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Telemedicine Monitoring System Based on fog/edge computing: A Survey

IEEE TRANSACTIONS ON SERVICES computing 2025年第1期18卷 479-498页

作者： He, Qiang Xi, Zhaolin Feng, Zheng Teng, Yueyang Ma, Lianbo Cai, Yuliang Yu, Keping Northeastern Univ Coll Med & Biol Informat Engn Shenyang 110169 Peoples R China Northeastern Univ Coll Software Shenyang 110169 Peoples R China Liaoning Univ Sch Math & Stat Shenyang 110036 Peoples R China Hosei Univ Grad Sch Sci & Engn Tokyo 1848584 Japan

Telemedicine Monitoring (TM) integrates mobile communication technology and Internet of Things (IoT) technology for health monitoring and data management. Amidst the escalating demand for telemedicine, traditional cloud computing struggles to guarantee real-time performance and data privacy. To address these challenges, we systematically survey the application of fog and edge computing technologies in TM systems. We focus on the following key aspects: (1) We delve into the theoretical foundations of fog and edge computing, underscoring their salient advantages including low latency, location awareness, high mobility, and more. (2) We elaborate on the architecture of a TM system hinged on fog and edge computing. (3) We outline key challenges facing fog/edge computing-based TM systems, including bandwidth limitations, low latency, data security, privacy, heterogeneity, and reliability. (4) We discuss the need for future advancements in the realms of security defense capability, system adaptability, and convergence of scheduling algorithms to refine the construction of the TM system and stimulate the development of telemedicine.

关键词： edge computing Cloud computing Telemedicine Medical services Surveys Real-time systems Internet of Things Reliability Data privacy Low latency communication fog/edge computing healthcare telemedicine monitoring system (TM)

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Person Re-Identification with Attribute-Guided, Robust-to-Low-Resolution Drone Footage Considering fog/edge computing

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SENSORS 2025年第6期25卷 1819-1819页

作者： Kim, Bongjun Kim, Sunkyu Park, Seokwon Jeong, Junho Dongguk Univ Dept Comp Sci & Engn Seoul 04620 South Korea

In aerial surveillance using drones, person re-identification (ReID) is crucial for public safety. However, low resolutions in drone footage often leads to a significant drop in ReID performance of subjects. To investigate this issue, rather than relying solely on real-world datasets, we employed a synthetic dataset that systematically captures variations in drone altitude and distance. We also utilized an eXplainable Artificial Intelligence (XAI) framework to analyze how low resolutions affect ReID. Based on our findings, we propose a method that improves ReID accuracy by filtering out attributes that are not robust in low-resolution environments and retaining only those features that remain reliable. Experiments on the Market1501 dataset show a 6.59% percentage point improvement in accuracy at a 16% resolution scale. We further discuss the effectiveness of our approach in drone-based aerial surveillance systems under fog/edge computing paradigms.

关键词： re-identification vision fog/edge computing drone footage aerial surveillance eXplainable Artificial Intelligence (XAI)

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Governing Resource Failures through Reinforcement Learning Scheduling in fog/edge computing: A Review

Governing Resource Failures through Reinforcement Learning S...

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IEEE International Conference on Automatic Control and Intelligent Systems (I2CACIS)

作者： Muhamad, Aliyu Hussin, Masnida Univ Putra Malaysia UPM Fac Comp Sci & Informat Technol Serdang 43400 Selangor Darul Malaysia

ISBN: (纸本)9798350372113;9798350372106

The exponential growth of IoT applications due to user demands necessitates the emergence of reliable fog/edge computing systems. Because of the wide-open area of these systems, the tendency for failure in communication channels and computing nodes (resources) is unpredictable, especially in real-time IoT applications. Several studies have aimed at tackling fault and failure issues by employing a variety of resource management strategies. Most studies use heuristics or conventional scheduling approaches that leverage the use of reinforcement learning. The element of reinforcement learning aims to support better governance when resource faults and failures occur. This paper provides an analysis of the implementation of reinforcement learning (RL) into fault-tolerant scheduling in fog/edge computing systems. The efficacy of fault-tolerant scheduling is claimed to be improved through RL with regard to system performance. We grouped the discussion based on three themes: resource failures, reinforcement learning (RL) in general, and RL scheduling for fault tolerance. The discussion could further derive solutions for overcoming the challenges of resource failures in fog/edge systems.

