关键词： Servers   Resource management   Delays   Reliability engineering   Error probability   Optimization   Temperature sensors   Sensor phenomena and characterization   Reliability theory   Energy consumption   Low-latency   mobile edge computing   end-to-end reliability   fixed frames structure   dynamic frames structure  

摘要： In this paper, we consider a multi-access mobile edge computing (MEC) network with multiple sensors and one MEC server in industrial Internet of Things networks, where the MEC server provides a joint computation service (in the computation phase) for a set of sub-tasks offloaded by different sensors (in the communication phase). Due to the requirements of low latency and ultra reliability, we utilize finite blocklength information theory to characterize the reliability of the communication phase and exploit extreme value theory to investigate the delay violation probability in the computation phase. Following these characterizations, we derive the average end-to-end error probability of the entire service and provide two average end-to-end reliability-optimal design frameworks considering fixed frames structure and dynamic frames structure, in both of which the goal is to minimize the average end-to-end error probability by optimally allocating the total time length to each frame, as well as allocating each frame length to the communication phase and the computation phase. For the fixed frames structure, the original problem is decomposed, and the joint convexity of the decomposed sub-problems is rigorously proved, and the optimal solutions are obtained by the proposed optimal time allocation algorithm. Moreover, for the dynamic frames structure, we reformulate the optimization problem by introducing an average time constraint. By exploiting Lagrange multipliers, we transform the reformulated optimization problem into a dual problem with strong duality, the solutions of which can be obtained by the proposed time allocation algorithm. Via simulations, we validate the proven convexity and the approximation in our analytical model and evaluate the performance for both fixed frames length structure and dynamic frames length structure.

关键词： Resource management   Dynamic scheduling   Cost accounting   Computational efficiency   Virtual machines   Computational modeling   Wireless communication   Pricing   Indexes   Delays   Mobile edge computing   task offloading   pricing   online auction   truthfulness  

摘要： Mobile edge computation (MEC) is envisioned as a prospective approach for processing the computation-intensive and delay-sensitive tasks of smart mobile devices (SMDs) through offloading them to base stations (BSs) nearby. In fact, efficient task offloading mechanisms are crucial to accomplish an MEC system. The key challenge is to make on-spot decisions upon the arrival of each task and at the same time achieve truthfulness of each SMD. The challenge further escalates, when the unique characteristics of an MEC system, such as locality constraint, delay constraint, etc., are explicitly considered. To solve the challenge, we present a truthful online combinatorial auction-based mechanism (TOCA) for task offloading in an MEC system. Specifically, we first devise the candidate offloading scheme determination algorithm, aiming to determine the candidate offloading schemes of an SMD upon the arrival of its task. Next, we devise the winning offloading scheme selection and pricing algorithm based on the online primal-dual optimization framework, to decide the winning scheme among the SMD's candidate offloading schemes and calculate its payment. By solid theoretical analysis, we verify that TOCA achieves truthfulness, individual rationality and computational efficiency and a smaller competitive ratio. Trace-driven simulation studies validate the effectiveness and efficacy of TOCA.

关键词： Internet of Things   mobile edge computing   status updates   layer-coded HARQ   stochastic hybrid system   age of information   mobile edge computing   status updates   layer-coded HARQ   stochastic hybrid system   age of information  

摘要： This work focuses on an Internet of Things (IoT) status update system with the assistance of mobile edge computing (MEC) under short packet communications. The edge server includes a successive interference canceller (SIC) and a buffer pool. To simultaneously improve both information timeliness and throughput, a layer-coded hybrid automatic repeat request (L-HARQ) protocol is proposed especially considering backtrack decoding (BD) delay. In the MEC-assisted L-HARQ IoT status update system, the source successively transmits mixing updates to an access point (AP). The mixing update consists of the new generation and the part of the previously failed one. All the undecoded mixing packets are delivered to the edge server. Once a successful feedforward decoding (FD) occurs, the edge server attempts to recover undecoded mixing packets until a BD failure occurs or all buffers empty. The successful BD results are also delivered to the IR and the obtained prior information is fed back to the edge server for the next BD. By employing the stochastic hybrid system (SHS) model, the average age of informations (AoIs) are derived under circle-shift preemption (CS-P) and fixed preemption (F-P) policies. Considering the fact that the number of buffers is limited and the successful FD and BD do not always cause the reduction of system AoI, this work proposes a non-binary age evolution model as well as the state simplification mothed in SHS. The presented numerical results show that the CS-P and F-P policies obtain the enhanced information freshness compared with the non-preemptive blocking policy, and the CS-P policy outperforms the F-P one in terms of information freshness. The average AoI greatly depends on BD depth. When it is small, the average AoI is small. However, the two policies have the same average throughput that mainly depends on the size of buffer pool and the success probabilities of both FD and BD.

