Search Results (220)

(1) Background: Fiberoptic Endoscopic Evaluation of Swallowing (FEES) is one of the two gold-standard tools for assessing oropharyngeal dysphagia (alongside Videofluoroscopic Swallowing Study). Although generally considered safe, concerns about complications persist, particularly in systems where FEES is not routine and professional roles differ. The aim of this study was to evaluate the safety of FEES performed by both speech-language pathologists (SLPs) and physicians, in order to provide evidence of its safety in a healthcare system where the procedure is not yet widely established and to identify patient subgroups potentially at higher risk of procedure-related complications. (2) Methods: This retrospective study analyzed 964 consecutive FEES procedures. Examinations were carried out by trained SLPs or physicians. Data included demographics, clinical status, operator qualifications, setting, and complications, classified as minor (vomiting, poor tolerance, early termination) or major (laryngospasm, epistaxis). (3) Results: The overall complication rate was 1.14% (11/964): 0.6% minor and 0.5% major. All events were self-limiting. Complication rates did not differ between SLPs (1.05%) and physicians (1.23%) or by experience, setting, drug use, penetration–aspiration scale score, or nasogastric tube. Four complications occurred in amyotrophic lateral sclerosis patients, suggesting higher risk. (4) Conclusions: FEES is safe and well tolerated when performed by either physicians or SLPs. These findings underscore the value of task sharing in dysphagia diagnostics, demonstrating that a shared model increases service capacity, reduces delays, and facilitates timely management of dysphagia. Full article

Operating systems such as Windows, Linux, and macOS include built-in commands that enable administrators to perform essential tasks. These same commands can be exploited by attackers for malicious purposes that may go undetected by traditional security solutions. This research identifies an unmitigated risk of misuse of a standard command to disconnect network services on victim devices. Thus, we developed a novel Proof-of-Concept (PoC) malware named DCmal-2025 and documented every step of its lifecycle, including the core idea of the malware, its development, impact, analysis, and possible countermeasures. The proposed DCmal-2025 malware can cause a Denial-of-Service (DoS) condition without exploiting any software vulnerabilities; instead, it misuses legitimate standard commands and manipulates the routing table to achieve this. We developed two types of DCmal-2025: one that triggers a DoS immediately and another that initiates it after a predefined delay before restoring connectivity. This study evaluated 72 antivirus detection rates of two malware types (DCmal-2025 Type 1 and Type 2) written in C and Rust using VirusTotal. The source code for both types was undetected by any of the antivirus engines. However, after compiling the source code into executable files, only some Windows executables were flagged by general keywords unrelated to DCmal-2024 behaviour; Linux executables remained undetected. Rust significantly reduced detection rates compared to C—from 7.04% to 1.39% for Type 1 and from 9.72% to 4.17% for Type 2. An educational institution was chosen as a case study. The institution’s network topology was simulated using the GNS3 simulator. The result of the case study reveals that both malware types could cause a successful DoS attack by disconnecting targeted devices from all network-based services. The findings underscore the need for enhanced detection methods and heightened awareness that unexplained network disconnections may be caused by undetected malware, such as DCmal-2025. Full article

The rapid development of telematics technology has greatly supported high-computing applications like autonomous driving and real-time road condition prediction. However, the limited computational resources and dynamic topology of in-vehicle terminals pose challenges such as delay, load imbalance, and bandwidth consumption. To address these, a three-layer vehicular network architecture based on cloud–edge–end collaboration was proposed, with V2X technology used for multi-hop transmission. Models for delay, energy consumption, and edge caching were designed to meet the requirements for low delay, energy efficiency, and effective caching. Additionally, a dynamic pricing model for edge resources, based on load-awareness, was proposed to balance service quality and cost-effectiveness. The enhanced NSGA-III algorithm (ADP-NSGA-III) was applied to optimize system delay, energy consumption, and system resource pricing. The experimental results (mean of 30 independent runs) indicate that, compared with the NSGA-II, NSGA-III, MOEA-D, and SPEA2 optimization schemes, the proposed scheme reduced system delay by 21.63%, 5.96%, 17.84%, and 8.30%, respectively, in a system with 55 tasks. The energy consumption was reduced by 11.87%, 7.58%, 15.59%, and 9.94%, respectively. Full article