关键词： fog/edge computing fault tolerance reinforcement learning scheduling review study

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Towards Efficient Offloading in fog/edge computing by Approximating Effect of Externalities

Towards Efficient Offloading in Fog/Edge Computing by Approx...

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IEEE Conference on Computer Communications (IEEE INFOCOM)

作者： Marbukh, Vladimir NIST 100 Bur DrStop 8910 Gaithersburg MD 20899 USA

ISBN: (纸本)9781538659793

fog/edge computing is an emerging architecture, which extends the Cloud computing paradigm to the edge of the network, enabling new applications and services, including Internet of Things (IoT). End devices have certain computation tasks, which can be completed either locally on the end device or remotely on the cloud via computation offloading. Due to mobility and highly dynamic nature of end users, the access is wireless with severe physical limitations on the access network capacity to sustain stringent latency requirements for streaming and real-time applications for large number of end users over wide-spread geographical area. Assuming that access network has cellular architecture and is interference limited, we quantify system performance by the aggregate utility. Optimization of the system computing and communication resources requires accounting for externalities due to interference created by the individual offloading transmissions. While each base station can account for the intracell externalities, exact accounting for the intercell externalities in a large-scale network is not feasible due to prohibitive exchange of the "microscopic" information on the intercell interference by each end user. We propose a "macroscopic" approximation for the intercell externalities, which significantly reduces information exchange, and thus allows for decentralized near-maximization of the aggregate system utility.

关键词： fog/edge computing offloading utility resource management externalities approximation

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Resource Management in fog/edge computing: A Survey on Architectures, Infrastructure, and Algorithms

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ACM computing SURVEYS 2019年第5期52卷 97-97页

作者： Hong, Cheol-Ho Varghese, Blesson Chung Ang Univ 84 Heukseok Ro Seoul 06974 South Korea Queens Univ Belfast Univ Rd Belfast BT7 1NN Antrim North Ireland

Contrary to using distant and centralized cloud data center resources, employing decentralized resources at the edge of a network for processing data closer to user devices, such as smartphones and tablets, is an upcoming computing paradigm, referred to as fog/edge computing. fog/edge resources are typically resource-constrained, heterogeneous, and dynamic compared to the cloud, thereby making resource management an important challenge that needs to be addressed. This article reviews publications as early as 1991, with 85% of the publications between 2013 and 2018, to identify and classify the architectures, infrastructure, and underlying algorithms for managing resources in fog/edge computing.

关键词： fog/edge computing resource management architectures infrastructure algorithms

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Blockchain and RL-Based Secured Task Offloading Framework for Software-Defined 5G edge Networks

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IEEE ACCESS 2025年 13卷 56820-56842页

作者： Subramanian, N. Sethu Krishnan, Prabhakar Jain, Kurunandan Kumar, K. B. Aneesh Pandey, Tulika Buyya, Rajkumar Amrita Vishwa Vidyapeetham Ctr Cybersecur Syst & Networks Amritapuri Campus Kollam 690525 India Ctr Dev Adv Comp CDAC Cyber Secur Grp Thiruvananthapuram 695033 Kerala India Govt India Res & Dev Cybersecur Grp Minist Elect & Informat Technol MeitY New Delhi 110003 India Univ Melbourne Sch Comp & Informat Syst Cloud Comp & Distributed Syst Lab Melbourne Vic 3010 Australia

5G (Fifth Generation) technology represents a significant advancement in telecommunications, facilitating industry innovation and advancement for applications across various sectors. It enhances high-speed, low-latency communication, making it ideal for task offloading in resource-constrained mobile devices. By leveraging task offloading, 5G networks maximize the efficiency of both computational and network resources, ensuring faster, more reliable data delivery and enabling high-performance requirements of modern applications. However, dynamic and secure task offloading remains a challenge due to fluctuating network conditions and trust concerns. This paper proposes SAGE (Secured Adaptive Generalized edge), a reinforcement learning-based task offloading framework that leverages contextual bandits for adaptive decision-making in Multi-Access edge computing (MEC) environments. By integrating blockchain smart contracts for security and SDN-based orchestration for dynamic resource management, SAGE ensures robust, low-latency offloading. Experimental evaluations demonstrate that SAGE reduces task offloading delays by 36% and task durations by 30% compared to baseline methods under varying load and energy constraints.