关键词： Wireless body area network   Mobile edge computing   Power control   Deep reinforcement learning  

摘要： With the widespread application of Wireless Body Area Network (WBAN) in healthcare, issues such as a lack of computational resources and communication interference in high-density WBAN scenarios have become increasingly prominent. To address these issues, this paper introduces Mobile Edge Computing (MEC) to tackle the lack of computational resources and employs power control to mitigate communication interference among WBANs. We describe the joint optimization problem of power control and task offloading in high-density WBAN scenarios as a Markov Decision Process (MDP) and propose a Power Control and Task Offloading (PCTO) algorithm based on the Deep Deterministic Policy Gradient (DDPG) method to achieve coordinated optimization of transmission power and task offloading. Furthermore, we improve the algorithm's learning efficiency and sample utilization by incorporating a prioritized experience replay mechanism. By constructing a multi-objective reward function that comprehensively considers delay, energy consumption, and Signal-to-Interference-plus-Noise Ratio (SINR), and incorporating Quality of Service (QoS) constraints, the algorithm is capable of making intelligent decisions in complex wireless communication environments. The simulation results demonstrate that, compared to existing methods, the proposed approach significantly reduces system delay while improving SINR and service satisfaction in various WBAN density scenarios.

关键词： Mobile edge computing  

摘要： Food defect detection is a critical link in ensuring food safety. Traditional machine vision-based detection systems rely on cloud servers to complete algorithmic inference, suffering from problems such as large detection equipment volume and insufficient real-time performance. This paper proposes a portable lightweight detection scheme based on Terminal Machine Learning (TinyML) and Mobile Edge Computing (MEC). Through lightweight neural network model compression technology and edge node task collaboration mechanisms, low-power operation and millisecond-level response of the detection equipment are achieved. Experimental results show that in the scenario of fruit surface defect detection, the system achieves a detection accuracy of (Formula presented.), with a single-frame inference power consumption of only 85 mW, meeting the practical application requirements of portable devices. © 2025 John Wiley & Sons Ltd.

关键词： Servers   Cloud computing   Internet of Things   Real-time systems   Autonomous vehicles   Low latency communication   Computational efficiency   Robustness   Multi-access edge computing   Computer architecture   distributed computing   mobile edge computing   verifiable computation  

摘要： With the rapid expansion of the Internet of Things (IoT), the shift from cloud computing to Mobile Edge Computing (MEC) has become necessary to address the low-latency requirements of real-time applications. Verifiable computation (VC) enables resource-limited clients to outsource their computation-intensive tasks to a powerful cloud while ensuring the correctness of the computation result. However, traditional VC schemes, originally designed for cloud computing, face challenges when applied to MEC environments, such as scalability issues, robustness, and efficiency concerns. To this end, we propose a verifiable distributed computation scheme for MEC, where computation tasks are distributed between a cloud server cluster (consisting of $n$n servers) and an edge server. The cloud handles most of the computation through parallel sub-tasks, while the edge server verifies intermediate results and performs minimal computation to recover the final outcome. Our scheme guarantees that the result can be recovered if at least $t$t servers, out of a total of $n$n servers in the cloud server cluster, perform their computations honestly. By leveraging batch verification and matrix-optimized polynomial evaluations, our scheme significantly enhances scalability, fault tolerance, and efficiency. The extensive analysis and simulations demonstrate that our proposed scheme is more feasible than existing solutions.

关键词： IoV   Mobile edge computing   Task offloading   Dynamic   V-DQN  

摘要： The rapid development of edge computing has an impact on the Internet of Vehicles (IoV). However, the high-speed mobility of vehicles makes the task offloading delay unstable and unreliable. Hence, this paper studies the task offloading problem to provide stable computing, communication and storage services for user vehicles in vehicle networks. The offloading problem is formulated to minimize cost consumption under the maximum delay constraint by jointly considering the positions, speeds and computation resources of vehicles. Due to the complexity of the problem, we propose the vehicle deep Q-network (V-DQN) algorithm. In V-DQN algorithm, we firstly propose a vehicle adaptive feedback (VAF) algorithm to obtain the priority setting of processing tasks for service vehicles. Then, the V-DQN algorithm is implemented based on the result of VAF to realize task offloading strategy. Specially, the interruption problem caused by the movement of the vehicle is formulated as a return function to evaluate the task offloading strategy. The simulation results show that our proposed scheme significantly reduces cost consumption.

关键词： Mobile edge computing   Authentication   Session key   Scyther   Authenticated Encryption (AE)  

摘要： By bringing services closer to the network edge, mobile edge computing (MEC) improves traditional cloud computing and increases end-user accessibility. This represents a significant improvement in Internet of Things (IoT) applications. Ensuring strong security is becoming more and more important as more mobile users access MEC services across several domains. Keeping IoT applications safe from attacks is a major problem in MEC contexts. Moreover, it becomes considerably challenging to maintain robust security in circumstances where users often move between networks. This demands a trustworthy authentication framework that facilitates mobility for users while protecting MEC from illegal access. The framework also needs to make sure that insider attacks do not jeopardize the MEC environment's or its servers' security. Establishing a secure session key between MEC users and external edge servers is also crucial for maintaining safe communication in MEC environments. Another essential security requirement is maintaining user anonymity, since most users want to keep their personal information private. This work addresses these problems by presenting a mutual authentication framework developed especially for mobility-supported MEC contexts, that utilizes PUF, ECC, and AE. The presented framework incorporates three factors for user authentication and provides resilience against various attacks, and session key establishment in mobile MEC environments. It also presents flexibility for users to update their passwords when required. Security and performance evaluations of the framework demonstrate its practicality and effectiveness, exhibiting a reduction in communication cost by 61% to 71% percent and computational cost by 41.16%, and 75.44%.

