EMCR : Routing in WSN Using Multi Criteria Decision Analysis and Entropy Weights

Bhunia, Suman Sankar; Das, Bijoy; Mukherjee, Nandini

doi:10.1007/978-3-319-11692-1_28

Cited by 10 publications

(7 citation statements)

References 13 publications

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“…In [18], a TDMA‐based routing protocol is given for improving the performance in terms of energy in WBAN. An effective multi‐hop routing strategy in [19] has been put forth that is helpful in detecting efficient routes among available routes while considering multiple significant criteria for making routing decisions, also providing balance in energy consumption across all the sensor nodes. In [20], the researchers gave a method for GSCM to create a strong ranking scheme by applying neutrosophic sets to dodge imprecise, indefinite opinion and concluded that GSCM practices could reduce waste, economic drops, etc.…”

Section: Related Workmentioning

confidence: 99%

AHP–neutrosophic decision model for selection of relay node in wireless body area network

Bilandi¹,

Verma²,

Dhir³

2020

CAAI trans. intell. technol.

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The medical health systems empowered by wireless body area networks (WBANs) are becoming a reliable technology with unmatched facilities for personalised health monitoring and managing real‐time health issues. A WBAN is primarily composed of a miniature, and smart devices called sensors that are worn in, on, or around the human body. In recent years, research has been mainly centred towards the reduction of the energy consumption and stability of the network. The selection of the relay node is one of the foremost issues for balancing the energy consumption in WBAN. To overcome this issue, the authors propose a hybrid analytic hierarchy process–neutrosophic method for making the decision of the relay node selection, in which the analytic hierarchy process (AHP) technique calculates the weights for different criteria and forwards them to the neutrosophic technique which finds out the best alternative solution or choice which is closest to the ideal solution. A detailed analysis is carried out on multi‐criteria decision‐making techniques by comparing the proposed AHP–neutrosophic and AHP techniques taking into account various criteria and alternatives. The experimental outcomes of this research indicate a significant reduction in terms of energy consumption and enhanced overall network‐stability.

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Section: Related Workmentioning

confidence: 99%

AHP–neutrosophic decision model for selection of relay node in wireless body area network

Bilandi¹,

Verma²,

Dhir³

2020

CAAI trans. intell. technol.

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“…The conducted survey reveals several basic techniques of cluster forming e.g. Random Competition based Clustering, RCC [52], which applies a random timer. The registration of nodes to each cluster is based on a rule called First Declaration Wins, FDW [53].…”

Section: Technologies Of Wsn Healthcare Application In Iotmentioning

confidence: 99%

Wirelessly Interfacing Objects and Subjects of Healthcare System – IoT Approach

Dziak¹,

Jachimczyk²,

Kulesza³

2016

ElAEE

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Wireless sensor networks, WSN, for which development has begun by military applications, are nowadays applied to all human activities; e.g. in medicine for patience monitoring or to reduce the effects of disasters. Therefore, the WSNs area has been also one of the emerging and fast growing scientific fields. Increasing interest of WSNs is even caused by equally intense growth of interest in the Internet of Things domain, IoT, in which WSNs constitute a significant part. These reasons have brought about developing low cost, lowpower and multi-function sensor nodes. However, the major fact that sensor nodes run quickly out of energy has been an issue and many energy efficient routing protocols have been proposed to solve this problem. Case study presented in this paper concern design of WSN in IoT concept from system lifetime perspective. A hierarchical routing technique, which shows energy efficiency, has been validated. Simulation results show that chosen technique prolongs the lifetime of the WSN compared to other investigated clustering schemes. The advantages of this method are validated by comparative studies.