The air transportation industry is challenged to address airport curbside delay problems that affect landside service quality and can potentially impact check-in operations. Methodological advances guided by industry requirements are needed to support curbside improvement studies. Existing methods require verification of assumptions prior to application or need expensive surveys to acquire data for use in microsimulations. A probability-based macrosimulation method is advanced for the evaluation of the level of service and capacity of the curbside processor. A key component of the method is the simulation of the stochastic balance of demand and available curb space for unloading/loading tasks using the Monte Carlo simulation model. The method meets the planning and operation requirements with the ability to analyze conditions commonly experienced at the curb area. Example applications illustrate the flexibility of the method in evaluating existing as well as planned facilities of diverse designs and sizes. The developed method can contribute to curbside processor delay reduction and due to the macroscopic nature of the method, the data requirements can be met by an airport authority without costly surveys. Full article

Vehicular edge computing (VEC) represents a concrete application of mobile edge computing (MEC) in the field of intelligent transportation, with task offloading serving as one of its core components. The design of efficient task offloading strategies poses significant challenges due to the dynamic network topology, stringent low-latency requirements, and massive data processing demands. This paper proposes a digital twin (DT)-assisted intelligent task offloading approach, which establishes a dynamic interaction and mapping between the virtual and physical worlds to enable real-time monitoring of VEC network states, thereby optimizing offloading decisions. First, to meet diverse user service requirements, an optimization model is formulated with the objective of minimizing task processing latency and energy consumption. Next, a gravity model-based vehicle clustering algorithm is integrated with digital twin technology to find the optimal offloading space and ensure link stability among vehicles within aggregated clusters. Furthermore, to minimize overall system costs, the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm is utilized to train the offloading policy, enabling automatic optimization of both latency and energy consumption. consumption. Finally, a feedback mechanism is introduced to dynamically adjust parameters and enhance the robustness of the clustering process. Simulation results demonstrate that the proposed approach significantly outperforms baseline methods in terms of task completion cost, energy consumption, delay, and success rate, thereby validating its potential and superior performance in dynamic vehicular network environments. Full article

Unmanned Aerial Vehicles (UAVs) exhibit significant potential in enhancing the wireless communication coverage and service quality of Mobile Edge Computing (MEC) systems due to their superior flexibility and ease of deployment. However, the rapid growth of tasks leads to transmission queuing in edge networks, while the uneven distribution of user nodes and services causes network load imbalance, resulting in increased user waiting delays. To address these issues, we propose a multi-UAV collaborative MEC network model based on Non-Orthogonal Multiple Access (NOMA). In this model, UAVs are endowed with the capability to dynamically offload tasks among one another, thereby fostering a more equitable load distribution across the UAV swarm. Furthermore, the integration of NOMA is strategically employed to alleviating the inherent queuing delays in the communication infrastructure. Considering delay and energy consumption constraints, we formulate a task offloading strategy optimization problem with the objective of minimizing the overall system delay. To solve this problem, we design a delay-optimized offloading strategy based on the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm. By jointly optimizing task offloading decisions and UAV flight trajectories, the system delay is significantly reduced. Simulation results show that, compared to traditional approaches, the proposed algorithm achieves a delay reduction of 20.2%, 9.8%, 17.0%, 12.7%, 15.0%, and 11.6% under different scenarios, including varying task volumes, the number of IoT devices, UAV flight speed, flight time, IoT device computing capacity, and UAV computing capability. These results demonstrate the effectiveness of the proposed solution and offloading decisions in reducing the overall system delay. Full article

