关键词： Internet of Things   Servers   Delays   Collaboration   Energy consumption   Cloud computing   Device-to-device communication   Training   Codes   Redundancy   Mobile edge computing   peer offloading   eliminate redundancy   spatial index   adaptive search   stable matching  

摘要： Mobile Edge Computing (MEC) and Device-to-Device (D2D) peer offloading are two promising paradigms in the mobile Internet of Things (IoT). In this paper, we study the collaborative task offloading with redundant data and codes in large-scale IoT networks, where computing resource-starved IoT devices can offload their tasks to MEC servers via cellular links or to nearby peer devices (PDs) with idle resources through D2D links for execution. IoT tasks usually consist of a series of dependent and parallel subtasks, and the difficulties in current research are (i) how to eliminate redundancy in data or codes between subtasks, and (ii) how to leverage previous experience to adaptively search a set of collaborative MEC servers and PDs for matching offloading of dependent and parallel subtasks. From this, we propose a redundancy-aware adaptive search offloading (RASO) method based on the deep Q-network (DQN). Specifically, we first design a fine-grained task recombination scheme by judging the consistency of subtask data and codes. After that, we organize the global devices into a spatial index MP-tree to reduce the search solution space, and propose a fast adaptive search method based on the DQN combined with MP-tree, where optimal path-guiding parameters training of inner and outer layers is involved to efficiently help achieve collaborative devices to complete specific tasks with the same type. After finding the collaborative MEC servers and PDs along MP-tree for a certain task, a centralized stable matching algorithm is further developed to give a decision of offloading each of its divided dependent and parallel subtasks to the matched one, thereby optimizing offloading delay and energy consumption. Extensive simulation results show that compared to other counterpart solutions, our proposed method has improved task offloading performance in terms of delay and energy consumption.

关键词： Autonomous aerial vehicles   Servers   Jamming   Optimization   Delays   Security   Wireless communication   Communication system security   Performance evaluation   Computational efficiency   Mobile edge computing   unmanned aerial vehicles   cooperative jamming   service placement   deep reinforcement learning  

摘要： Unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) has emerged as a promising solution to support wireless devices' computation-intensive services in the absence of terrestrial infrastructures. Nevertheless, the heterogeneous nature of MEC services and the security vulnerability of wireless channels present significant challenges to achieving efficient and secure computation offloading. In this paper, we investigate a multi-UAV-assisted MEC network in which wireless devices need to process diverse computation tasks. The devices can perform local computing or offload their computation tasks to UAV servers that have pre-cached relevant service programs in the presence of eavesdroppers. To facilitate secure service provisioning, we propose a cooperative jamming-based scheme in which a UAV jammer transmits jamming signals to interfere with eavesdroppers during devices' computation offloading processes. Taking into account UAV servers' constrained caching spaces and secure offloading requirements, we minimize the total task completion delay of devices by jointly optimizing multi-UAV deployment, task offloading decisions, service placement, UAV jammer's transmit power, and devices' transmit power. To tackle the formulated mixed-integer nonlinear programming problem, we design an optimization-embedding multi-agent twin delayed deep deterministic policy gradient (OE-MATD3) algorithm. Specifically, the MATD3 approach is leveraged to deal with optimization variables concerning UAVs, while a closed-form solution for devices' transmit power is derived and guides MATD3-based decision-making. Simulation results demonstrate that the proposed scheme outperforms baselines in terms of devices' task completion delay.

关键词： Mobile edge computing   Spectrum resource allocation   Internet of vehicles   Data prioritization   Multi-agent deep reinforcement learning  

摘要： Intelligent transportation systems (ITS) are facing the limitation of spectral resources and stringent real time communication requirements. How to effectively allocate system resources for maximizing the performance in Internet of Vehicles (IoV) is still a substantial challenge, particularly the priority and urgency of different types of data need to be focused. To improve the allocation spectrum resources and optimize transmission power while taking into the dynamic characteristics of vehicles and data priorities account, we design a time-series-based multi-agent deep reinforcement learning framework (NL-MAPPO for short), in this paper. First, we formulate the joint optimization problem as a multi-agent Markov decision process to ensure the minimization of transmission delays and energy consumption when the total vehicle-to-vehicle (V2V) link capacity is maximized. Here, V2V link capacity refers to the maximum achievable data rate for direct communication between vehicles, which depends on factors such as signal strength, interference, and available bandwidth. Then, we design a multi-agent resource allocation algorithm based on a shared-critic mechanism to realize the global sharing of channel information and solve the optimization problem. Finally, to improve efficiency, we also introduce a time series-based channel information extraction mechanism to capture the temporal characteristics of channel information. The simulation experiments were conducted and the results demonstrated that our proposed NL-MAPPO can demonstrate superiority in multiple metrics.

