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Research papers on Edge computing and IoT

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  1. iFogSim: A toolkit for modeling and simulation of resource management techniques in the Internet of Things, Edge and Fog computing environments

    Harshit Gupta, Amir Vahid Dastjerdi, S. Ghosh, et al. · 2016 · Software: Practice and Experience · 1,598 citations

    Internet of Things (IoT) aims to bring every object (eg, smart cameras, wearable, environmental sensors, home appliances, and vehicles) online, hence generating massive volume of data that can overwhelm storage systems and data analytics applications. Cloud computing offers services at the infrastructure level that can scale to IoT storage and processing requirements. However, there are applications such as health monitoring and emergency response that require low latency, and delay that is caused by transferring data to the cloud and then back to the application can seriously impact their performances. To overcome this limitation, Fog computing paradigm has been proposed, where cloud servic

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

    Wei Yu, Fan Liang, Xiaofei He, et al. · 2017 · IEEE Access · 1,515 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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  3. Edge Computing in Industrial Internet of Things: Architecture, Advances and Challenges

    T. Qiu, J. Chi, Xiaobo Zhou, et al. · 2020 · IEEE Communications Surveys & Tutorials · 718 citations

    The Industrial Internet of Things (IIoT) is a crucial research field spawned by the Internet of Things (IoT). IIoT links all types of industrial equipment through the network; establishes data acquisition, exchange, and analysis systems; and optimizes processes and services, so as to reduce cost and enhance productivity. The introduction of edge computing in IIoT can significantly reduce the decision-making latency, save bandwidth resources, and to some extent, protect privacy. This paper outlines the research progress concerning edge computing in IIoT. First, the concepts of IIoT and edge computing are discussed, and subsequently, the research progress of edge computing is discussed and sum

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  4. Edge-computing-driven Internet of Things: A Survey

    L. Kong, Jinlin Tan, Junqin Huang, et al. · 2022 · ACM Computing Surveys · 404 citations

    The Internet of Things (IoT) is impacting the world’s connectivity landscape. More and more IoT devices are connected, bringing many benefits to our daily lives. However, the influx of IoT devices poses non-trivial challenges for the existing cloud-based computing paradigm. In the cloud-based architecture, a large amount of IoT data is transferred to the cloud for data management, analysis, and decision making. It could not only cause a heavy workload on the cloud but also result in unacceptable network latency, ultimately undermining the benefits of cloud-based computing. To address these challenges, researchers are looking for new computing models for the IoT. Edge computing, a new decentr

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  5. 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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  6. Edge Computing and Cloud Computing for Internet of Things: A Review

    Francesco Cosimo Andriulo, Marco Fiore, Marina Mongiello, et al. · 2024 · Informatics · 216 citations

    The rapid expansion of the Internet of Things ecosystem has created an urgent need for efficient data processing and analysis technologies. This review aims to systematically examine and compare edge computing, cloud computing, and hybrid architectures, focusing on their applications within IoT environments. The methodology involved a comprehensive search and analysis of peer-reviewed journals, conference proceedings, and industry reports, highlighting recent advancements in computing technologies for IoT. Key findings reveal that edge computing excels in reducing latency and enhancing data privacy through localized processing, while cloud computing offers superior scalability and flexibilit

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  7. 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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  9. 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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  12. A lightweight scalable hybrid authentication framework for Internet of Medical Things (IoMT) using blockchain hyperledger consortium network with edge computing

    Abdullah Ayub Khan, A. A. Laghari, Roobaea Alroobaea, et al. · 2025 · Scientific Reports · 62 citations

    The Internet of Things (IoMT) has revolutionized the global landscape by enabling the hierarchy of interconnectivity between medical devices, sensors, and healthcare applications. Significant limitations in terms of scalability, privacy, and security are associated with this connection. This study presents a scalable, lightweight hybrid authentication system that integrates blockchain and edge computing within a Hyperledger Consortium network to address such real-time problems, particularly the use of Hyperledger Indy. For secure authentication, Hyperledger ensures a permissioned, decentralized, and impenetrable environment, while edge computing lowers latency by processing data closer to Io

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  13. Current state and prospects of edge computing within the Internet of Things (IoT) ecosystem

    Enoch Oluwademilade Sodiya, Uchenna Joseph Umoga, Alexander Obaigbena, et al. · 2024 · International Journal of Science and Research Archive · 59 citations