关键词： Abbr.   Full Form   5G   Fifth Generation   AAL   Assisted Ambient Living   AES   Advanced Encryption Standard   AI   Artificial Intelligence   ANN   Artificial Neural Networks   AR   Augmented Reality   BAN   Body Area Networks   BCI   Brain Computer Interfacing   BSN   Body Sensor Networks   CHF   Congestive Heart Failure   CNN   Convolution Neural Networks   CVD   Cardiovascular Diseases   DBN   Deep Belief Network   DL   Deep Learning   DQN   Deep Q-Networks   ECG   Electrocardiogram   EEG   Electroencephalogram   ER   Emergency Room   ETE   End to End   H-IoT   Healthcare Internet of Things   IDS   Intrusion Detection Systems   IEEE   The Institute of Electrical and Electronics Engineers   IoNT   Internet of Bio Nano Things   IoNT   Internet of Nano Things   ITU   International Telecommunications Union   kNN   k-Nearest Neighbour   KPI   Key Performance Indicators   LTE   Long Term Evolution   MAC   Medium Access Control   MC   Molecular Communication   MEC   Mobile Edge Computing   MEMS   Micro Electro Mechanical Systems   ML   Machine Learning   MQTT   Message Queue Telemetry Transport   NFV   Network Function Virtualization   NOS   Network Operating System   OS   Operating System   PHY   Physical Layer   PoCD   Point of Care Devices   QoL   Quality of Life   QoS   Quality of Service   RSA   Rivest–Shamir–Adleman Algorithm   SDN   Software Defined Network   SVM   Support Vector Machines   TI   Tactile Internet   VR   Virtual Reality   WBAN   Wireless Body Area Network   WSN   Wireless Sensor Network   Healthcare Delivery   Healthcare Technology   Artificial Intelligence   5   G Technology  

摘要： The integration of 5   G with emerging technologies, including big data, artificial intelligence (AI), machine learning (ML), and the Internet of Things (IoT), is anticipated to revolutionize the healthcare sector. By providing enhanced Mobile Broadband (eMBB), ultra-reliable low-latency communication (URLLC), and ubiquitous connectivity, 5   G is set to transform patient care through real-time communication, remote diagnostics, and tactile internet-enabled telemedicine. These advancements are expected to foster high-performance, reliable, and personalized healthcare solutions. Furthermore, the convergence of sensor technologies, AI, and ML will enhance diagnostic accuracy and support proactive, data-driven medical decision-making. Notably, AI is also emerging as a powerful tool in cancer pharmacology, facilitating early cancer detection, drug discovery, and personalized oncology through advanced predictive models and real-time analytics. AI-driven platforms can analyze complex oncological datasets to identify optimal treatment pathways and predict therapeutic responses, significantly improving outcomes for cancer patients. This report offers a comprehensive analysis of the impending 5G-enabled healthcare revolution, examining the interplay of enabling technologies, current research, and practical applications. It includes a case study on the economic benefits of 5   G in healthcare and discusses the key challenges and risks that must be addressed to fully realize its potential.

关键词： UAV-assisted platooning   Dual-channel switching   Energy optimization   Reliability enhancement   Ground-air networks   Mobile edge computing  

摘要： Connected and Autonomous Vehicle (CAV) networks are rapidly advancing as a critical technology for enabling intelligent transportation systems. One promising approach to support such vehicular networks is the use of Unmanned Aerial Vehicles (UAVs) to enhance communication and computational capabilities through air-ground collaboration. However, existing UAV-assisted vehicular communication methods often face challenges, particularly in urban environments with frequent obstructions that degrade communication quality and reliability. Additionally, ensuring energy efficiency while maintaining high communication reliability remains an unsolved issue. In this paper, we investigate a UAV-assisted ground-air collaborative network for a platoon of vehicles navigating urban areas with frequent obstacles. Our objective is to maximize communication reliability while minimizing system energy consumption, specifically utilizing a dual-channel switching mechanism to adapt between Line-of-Sight (LoS) and Non-Line-of-Sight (NLoS) communication channels. We propose a joint optimization model that integrates UAV trajectory planning with communication and computation resource allocation for platooning vehicles. To solve the non-convex optimization problem, we propose a novel optimization algorithm that integrates the Broyden-Fletcher-Goldfarb-Shanno (BFGS) method with Sequential Quadratic Programming (SQP). This combined approach iteratively determines effective search directions, ensuring convergence towards a locally optimal solution. Furthermore, we theoretically analyze the convergence of the proposed joint optimization model. Extensive simulations are carried out to demonstrate the effectiveness of the proposed optimization model and algorithm, showing significant improvements in communication reliability and energy efficiency compared to conventional single-channel approaches.