关键词： Mobile edge computing   Multi-UAV network   Task offloading   Path optimization   Multi-agent deep reinforcement learning  

摘要： To tackle task offloading and path planning challenges in multi-UAV-assisted mobile edge computing, this paper proposes a task offloading and path optimization approach via multi -agent deep reinforcement learning. The primary goal is to minimize the overall energy consumption of the system and improve computational performance. Initially, we established a model for a multi-UAV-assisted mobile edge computing system that centrally manages the UAV network through software -defined networking technology. Subsequently, considering UAV load and fairness in user equipment -related services, we employ the multi -agent deep deterministic policy gradient algorithm to optimize task offloading and UAV path management, aiming at load balancing and reducing overall system energy consumption. Simulation results demonstrate our method's effectiveness in reducing energy consumption and computation latency compared to benchmark algorithms. It ensures system efficiency, stability, and reliability, meeting mobile edge users' service requests while utilizing computing resources efficiently.

关键词： acoustic sensing   mobile edge computing   Parkinson's disease   privacy protection  

摘要： In this article, we propose a mobile edge computing (MEC)-related system named PD-Gait, which can measure gait parameters of Parkinson's disease patients in a contactless and privacy-preserving manner. We utilize inaudible acoustic signals and band-pass filters to achieve privacy data protection in the physical layer. The proposed framework can be easily deployed in the mobile end of MEC, and hence release the edge server in cybersecurity attacks fighting. The gait parameters include stride cycle time length and moving speed, and hence providing an objective basis for the doctors' judgment. PD-Gait utilizes acoustic signals in bands from 16 to 23 kHz to achieve device-free sensing, which would release both doctors and patients from the tedious wearing process and psychological burden caused by traditional wearable devices. To achieve robust measurement, we propose a novel acoustic ranging method to avoid "broken tones" and "uneven peak distribution" in the received data. The corresponding ranging accuracy is 0.1 m. We also propose auto-focus micro-Doppler features to extract robust stride cycle time length, and can achieve an accuracy of 0.052 s. We deployed PD-Gait in a brain hospital and collected data from 8 patients. The total walked distance is over 330 m. From the overall trend, our results are highly correlated with the doctor's judgment.

关键词： Computational modeling   Task analysis   Artificial intelligence   Sensors   Wireless communication   Data models   Wireless sensor networks   Wireless sensing   collaborative inference   model splitting   mobile edge computing  

摘要： With recent advancements in artificial intelligence (AI), wireless sensing has recently been accepted as an attractive solution to enable accurate detection of human activities by analyzing the radio signal variations of sensor devices (SDs) using a well-trained AI model. However, due to the limited communication and computation resources at SDs, it is impractical to support energy- and delay-sensitive sensing services by solely processing the massive computation workload at local or offloading it to the edge server (ES) for edge inference. To address this problem, we consider in this paper device-edge co-inference in a wireless sensing system where multiple users collaboratively perform a common inference task. In particular, the AI model deployed at each SD can be split into two sequential parts. Each SD executes the former part of AI model at local, and leaves the remaining part computed at the ES. We aim to minimize the energy consumption of SDs subject to a prescribed inference latency requirement. To this end, we formulate a mixed integer non-linear programming (MINLP) to jointly optimize the model splitting point and system resource allocation, where the major difficulty lies in the tight couplings among splitting decisions of collaborative SDs. To solve the problem, we propose an integrated learning and optimization algorithm named LOP, which tackles the combinatorial model splitting by using a deep reinforcement learning (DRL)-based method, and deals with the remaining resource allocation problem via convex optimization. To gain some engineering insights, we study the optimal model splitting design in a practical wireless indoor crowd counting system, where the optimal splitting point exhibits a threshold-based structure related to the user channel gain. Simulation results demonstrate that the proposed LOP algorithm can achieve a near-optimal energy performance with on average 0.8% optimality gap while enjoying a hundredfold reduction in computation delay.

关键词： Task analysis   Internet of Things   NOMA   Wireless communication   Servers   Uplink   Array signal processing   Computation offloading   intelligent reflecting surface   mobile edge computing   non-orthogonal multiple access   wireless power transfer  

摘要： To address the power and computation limitations of the Internet-of-Things (IoT) devices, this letter investigates integrating wireless power transfer (WPT) and mobile edge computing (MEC) with intelligent reflective surfaces (IRS) in 6G networks. An optimization problem is formulated to minimize the energy consumption of the access point (AP) for WPT while ensuring timely completion of task offloading by jointly optimizing the transmission power of the AP and IoT devices, and the IRS phase shifts and time allocation for downlink WPT and hybrid non-orthogonal multiple access (NOMA)-based uplink task offloading. By solving the problem, a hybrid NOMA offloading scheme in conjunction with a semi-dynamic IRS beamforming strategy to enhance spectral and energy efficiency. Simulations demonstrate its superiority or OMA-based offloading schemes over pure NOMA or OMA-based offloading schemes.

