关键词： mobile edge computing   satellite-terrestrial networks   inter-satellite cooperation   offloading decision   resource allocation  

摘要： Mobile edge computing (MEC)-enabled satellite-terrestrial networks (STNs) can provide task computing services for Internet of Things (IoT) devices. However, since some applications' tasks require huge amounts of computing resources, sometimes the computing resources of a local satellite's MEC server are insufficient, but the computing resources of neighboring satellites' MEC servers are redundant. Therefore, we investigated inter-satellite cooperation in MEC-enabled STNs. First, we designed a system model of the MEC-enabled STN architecture, where the local satellite and the neighboring satellites assist IoT devices in computing tasks through inter-satellite cooperation. The local satellite migrates some tasks to the neighboring satellites to utilize their idle resources. Next, the task completion delay minimization problem for all IoT devices is formulated and decomposed. Then, we propose an inter-satellite cooperative joint offloading decision and resource allocation optimization scheme, which consists of a task offloading decision algorithm based on the Grey Wolf Optimizer (GWO) algorithm and a computing resource allocation algorithm based on the Lagrange multiplier method. The optimal solution is obtained by continuous iterations. Finally, simulation results demonstrate that the proposed scheme achieves relatively better performance than other baseline schemes.

关键词： caching   mobile edge computing   content preferences   user mobility   MovieLens  

摘要： High-speed internet has boosted clients' traffic needs. Content caching on mobile edge computing (MEC) servers reduces traffic and latency. Caching with MEC faces difficulties such as user mobility, limited storage, varying user preferences, and rising video streaming needs. The current content caching techniques consider user mobility and content popularity to improve the experience. However, no present solution addresses user preferences and mobility, affecting caching decisions. We propose mobility- and user-preferences-aware caching for MEC. Using time series, the proposed system finds mobility patterns and groups nearby trajectories. Using cosine similarity and CF, we predict and cache user-requested content. CF predicts the popularity of grouped-based content to improve the cache hit ratio and reduce delay compared to baseline techniques.

关键词： Industrial IoT   Internet of Things   microservice offloading   mobile edge computing   resource-agnostic  

摘要： Due to increase in real-time mobile applications and Industrial Internet-of-Things (IIoT) devices, the edge computing paradigm provides a systematic and eccentric platform for real-time Internet-of-Things applications. Though the paradigm provides an effective infrastructure, however the resource requirements of IIoT devices change radically with time, which is described as a resource-agnostic property. Therefore, the estimation of resource requirements of IIoT devices is a critical and resilient assignment. In addition, it requires an extensive amount of resources to process the data traffic flows and microservice offloading. Hence, we present RAISE, a novel resource-agnostic microservice offloading scheme for mobile IIoT devices. RAISE efficiently estimates the resource-agnostic nature of IIoT devices to maximize their resource utilization in the network. Based on the estimated resource requirement, we propose a resource-agnostic microservice offloading scheme to maximize the success rate. Extensive experiments show that RAISE provides better performance in terms of network throughput and Quality-of-Service (QoS) than the other existing methods, SDTO and DTOS, in terms of cost and reliability.

关键词： mobile edge computing   partial-di ff erential equations   UAV   polling system   neural   network   machine learning   applied numerical methods  

摘要： As the demand for the internet of things (IoT) continues to grow, there is an increasing need for low-latency networks. Mobile edge computing (MEC) provides a solution to reduce latency by offloading computational tasks to edge servers. However, this study primarily focuses on the integration of back propagation (BP) neural networks into the realm of MEC, aiming to address intricate network challenges. Our innovation lies in the fusion of BP neural networks with MEC, particularly for optimizing task scheduling and processing. Firstly, we introduce a drone-assisted MEC model that categorizes computation offloading into synchronous and asynchronous modes based on task scheduling. Secondly, we employ Markov chains and probability-generation functions to accurately compute parameters such as average queue length, cycle time, throughput, and average delay in the synchronous mode. We also derive the first and second-order derivatives of the probability-generation function to support these computations. Finally, we establish a BP neural network to solve for the average queue length and latency in the asynchronous mode. Our results from the BP neural network closely align with the theoretical values obtained through the probability-generation function, demonstrating the effectiveness of our approach. Additionally, our proposed UAV-assisted MEC model outperforms the synchronous mode. Overall, our MEC scheduling approach significantly reduces latency, enhances speed, and improves throughput, with our model reducing latency by approximately 11.72% and queue length by around 9.45%.