关键词： Blockchains Security 5G mobile communication Delays Resource management Cloud computing Vehicle dynamics Servers Privacy Quality of service 5G mobile software-defined-networking (SDN) mobile edge-computing (MEC) blockchain-assisted offloading fog/edge computing security trust modeling dynamic edge server offload (DESO) secured adaptive generalized edge (SAGE)

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Enhanced fall prevention: A real-time hybrid analysis with smart walking stick & edge-based IoMT

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COMPUTERS & ELECTRICAL ENGINEERING 2025年 124卷

作者： Bhattacharjee, Pratik Sarkar, Indranil Biswas, Suparna Sister Nivedita Univ Kolkata W Bengal India Maulana Abul Kalam Azad Univ Technol Kolkata W Bengal India

Human fall poses a significant risk for the elderly. A fall can result in hospitalization or, tragically, death. The primary causes of falls among the elderly are often linked to a loss of balance or insufficient limb support. While fall prevention is difficult, fall risk analysis can help predict and prevent future falls. The present work proposes a real-time 3-way hybrid fall risk factor analysis methodology, , employing a smart walking stick. It uses an edge-based IoMT (Internet of Medical Things) architecture that is extendable to the Cloud. The smart walking stick has a 10 kg load cell (YZC-133) with HX711 (mounted on the grip) and a MPU 6050 kinematic sensor paired with an ESP8266 WiFi Micro Controller (MCU) to transfer the accelerometer, gyroscope and load cell data to the processing unit. A Raspberry Pi-based edge device evaluates pressure (support) on the grip and walking patterns using an accelerometer, gyroscope, and load sensor signals connected with ESP 8266. The system could perform fine grain fall analysis by classifying the subjects into the risk categories of High/Medium/Low/None. The system used the clinically established parameters and tests for its multimodal analysis at low cost based on Timed Up and Go (TUG), Force test and Gait analysis modules. The individual results from each module were then combined to predict the final risk category. The long-term analysis is done on a cloud server and the system could predict falls with a maximum of 92% accuracy.

关键词： Fall risk analysis Smart walking stick ESP8266 MPU 6050 HX711 Multi-modal analysis fog/edge computing IoMT

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Privacy-preserving security of IoT networks: A comparative analysis of methods and applications

Cyber Security and Applications

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Cyber Security and Applications 2025年 3卷

作者： Wakili, Abubakar Bakkali, Sara Euromed University of Fez Morocco

The Internet of Things (IoT) connects devices to enhance efficiency, productivity, and quality of life. However, deploying IoT networks introduces critical privacy and security challenges, including resource constraints, scalability issues, interoperability gaps, and risks to data privacy. Addressing these challenges is vital to ensure the reliability and trustworthiness of IoT applications. This study provides a comprehensive analysis of privacy-preserving security methods, evaluating cryptography, blockchain, machine learning, and fog/edge computing against performance indicators such as scalability, efficiency, robustness, and usability. Through a structured literature review and thorough data analysis, the study reveals that while cryptography offers high security, it faces scalability challenges;blockchain excels in decentralization but struggles with efficiency;machine learning provides adaptive intelligence but raises privacy concerns;and fog/edge computing delivers low-latency processing yet encounters operational complexities. The findings highlight the importance of adopting a hybrid approach that combines the strengths of various methods to overcome their limitations. This study serves as a valuable resource for academia, industry professionals, and policymakers, providing guidance to strengthen IoT infrastructures and influence the direction of future research. © 2025

关键词： Blockchain Cryptography fog/edge computing Internet of Things (IoT) networks Machine learning Privacy-preserving security