关键词： satellite edge computing   earth observation satellite   task offloading   mobile edge computing  

摘要： Integrated communication-sensing-computing (ICSC) satellites, which integrate edge computing servers on Earth observation satellites to process collected data directly in orbit, are attracting growing attention. Nevertheless, some monitoring tasks involve sequential sub-tasks like target observation and movement prediction, leading to data dependencies. Moreover, the limited energy supply on satellites requires the sequential execution of sub-tasks. Therefore, inappropriate assignments can cause circular waiting among satellites, resulting in deadlocks. This paper formulates task offloading in ICSC satellites with data-dependent constraints as a mixed-integer linear programming (MILP) problem, aiming to minimize service latency and energy consumption simultaneously. Given the non-centrality of ICSC satellites, we propose a distributed deadlock-free task offloading (DDFTO) algorithm. DDFTO operates in parallel on each satellite, alternating between sub-task inclusion and consensus and sub-task removal until a common offloading assignment is reached. To avoid deadlocks arising from sub-task inclusion, we introduce the deadlock-free insertion mechanism (DFIM), which strategically restricts the insertion positions of sub-tasks based on interval relationships, ensuring deadlock-free assignments. Extensive experiments demonstrate the effectiveness of DFIM in avoiding deadlocks and show that the DDFTO algorithm outperforms benchmark algorithms in achieving deadlock-free offloading assignments.

关键词： Mobile edge computing   Task graph   Computation offloading   Reinforcement learning  

摘要： Various mobile applications that comprise dependent tasks are gaining widespread popularity and are increasingly complex. These applications often have low-latency requirements, resulting in a significant surge in demand for computing resources. With the emergence of mobile edge computing (MEC), it becomes the most significant issue to offload the application tasks onto small-scale devices deployed at the edge of the mobile network for obtaining a high-quality user experience. However, since the environment of MEC is dynamic, most existing works focusing on task graph offloading, which rely heavily on expert knowledge or accurate analytical models, fail to fully adapt to such environmental changes, resulting in the reduction of user experience. This paper investigates the task graph offloading in MEC, considering the time-varying computation capabilities of edge computing devices. To adapt to environmental changes, we model the task graph scheduling for computation offloading as a Markov Decision Process (MDP). Then, we design a deep reinforcement learning algorithm (SATA-DRL) to learn the task scheduling strategy from the interaction with the environment, to improve user experience. Extensive simulations validate that SATA-DRL is superior to existing strategies in terms of reducing average makespan and deadline violation.

关键词： Video caching   Mobile edge computing   Asynchronous federated learning   User mobility  

摘要： With the rapid advance of the Internet of Things (IoT) and the surging user-centric smart devices, video services such as short videos, real -time video streaming, and Virtual Reality (VR) gaming are emerging. Traditional cloud-centric caching is no longer able to satisfy the low-latency needs of mobile user groups. Predicting the most popular content in advance and caching it on edge devices such as small base stations (SBS) is a promising solution to alleviate network congestion and supply better Quality of Service (QoS) for mobile users. However, frequently changing popular content, limited edge caching resources, privacy of user data, and user mobility pose significant challenges to edge video caching. To tackle these challenges, we propose an efficient asynchronous federated learning (AFL)-based mobility-aware proactive video caching scheme (AFMPVC), which significantly improves the overall network performance while preventing the risk of user data leakage. First, we model the proactive video caching problem in mobile edge computing systems and consider the impact of user mobility on AFL, representing the problem as an autoencoder (AE) model loss minimization problem. Then, a proactive video caching algorithm is proposed based on a filtering model, which uses the hidden features obtained from the trained AE model to compute the user similarity to find the popular videos that are most likely to be accessed by users and combine them with the most frequently requested videos. Meanwhile, we take into account the short duration of high mobility users staying on the current SBS and cache some videos to neighboring edge servers to have more redundant space to store more less popular videos. Finally, experiments on three real-world datasets demonstrate that the presented caching scheme outperforms other baseline caching schemes with respect to cache hit rate and latency reduction.