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： Mobile edge computing   edge server side   game   multi server multi task offloading   deadline-aware  

摘要： In this paper, a novel task offloading architecture called Flex-MEC is proposed, which achieves efficient task allocation and scheduling (TAS) between MEC servers. By adding metadata before task data, we redesign the offloading process in Flex-MEC, the TAS planning can be conducted without finishing the task data receiving. Once planning is done the task data can be directly forwarded to the allocated server and executed. This reduces latency compared to the traditional way of transmitting, planning, forwarding and executing sequentially. For TAS planning, a multi-server multi-task allocation and scheduling (MMAS) problem is formulated to maximize the MEC system profit. The MMAS problem is proven as an NP-complete problem, thus is challenging to solve. Then, a distributed scheme and a centralized scheme are proposed to solve the MMAS problem with low complexity. In the distributed scheme, the MMAS problem is converted into a non-cooperative game and the existence of Nash Equilibrium (NE) is proven and a low complexity response update algorithm is proposed to converge to NE. And the centralized scheme is based on a greedy idea and runs on a MEC controller in a centralized way. Verified by experiments, these two schemes can achieve better performance than compared schemes.

关键词： Mobile edge computing   offloading strategy   joint optimization  

摘要： Offloading strategies in mobile edge computing are hot research, whereas, existing offloading strategies at the edge hard handle the issues of multi-user intensive task scheduling, resulting in the poor utilization of network resource. Therefore, this makes the quality of experience for end users far from satisfactory. To address this, this paper proposes a novel joint offloading strategy consisting of the back propagation neural network and the genetic algorithm. Firstly, using the genetic algorithm optimizes the learning error of the back propagation neural network, and then energy consumption in the system and response delay are jointly optimized by the back propagation neural network. Under long-term total overhead-cost constraints, the joint strategy can achieve the search of the optimal solutions to generate superior calculated offloading results. Unlike those approaches devoting into reducing response delay only for end users, this work takes account into the total overhead-cost in the system thereby affording more efficient for application service providers. Multiple simulation results indicate that the proposed strategy can not only reduce the average response delay of the mobile edge computing system, but also remain a low average energy consumption.

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： Data integrity   Protocols   Cloud computing   Edge computing   Servers   Time factors   Privacy   Mobile edge computing   data integrity checking   privacy-preserving  

摘要： Mobile edge computing (MEC) is proposed as an extension of cloud computing in the scenarios where the end devices desire better services in terms of response time. Because the edges are usually owned by individuals or small organizations with limited operation capabilities, the data on the edges are easily corrupted (due to external attacks or internal hardware failures). Therefore, it is essential to verify data integrity i the MEC. We propose two Integrity Checking protocols for the mobile Edge storage, called ICE-basic and ICE-batch. Our protocols allow a third-party verifier to check the data integrity on the edges without violating users' data privacy and query pattern privacy. We rigorously prove the security and privacy guarantees of the protocols. In addition, we have investigated how to let the end devices cache some verification tags such that the communication cost between end devices and the cloud can be further reduced when a user connects to multiple edges in sequence. We have implemented a proof-of-concept system that runs ICE, and extensive experiments are conducted to evaluate the performance of the proposed protocols. The theoretical analysis and experimental results demonstrate the proposed protocols are efficient both in computation and communication.

关键词： blockchain   BC   federated learning   FL   resource scheduling   mobile edge computing   MEC   multi-task execution   task running time   utility   energy loss   resource utilisation cost   saturation throughput   trust score  

摘要： Federated learning (FL) can improve the privacy-preserving issue of users' IoT devices, in which users complete the local training and transfer the updated model data to the central server for a global update. Due to high latency, the central server-based FL may suffer from huge energy loss at local user devices. MEC-based FL can improve the model accuracy and energy consumption at user devices via edge server-based task execution. Along with FL, blockchain can improve data security via permission-based access. Existing works explored only single type of IoT task without any appropriate resource scheduling for multiple tasks with different preferences, FL, and blockchain operations. This paper provides a merit-based resource scheduling scheme for different tasks with preferences, blockchain, and FL operations by checking resources, deadlines, delays, and resource costs. The simulation results verify that 45% running time and 53% cost gain is achieved in proposed scheme over the baseline schemes.