Search Results (506)

Diabetic foot ulcers (DFUs) are a serious complication of diabetes, associated with high recurrence and amputation rates. Adherence to offloading devices is critical for wound healing but remains inadequately monitored in real-world settings. This study evaluates the SmartBoot edge-computing system—a wearable, real-time remote monitoring solution integrating an inertial measurement unit (Sensoria Core) and smartwatch—for its validity in quantifying cadence and step count as digital biomarkers of frailty, and for detecting adherence. Twelve healthy adults wore two types of removable offloading boots (Össur and Foot Defender) during walking tasks at varied speeds; system outputs were validated against a gold-standard wearable and compared with staff-recorded adherence logs. Additionally, user experience was assessed using the Technology Acceptance Model (TAM) in healthy participants (n = 12) and patients with DFU (n = 81). The SmartBoot demonstrated high accuracy in cadence and step count across conditions (bias < 5.5%), with an adherence detection accuracy of 96% (Össur) and 97% (Foot Defender). TAM results indicated strong user acceptance and perceived ease of use across both cohorts. These findings support the SmartBoot system’s potential as a valid, scalable solution for real-time remote monitoring of adherence and mobility in DFU management. Further clinical validation in ongoing studies involving DFU patients is underway. Full article

This paper presents a novel framework for optimizing multimodal large language model (MLLM) inference through task offloading and resource allocation in UAV-assisted satellite edge computing (SEC) networks. MLLMs leverage transformer architectures to integrate heterogeneous data modalities for IoT applications, particularly real-time monitoring in remote areas. However, cloud computing dependency introduces latency, bandwidth, and privacy challenges, while IoT device limitations require efficient distributed computing solutions. SEC, utilizing low-earth orbit (LEO) satellites and unmanned aerial vehicles (UAVs), extends mobile edge computing to provide ubiquitous computational resources for remote IoTDs. We formulate the joint optimization of MLLM task offloading and resource allocation as a mixed-integer nonlinear programming (MINLP) problem, minimizing latency and energy consumption while optimizing offloading decisions, power allocation, and UAV trajectories. To address the dynamic SEC environment characterized by satellite mobility, we propose an action-decoupled soft actor–critic (AD-SAC) algorithm with discrete–continuous hybrid action spaces. The simulation results demonstrate that our approach significantly outperforms conventional deep reinforcement learning methods in convergence and system cost reduction compared to baseline algorithms. Full article

Soft continuum robots are constructed from soft and compliant materials and can provide high flexibility and adaptability to various applications. They have theoretically infinite degrees of freedom (DOFs) and can generate highly nonlinear behaviors, which leads to challenges in accurately modeling and controlling their deformation, compliance, and behaviors. Inspired by animals, embodied intelligence utilizes physical bodies as an intelligent resource for information processing and task completion and offloads the computational cost of central control, which provides a unique approach to understanding and modeling soft robotics. In this study, we propose a theoretical framework to explain and guide distributed sensing enabled embodied intelligence for soft finger manipulation from a physics-based perspective. Specifically, we aim to provide a theoretical foundation to guide future sensor design and placement by addressing two key questions: (1) whether and why the state of a specific material point such as the tip trajectory of a soft finger can be predicted using distributed sensing, and, (2) how many sensors are sufficient for accurate prediction. These questions are critical for the design of soft and compliant robotic systems with embedded sensing for embodied intelligence. In addition to theoretical analysis, the study presents a feasible approach for real-time trajectory prediction through optimized sensor placement, with results validated through both simulation and experiment. The results showed that the tip trajectory of a soft finger can be predicted with a finite number of sensors with proper placement. While the proposed method is demonstrated in the context of soft finger manipulation, the framework is theoretically generalizable to other compliant soft robotic systems. Full article