关键词： Ultra reliable low latency communication   Reconfigurable intelligent surfaces   Minimization   Task analysis   Optimization   Computer architecture   Power amplifiers   Active reconfigurable intelligent surface   fully-connected architecture   sub-connected architecture   ultra-reliable and low latency communication   digital twin   alternating optimization   mobile edge computing  

摘要： The substantial power consumption attributed to the active components within fully-connected reconfigurable intelligent surface (RIS) architecture significantly hinders the efficiency and sustainability of DT-enabled MEC networks. To tackle this challenge, we present an innovative sub-connected architecture for active RIS within the digital twin (DT) integrated mobile edge computing (MEC) framework of an Internet-of-Things (IoT) networks, capitalizing on edge intelligence to enhance ultra-reliable and low-latency communication (URLLC) services. The primary aim of our research is to improve uplink data transmission from IoT URLLC user nodes (UNs) to a base station (BS) with the aid of an active RIS, even under an imperfect channel state information (CSI). We have formulated the total end-to-end (e2e) latency minimization problem, which is solved by using an efficient alternating optimization (AO) algorithm. The algorithm breaks down the proposed non-convex problem into five subproblems, namely, beamforming design, caching and offloading policy optimization, joint communication and computation optimization, and joint active RIS phase shift and amplification factor vector optimization. We conducted a thorough analysis of the convergence properties of the proposed AO algorithm, benchmarking its performance against the established Heuristic algorithm. Our simulation results consistently demonstrate the superiority of our proposed DT-assisted optimal phase sub-connected active RIS scheme over various benchmark schemes, taking into account various factors such as the number of RIS elements, power budget constraints, imperfect CSI, edge computing server (ECS) cache capacity, number of IoT UNs, and the number of power amplifiers.

关键词： Vehicular networking   Mobile edge computing   DDPG algorithm   Multi-intelligent body reinforcement learning  

摘要： As a reliable computational model in telematics, Mobile Edge Computing (MEC) is utilized to lighten the load on individual autonomous vehicles. It enables computationally expensive and latency-sensitive jobs to be offloaded from cars with limited processing power to servers at edge nodes. This paper presents the joint optimization problem as a mixed nonlinear integer programming problem. The scheduling of vehicle tasks and the distribution of communication resources are then rationalized using a resource allocation algorithm based on Deep Deterministic Policy Gradient (DDPG) reinforcement learning to arrive at a suboptimal solution based on the resource allocation policies of single-intelligent DDPG and multi-intelligent DDPG. The numerical simulation demonstrates the superior service experience of the proposed joint optimization strategy of distributed task offloading and resource allocation based on multi-intelligent DDPG over other association and channel selection strategies and joint resource allocation based on single-intelligent DDPG.

关键词： mobile edge computing   computing offloading   resource allocation   multi-agent  

摘要： Task offloading and resource allocation is a research hotspot in cloud-edge collaborative computing. Many existing pieces of research adopted single-agent reinforcement learning to solve this problem, which has some defects such as low robustness, large decision space, and ignoring delayed rewards. In view of the above deficiencies, this paper constructs a cloud-edge collaborative computing model, and related task queue, delay, and energy consumption model, and gives joint optimization problem modeling for task offloading and resource allocation with multiple constraints. Then, in order to solve the joint optimization problem, this paper designs a decentralized offloading and scheduling scheme based on "task-oriented" multi-agent reinforcement learning. In this scheme, we present information synchronization protocols and offloading scheduling rules and use edge servers as agents to construct a multi-agent system based on the Actor-Critic framework. In order to solve delayed rewards, this paper models the offloading and scheduling problem as a "task-oriented" Markov decision process. This process abandons the commonly used equidistant time slot model but uses dynamic and parallel slots in the step of task processing time. Finally, an offloading decision algorithm TOMAC-PPO is proposed. The algorithm applies the proximal policy optimization to the multi-agent system and combines the Transformer neural network model to realize the memory and prediction of network state information. Experimental results show that this algorithm has better convergence speed and can effectively reduce the service cost, energy consumption, and task drop rate under high load and high failure rates. For example, the proposed TOMAC-PPO can reduce the average cost by from 19.4% to 66.6% compared to other offloading schemes under the same network load. In addition, the drop rate of some baseline algorithms with 50 users can achieve 62.5% for critical tasks, while the proposed TOMAC-PPO only has