关键词： Edge server placement   Mobile edge computing   Quality of service   Quality of experience   Network resilience   Load balancing   Fault tolerance   K-medoids clustering  

摘要： Mobile edge computing (MEC) enhances quality of service (QoS) by offloading workloads to nearby edge servers (ESs). The performance of MEC networks critically depends on edge server placement (ESP), the strategic decision of where to locate ESs and how to assign base stations (BSs) to them. While many studies focus on minimizing latency, they often overlook network resilience, creating a critical performance gap. Addressing this, our study presents a framework that formulates ESP as a unified optimization problem to simultaneously minimize access delay, balance workload distribution, and provide fault tolerance. To solve this, we propose an efficient two-step heuristic algorithm. Step one determines the optimal physical locations for ESs by applying the K-medoids clustering algorithm to BS traffic data, placing servers at the center of demand clusters to minimize latency. Step two then implements a resilient assignment mechanism: A custom heuristic allocates each BS to both a primary ES for normal operation and hot-backup ES (dual-active) for failover, thereby embedding fault tolerance directly into the network topology. This integrated approach is governed by constraints on ES capacity, latency thresholds, and service-level agreements (SLAs). Extensive experiments using real-world datasets validate our approach. In a unified benchmark conducted on the Shanghai Telecom dataset, comprising 3000 BSs and 300 ESs, our Resilient Edge Server Placement (RESP) method achieves the lowest mean access delay of 0.038, with strong workload balance (variance sigma = 6.8 x 105 requests per minute) and the highest resilience, with a failover success rate (FSR) of 0.90 and a redundancy coverage (RC) of 0.93. This performance approaches the FSR of a robust optimization baseline (0.93) while maintaining a lower delay. Under outage scenarios affecting up to 20% of ESs, RESP sustains an FSR of at least 0.90 for random outages and at least 0.85 for correlated outages, whereas the best co

关键词： age of process information   backtrack decoding delay   information freshness   internet of things   layered superposition coding   mobile edge computing   non-orthogonal multiple access   throughput  

摘要： This work focuses on a mobile edge computing (MEC) assisted IoT status update network with multi-objective sensing, which consists of a wireless sensing network, MEC network, and an information receiver (IR). To simultaneously guarantee information freshness and system throughput, a layer-superposed non-orthogonal multiple access (NOMA) HARQ (LS-NOMA-HARQ) scheme is proposed. In the proposed LS-NOMA-HARQ scheme, an entire status update delivery circle consists of multiple rounds for NOMA symbol feedforward transmission. In each round, the source constructs and transmits a NOMA symbol to the AP that first performs feedforward decoding (FD). Each NOMA symbol includes the newly generated packet, termed the primary packet, and the part of the currently failed packet, termed the secondary one. If the received primary packet can not be correctly recovered by the AP, the NOMA symbol is offloaded to the edge server and stored in the buffer of the edge server. The source continuously generates and sends new NOMA symbols to AP until a successful FD occurs. On the contrary, the decoded result is directly delivered to IR, and backtrack decoding (BD) is triggered at the edge server. Then, the edge server successively decodes the previously stored NOMA symbols by using sophisticated successive interference cancellation (SIC), and delivers the recovered packet to IR. Once SIC-based BD fails, it is declared that a circle of LS-NOMA-HARQ status update delivery completes. Because the primary and secondary packets are independently modulated and superposed in the MAC layer, the proposed LS-NOMA-HARQ outperforms the layer-coded HARQ scheme that is executed in the physical layer. Moreover, this work also considers the two cases of finite and infinite buffer size at the edge server, and truncated HARQ is used for the retransmission of a single NOMA symbol. Under the finite buffer size case, the circle-shift preemption is used at the buffer edge server. The edge service follows exponentia

关键词： Mobile edge computing   Computational offloading   UAV trajectory planning   Reinforcement learning  

摘要： Unmanned Aerial Vehicle (UAV) assisted Mobile Edge Computing (UAV-MEC), with its high flexibility and mobility, enables efficient computing services for terminal devices (TD) in remote or emergency scenarios, with broad application potential. However, current UAV-MEC systems primarily employ binary offloading or basic dual-node partial offloading mechanisms. Although these simplify offloading decisions, they fail to fully exploit heterogeneous resources in dynamic and complex environments. To address this limitation, we propose a collaborative offloading framework based on Deep Reinforcement Learning (DRL), termed the Dual-Agent SAC-Based UAV Trajectory and Task Offloading Optimization (SAC-UTO). This framework incorporates Ground-MEC as an auxiliary computing node, forming a heterogeneous resource pool with UAV-MEC and local devices. The core innovation of SAC-UTO is its dual-agent system with distinct yet complementary roles: Agent 1 manages global strategy to optimize TD task offloading priorities, while Agent 2 dynamically adjusts UAV flight trajectory and task distribution across heterogeneous nodes based on these priorities. By incorporating flight distance and the impact of complex environments into the reward function for system latency, SAC-UTO effectively reduces UAV flight distance while preserving equivalent system latency.

