Wireless sensor networks mobility management using fuzzy logic

Zinonos, Zinon; Chrysostomou, Chrysostomos; Vassiliou, Vasos

doi:10.1016/j.adhoc.2013.12.003

Cited by 38 publications

(22 citation statements)

References 41 publications

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“…The LQI is the signal parameter calculated from RSS. 42 LQI is defined by the linguistic variables Low, Moderate, and High. The membership function of each linguistic variable is given in Figure 8.…”

Section: Selection Based On Fl Systemmentioning

confidence: 99%

Fuzzy logic–based distributed clustering protocol to improve energy efficiency and stability of wireless smart sensor networks for farmland monitoring systems

Rajput

Kumaravelu

2019

Int J Communication

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Wireless smart sensor networks (WSSNs) are emerging as the physical backbone of the internet of things (IoT) technology. On the basis of the IoT platform, web-based systems and services are been developing such as esurveillance, industrial-IoT, and precision agriculture. For farmland monitoring systems, WSSNs need to be scalable in terms of coverage area. Sensor nodes are energy-constrained devices, and hence, many energy-efficient clustering protocols are developed in the literature. But these methods overload the cluster leaders (CLs) with cluster computation and data communication costs. An improper CL selection may lead to the early death of such nodes and hence does not prolong the network lifetime stability. We propose a fuzzy logic (FL)-based distributed clustering protocol to enhance the energy efficiency of WSSN while maximizing the coverage area. The load of CLs is shared by originators and super-CLs (SCLs) selected in the network. The wireless link and received signal strength (RSS) are greatly affected by environmental conditions and thus cannot be considered as ideal network parameters. We use FL systems to tackle the uncertainty of such network parameters. The proposed protocol is simulated for different scalable WSSNs. The results indicate that the proposed protocol provides better lifetime stability than the recent conventional protocols. The functionalities of the protocol are proposed considering the recent wireless standards. Hence, the proposed protocol can be suitably implemented for farmland monitoring systems. KEYWORDSfarmland monitoring, fuzzy logic (FL), network lifetime stability (NLS), originator node, super-cluster leader (SCL)

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Section: Selection Based On Fl Systemmentioning

confidence: 99%

Fuzzy logic–based distributed clustering protocol to improve energy efficiency and stability of wireless smart sensor networks for farmland monitoring systems

Rajput

Kumaravelu

2019

Int J Communication

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“…The selection of fuzzy logic is supported by the fact that it can handle multiple inputs with minimum overhead. Thus, we utilized a two-input, singleoutput fuzzy controller on each sensor MN in WSNs [19].…”

Section: B Fuzzy Logic-based Mobility Controller Solution (Flmc)mentioning

confidence: 99%

Reliable Mobility Support for e-Health Monitoring: A Performance Evaluation

Zinonos¹,

Vassiliou²,

Christodoulou³

2018

2018 Innovations in Intelligent Systems and Applications (INISTA)

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A number of sensor network applications are envisioned to be applied to industry settings where the existence of mobile nodes (MN) is required. In critical applications, the realtime monitoring of a MN must always be available, something that requires the existence of a suitable mobility protocol to control the handoff procedure. In this paper, we use data from an industrial testbed to perform a comprehensive performance evaluation of different mobility solutions based on single-and multiple-metric options. The results show that a fuzzy logic based approach performs better compared to any single metricbased approach.

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“…In this paper, 3-CEDF utilizes three-layer cooperation relations in WSAN to make normal sensor nodes, cluster-head nodes and actor nodes work together, which reducing energy consumption and prolonging working life of WSAN. At the same time, all kinds of nodes use their own sliding windows [16] to ensure the real-time requirement of event detection for WSAN.…”

Section: Related Workmentioning

confidence: 99%

A Novel Event Detection Framework for Wireless Sensor and Actor Networks

Wang¹,

Fei²,

Liu³

et al. 2016

jcm

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Abstract-Wireless Sensor and Actor Network (WSAN) can observe the physical world using event detection methods by sensor nodes and implement appropriate operation by actor nodes. Compared with Wireless Sensor Network (WSN), WSAN set high requests for real-time, collaboration, mobility, etc. Therefore, the event detection methods applied to WSN cannot be adopted for WSAN. In this paper, we present a threelayer Cooperative Event Detection Framework (3-CEDF) for WSAN. Firstly, sensor nodes cooperate with each other to affirm the occurrence of events via voting mechanism. Then, cluster-head nodes use a modified K-means algorithm and a proposed type matching mechanism to detect the event types with the boundary information of event data. Finally, actor nodes get the priority of events processing using a modified knearest neighbor algorithm with the slope similarity measure. The performances of effectiveness, real-time, mobility and communication traffic for 3-CEDF are obtained through analysis and simulations, when 3-CEDF is compared to the centralized event detection method adopted for WSN.Index Terms-Wireless sensor and actor network, event detection, cooperative algorithm, type matching, similarity measure

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Wireless sensor networks mobility management using fuzzy logic

Cited by 38 publications

References 41 publications

Fuzzy logic–based distributed clustering protocol to improve energy efficiency and stability of wireless smart sensor networks for farmland monitoring systems

Fuzzy logic–based distributed clustering protocol to improve energy efficiency and stability of wireless smart sensor networks for farmland monitoring systems

Reliable Mobility Support for e-Health Monitoring: A Performance Evaluation

A Novel Event Detection Framework for Wireless Sensor and Actor Networks

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