    The burgeoning growth of the Internet of Things (IoT) has prompted a paradigm shift in computing architectures, leading to the emergence and rapid evolution of edge computing. This review delves into the current state and prospects of edge computing within the IoT ecosystem, exploring its significance, challenges, and future potential. Edge computing, characterized by decentralized data processing at or near the source of data generation, has gained substantial traction owing to its ability to address critical concerns such as latency, bandwidth consumption, and privacy issues inherent in centralized cloud-based systems. By enabling data processing closer to the point of collection, edge com

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  15. SCOF: Security-Aware Computation Offloading Using Federated Reinforcement Learning in Industrial Internet of Things With Edge Computing

    Kai Peng, Peiyun Xiao, Shang-Guang Wang, et al. · 2024 · IEEE Transactions on Services Computing · 41 citations

    Industry 5.0 facilitates the intelligent upgrade of smart factories in Industrial Internet of Things (IIoT), and also introduces a plethora of data processing challenges. Mobile edge computing offloads data to edge servers for processing, easing the data processing pressure and reducing system cost. However, smart factories contain numerous sensitive information, and offloading them to edge servers directly may pose a risk of data leakage. To address these challenges, we investigate a local-edge collaborative smart factory system. Specifically, we first model the tasks as a directed acyclic graph, and formulate the offloading problem as a Markov decision process, considering the optimization

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  17. AI augmented edge and fog computing for Internet of Health Things (IoHT)

    D. Rajagopal, P. Subramanian · 2025 · PeerJ Computer Science · 36 citations

    Patients today seek a more advanced and personalized health-care system that keeps up with the pace of modern living. Cloud computing delivers resources over the Internet and enables the deployment of an infinite number of applications to provide services to many sectors. The primary limitation of these cloud frameworks right now is their limited scalability, which results in their inability to meet needs. An edge/fog computing environment, paired with current computing techniques, is the answer to fulfill the energy efficiency and latency requirements for the real-time collection and analysis of health data. Additionally, the Internet of Things (IoT) revolution has been essential in changin

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  19. Edge Computing Enabling Internet of Ships: A Survey on Architectures, Emerging Applications, and Challenges

    Hua-Long Chen, Yuan-Qiao Wen, Ya-Min Huang, et al. · 2025 · IEEE Internet of Things Journal · 31 citations

    The Internet of Ships (IoS), by integrating advanced technologies, such as the Internet of Things, cloud computing, and artificial intelligence, aims to interconnect and communicate various physical devices related to maritime transportation, including ships, ports, traffic infrastructure, and warehouses. This integration is designed to optimize transportation decision making, reduce costs, enhance efficiency, improve safety, and promote environmental sustainability. Traditional IoS adopts a cloud computing-based data processing and service model, which, due to its centralized and remotely deployed nature, often places computing nodes far from the data collection and service demand points. T

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  22. Analyzing the impact of edge, fog and cloud computing on predictive maintenance in the Industrial Internet of Things

    Dimple Kapoor, Deepali Gupta, Mudita Uppal · 2025 · Discover Computing · 21 citations

    The Industrial Internet of Things is transforming industrial processes through the integration of edge, fog, and cloud computing to offer predictive maintenance to reduce unexpected downtime and increase operational effectiveness. Limited use of predictive maintenance is due to antiquated legacy systems, the availability and security of dependable communication networks, and the provision of big datasets in real time. This study proposes a novel multilayer framework that uses MQTT protocol for transferring data across the layers along with systematically integrating pre-processing and machine learning algorithms to ensure robust and accurate predictions. It uses real-time data collection, lo

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  23. End-Edge Collaborative Inference of Convolutional Fuzzy Neural Networks for Big Data-Driven Internet of Things

    Yu-Hao Hu, Xiao-Long Xu, Li Duan, et al. · 2025 · IEEE Transactions on Fuzzy Systems · 21 citations

    Deep neural networks (DNN) has been widely applied in big data-driven Internet of Things (IoT) for excellent learning ability, while the black-box nature of DNN leads to uncertainty of inference results. With higher interpretability, convolutional fuzzy neural network (CFNN) becomes an alternative choice for the model of IoT applications. IoT applications are often latency-sensitive. By jointly utilizing computing power of IoT devices and edge servers, end-edge collaborative CFNN inference improves the insufficiency of local computing resources and reduces the latency of computing-intensive CFNN inference. However, the calculation amount of fuzzy layers is hard to get directly, bringing diff

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