关键词： highly dynamic mobile terminals   mobile edge computing   scalable video coding   resource cooperative optimization   lyapunov optimization  

摘要： Under the typical scenario of high-speed mobility, channel disturbances at the physical layer may disturb the transmission of video base layers. Due to the close dependency of Scalable Video Coding (SVC) on base layers, such disturbances will result in retransmissions and handover delays. Meanwhile, ineffective enhancement layers continue to occupy resources, ultimately causing system performance collapse and further exacerbating physical-layer disturbances. To address this challenge, we propose an edge computing resource coordination optimization scheme for highly dynamic mobile terminals. The scheme first empowers the SVC layered transmission with the local caching capabilities, enabling rapid retransmission of base layer data by employing a Lyapunov optimization framework for transmission queue scheduling. Secondly, we design a strategy for dynamically releasing the enhancement layer (EL) cache. This can mitigate resource waste caused by invalid enhancement layers. Finally, Lyapunov drift optimization is implemented to ensure base layer transmission stability and accelerate system state convergence. Simulation and experimental results demonstrate that the proposed scheme significantly improves video transmission reliability and user experience in highly dynamic network environments.

关键词： computational offloading   Internet of Vehicles   mobile edge computing   resource optimization   unmanned aerial vehicle  

摘要： As an essential element of intelligent trans-port systems,Internet of vehicles(IoV)has brought an immersive user experience ***,the emergence of mobile edge computing(MEC)has enhanced the computational capability of the vehicle which reduces task processing latency and power con-sumption effectively and meets the quality of service requirements of vehicle ***,there are still some problems in the MEC-assisted IoV system such as poor connectivity and high *** aerial vehicles(UAVs)equipped with MEC servers have become a promising approach for providing com-munication and computing services to mobile ***,in this article,an optimal framework for the UAV-assisted MEC system for IoV to minimize the average system cost is *** joint consideration of computational offloading decisions and computational resource allocation,the optimiza-tion problem of our proposed architecture is presented to reduce system energy consumption and *** purpose of tackling this issue,the original non-convex issue is converted into a convex issue and the alternat-ing direction method of multipliers-based distributed optimal scheme is *** simulation results illustrate that the presented scheme can enhance the system performance dramatically with regard to other schemes,and the convergence of the proposed scheme is also significant.

关键词： Mobile edge computing   Unmanned aerial vehicle (UAV)   Task offloading   Trajectory optimization   Resource allocation  

摘要： Integrating Mobile Edge Computing (MEC) with Unmanned Aerial Vehicles (UAVs) offers enhanced coverage and computational support for mobile IoT devices (MIDs). Due to the inherent computational capacity and energy constraints of UAV, existing UAV-assisted MEC systems struggle to satisfy computation-intensive network services for numerous MIDs. To address this issue, this paper proposes a UAV and ground-based Edge Cloud (EC) collaboration MEC system. Specifically, the EC is equipped with an energy transmitter to provide wireless power transfer (WPT) to the UAV, thereby collaboratively managing backlog tasks in scenarios where task and energy arrive stochastically. To achieve optimal service delivery, we formulate a long-term stochastic optimization problem aiming to jointly optimize UAV energy consumption and system throughput while ensuring task queue stability. However, this NP-hard problem posed by the stochastic nature of task arrivals and energy constraints, we develop an online offloading and trajectory control (OOTC) algorithm. This algorithm uses Lyapunov optimization theory to transform the long-term stochastic optimization problem into a deterministic per-slot optimization problem. The OOTC algorithm decouples control decisions across consecutive time slots, reducing computational complexity and ensuring queue stability without relying on statistical knowledge. We further decompose it into three subproblems, and derive suboptimal solutions by the Successive Convex Approximation (SCA) and the Lagrangian duality. Simulations show OOTC algorithm outperforms benchmarks and maintains stability.