Platform support for mobile collaboration among multiple smart devices has been an active research issues in the computing community. Using platform-level collaboration functionalities, a mobile device can share its resources, I/O events, and even apps easily with other devices, which enables developing a new kind of application that runs across multiple devices. In this work, we further extend the collaboration functionalities in mobile platforms by developing a novel platform service, remote intent service (RIS),which enables a running application in a device to outsource the execution of a specific task to another application in a remote device. Using the remote intent service, for example, we can view an attached document to an email, using a document viewer application in a remote device that has a larger screen, or conveniently browse an audio file that exists on another mobile device and play it locally. We implemented the remote intent service to the Android platform and measured the latency for executing such tasks in a remote device. The experimental results confirm that the remote intent service, for sending the intent plus retrieving the result, incurs an additional delay of less than 250 ms in total, and thus, it is practical. 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

Green data centers (GDCs) are increasingly deployed worldwide to power digital infrastructure sustainably. These centers integrate renewable energy sources, such as solar and wind, to reduce dependence on grid electricity and lower operational costs. When distributed geographically, GDCs face considerable challenges due to spatial variations in renewable energy availability, electricity pricing, and bandwidth costs. This paper addresses the joint optimization of operational cost and service quality for delay-sensitive applications scheduled across distributed green data centers (GDDCs). We formulate a multi-objective optimization problem that minimizes total operational costs while reducing the Average Task Loss Probability (ATLP), a key Quality of Service (QoS) metric. To solve this, we propose an Adaptive Firefly-Based Bi-Objective Optimization (AFBO) algorithm that introduces multiple adaptive mechanisms to improve convergence and diversity. The minimum Manhattan distance method is adopted to select a representative knee solution from each algorithm’s Pareto front, determining optimal task service rates and ISP task splits into each time slot. AFBO is evaluated using real-world trace-driven simulations and compared against benchmark multi-objective algorithms, including multi-objective particle swarm optimization (MOPSO), simulated annealing-based bi-objective differential evolution (SBDE), and the baseline Multi-Objective Firefly Algorithm (MOFA). The results show that AFBO achieves up to 64-fold reductions in operational cost and produces an extremely low ATLP value ($1.875\times {10}^{-7}$) that is nearly two orders of magnitude lower than SBDE and MOFA and several orders better than MOPSO. These findings confirm AFBO’s superior capability to balance energy cost savings and Quality of Service (QoS), outperforming existing methods in both solution quality and convergence speed. Full article

In Wireless Sensor Networks (WSNs) used for real-time applications, ensuring Quality of Service (QoS) is essential for maintaining end-to-end performance guarantees. QoS requirements are typically defined by a set of end-to-end constraints, including delay, jitter, and packet loss. In multi-hop scenarios, this requires multi-constrained path computation. This research examines the standard Routing Protocol for Low-Power and Lossy Networks (RPL), which employs a Destination-Oriented Directed Acyclic Graph (DODAG) for data transmission. Nonetheless, there are several challenges related to multi-constrained route computation in the RPL: (1) The DODAG originates from an objective function that cannot manage multiple constraints. (2) The process of computing multi-constrained routes is resource-intensive, even for a single path. (3) The collection of QoS-compatible paths does not necessarily form a DODAG. To address these challenges, this paper suggests modifications to the existing protocols that shift computationally demanding tasks to edge servers. Such a strategic adjustment allows for the implementation of QoS-compatible route computation in WSNs using the RPL. It enhances their ability to meet increasingly stringent demands for QoS in numerous application environments. Full article

The growing adoption of the Internet of Things (IoT) in healthcare has led to a surge in real-time data from wearable devices, medical sensors, and patient monitoring systems. This latency-sensitive environment poses significant challenges to traditional cloud-centric infrastructures, which often struggle with unpredictable service demands, network congestion, and end-to-end delay constraints. Consistently meeting the stringent QoS requirements of smart healthcare, particularly for life-critical applications, requires new adaptive architectures. We propose ML-RASPF, a machine learning-based framework for efficient service delivery in smart healthcare systems. Unlike existing methods, ML-RASPF jointly optimizes latency and service delivery rate through predictive analytics and adaptive control across a modular mist–edge–cloud architecture. The framework formulates task provisioning as a joint optimization problem that aims to minimize service latency and maximize delivery throughput. We evaluate ML-RASPF using a realistic smart hospital scenario involving IoT-enabled kiosks and wearable devices that generate both latency-sensitive and latency-tolerant service requests. Experimental results demonstrate that ML-RASPF achieves up to 20% lower latency, 18% higher service delivery rate, and 19% reduced energy consumption compared to leading baselines. Full article

