关键词： PV system   WECS   ESS   edge computing enabled IoT   sharded blockchain  

摘要： An innovative cutting-edge smart grid system that integrates Internet of Things (IoT) with Blockchain technology is presented in this work. The smart grid harnesses the potential of Renewable Energy Sources (RESs) like Photovoltaic (PV) system and Wind Energy Conversion System (WECS) to meet the growing energy demands. The Edge computing enabled IoT technology and sharded blockchain is proposed to strengthen the functionality of smart grid. Moreover, the IoT infrastructure enables seamless communication and coordination between various grid components, facilitating optimal energy generation, consumption, and management. Furthermore, implementation of sharded blockchain architecture ensures secure and transparent transactions within smart grid ecosystem. The proposed smart grid system represents significant advancement in renewable energy integration, combining PV, WECS, and Energy Storage System (ESS) with edge computing enabled IoT and sharded blockchain. Through rigorous simulation and analysis, the remarkable advantages of these innovations are quantified. The sharded blockchain integration enhances transaction throughput by 300%, achieving 5000 transactions per second, while reducing confirmation latency to 3 s. It maintains 99.9% data integrity, achieves 98% privacy compliance via encryption, and exhibits robust resilience against cyber threats, securing energy-related data in the smart grid ecosystem.

关键词： Mobile edge computing  

摘要： The concept of the Metaverse has garnered growing interest from both academic and industry circles. The decentralization of both the integrity and security of digital items has spurred the popularity of play-to-earn (P2E) games, where players are entitled to earn and own digital assets which they may trade for physical-world currencies. However, these computationally-intensive games are hardly playable on resource-limited mobile devices and the computational tasks have to be offloaded to an edge server. Through mobile edge computing (MEC), users can upload data to the Metaverse Service Provider (MSP) edge servers for computing. Nevertheless, there is a trade-off between user-perceived in-game latency and user visual experience. The downlink transmission of lower-resolution videos lowers user-perceived latency while lowering the visual fidelity and consequently, earnings of users. In this paper, we design a method to enhance the Metaverse-based mobile augmented reality (MAR) in-game user experience. Specifically, we formulate and solve a multi-objective optimization problem. Given the inherent NP-hardness of the problem, we present a low-complexity algorithm to address it, mitigating the trade-off between delay and earnings. The experiment results show that our method can effectively balance the user-perceived latency and profitability, thus improving the performance of Metaverse-based MAR systems. Copyright © 2023, The Authors. All rights reserved.

关键词： Mobile edge computing   Latency   Offloading reliability   Task offloading   Niching genetic algorithm  

摘要： In the field of mobile edge computing (MEC) research, many studies under common research scenarios focus on the optimization of energy consumption and processing delay. Meanwhile, some researchers only discussed the task offloading of terminals to a single server, without considering the collaborative execution of task offloading by multiple servers. Motivated by their observations, this paper conducted research in the scenario of offloading tasks from a single terminal to multiple edge servers. We formulated the multi-objective optimization problem of latency and offloading reliability, and proposed an intensive task offloading strategy based on the Sequential Waiting Model(SWM). The scheme allocates tasks according to the wireless channel environment and the computing power of the server. Besides, this approach minimized the combined cost consisting of latency and offloading failure probability. Since this kind of optimization problem has been proved to be an NP-hard problem. And we designed a Niching Preservation Genetic Algorithm (NPGA), which is based on Niching Genetic Algorithm (NGA). Besides, to obtain better system performance, we simulated the influence of different computing factors on the proposed algorithm NPGA and analyzed the convergence of the NPGA. Finally, the simulation results illustrated the proposed algorithm NPGA can effectively reduce the total latency of task completion and the probability of task offloading failure. Compared with the most advanced algorithms in the previous literature, the cost function value of the combined latency and offloading failure probability reduces 2%-16%. At the same time, we conducted simulation experiments using real base station locations, and also verified that the NPGA algorithm can achieve lower cost values under multiple edge server counts.

