An Adaptive Edge Router Enabling Internet of Things

Jutila, Mirjami

doi:10.1109/jiot.2016.2550561

Cited by 72 publications

(30 citation statements)

References 12 publications

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“…EC carries the data computing that is outperformed at the "edge" of Industrial wireless networks. In addition, distributing computation nodes deployed at the edge can allow the offloading of traffic from the cloud for better response time [21], [22]. Thus, our proposed work reasonably decreases the transmission latency during data computing as well as power consumption in the SDN based-IIoT architecture with edge computing.…”

Section: Iiot and Edge Computingmentioning

confidence: 87%

A Self Configurable Edge Computing for Industrial IoT

Chandramohan¹,

Senthilkumaran²

2019

IJEAT

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The proliferation of Industrial Internet of Things (IIoT) introduces the concept of a smarter production environment. The emerging technologies like software defined network (SDN), IIoT and cloud computing will bring great advancements in the modern industrial revolution called Industry 4.0. Therefore, with the rapid development of IIoT technology, the proposed work incorporates with Edge Computing (EC). The current manufacturing process and automation, computing and wireless network reaches out to headways in innovation from easy to the point where all things (devices) and machines can interface through an Internet of Everything (IoE). This paper extends the work carried out in traditional methods, by integrating the cloud layer, Automatic Guided Vehicles (AGV), Industrial Wireless networks (IWN) and Industrial robots through EC is conferred to make autonomous decision-making capabilities. EC is emerging as a significant element in the smart industry to bring legacy in the context of Industrial IoT (IIoT). Finally, our proposed framework demonstrates that the active RFID-enabled AGV and industrial robots are brought in to exploit for effective resource management under the EC-based IIoT architecture, subsequently, it improves the conveyor efficiency and overall energy consumption in the warehouse for material handling.

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Section: Iiot and Edge Computingmentioning

confidence: 87%

A Self Configurable Edge Computing for Industrial IoT

Chandramohan¹,

Senthilkumaran²

2019

IJEAT

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“…After reviewing the selected papers, we found that the recent literature has focused on using edge technologies as a new concept that provides localized computing resources for reducing the latency, power consumption of IoT devices, and time of data processing at the edge network. Furthermore, among the selected works, we found that the usability of MCDA algorithms is still in an early stage at edge computing, such as using fuzzy for making decision of whether to execute tasks locally or offload them to fog servers [33], or for monitoring and controlling the network quality-of-service changes within heterogeneous networks at edge computing [48]. Another work proposed a framework based on AHP (Analytic Hierarchy Process) and TOPSIS algorithms for selecting an adaptive mobile cloud service from clouds or cloudlets [49].…”

Section: Discussionmentioning

confidence: 99%

Moving towards Smart Cities: A Selection of Middleware for Fog-to-Cloud Services

et al. 2018

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Smart cities aim at integrating various IoT (Internet of Things) technologies by providing many opportunities for the development, governance, and management of user services. One of the ways to support this idea is to use cloud and edge computing techniques to reduce costs, manage resource consumption, enhance performance, and connect the IoT devices more effectively. However, the selection of services remains a significant research question since there are currently different strategies towards cloud computing, including services for central remote computing (traditional cloud model) as well as distributed local computing (edge computing). In this paper, we offer an integrated view of these two directions and the selection among the edge technologies based on MCDA (Multiple Criteria Decision Analysis) algorithms. To this end, we propose a foglet as a middleware that aims at achieving satisfactory levels of customer services by using fuzzy similarity and TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) to facilitate the rating and selection of services in the fog-to-cloud environment. Then, we describe the selection process with a numerical example, and conclude our work with an outline of future perspectives.

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“…Several researchers attempted to integrate edge computing with IoT. Jutila et al proposed an adaptive computing method deployed at the edges of an IoT network for the optimization and control of traffic. Ahmed et al discussed various scenarios where edge computing can be used for the efficient processing of data.…”

Section: Related Paradigmsmentioning

confidence: 99%

Elucidating the challenges for the praxis of fog computing: An aspect‐based study

Martin

Kandasamy

Chandrasekaran

et al. 2019

Int J Communication

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Summary The evolutionary advancements in the field of technology have led to the instigation of cloud computing. The Internet of Things paradigm stimulated the extensive use of sensors distributed across the network edges. The cloud datacenters are assigned the responsibility for processing the collected sensor data. Recently, fog computing was conceptuated as a solution for the overwhelmed narrow bandwidth. The fog acts as a complementary layer that interplays with the cloud and edge computing layers, for processing the data streams. The fog paradigm, as any distributed paradigm, has its set of inherent challenges. The fog environment necessitates the development of management platforms that effectuates the orchestration of fog entities. Owing to the plenitude of research efforts directed toward these issues in a relatively young field, there is a need to organize the different research works. In this study, we provide a compendious review of the research approaches in the domain, with special emphasis on the approaches for orchestration and propose a multilevel taxonomy to classify the existing research. The study also highlights the application realms of fog computing and delineates the open research challenges in the domain.

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An Adaptive Edge Router Enabling Internet of Things

Cited by 72 publications

References 12 publications

A Self Configurable Edge Computing for Industrial IoT

A Self Configurable Edge Computing for Industrial IoT

Moving towards Smart Cities: A Selection of Middleware for Fog-to-Cloud Services

Elucidating the challenges for the praxis of fog computing: An aspect‐based study

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