The integration of Unmanned Aerial Vehicles (UAVs) into Mobile Edge Computing (MEC) systems has emerged as a transformative solution for latency-sensitive applications, leveraging UAVs’ unique advantages in mobility, flexible deployment, and on-demand service provisioning. This paper proposes a novel multi-agent reinforcement learning framework, termed Multi-Agent Twin Delayed Deep Deterministic Policy Gradient for Task Offloading and Resource Allocation (MATD3-TORA), to optimize task offloading and resource allocation in UAV-assisted MEC networks. The framework enables collaborative decision making among multiple UAVs to efficiently serve sparsely distributed ground mobile devices (MDs) and establish an integrated mobility, communication, and computational offloading model, which formulates a joint optimization problem aimed at minimizing the weighted sum of task processing latency and UAV energy consumption. Extensive experiments demonstrate that the algorithm achieves improvements in system latency and energy efficiency compared to conventional approaches. The results highlight MATD3-TORA’s effectiveness in addressing UAV-MEC challenges, including mobility–energy tradeoffs, distributed decision making, and real-time resource allocation. Full article

As an emerging paradigm for supporting computation-intensive and latency-sensitive services, mobile edge computing (MEC) faces significant challenges in terms of efficient resource utilization and intelligent task coordination among heterogeneous user equipment (UE), especially in dense MEC scenarios with severe interference. Generally, task similarity and cooperation opportunities among UE are usually ignored in existing studies when dealing with reusable tasks. In this paper, we investigate the problem of cooperative computation offloading and resource allocation for reusable tasks, with a focus on minimizing the energy consumption of UE while ensuring delay limits. The problem is formulated as an intractable mixed-integer nonlinear programming (MINLP) problem, and we design a similarity-based cooperative offloading and resource allocation (SCORA) algorithm to obtain a solution. Specifically, the proposed SCORA algorithm decomposes the original problem into three subproblems, i.e., task offloading, resource allocation, and power allocation, which are solved using a similarity-based matching offloading algorithm, a cooperative-based resources allocation algorithm, and a concave–convex procedure (CCCP)-based power allocation algorithm, respectively. Simulation results show that compared to the benchmark schemes, the SCORA scheme can reduce energy consumption by up to 51.52% while maintaining low latency. Moreover, the energy of UE with low remaining energy levels is largely saved. Full article

Mobile edge computing (MEC) has become an effective framework for latency-sensitive and computation-intensive applications by deploying computing resources at network edge. The unmanned aerial vehicle (UAV)-assisted MEC leverages UAV mobility and communication advantages to enable services in dynamic environments, where frequent adjustments to flight trajectories and user association are required due to dynamic factors such as time-varying task requirements, user mobility, and communication environment variation. This paper addresses the joint optimization problem of UAV flight trajectory control and user association in dynamic environments, which explicitly incorporates the constraints governed by UAV flight dynamics. The joint problem is formulated as a non-convex optimization formulation that involves continuous–discrete hybrid decision variables. To overcome the inherent complexity of this problem, a novel proximal policy optimization-based dynamic control (PPO-DC) algorithm is developed. This algorithm aims to reduce the weighted combination of delay and energy consumption by dynamically controlling the UAV trajectory and user association. The numerical results validate that the PPO-DC algorithm successfully enables real-time UAV trajectory control under flight dynamics constraints, ensuring feasible and efficient flight trajectory. Compared to the state-of-the-art hybrid-action deep reinforcement learning (DRL) algorithms or metaheuristics, the PPO-DC achieves notable improvements in system performance by simultaneously lowering system delay and energy consumption. Full article