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Collaborative Energy Beamforming for Wireless Powered fog computing Networks

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2022年第10期21卷 7942-7956页

作者： Chiu, Te-Chuan Wang, Chih-Yu Pang, Ai-Chun Chung, Wei-Ho Natl Tsing Hua Univ Dept Comp Sci Hsinchu 300044 Taiwan Acad Sinica Res Ctr Informat Technol Innovat CITI Taipei 11529 Taiwan Natl Taiwan Univ Grad Inst Networking & Multimedia Dept Comp Sci & Informat Engn Taipei 10617 Taiwan Natl Tsing Hua Univ Dept Elect Engn Hsinchu 300044 Taiwan

Beam-based wireless power transfer and fog/edge computing are promising dual technologies for realizing wireless powered fog computing networks to support the upcoming 135C/6G IoT applications, which require latency-aware and intensive computing, with a limited energy supply. In such systems, IoT devices can either offload their computing tasks to the proximal fog nodes or execute local computing with replenishing energy from the dedicated beamforming. However, effective integration of these techniques is still challenging, where two new issues arise: energy-aware task offloading and signal interferences from spillovers of wireless beamforming. In this paper, we observe that the beam-ripple phenomenon, which takes advantage of beamformer defects to transfer energy to IoT devices, is the key to jointly addressing these two issues. Different from traditional SWEPT technology, as in our approach the stream is not separately divided into data/energy streams, but target IoT devices can potentially harvest the whole stream. Inspired by this phenomenon, we treat the collaborative energy beamforming and edge computing design as a strongly NP-hard optimization problem. The proposed solution is an iterative algorithm to cascadingly integrate a polynomial-time (1 - 1/e)-approximation algorithm, which achieves the theoretical upper bound in approximation ratio unless P = NP, and an optimal dynamic programming algorithm. The numerical results show that the energy minimization goal among IoT devices can achieve, and the developed harvest-when-interfered protocol is practical in the wireless powered fog computing networks.

关键词： B5G/6G cellular networks fog/edge computing wireless powered fog computing networks energy beamforming wireless power transfer beam-ripple phenomenon

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Combination of Task Allocation and Approximate computing for fog-Architecture-Based IoT

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IEEE INTERNET OF THINGS JOURNAL 2021年第9期8卷 7638-7648页

作者： Yu, Wanli Najafi, Ardalan Huang, Yanqiu Garcia-Ortiz, Alberto Univ Bremen Inst Electrodynam & Microelect D-28359 Bremen Germany Univ Twente Fac Elect Engn Math & Comp Sci NL-7522 NH Enschede Netherlands

Achieving energy efficiency is always a primary concern for fog-architecture-based Internet of Things (IoT) applications. As the IoT devices are typically of small sizes and powered by battery energy, it is essential to address the energy consumption at all levels from the circuit to the system. Two of the promising solutions at circuit and system levels are approximate computing and energy-aware task allocation, respectively. However, the existing task allocation approaches are designed without considering the aspect of approximate computing. In this work, we fill this gap and aim to maximize the network lifetime subject to the accuracy requirements of the applications. By considering both the approximate computing and task allocation simultaneously, a nonlinear problem is obtained to allocate the tasks for the devices (fog nodes and IoT end devices) and to select the corresponding execution modes (tasks in approximate or exact modes). To efficiently solve this problem, a centralized algorithm is first proposed by transferring the above nonlinear problem as a linear programming (LP) problem. As executing the centralized algorithm is a challenge for the resource-limited IoT devices, this work further proposes an optimal distributed algorithm based on Dantzig-Wolfe decomposition to solve the problem of tasks distribution and execution modes selection. The centralized large-scaled LP problem is decomposed into small-scaled subproblems, which can be efficiently solved by each IoT device. The proposed algorithms are tested by extensive simulations. The results demonstrate that the distributed algorithm achieves the same results as the centralized algorithm, and both of them significantly outperform the previous approaches.

关键词： Approximate computing fog/edge computing Internet of Things (IoT) network lifetime task allocation

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