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“…Keeping in view the opportunistic connection between sensor nodes in heterogeneous clustering, we selected multiple parameters including an asynchronous working-sleeping cycle, status transition frequencies, residual energy, link quality factor in terms of signal-to-noise ratio, distance between sensor node and BS, and number of supported sensor nodes by a potential CH as our attributes of hesitant fuzzy set. Furthermore, we need Multi-Attribute Decision Modeling (MADM) to efficiently utilize our hesitant fuzzy set to generate hesitant fuzzy entropy matrix and determine our entropy weight coefficients [14,15,17,18].…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, the hesitant entropy matrix is generated after finding entropy values of each attribute in the hesitant fuzzy set using data standardization process. Subsequently, the entropy weight model [14,15,17] is employed to determine the entropy weight coefficients for each sensor node and, finally, the threshold attribute values are determined and then compared with the original attribute values for making a decision about new CH. This entire process is part of the CH election procedure which is initiated by the BS and continued by every CH for all communication rounds.…”

Section: Introductionmentioning

confidence: 99%

“…In previous studies, several researchers have adopted entropy weight coefficient method for making decision in clustering environment [14][15][16]18]. Entropy weight-based multi-criteria decision routing is a routing technique in which the next hop decision is based on Multi-Criteria Decision Analysis (MCDA) and Multi-Attribute Sensors 2020, 20, 913 5 of 28 Decision Modeling (MADM) using entropy weight coefficients [14,15,17,18]. Qiang et al [16] presented an optimization of objective and subjective weights based on fuzzy MADM routing for selection of next best hop in a heterogeneous WSN.…”

Section: Introductionmentioning

confidence: 99%

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Hesitant Fuzzy Entropy-Based Opportunistic Clustering and Data Fusion Algorithm for Heterogeneous Wireless Sensor Networks

Anees

Zhang

Baig

et al. 2020

Sensors

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Limited energy resources of sensor nodes in Wireless Sensor Networks (WSNs) make energy consumption the most significant problem in practice. This paper proposes a novel, dynamic, self-organizing Hesitant Fuzzy Entropy-based Opportunistic Clustering and data fusion Scheme (HFECS) in order to overcome the energy consumption and network lifetime bottlenecks. The asynchronous working-sleeping cycle of sensor nodes could be exploited to make an opportunistic connection between sensor nodes in heterogeneous clustering. HFECS incorporates two levels of hierarchy in the network and energy heterogeneity is characterized using three levels of energy in sensor nodes. HFECS gathers local sensory data from sensor nodes and utilizes multi-attribute decision modeling and the entropy weight coefficient method for cluster formation and the cluster head election procedure. After cluster formation, HFECS uses the same techniques for performing data fusion at the first hierarchical level to reduce the redundant information flow from the first-second hierarchical levels, which can lead to an improvement in energy consumption, better utilization of bandwidth and extension of network lifetime. Our simulation results reveal that HFECS outperforms the existing benchmark schemes of heterogeneous clustering for larger network sizes in terms of half-life period, stability period, average residual energy, network lifetime, and packet delivery ratio.Sensors 2020, 20, 913 2 of 28 overall decrease in energy consumption and also reduces the interference among sensor nodes due to specific allocation of timeslots for communication purposes [4,5]. The clustering topology in WSN can effectively optimize the data redundancy by significantly reducing the size of the collected data using data aggregation and data fusion techniques at CH level. The aggregated or fused data can then be forwarded to the Base Station (BS) for further processing and accurate decision making of interested events [4][5][6].The recent literature shows that researchers have proposed a working-sleeping cycle strategy in WSN to save battery power in case the sensor nodes are idle and not performing any of the designated tasks. Alfayez et al. [7] discussed that the sensor nodes go to sleep to save their battery power and wake up before performing their routine operations. These node scheduling techniques can be categorized as synchronous and asynchronous working-sleeping scheduling. These node scheduling techniques are designed in accordance with the scenario to prolong the network lifetime and improve energy utilization by creating an opportunistic node connection between sensor nodes. According to [8][9][10][11][12], Opportunistic Routing (OR) is a paradigm for wireless networks that benefits from broadcast characteristics of a wireless medium by selecting multiple sensor nodes as candidate forwarders. In [10-12], a set of nodes were selected as potential forwarders that transmitted the data packet according to some special criteria after receiving data packet from their neighb...

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EMCR : Routing in WSN Using Multi Criteria Decision Analysis and Entropy Weights

Cited by 10 publications

References 13 publications

AHP–neutrosophic decision model for selection of relay node in wireless body area network

AHP–neutrosophic decision model for selection of relay node in wireless body area network

Wirelessly Interfacing Objects and Subjects of Healthcare System – IoT Approach

Hesitant Fuzzy Entropy-Based Opportunistic Clustering and Data Fusion Algorithm for Heterogeneous Wireless Sensor Networks

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