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

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. © 2023, Malaysian Journal of Computer Science. All Rights Reserved.

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： 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： 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： 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： Age of information   low-latency communications   mobile edge computing   offloading ratio  

摘要： In this paper, we analyze the average age of information (AoI) and the average peak AoI (PAoI) of a multiuser mobile edge computing (MEC) system where a base station (BS) generates and transmits computation-intensive packets to user equipments (UEs). In this MEC system, we focus on three computing schemes: (i) The local computing scheme where all computational tasks are computed by the local server at the UE, (ii) The edge computing scheme where all computational tasks are computed by the edge server at the BS, and (iii) The partial computing scheme where computational tasks are partially allocated at the edge server and the rest are computed by the local server. Considering exponentially distributed transmission time and computation time and adopting the first come first serve (FCFS) queuing policy, we derive closed-form expressions for the average AoI and average PAoI. To address the complexity of the average AoI expression, we derive simple upper and lower bounds on the average AoI, which allow us to explicitly examine the dependence of the optimal offloading decision on the MEC system parameters. Aided by simulation results, we verify our analysis and illustrate the impact of system parameters on the AoI performance.

关键词： Computation offloading   reconfigurable intelligent surface   mobile edge computing   backscatter communications   wireless powered communication networks   MOOP   SOOP   Pareto Optimality  

摘要： We investigate a wireless power transfer (WPT)-based backscatter-mobile edge computing (MEC) network with a reconfigurable intelligent surface (RIS). In this network, wireless devices (WDs) offload task bits and harvest energy, and they can switch between backscatter communication (BC) and active transmission (AT) modes. We exploit the RIS to maximize energy efficiency (EE). To this end, we optimize the time/power allocations, local computing frequencies, execution times, backscattering coefficients, and RIS phase shifts. This goal results in a multi-objective optimization problem (MOOP) with conflicting objectives. Thus, we simultaneously maximize system throughput and minimize energy consumption via the Tchebycheff method, transforming into two single-objective optimization problems (SOOPs). For throughput maximization, we exploit alternating optimization (AO) to yield two sub-problems. For the first one, we derive closed-form resource allocations. For the second one, we design the RIS phase shifts via semi-definite relaxation, a difference of convex functions programming, majorization minimization techniques, and a penalty function to enforce a rank-one solution. For energy minimization, we derive closed-form resource allocations. We demonstrate the gains over several benchmarks. For instance, with a 20-element RIS, EE can be as high as 3 (Mbits/Joule), a 150% improvement over the no-RIS case (achieving only 2 (Mbits/Joule)).

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.