关键词： Blockchain   Oracle machine   Key management   Mobile Internet of things   Mobile edge computing  

摘要： To solve the issue of perception data trusted cochian in the external IoT edge server of the blockchain system, as well as the high costs of computing, communication, and storage in the process of perception data trusted onchian, an edge computing key management approach based on oracle machine or perception data trusted cochian (OMECKM) was proposed. A key pre-distribution strategy based on key priority was designed to calculate the key priority of smart devices. It comprehensively measures three factors such as geographical distance proximity, network connection bandwidth, and spatial mobility of IoT users over time. By saving keys with higher key priority, the probability of adjacent smart devices obtaining the same key was improved. This improves the connectivity of the external edge network of the blockchain and the performance of smart devices in resisting capture. A sub-edge network key management system model based on oracle machine was designed. It can quickly verify the smart devices who have moved into the sub-edge network and resisting double consumption attacks in the blockchain network. Compared with the other four blockchain key management approaches based on mobile edge computing, the OMECKM approach reduces the costs of calculation, communication and storage.

关键词： Multi-Unmanned Aerial Vehicle   Mobile edge computing   Offloading scheduling   Differential evolution   Greedy algorithm  

摘要： Multi-Unmanned Aerial Vehicle (UAV)-supported Mobile Edge Computing (MEC) can meet the computational requirements of tasks with high complexity and latency sensitivity to compensate for the lack of computational resources and coverage. In this paper, a multi-user and multi-UAV MEC networks is built as a two-tier UAV system in a task-intensive region where base stations are insufficient, with a centralized top-center UAV and a set of distributed bottom-UAVs providing computing services. The total energy consumption of the system is minimized by jointly optimizing the task offloading decision, 3D deployment of two-tier UAVs, the elevation angle of the bottom UAV, the number of UAVs, and computational resource allocation. To this end, an algorithm based on Differential Evolution and greedy algorithm with the objective of minimizing Energy Consumption (DEEC) is proposed in this paper. The algorithm uses a two-tier optimization framework where the upper tier uses a population optimization algorithm to solve for the location and elevation angle of the bottom UAV and the number of UAVs based on the actual ground equipment and the lower tier uses clustering and greedy algorithms to solve for the position of the top UAV, the offloading decision of the task, and the allocation of computational resources based on the results of the upper layer. The simulation results show that the algorithm effectively reduces the total energy consumption of the system while satisfying the task computation success rate and time delay.

关键词： Signal processing   Costs   Relays   Energy consumption   Resource management   Power systems   Collaboration   Computer architecture   Servers   Cloud computing   signal processing   mobile edge computing   non-orthogonal multiple access   resource allocation  

摘要： This brief presents a signal processing system and its optimize control strategy for power systems. The proposed approach addresses the challenges of increased computational energy consumption and unbalanced processing task allocation. Firstly, a joint edge collaboration and relay assistance signal processing system architecture for power systems is proposed to address the issue of uneven signal processing task allocation. Secondly, a five-slot signal transmission architecture based on non-orthogonal multiple access technology is proposed, along with a method for representing signal processing costs, which solves the problem of signal processing cost quantification. Thirdly, a collaboration assistance computing and resource allocation algorithm is proposed to minimize signal processing costs. Finally, the proposed signal processing system is tested on a power system in China. The results demonstrate that it effectively mitigates the uneven resource allocation issue while significantly reducing signal processing costs.

关键词： Intelligent reflecting surface   Mobile edge computing   Cooperative offloading   Wireless energy transfer   Alternating optimization approach  

摘要： The intelligent reflecting surface (IRS) can intelligently control the wireless communication environment to improve communication quality and wireless power transfer (WPT) technology enables wireless devices powered over the air by dedicated wireless power transmitters for communications. Hence, the integration of IRS and WPT is a promising technology to provide sustainable energy supply and improve computing service in mobile edge computing (MEC) system. Inspired by this, we consider an IRS assisted wireless power transfer-mobile edge computing (WPT-MEC) system, where a hybrid access point (HAP) with server is deployed to charge the low-power wireless devices for sustainable energy supply by the radio-frequency (RF) signal. And the cooperative offloading scheme is adopted in such a system to improve the computation performance of wireless devices, in which the surrounding near devices with idle computation resource are enabled as helpers to use the harvested energy help far device execute computing tasks. All the devices are subject to the energy balance constraints, thus their energy consumption does not exceed the harvested energy from HAP. Under above setup, we maximize the total computation bits for all devices by jointly optimizing the time allocation, power allocation, local computing capability and IRS phase shift matrices during charging process and offloading process. Then, the non-convex optimization problem is transformed into four sub-problems solved by alternative optimization approach. Numerical results show that the IRS assisted WPT-MEC system with cooperation offloading scheme can significantly improve the performance of MEC system.

