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Research papers on Edge computing and IoT

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  2. Internet of Things: A Survey on Enabling Technologies, Protocols, and Applications

    Ala Al‐Fuqaha, Mohsen Guizani, Mehdi Mohammadi, et al. · 2015 · IEEE Communications Surveys & Tutorials · 8,576 citations

    This paper provides an overview of the Internet of Things (IoT) with emphasis on enabling technologies, protocols, and application issues. The IoT is enabled by the latest developments in RFID, smart sensors, communication technologies, and Internet protocols. The basic premise is to have smart sensors collaborate directly without human involvement to deliver a new class of applications. The current revolution in Internet, mobile, and machine-to-machine (M2M) technologies can be seen as the first phase of the IoT. In the coming years, the IoT is expected to bridge diverse technologies to enable new applications by connecting physical objects together in support of intelligent decision making

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  3. Internet of Things for Smart Cities

    Andréa Zanella, Nicola Bui, Angelo Castellani, et al. · 2014 · IEEE Internet of Things Journal · 6,295 citations

    The Internet of Things (IoT) shall be able to incorporate transparently and seamlessly a large number of different and heterogeneous end systems, while providing open access to selected subsets of data for the development of a plethora of digital services. Building a general architecture for the IoT is hence a very complex task, mainly because of the extremely large variety of devices, link layer technologies, and services that may be involved in such a system. In this paper, we focus specifically to an urban IoT system that, while still being quite a broad category, are characterized by their specific application domain. Urban IoTs, in fact, are designed to support the Smart City vision, wh

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  4. Fog computing and its role in the internet of things

    Flavio Bonomi, Rodolfo Milito, Jiang Zhu, et al. · 2012 · 6,071 citations

    Fog Computing extends the Cloud Computing paradigm to the edge of the network, thus enabling a new breed of applications and services. Defining characteristics of the Fog are: a) Low latency and location awareness; b) Wide-spread geographical distribution; c) Mobility; d) Very large number of nodes, e) Predominant role of wireless access, f) Strong presence of streaming and real time applications, g) Heterogeneity. In this paper we argue that the above characteristics make the Fog the appropriate platform for a number of critical Internet of Things (IoT) services and applications, namely, Connected Vehicle, Smart Grid, Smart Cities, and, in general, Wireless Sensors and Actuators Networks (W

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  5. A Survey on Mobile Edge Computing: The Communication Perspective

    Yuyi Mao, Changsheng You, Jun Zhang, et al. · 2017 · IEEE Communications Surveys & Tutorials · 5,676 citations

    Driven by the visions of Internet of Things and 5G communications, recent years have seen a paradigm shift in mobile computing, from the centralized mobile cloud computing toward mobile edge computing (MEC). The main feature of MEC is to push mobile computing, network control and storage to the network edges (e.g., base stations and access points) so as to enable computation-intensive and latency-critical applications at the resource-limited mobile devices. MEC promises dramatic reduction in latency and mobile energy consumption, tackling the key challenges for materializing 5G vision. The promised gains of MEC have motivated extensive efforts in both academia and industry on developing the

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

    Harshit Gupta, A. V. Dastjerdi, S. Ghosh, et al. · 2016 · Software: Practice and Experience · 1,639 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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  7. A Survey on the Edge Computing for the Internet of Things

    Wei Yu, Fan Liang, Xiaofei He, et al. · 2018 · 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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  8. The Promise of Edge Computing

    Weisong Shi, Schahram Dustdar · 2016 · Computer · 1,246 citations

    The success of the Internet of Things and rich cloud services have helped create the need for edge computing, in which data processing occurs in part at the network edge, rather than completely in the cloud. Edge computing could address concerns such as latency, mobile devices' limited battery life, bandwidth costs, security, and privacy.

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  9. Future Edge Cloud and Edge Computing for Internet of Things Applications

    Jianli Pan, James McElhannon · 2017 · IEEE Internet of Things Journal · 881 citations

    The Internet is evolving rapidly toward the future Internet of Things (IoT) which will potentially connect billions or even trillions of edge devices which could generate huge amount of data at a very high speed and some of the applications may require very low latency. The traditional cloud infrastructure will run into a series of difficulties due to centralized computation, storage, and networking in a small number of datacenters, and due to the relative long distance between the edge devices and the remote datacenters. To tackle this challenge, edge cloud and edge computing seem to be a promising possibility which provides resources closer to the resource-poor edge IoT devices and potenti

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  10. Edge Computing in Industrial Internet of Things: Architecture, Advances and Challenges

    T. Qiu, J. Chi, Xiaobo Zhou, et al. · 2020 · IEEE Communications Surveys & Tutorials · 861 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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  11. Edge-computing-driven Internet of Things: A Survey

    L. Kong, Jinlin Tan, Junqin Huang, et al. · 2022 · ACM Computing Surveys · 380 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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  12. 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 · 257 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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  13. Energy-Efficient Multiaccess Edge Computing for Terrestrial-Satellite Internet of Things

    Zhengyu Song, Yuanyuan Hao, Yuanwei Liu, et al. · 2021 · IEEE Internet of Things Journal · 213 citations

    The recent advances in low earth orbit (LEO) satellites enable the satellites to provide task processing capability for remote Internet-of-Things (IoT) mobile devices (IMDs) without proximal multiaccess edge computing (MEC) servers. In this article, by leveraging the LEO satellites, a novel MEC framework for terrestrial-satellite IoT is proposed. With the aid of terrestrial-satellite terminal (TST), the computation offloading from IMDs to LEO satellites is divided into two stages in the ground and space segments. In order to minimize the weighted-sum energy consumption of IMDs, we decompose the formulated problem into two layered subproblems: 1) the lower layer subproblem minimizing the late

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

    Francesco Cosimo Andriulo, Marco Fiore, Marina Mongiello, et al. · 2024 · Informatics · 200 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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  15. Joint Optimization of Energy Consumption and Latency in Mobile Edge Computing for Internet of Things

    Laizhong Cui, Chong Xu, Shu Yang, et al. · 2019 · IEEE Internet of Things Journal · 185 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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  18. 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. · 2020 · IEEE Internet of Things Journal · 96 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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  20. 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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  22. 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 · 55 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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  23. SCOF: Security-Aware Computation Offloading Using Federated Reinforcement Learning in Industrial Internet of Things With Edge Computing

    Kai Peng, Peiyun Xiao, Shangguang 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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