The rapid increase in the deployment of Internet of Things (IoT) sensor networks has led to an exponential growth in data generation and an unprecedented demand for efficient resource management infrastructure. Ensuring end-to-end communication across multiple heterogeneous network domains is crucial to maintaining Quality of Service (QoS) requirements, such as low latency and high computational capacity, for IoT applications. However, limited computing resources at multi-access edge computing (MEC), coupled with increasing IoT network requests during task offloading, often lead to network congestion, service latency, and inefficient resource utilization, degrading overall system performance. This paper proposes an intelligent task offloading and resource orchestration framework to address these challenges, thereby optimizing energy consumption, computational cost, network congestion, and service latency in dynamic IoT-MEC environments. The framework introduces task offloading and a dynamic resource orchestration strategy, where task offloading to the MEC server ensures an efficient distribution of computation workloads. The dynamic resource orchestration process, Service Function Chaining (SFC) for Virtual Network Functions (VNFs) placement, and routing path determination optimize service execution across the network. To achieve adaptive and intelligent decision-making, the proposed approach leverages Deep Reinforcement Learning (DRL) to dynamically allocate resources and offload task execution, thereby improving overall system efficiency and addressing the optimal policy in edge computing. Deep Q-network (DQN), which is leveraged to learn an optimal network resource adjustment policy and task offloading, ensures flexible adaptation in SFC deployment evaluations. The simulation result demonstrates that the DRL-based scheme significantly outperforms the reference scheme in terms of cumulative reward, reduced service latency, lowered energy consumption, and improved delivery and throughput. Full article

The rapid growth of ultra-dense mobile edge computing (UDEC) in 5G IoT networks has intensified energy inefficiencies and latency bottlenecks exacerbated by dynamic channel conditions and imperfect CSI in real-world deployments. This paper introduces POTMEC, a power optimization framework that combines a channel-aware adaptive power allocator using real-time SNR measurements, a MATLAB-trained RL model for joint offloading decisions and a decaying step-size algorithm guaranteeing convergence. Computational offloading is a productive technique to overcome mobile battery life issues by processing a few parts of the mobile application on the cloud. It investigated how multi-access edge computing can reduce latency and energy usage. The experiments demonstrate that the proposed model reduces transmission energy consumption by 27.5% compared to baseline methods while maintaining the latency below 15 ms in ultra-dense scenarios. The simulation results confirm a 92% accuracy in near-optimal offloading decisions under dynamic channel conditions. This work advances sustainable edge computing by enabling energy-efficient IoT deployments in 5G ultra-dense networks without compromising QoS. Full article

Swarm-based unmanned aerial vehicle (UAV) systems offer enhanced spatial coverage, collaborative intelligence, and mission scalability for various applications, including environmental monitoring and emergency response. However, their onboard processing is limited by stringent size, weight, and power constraints, posing challenges for real-time computation and autonomous control. This paper presents an integrated communication and computation framework that combines cloud–edge–end collaboration with cell-free massive multiple-input multiple-output (CF-mMIMO) to enable scalable and efficient task offloading in UAV swarms. Furthermore, we implement a prototype system testbed with nine UAVs and validate the proposed framework through real-time object detection tasks. Results demonstrate over $30\%$ reduction in onboard computation and significant improvements in communication reliability, highlighting the framework’s potential for enabling intelligent, cooperative aerial systems. Full article

In the UAV-assisted mobile edge computing (MEC) communication system, the UAV receives the data offloaded by multiple ground user devices as an aerial base station. Among them, due to the limited battery storage of a UAV, energy saving is a key issue in a UAV-assisted MEC system. However, for a low-altitude flying UAV, successful obstacle avoidance is also very necessary. This paper aims to maximize the system energy efficiency (defined as the ratio of the total amount of offloaded data to the energy consumption of the UAV) to meet the maneuverability and three-dimensional obstacle avoidance constraints of a UAV. A joint optimization strategy with maximized energy efficiency for the UAV flight trajectory and user device task offloading rate is proposed. In order to solve this problem, hybrid alternating metaheuristics for energy optimization are given. Due to the non-convexity and fractional structure of the optimization problem, it can be transformed into an equivalent parameter optimization problem using the Dinkelbach method and then divided into two sub-optimization problems that are alternately optimized using metaheuristic algorithms. The experimental results show that the strategy proposed in this paper can enable a UAV to avoid obstacles during flight by detouring or crossing, and the trajectory does not overlap with obstacles, effectively achieving two-dimensional and three-dimensional obstacle avoidance. In addition, compared with related solving methods, the solving method in this paper has significantly higher success than traditional algorithms. In comparison with related optimization strategies, the strategy proposed in this paper can effectively reduce the overall energy consumption of UAV. Full article

