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Research papers on Edge computing and IoT

Recent and highly-cited academic work on edge computing and iot, gathered from Semantic Scholar, CrossRef and OpenAlex.

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  1. A Survey on the Edge Computing for the Internet of Things

    Wei Yu, Fan Liang, Xiaofei He, et al. · 2017 · IEEE Access · 1,516 citations

    The Internet of Things (IoT) now permeates our daily lives, providing important measurement and collection tools to inform our every decision. Millions of sensors and devices are continuously producing data and exchanging important messages via complex networks supporting machine-to-machine communications and monitoring and controlling critical smart-world infrastructures. As a strategy to mitigate the escalation in resource congestion, edge computing has emerged as a new paradigm to solve IoT and localized computing needs. Compared with the well-known cloud computing, edge computing will migrate data computation or storage to the network “edge”, near the end users. Thus, a number of computa

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  2. Mobile Edge Computing and Networking for Green and Low-Latency Internet of Things

    Ke Zhang, Supeng Leng, Yejun He, et al. · 2018 · IEEE Communications Magazine · 252 citations

    IoT, a heterogeneous interconnection of smart devices, is a great platform to develop novel mobile applications. Resource constrained smart devices, however, often become the bottlenecks to fully realize such developments, especially when it comes to intensive-computation-oriented and low-latency-demanding applications. MEC is a promising approach to address such challenges. In this article, we focus on MEC applications for IoT, and address energy efficiency as well as offloading performance of such applications in terms of end-user experience. In this regard, we present a mobility-aware hierarchical MEC framework for green and low-latency IoT. We deploy a game theoretic approach for computa

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  3. Joint Optimization of Energy Consumption and Latency in Mobile Edge Computing for Internet of Things

    Laizhong Cui, Chong Xu, Shu Yang, et al. · 2018 · IEEE Internet of Things Journal · 180 citations

    With wide adoption of Internet of Things (IoT) across the world, the IoT devices are facing more and more intensive computation task nowadays. However, the IoT devices are usually limited by their computing capability and battery lifetime. Mobile edge computing provides new opportunities for developments of IoT, since edge computing servers which are close to devices can provide more powerful computing resources. The IoT devices can offload the intensive computing tasks to edge computing servers, while saving their own computing resources and reducing energy consumption. However, the benefits come at the cost of higher latency, mainly due to additional transmission time, and it may be unacce

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  5. Two-Stage Offloading Optimization for Energy–Latency Tradeoff With Mobile Edge Computing in Maritime Internet of Things

    Tingting Yang, Hailong Feng, Shan Gao, et al. · 2019 · IEEE Internet of Things Journal · 130 citations

    The ever-increasing growth in maritime activities with large amounts of Maritime Internet-of-Things (M-IoT) devices and the exploration of ocean network leads to a great challenge for dealing with a massive amount of maritime data in a cost-effective and energy-efficient way. However, the resources-constrained maritime users cannot meet the high requirements of transmission delay and energy consumption, due to the excessive traffic and limited resources in maritime networks. To solve this problem, mobile edge computing is taken as a promising paradigm to help mobile devices from edge servers via computation offloading considering the different quality of service (QoS) with the complex ocean

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  14. Energy and Latency Optimization in Edge-Fog-Cloud Computing for the Internet of Medical Things

    Hatem A. Alharbi, Barzan A. Yosuf, Mohammad Aldossary, et al. · 2023 · Computer Systems Science and Engineering · 13 citations

    In this paper, the Internet of Medical Things (IoMT) is identified as a promising solution, which integrates with the cloud computing environment to provide remote health monitoring solutions and improve the quality of service (QoS) in the healthcare sector. However, problems with the present architectural models such as those related to energy consumption, service latency, execution cost, and resource usage, remain a major concern for adopting IoMT applications. To address these problems, this work presents a four-tier IoMT-edge-fog-cloud architecture along with an optimization model formulated using Mixed Integer Linear Programming (MILP), with the objective of efficiently processing and p