关键词： computation offloading   mobile edge computing   non-orthogonal multiple access   resource allocation  

摘要： Mobile Edge Computing(MEC)is a technology for the fifth-generation(5G)wireless communications to enable User Equipment(UE)to offload tasks to servers deployed at the edge of ***,taking both delay and energy consumption into consideration in the 5G MEC system is usually complex and ***-orthogonal multiple access(NOMA)enable more UEs to offload their computing tasks to MEC servers using the same spectrum resources to enhance the spectrum efficiency for 5G,which makes the problem even more complex in the NOMA-MEC *** this work,a system utility maximization model is present to NOMA-MEC system,and two optimization algorithms based on Newton method and greedy algorithm respectively are proposed to jointly optimize the computing resource allocation,SIC order,transmission time slot allocation,which can easily achieve a better trade-off between the delay and energy *** simulation results prove that the proposed method is effective for NOMA-MEC systems.

关键词： Internet of medical things   Mobile cloud computing   Mobile edge computing   Network function virtualization   Software-defined network  

摘要： A typical healthcare system needs further participation with patient monitoring, vital signs sensors and other medical devices. Healthcare moved from a traditional central hospital to scattered patients. Healthcare systems receive help from emerging technology innovations such as fifth generation (5G) communication infrastructure: internet of things (IoT), machine learning (ML), and artificial intelligence (AI). Healthcare providers benefit from IoT capabilities to comfort patients by using smart appliances that improve the healthcare level they receive. These IoT smart healthcare gadgets produce massive data volume. It is crucial to use very high-speed communication networks such as 5G wireless technology with the increased communication bandwidth, data transmission efficiency and reduced communication delay and latency, thus leading to strengthen the precise requirements of healthcare big data utilities. The adaptation of 5G in smart healthcare networks allows increasing number of IoT devices that supplies an augmentation in network performance. This paper reviewed distinctive aspects of internet of medical things (IoMT) and 5G architectures with their future and present sides, which can lead to improve healthcare of patients in the near future. © 2023 Institute of Advanced Engineering and Science. All rights reserved.

关键词： Mobile edge computing   Non-orthogonal multiple access   Unmanned aerial vehicle   Electric vehicle  

摘要： In this paper, we studied a collaborative mobile edge computing(MEC) system based on unmanned aerial vehicle(UAV) and electric vehicle(EV), in which the EV moves to the target area carrying the UAV to provide computing and offloading services to the user equipment(UE). We aimed at minimizing all the UE tasks delay by optimizing the task offloading ratio, the UAV hovering position, and the computing resource allocation of EV and UAV, respectively. The problem was formulated as a Nonlinear Programming(NLP) problem, and we decomposed it into EV related subproblem and UAV related subproblem by the Block-Coordinate Descent(BCD) method. For EV related subproblem, we obtained the optimal offloading ratio by making the computing time on the UAV equal to its offloading time, and proved the method is feasible. For the UAV related subproblem, we introduced the non-orthogonal multiple access(NOMA) and successive interference cancellation(SIC) techniques to improve the communication efficiency, and we obtained the optimal hovering position by using the successive convex approximation(SCA) technique twice to transfer this subproblem into a convex problem. Finally, an overall iterative algorithm was proposed. To verify the effectiveness of our algorithm, we compared our scheme with 'PSO', 'GT', 'MC' and benchmark algorithm.

关键词： Mobile edge computing   Computation offloading   Multiple priority tasks   Simulated annealing algorithm   Evaluation value   Energy consumption model   Energy efficient  

摘要： To solve the problem of insufficient energy consumption for multi-priority tasks in the computing and unloading environments of mobile devices, we designed evaluation methods to improve the traditional simulated annealing algorithm and obtain the optimal allocation scheme for these tasks under multiple computing resources. Firstly, we proposed the task model and discussed the classification and definition of task priority as well as the procedure of task processing in detail. Secondly, a calculation model for the energy consumption of mobile devices is provided. Thirdly, an evaluation mechanism is designed to evaluate the unloading distribution scheme of mobile devices effectively. Finally, the improved traditional simulated annealing algorithm was applied to find the optimal distribution scheme for this computing environment. All allocation schemes obtained in this study were compared and analyzed via simulation. The results showed that the proposed algorithm can reduce the energy consumption of mobile devices more significantly, shorten the system response time, and complete high-priority tasks based on time constraints.

