Routing in Industrial Wireless Sensor Networks: A Survey

Tiab, Amal; Bouallouche-Medjkoune, Louiza

doi:10.1155/2014/579874

Cited by 6 publications

(2 citation statements)

References 13 publications

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“…Hence, these two applications have gained low interest to both the users and the vendors, compared to the monitoring applications on which most of the users and vendors (88%) are of paramount interest according to the survey result carried out by the International Society of Automation [4]. e monitoring applications, otherwise, possess soft QoS requirements where successful data delivery is the main concern and acceptable delay requirements are in the order of seconds to minutes [26]. Hence, in this paper, our primary focus is on industrial monitoring applications.…”

Section: Instance Classificationmentioning

confidence: 99%

On Providing Differentiated Service Exploiting Multi-Instance RPL for Industrial Low-Power and Lossy Networks

Monowar

Basheri

2020

Wireless Communications and Mobile Computing

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The rapid proliferation of low-power wireless devices enables the industrial users to improve the productivity and safety of the plants as well as efficient management of the system. This can be achieved through significant increase in data collection, remote monitoring, and control of the plants and promoting the development of industrial Internet of Things (IoT) applications. However, the industrial environment is typically harsh causing high link quality variations and topology changes. The wireless devices used in this environment are also resource constrained in terms of energy, memory, and processing power. In spite of their low-power and lossy nature, these networks demand provisioning of differentiated services for various industrial applications having diverse quality of service (QoS) requirements. Considering the unique characteristics of low-power and lossy networks (LLN), routing for low-power and lossy networks (RPL) is devised which was standardized by IETF in 2012. To meet the demand of diverse traffic, RPL supports multiple instances in a single network. This paper proposes MI-RPL, a multi-instance solution of RPL for industrial low-power and lossy networks (LLNs). MI-RPL defines four instances for four distinct traffic classes of industrial monitoring applications in terms of delay and reliability. MI-RPL also introduces composite routing metrics and proposes an objective function (OF) to compute the most suitable path for each instance. The performance of MI-RPL is investigated through simulations that exhibit MI-RPL has better delay and packet delivery performance for delay- and reliability-constrained traffic along with lower energy consumption compared to the standard RPL.

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Section: Instance Classificationmentioning

confidence: 99%

On Providing Differentiated Service Exploiting Multi-Instance RPL for Industrial Low-Power and Lossy Networks

Monowar

Basheri

2020

Wireless Communications and Mobile Computing

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“…The aforementioned routing protocols obtain good performance for different application environments. However, their performances are not ideal when gathering real-time big data for risk analysis of industrial operations because they did not comprehensively consider the signal transmission characteristics and routing requirements for industrial WSN [34].…”

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confidence: 99%

A Real-Time Big Data Gathering Algorithm Based on Indoor Wireless Sensor Networks for Risk Analysis of Industrial Operations

Ding

Tian

2016

IEEE Trans. Ind. Inf.

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The era of big data has begun, and an enormous amount of real-time data is used for the risk analysis of various industrial applications. However, a technical challenge exists in gathering real-time big data in a complex indoor industrial environment. Indoor wireless sensor networks (WSN) technology can overcome this limitation by collecting the big data generated from source nodes and transmitting them to the data center in real time. In this study, typical residence, office, and manufacturing environments were chosen. The signal transmission characteristics of an indoor WSN were obtained by analyzing the test data. According to these characteristics, a real-time big data gathering (RTBDG) algorithm based on an indoor WSN is proposed for the risk analysis of industrial operations. In this algorithm, sensor nodes can screen the data collected from the environment and equipment according to the requirements of risk analysis. Clustering data transmission structure is then established on the basis of the received signal strength indicator (RSSI) and residual energy information.Experimental results show that RTBDG not only efficiently uses the limited energy of network nodes but also balances the energy consumption of all nodes. In the near future, the algorithm will be widely applied to risk analysis in different industrial operations.

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Energy-Efficient based Multi-path Routing with Static Sink using Fuzzy Logic Controller in Wireless Sensor Networks

Chavva

Sangam

2020

Proceedings of the Third International Conference on Computational Intelligence and Informatics

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Routing in Industrial Wireless Sensor Networks: A Survey

Cited by 6 publications

References 13 publications

On Providing Differentiated Service Exploiting Multi-Instance RPL for Industrial Low-Power and Lossy Networks

On Providing Differentiated Service Exploiting Multi-Instance RPL for Industrial Low-Power and Lossy Networks

A Real-Time Big Data Gathering Algorithm Based on Indoor Wireless Sensor Networks for Risk Analysis of Industrial Operations

Energy-Efficient based Multi-path Routing with Static Sink using Fuzzy Logic Controller in Wireless Sensor Networks

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