关键词： Cloud server   Digital twin   Deep neural network   Mobile edge computing   Optimization   Multi-layer networks   Unmanned aerial vehicles  

摘要： The sixth-generation (6G) wireless networks are expected to provide ubiquitous connectivity, high data rate, low latency, energy efficiency, and edge intelligence for Internet of Things (IoT) applications. Digital twin technology is a promising solution to enable multi-layer wireless networks that incorporate IoT devices on the ground, unmanned aerial vehicles (UAVs) as mobile edge computing (MEC) servers, and cloud servers. Multi-layer processing can handle time-sensitive and computationally intensive tasks from IoT devices. This paper proposes a digital twin-assisted multi-layer network for low-latency and energy-efficient communication and computation. We mathematically formulate an optimization problem to minimize the latency and energy consumption of IoT devices by optimizing their association with the UAV-MECs, computation resources, communication resources, and offloading portions of tasks. We propose a two-stage scheme based on the Kmeans method and the deep neural network approach to solve the above optimization problem. We compare the proposed two-stage scheme with existing schemes to highlight the scalability of the proposed solution. Simulation results demonstrate that the proposed multi-layer network achieved optimization results comparable to existing schemes with less computational cost, highlighting its usefulness in achieving low latency and energy-efficient computation and communication.

关键词： Mobile edge computing   IoT   robust MDP  

摘要： With the advent of Mobile Edge Computing (MEC), the arriving tasks in an Internet of Things (IoT) network can be executed locally or at an MEC server. A Constrained Markov Decision Process (CMDP) formulation can capture the trade-off between computation time and power consumption. However, the optimal policy obtained by solving the CMDP problem may be sensitive to the changes in the task arrival rate. Moreover, there may be constraint violations. To address these issues, in this paper, we provide a Robust Return Robust CMDP (R3CMDP) formulation that minimizes the worst-case total discounted power consumption subject to a constraint on the worst-case total discounted deadline violations. Based on robust Dynamic Programming (DP) methods, we propose a task allocation algorithm that provably provides the optimal R3C policy. We also establish that the proposed algorithm incorporates robustness into the MDP framework with almost no additional complexity. Furthermore, we propose a low-complexity robust heuristic that can be implemented online, unlike the former algorithm. The proposed algorithms are implemented in a Network Simulator-3 (ns-3) based IoT simulation package. Numerical and simulation results establish that the proposed algorithms are more robust compared to the state-of-the-art algorithms in the face of varying task arrival rates.

关键词： Unmanned aerial vehicle   mobile edge computing   trajectory design   reconfigurable intelligence surface   reconfigurable intelligence surface   Lyapunov   Lyapunov   Lyapunov  

摘要： We consider a mobile edge computing (MEC) framework empowered by unmanned aerial vehicle (UAV) and reflecting intelligent surface (RIS) serving multiple ground users in a practical environment, where mobile ground users generate movements and tasks randomly. Our objective is to optimize energy efficiency while ensuring long-term data queue stability, assuming knowledge of the channel state information. The problem is formulated as a stochastic optimization problem, and the Lyapunov method is applied to convert the initial problem into per-slot problems. Without the future knowledge of user movement, we consider the outage constraint into the per-slot problem to derive robust resource allocation and trajectory design in the MEC system. For each per-slot problem, an alternating optimization algorithm utilizing successive convex approximation technique is designed to solve it. This solution guarantees adherence to the UAV energy budget constraint while achieving a balance between system energy efficiency and the length of the queue backlog. Simulation results demonstrate that the proposed algorithm achieves better performance than other benchmark methods in terms of improving energy efficiency and maintaining queue stability.

关键词： Integrated sensing and communication   Integrated sensing and communication   mobile edge computing   mobile edge computing   computation offloading   computation offloading   beamforming design   beamforming design  

摘要： Mobile edge computing (MEC) is powerful in alleviating the heavy computing tasks in integrated sensing and communication (ISAC) systems. In this paper, we investigate joint beamforming and offloading design within a three-tier integrated sensing, communication, and computation (ISCC) framework comprising one cloud server, multiple mobile edge servers, and multiple terminals. While executing sensing tasks, the user terminals can optionally offload sensing data to either MEC servers or the cloud server. To minimize the execution latency, we jointly optimize the transmit beamforming vectors and offloading decision variables under the constraint of sensing performance. An alternating optimization algorithm based on fractional programming is proposed to tackle the non-convex problem. Simulation results demonstrate the superiority of the proposed mechanism in terms of convergence and task execution time reduction, compared with the state-of-the-art two-tier ISCC framework.