The rapid growth of computationally intensive tasks in the Internet of Vehicles (IoV) poses a triple challenge to the efficiency, security, and stability of Mobile Edge Computing (MEC). Aiming at the problems that traditional optimization algorithms tend to fall into, where local optimum in task offloading and edge computing nodes are exposed to the risk of data tampering, this paper proposes a secure offloading strategy that integrates the Improved Polar Lights Optimization algorithm (IPLO) and blockchain. First, the truncation operation when a particle crosses the boundary is improved to dynamic rebound by introducing a rebound boundary processing mechanism, which enhances the global search capability of the algorithm; second, the blockchain framework based on the Delegated Byzantine Fault Tolerance (dBFT) consensus is designed to ensure data tampering and cross-node trustworthy sharing in the offloading process. Simulation results show that the strategy significantly reduces the average task processing latency (64.4%), the average system energy consumption (71.1%), and the average system overhead (75.2%), and at the same time effectively extends the vehicle’s power range, improves the real-time performance of the emergency accident warning and dynamic path planning, and significantly reduces the cost of edge computing usage for small and medium-sized fleets, providing an efficient, secure, and stable collaborative computing solution for IoV. Full article

This paper addresses the challenge of offloading resource-intensive image segmentation tasks and allocating computing resources within the Internet of Vehicles (IoV) using edge-based AI. To overcome the limitations of onboard computing in smart vehicles, this study develops an efficient edge computing resource allocation system. The core of this system is an improved model-free soft actor–critic (iSAC) algorithm, which is enhanced by incorporating prioritized experience replay (PER). This PER-iSAC algorithm is designed to accelerate the learning process, maintain stability, and improve the efficiency and accuracy of computation offloading. Furthermore, an integrated computing and networking scheduling framework is employed to minimize overall task completion time. Simulation experiments were conducted to compare the PER-iSAC algorithm against baseline algorithms (Standard SAC and PPO). The results demonstrate that the proposed PER-iSAC significantly reduces task allocation error rates and optimizes task completion times. This research offers a practical engineering solution for enhancing the computational capabilities of IoV systems, thereby contributing to the development of more responsive and reliable autonomous driving applications. Full article

Intelligent Connected Vehicles (ICVs) impose stringent requirements on real-time computational services. However, limited onboard resources and the high latency of remote cloud servers restrict traditional solutions. Unmanned Aerial Vehicle (UAV)-assisted Mobile Edge Computing (MEC), which deploys computing and storage resources at the network edge, offers a promising solution. In UAV-assisted vehicular networks, jointly optimizing content and service caching, computation offloading, and UAV trajectories to maximize system performance is a critical challenge. This requires balancing system energy consumption and resource allocation fairness while maximizing cache hit rate and minimizing task latency. To this end, we introduce system efficiency as a unified metric, aiming to maximize overall system performance through joint optimization. This metric comprehensively considers cache hit rate, task computation latency, system energy consumption, and resource allocation fairness. The problem involves discrete decisions (caching, offloading) and continuous variables (UAV trajectories), exhibiting high dynamism and non-convexity, making it challenging for traditional optimization methods. Concurrently, existing multi-agent deep reinforcement learning (MADRL) methods often encounter training instability and convergence issues in such dynamic and non-stationary environments. To address these challenges, this paper proposes a MADRL-based joint optimization approach. We precisely model the problem as a Decentralized Partially Observable Markov Decision Process (Dec-POMDP) and adopt the Multi-Agent Proximal Policy Optimization (MAPPO) algorithm, which follows the Centralized Training Decentralized Execution (CTDE) paradigm. Our method aims to maximize system efficiency by achieving a judicious balance among multiple performance metrics, such as cache hit rate, task delay, energy consumption, and fairness. Simulation results demonstrate that, compared to various representative baseline methods, the proposed MAPPO algorithm exhibits significant superiority in achieving higher cumulative rewards and an approximately 82% cache hit rate. Full article

