution methods. The average delay is 37.5% lower than all MEC execution methods. At the same time, the uplink rate of a single base station can reach 1 Gbps and the downlink rate can reach 1.5 Gbps. Our method can significantly improve the reliability of mining 5G communication systems and the rational allocation of resources.

关键词： computation offloading   energy   mobile edge computing   multitask   optimization   processing time  

摘要： This paper addresses the issue of efficient resource allocation in a Mobile Edge Computing (MEC) system, taking into account the trade-off between energy consumption and operation latency. The increasing deployment of connected devices and data-intensive services in the Internet of Things (IoT) poses significant challenges in terms of managing computational resources. In this study, we propose a MEC system model that considers energy constraints and the need to minimize latency to ensure optimal performance. We formulate the resource allocation problem in terms of a trade-off between energy consumption and latency, and explore solutions based on heuristic task offloading techniques. Our experiments demonstrate that our approach achieves improved latency performance while reducing energy consumption. We also evaluate the impact of various parameters, such as workload and resource availability, on the energy-latency trade-off. © 2023 by the authors of this article. Published under CC-BY.

关键词： connectivity management   constrained devices   Internet of Things   mobile edge computing   real-time systems   service middle-ware and platforms   smart environment  

摘要： The Internet of Things has a wide range of potential applications in environmental monitoring, including environmental protection, water safety, monitoring of severe weather, protection of endangered species, and commercial farming, amongst others. In these kinds of applications, sensors monitor and record any conceivable change in the surrounding environment. In order to facilitate the computations that utilises high energy, Mobile Edge Computing (MEC) has evolved. Using the techniques in MEC, energy-based task scheduling can be done with higher efficiency. For enhancing the energy-based data transmissions and performing computations, a novel MEC framework which incorporates heterogeneous components is proposed. Base stations and MEC servers are also included in the framework to perform computation-intensive tasks. Though the state change in base stations is continuous, the time is discretised to make the system effective. Communication and bandwidth allocation, computation tasks, and energy utilisation are prioritised and meticulously handled using statistical modeling. Queuing policy is introduced at various components of the proposed system to ensure the consistency of the computing task. The queued tasks are effectively managed using the optimidation algorithms based on energy conservation parameters. The neural network-based Reinforcement learning technique is proposed for optimising the data offloading and energy utilisation. By considering the contemporary system parameters, the actions are taken upon based on the optimisation technique. The proposed optimisation technique could adaptively learn the efficient policy for the given system and so it out performs the benchmark algorithms like Dueling Deep Q-Network (DQN), Double Deep Q-Network (DDQN), and Greedy policy. © 2023, Scibulcom Ltd. All rights reserved.

关键词： Internet of Things   unmanned aerial vehicles   energy harvesting   wireless power transfer   non-orthogonal multiple access   mobile edge computing   physical layer security  

摘要： This paper investigates a system for unmanned aerial vehicle (UAV)-enabled wireless power transfer (WPT) and non-orthogonal multiple access (NOMA) mobile edge computing (MEC) in the Internet of Things (IoT) -UWNMI, wherein a UAV equipped with an energy transmitter (ET) acts as a relay. In particular, we consider a situation involving two clusters of IoT devices (IDs) that have limited resources and therefore are unable to compute their own tasks and must instead offload them to a base station (BS) via the UAV relay (UR) in the presence of a passive eavesdropper. In the UWNMI system, the effects of imperfect channel state information (ICSI) and imperfect successive interference cancellation (ISIC) are considered, along with the usage of artificial noise (AN) to improve the physical layer security (PLS) of the system. We derive closed-form expressions for the successful computation probability (SCP) and secrecy outage probability (SOP) under the Nakagami-m fading channel model in order to evaluate the performance of the system. Moreover, we present optimization problems with the objectives of optimizing the position and height of the UR, the time switching ratio (TSR), and the power allocation for the AN in order to maximize the SCP and minimize the SOP. These problems are solved using a particle swarm optimization (PSO)-based algorithm. In addition, Monte Carlo simulation results are presented to validate the accuracy of our analysis based on simulations of the system performance under different values of the system parameters, including the number of antennas at the BS, the number of IDs in each cluster, the TSR, and the position and height of the UR.