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  15. MADRL-Based Edge Computing: Joint Energy-Latency Optimization for Marine Internet of Things

    Weijian Xu, Wenqian Luo, Yanglong Sun, et al. · 2025 · IEEE Internet of Things Journal · 4 citations

    Mobile edge computing technology has facilitated the deployment of high computational algorithms on Marine Internet of things (MIoT) devices that equipped with limited computing resources. However, the dynamic changes in the network environment, the strict requirements of system latency and energy consumption of mobile devices, restrict the task execution in MIoT. This paper proposes an offloading framework in a marine mobile edge computing scenario, which is assisted by sea buoys and unmanned aerial vehicles (UAVs). Aiming to achieve long-term system optimization goals under resource-constrained conditions, with task partitioning, user scheduling, and resource allocation joint optimization

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  17. Edge Computing Architectures for Low-Latency Data Processing in Internet of Things Applications

    Sreenu Banoth, M Vineesha, Hari Shankar Punna, et al. · 2025 · ITM Web of Conferences · 3 citations

    The explosion of Internet of Things (IoT) devices is leading to a need for ever-increasing low-latency data processing and real-time decision-making. Conventional cloud-based architectures, on the other hand, usually lead to high latency and bandwidth constraints which are not compliant to time-sensitive IoT applications. Existing paradigms emphasis on cloud computing, the emerging edge computing architecture enable us to take care of of real-time processing, scalability, energy efficiency as well with similar security and fault tolerance. In contrast with literature which are not tied in real-life applications and lack practical validations, this paper does extensive benchmarking on multipl

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  18. Fine-grained latency analysis of real-world 5G-enabled multi-tier edge computing for school zone traffic safety

    Ahmed Chebaane, Dominic Scholze, Yassine Rezgui, et al. · 2025 · Discover Internet of Things · 2 citations

    This article presents a real-world evaluation of 5-Safe, an innovative multi-tier 5G edge computing system designed to enhance traffic safety in school zones. The study addresses the critical challenge of implementing reliable and low-latency communication, data processing, and decision-making in complex urban environments. We employ a diverse methodology, including User Datagram Protocol (UDP) transmission analysis, Graphic Processing Unit (GPU) evaluation, and comparative studies of the messaging protocol Message Queueing Telemetry Transport (MQTT) and the Apache Kafka streaming platform. Our research revealed significant insights into system performance, such as latency variability in UDP

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  19. APPLICATION OF EDGE COMPUTING TO REDUCE LATENCY IN INDUSTRIAL INTERNET OF THINGS SYSTEMS

    Rudnev Arseny Vadimovich · CyberLeninK (CyberLeninka)

    The article examines the application of EC to reduce latency in IIoT systems. The relevance of the study is determined by the increasing need for real-time data processing in industrial environments, where delayed responses may affect equipment control, anomaly detection, machine vision, safety monitoring, and predictive maintenance. The article shows that cloud-centric architectures are not always sufficient for time-sensitive industrial tasks because they increase data transmission delay and dependence on network stability. EC is considered as an architectural layer that moves filtering, local inference, task offloading, and preliminary analytics closer to industrial equipment. Particular

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  21. Optimization of Mobile Edge Computing and Networking for Ultra-Low Latency and Green Internet of Things Applications

    Prasidhee Koli · 2026 · International Journal for Research in Applied Science and Engineering Technology

    IoT growth has quietly outpaced what cloud-centric network architectures were built to handle. When billions of sensors, actuators, and embedded controllers started producing continuous data streams, the assumption that round-trip latency toadistantserverwasanacceptablecoststoppedholding.In industrial automation lines, hospital monitoring loops, and vehicle-tovehicle coordination, tens of milliseconds separate a correctly functioning system from a failed one. This paper examines Mobile Edge Computing (MEC) as a structural fix: computation moves physically closer to wheredataoriginates,cuttingpropagationdelayandrelievingbackhaul pressureatthesametime.Welookathowtaskoffloading,energy budgeting

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