关键词： Mobile edge computing   Optimization algorithm   LEO satellite network   Game theory   Task offloading and resource allocation  

摘要： With the rapid growth of Internet of Things (IoT) devices, Mobile Edge Computing (MEC) faces challenges in meeting increasing computational demands, especially in resource-constrained environments. To address this issue, we propose the LEO Satellite-MS-RSU Edge Computing (LMREC) framework, which integrates Mobile Servers (MSs), Low Earth Orbit (LEO) satellite networks, and Roadside Units (RSUs) into an innovative edge computing architecture. We first introduce "attraction" and "repulsion" metrics to model the willingness of vehicular satellite servers to serve specific users. Subsequently, we design a Magnetic Equilibrium Algorithm (MEA), which dynamically adjusts the MS deployment and service allocation by balancing user-driven attraction and server repulsion. To address the latency sensitivity of task scheduling and user satisfaction in LMREC, we formulate a mixed-integer nonlinear programming (MINLP) optimization problem for task offloading and resource allocation. Since this optimization problem is intractable to solve in polynomial time, we propose a Magnetic Domain Migration Algorithm (MDMA) to obtain a near-optimal solution. In MDMA, tasks are modeled as magnetic domains migrating in a magnetic field, and the optimization problem is decomposed into subproblems, which are solved using Exact Potential Game Theory, convex optimization, and a hybrid genetic algorithm. Finally, simulation results validate the effectiveness of the LMREC framework, demonstrating its superiority over existing methods and its potential to enhance collaboration among end devices, RSUs, and LEO satellite networks.

关键词： Mobile edge computing   Dynamic computational window optimization   UAV deployment   Energy consumption optimization  

摘要： Unmanned aerial vehicles (UAVs) are viewed as a potential technology for handling user offloading duties as edge nodes. With their adaptable qualities, UAVs may be quickly deployed to useful locations and service consumers. However, the inability of UAVs to operate continuously for an extended time is a challenge for the current UAV-assisted mobile edge computing solutions. We put forth an optimization problem that involves the dynamic division of computational windows for UAVs, the optimization of user grouping and user transmission power, and the optimization of UAV deployment locations to save energy. We design a Communication-Computing Resource Scheduling with Dynamic computational Window allocation (CCRS-DW) algorithm to realize the problem decomposition and optimization. Specifically, the K-means clustering technique and the bisection search are used to tackle this problem. Simulation results show that the energy consumption of the proposed CCRS-DW scheme is significantly lower than that of other benchmark schemes.

关键词： Training   Noise   Privacy   Wireless communication   Data models   Protection   Games   Federated learning   Convergence   Computational modeling   Mobile edge computing   FL   DP   incentive mechanism   fairness  

摘要： As a distributed machine learning method, federated learning (FL) can collaboratively train a global model with multiple devices without sharing the original data, thus protecting certain privacy. However, due to the strong heterogeneity of edge nodes (ENs) participating in FL, the quality of data uploaded to the parameter server (PS) varies significantly. Without an appropriate incentive mechanism, low-quality contributors may receive disproportionately high rewards, while high-quality contributors may lack sufficient motivation, leading to inefficient participation and suboptimal global model performance. Consequently, it is critical to develop an effective incentive mechanism to promote fairness for the FL process. To address the issues of existing FL incentive mechanisms lacking privacy protection performance analysis, we propose a fairness-aware incentive mechanism for multi-server FL in edge-enabled wireless differential privacy (DP) networks. Specifically, the wireless channel noise is used to provide DP protection for the local model gradients uploaded by ENs. Next, the interaction between the PSs and ENs is modeled as a Stackelberg game. Furthermore, we solve the Stackelberg game process using backward induction and theoretically propose optimal strategies for both the PSs and ENs. Finally, extensive numerical simulations using real datasets demonstrate the superior performance of our theoretical analysis of the proposed scheme.