With the continuous development of the Internet of Things (IoT) and communication technologies, the demand for low latency in practical applications is becoming increasingly significant. Mobile edge computing, as a promising computational model, is receiving growing attention. However, most existing studies fail to consider two critical factors: task dependency and service caching. Additionally, the majority of proposed solutions are not related to the optimal solution. We investigate the task offloading problem in mobile edge computing. Considering the requirements of applications for service caching and task dependency, we define an optimization problem to minimize the delay under the constraint of maximum completion cost and present a $(1+ϵ)$-approximation algorithm and an exact algorithm. Specifically, the offloading scheme is determined based on the relationships between tasks as well as the cost and delay incurred by data transmission and task execution. Simulation results demonstrate that in all cases, the offloading schemes obtained by our algorithm consistently outperform other algorithms. Moreover, the approximation ratio to the optimal solution from the approximation algorithm is validated to be less than $(1+ϵ)$, and the exact algorithm consistently produces the optimal solution. Full article

The emergence of Flexible Manufacturing Systems (FMS) presents new challenges in Industrial IoT (IIoT) environments. Unlike traditional real-time systems, FMS must accommodate task set variability driven by human–machine interaction. As such variations can lead to abrupt resource overload or idleness, a dynamic scheduling mechanism is required. Although prior studies have explored dynamic scheduling, they often relax deadlines for lower-criticality tasks, which is not well suited to IIoT systems with strict deadline constraints. In this paper, instead of treating dynamic scheduling as a prediction problem, we model it as deterministic planning in response to explicit, observable user input. To this end, we precompute feasible resource plans for anticipated task set variations through offline optimization and switch to the appropriate plan at runtime. During this process, our approach jointly optimizes processor speeds, memory allocations, and edge/cloud offloading decisions, which are mutually interdependent. Simulation results show that the proposed framework achieves up to 73.1% energy savings compared to a baseline system, 100% deadline compliance for real-time production tasks, and low-latency responsiveness for user-interaction tasks. We anticipate that the proposed framework will contribute to the design of efficient, adaptive, and sustainable manufacturing systems. Full article

Edge computing has become a key framework for meeting the demands of low latency and high reliability across various applications. To ensure efficient task execution in edge computing, many offloading strategies have been proposed; however, most lack quantitative analysis and formal verification to guarantee their correctness. This paper addresses this gap by presenting an integrated offloading strategy that combines delay-based, energy-efficient, and energy-delay trade-off approaches, enhancing both the clarity and verifiability of offloading methods. Furthermore, we apply probabilistic model checking using PRISM to rigorously analyze and validate the correctness of the proposed hybrid strategy. Our approach provides practical solutions and valuable insights, promoting the development of reliable and efficient offloading strategies for edge computing systems. Full article

Mobile edge computing was conceived to address the increasing computing demand generated by users at the communication network edge. It is expected to play a significant role in next-generation (5G, 6G, and beyond) communication systems as new applications such as augmented/extended reality, teleoperations, telemedicine, and gaming become prolific. As the networks become denser, more and more edge servers are expected to be deployed, and the question of task offloading becomes more complicated. In this study, we present a framework for task offloading in the presence of multiple edge servers that employ round-robin task scheduling. Most studies in the literature attempt to optimize the offloading process under the assumption that each user generates just a single task, or they generate one task every time slot in a discrete-time system where all the tasks are handled within a slot. Furthermore, first-come-first-served queueing models are typically used in studies where queueing is considered at all. The work presented is novel in that we assume continuous and stochastic task arrivals generated by multiple users and round-robin task scheduling at the edge servers. This setting is considerably more realistic with respect to the existing works, and we demonstrate through extensive simulations that round-robin task scheduling significantly reduces task delay. We also present a comparison of a number of server selection mechanisms. Full article

This paper addresses the resource allocation challenges in autonomous vehicle (AV) networks for delay-sensitive perception tasks. Current vehicular networks face sub-optimal resource distribution and excessive communication overhead, hindering the performance of AVs. We propose an integrated approach that combines a platoon-based system model with optimization techniques using Deep Q-Networks (DQN) and Particle Swarm Optimization (PSO). The platoon-based model enables AVs to share resources effectively, while the DQN and PSO models optimize task offloading and reduce overhead. Simulation results across various traffic scenarios demonstrate that the PSO algorithm outperforms traditional methods in task completion rates, overhead minimization, and platoon formation. This approach offers a significant advancement in enhancing AV network performance and ensuring timely task execution. Full article