关键词： Task analysis   Internet of Things   Array signal processing   Backscatter   Resource management   Computational efficiency   Energy efficiency   Multi-access edge computing   Reconfigurable intelligent surfaces   Symbiotic radio   mobile edge computing   reconfigurable intelligent surface   computation energy efficiency  

摘要： This paper studies symbiotic radio (SR)-enabled computation offloading with the aid of the reconfigurable intelligent surface (RIS) for ultra-low-power or even battery-free Internet-of-Things (IoT). Specifically, multiple IoT devices can passively modulate their small-size computation tasks over computation offloading transmission of the primary transmitter (PTx) by backscattering. The multi-antenna base station integrated with the mobile edge computing (MEC) server conducts joint decoding to separate and recover data of computation tasks from the PTx and IoT devices. Our target is to maximize the computation energy efficiency (CEE) of the PTx while guaranteeing IoT devices' computation requirements under PTx's resource constraints. The passive beamforming of the RIS, the receive beamforming at the receiver, as well as the local computing frequency and transmission power of the PTx, are jointly optimized. We utilize the Dinkelbach algorithm to transform the problem into a tractable form and then use the AO technique to decouple it into several subproblems, which are solved by convex optimization methods. Simulation results show that with our algorithm, not only the task offloading of IoT devices can be supported effectively and incidentally in the RIS-enhanced SR system, but also the CEE of the PTx is improved significantly.

关键词： Mobile edge computing   Unmanned aerial vehicle   Intelligent reflecting surfaces   Deep reinforcement learning   Physical layer security  

摘要： The deployment of intelligent reflecting surfaces (IRS) on dynamically moving unmanned aerial vehicles (UAVs) can enhance the communication performance of mobile edge computing (MEC), improve the system flexibility, and alleviate eavesdropping on air-ground channels. In this paper, an IRS-equipped unmanned aerial vehicle (UAV)-assisted secure MEC network is proposed. By jointly optimizing the Relay-UAV stopping point, IRS-UAV stopping point, IRS reflection coefficients and the task offloading ratio, the objective of our proposed optimization scheme is to minimize the transmission delay and computing delay while considering the secure transmission performance. To solve this non-convex optimization problem with coupled variables, we propose an intelligent optimization algorithm based on dueling double deep Q networks (D3QN)-deep deterministic policy gradient (DDPG) that can efficiently explore the trajectories and a great number of the IRS reflection elements. Simulation results demonstrate that the intelligent algorithm exhibits good convergence and our proposed scheme can achieve a good balance between system consumption and secrecy rate.

关键词： Task analysis   Cloud computing   Resource management   Energy consumption   Wireless communication   Logic gates   Collaboration   Collaborative task offloading   mobile edge computing   distributed wireless backhaul  

摘要： Collaborative cloud-edge-end computing is a promising solution to support computation-intensive and latency-sensitive tasks by utilizing rich computing resources of cloud datacenters and low access delay of mobile edge computing (MEC) servers. Compared with traditional cloud computing and MEC, the cloud-edge environment has a stronger heterogeneity of servers and networks, resulting in significant differences between servers in the computation speed and access delay. However, few studies on cloud-edge-end task offloading focused on the characteristic of 5G heterogeneous networks in the cloud-edge environment. In this paper, we study the task offloading problem for collaborative cloud-edge-end computing in MEC-enabled small cell networks with low-cost distributed wireless backhaul. We aim to minimize the energy consumption of all user devices (UDs) via jointly optimizing the offloading decision, UDs' transmission power, and the allocation of spectrum and computation resources. To solve the non-convex problem, we decouple the original problem into three subproblems, and design an efficient method with solving these three subproblems iteratively to obtain a high-quality solution. The simulation results indicate that our proposed method can lead to significant reduction in the energy consumption of all UDs compared with other conventional methods.