Fuzzy logic based clustering in wireless sensor networks: a survey

Singh, Ashutosh Kumar; Purohit, Neetesh; Varma, Shirshu

doi:10.1080/00207217.2012.687191

Cited by 52 publications

(20 citation statements)

References 17 publications

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“…Fuzzy logic-based algorithms for clustering give better lifetime than that of LEACH. In Singh, Purohit, and Varma (2012), authors have surveyed fuzzybased algorithms for clustering and their performances in terms of lifetime of wireless sensor network. In Singh, Purohit, Singh, and Shukla (2011), authors have shown that the fuzzy-based algorithm has been successful in the extending the lifetime of sensor network.…”

Section: Introductionmentioning

confidence: 99%

Performance analysis of fuzzy logic-based geographical node-disjoint path routing for WSNs

Goutele

Singh

Gurjar

et al. 2014

International Journal of Electronics

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Geographical routing in wireless sensor networks is an effective approach for monitoring of seismically sensitive zones where the probability of earthquake and hazards is very high. In this letter, we are proposing an efficient fuzzy logic-based geographical routing approach for seismically sensitive areas using wireless sensor network (WSN). The proposed system uses the concept of basic geographical node-disjoint path routing (GNPR). The difference between proposed system and its conventional GNPR is that, it uses fuzzy logic to find out the shortest path per iteration and repeatedly apply the fuzzy inference rules in subsequent iteration to find the next on-demand node. Simulation results show that the proposed scheme gives better performance in terms of higher throughput and lower delay as compared to basic GNPR approach.

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Section: Introductionmentioning

confidence: 99%

Performance analysis of fuzzy logic-based geographical node-disjoint path routing for WSNs

Goutele

Singh

Gurjar

et al. 2014

International Journal of Electronics

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“…In clustering, the network nodes are grouped into small clusters (Singh & Purohit, 2014). Also, the routes are established between clusters in place of nodes (Singh, Purohit, & Varma, 2013). Clustering techniques for ad hoc networks may be classified as follows.…”

Section: Literature Reviewmentioning

confidence: 99%

Performance optimisation through EPT-WBC in mobile ad hoc networks

Agarwal

Gupta

Motwani

2015

International Journal of Electronics

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Mobile ad hoc networks are self-organised, infrastructure-less networks in which each mobile host works as a router to provide connectivity within the network. Nodes out of reach to each other can communicate with the help of intermediate routers (nodes). Routing protocols are the rules which determine the way in which these routing activities are to be performed. In cluster-based architecture, some selected nodes (clusterheads) are identified to bear the extra burden of network activities like routing. Selection of clusterheads is a critical issue which significantly affects the performance of the network. This paper proposes an enhanced performance and trusted weightbased clustering approach in which a number of performance factors such as trust, load balancing, energy consumption, mobility and battery power are considered for the selection of clusterheads. Moreover, the performance of the proposed scheme is compared with other existing approaches to demonstrate the effectiveness of the work.

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“…It reproduces the human mind capacity to employ approximate reasoning. In classic logic the decision is binary, true or false, whereas in Fuzzy Logic variables have a range between 0 and 1, of degree of membership towards a fuzzy set [13]. Imprecise inputs can be considered and used as "linguistic variables" denoting values such as big and small, low/medium/high are used in the model.…”

Section: Fuzzy Logicmentioning

confidence: 99%

“…The Fuzzification changes crisp data into linguistic values using linguistic variables, membership functions map every element of the input variables onto a membership rate from 0 to 1. The evaluation makes approximate reasoning [13].…”

Section: Fuzzy Logicmentioning

confidence: 99%

Using Fuzzy Logic to Improve BLE Indoor Positioning System

Onofre¹,

Caseiro²,

Pimentão³

et al. 2016

Technological Innovation for Cyber-Physical Systems

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Accuracy and precision are key parameters in the definition of indoor positioning systems. We want to provide a mobile robot with the capacity to autonomously determining its location inside buildings, to allow it to autonomously navigate. The solution developed is based on spreading emitter beacons of Bluetooth Low Energy in the building and use location finding techniques to determine the robot's location. The main challenge is the capacity to obtain accurate readings of signal strength and the low repeatability of readings even under unchanged conditions. To improve the signal strength measurements it is necessary to deal with this imprecision. Our approach is based on the use of Fuzzy Logic to deal with the accuracy problem. Once better signal strength readings are achieved, using this method, approximate distances are calculated based on signal strength and the trilateration method is implemented to provide the location of the mobile robot.

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Fuzzy logic based clustering in wireless sensor networks: a survey

Cited by 52 publications

References 17 publications

Performance analysis of fuzzy logic-based geographical node-disjoint path routing for WSNs

Performance analysis of fuzzy logic-based geographical node-disjoint path routing for WSNs

Performance optimisation through EPT-WBC in mobile ad hoc networks

Using Fuzzy Logic to Improve BLE Indoor Positioning System

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