Performance Evaluation of Wireless Sensor Networks Based on Hesitant Fuzzy Linguistic Preference Relations

Zhai, Wei

doi:10.3991/ijoe.v14i05.8650

Cited by 4 publications

(6 citation statements)

References 4 publications

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“…Non-hierarchical routing protocols are designed in accordance with overhearing, flooding, and sink node position advertisement, whereas hierarchical routing protocols are designed on the basis of grid, tree, cluster and area [1-3][12]. Different hierarchical routing protocols have their own merits and demerits, but as far as cluster-based hierarchical routing protocols are concerned, researchers have been challenged with a task of achieving an optimal balance between end-end delay and energy consumption [9][10][11][12][13].…”

Section: Related Workmentioning

confidence: 99%

“…In this algorithm, the local sensory data is gathered from sensor nodes by utilizing hesitant fuzzy entropy based multi-attribute decision modelling for cluster head election procedure. Zhai et al [10] developed Hesitant Language Preference Relationships (HLPR) to improve the credibility of WSNs by fusing uncertain information and putting forward exact opinion about different WSN schemes.…”

Section: Related Workmentioning

confidence: 99%

“…. 𝑥𝑛} having probability distribution as p(X) can be given as 𝐻(𝑥) = ∑ 𝑝(𝑥)𝑙𝑜𝑔 2 𝑝(𝑥) 𝑥∈𝑋 for 0 ≤ H(X) ≤ 1 [9][10]. It should be kept in focus that CH or BS should not be hesitant or irresolute about any of their decisions regarding cluster formation and data fusion.…”

Section: Introductionmentioning

confidence: 99%

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Thermal entropy based hesitant fuzzy linguistic term set analysis in energy efficient opportunistic clustering

Anees¹,

Zhang²

2021

Natural Language Computing

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Limited energy resources and sensor nodes’ adaptability with the surrounding environment play a significant role in the sustainable Wireless Sensor Networks. This paper proposes a novel, dynamic, self-organizing opportunistic clustering using Hesitant Fuzzy Linguistic Term Analysis- based Multi-Criteria Decision Modeling methodology in order to overcome the CH decision making problems and network lifetime bottlenecks. The asynchronous sleep/awake cycle strategy could be exploited to make an opportunistic connection between sensor nodes using opportunistic connection random graph. Every node in the network observe the node gain degree, energy welfare, relative thermal entropy, link connectivity, expected optimal hop, link quality factor etc. to form the criteria for Hesitant Fuzzy Linguistic Term Set. It makes the node to evaluate its current state and make the decision about the required action (‘CH’, ‘CM’ or ‘relay’). The simulation results reveal that our proposed scheme leads to an improvement in network lifetime, packet delivery ratio and overall energy consumption against existing benchmarks.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Thermal entropy based hesitant fuzzy linguistic term set analysis in energy efficient opportunistic clustering

Anees¹,

Zhang²

2021

Natural Language Computing

View full text Add to dashboard Cite

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“…The base station then used the uploaded local decisions to calculate the fusion support degree of clusters and conducted the final fusion of events. Zhai et al [ 20 ] mainly adopted hesitant language preference relationships (HLPRs) to put forward correct opinions on different wireless sensor network schemes, and fuse uncertain data information to improve the credibility of the overall network. The state-of-the-art approaches as explained above can improve the reliability of information collection, reduce the transmission of redundant information, or be employed to make accurate decisions on events of interest through data fusion.…”

Section: Related Workmentioning

confidence: 99%

A New Data Fusion Algorithm for Wireless Sensor Networks Inspired by Hesitant Fuzzy Entropy

Wang

Tawose

Jiang

et al. 2019

Sensors

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The wireless sensor network (WSN) is mainly composed of a large number of sensor nodes that are equipped with limited energy and resources. Therefore, energy consumption in wireless sensor networks is one of the most challenging problems in practice. On the other hand, data fusion can effectively decrease data redundancy, reduce the amount of data transmission and energy consumption in the network, extend the network life cycle, improve the utilization of bandwidth, and thus overcome the bottleneck on energy and bandwidth consumption. This paper proposes a new data fusion algorithm based on Hesitant Fuzzy Entropy (DFHFE). The new algorithm aims to reduce the collection of repeated data on sensor nodes from the source, and strives to utilize the information provided by redundant data to improve the data reliability. Hesitant fuzzy entropy is exploited to fuse the original data from sensor nodes in the cluster at the sink node to obtain higher quality data and make local decisions on the events of interest. The sink nodes periodically send local decisions to the base station that aggregates the local decisions and makes the final judgment, in which process the burden for the base station to process all the data is significantly released. According to our experiments, the proposed data fusion algorithm greatly improves the robustness, accuracy, and real-time performance of the entire network. The simulation results demonstrate that the new algorithm is more efficient than the state-of-the-art in terms of both energy consumption and real-time performance.

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“…Chaurasia et al [44] presented an adaptive fuzzy logic algorithm to address the inaccuracies in data fusion. Zhai et al [45] developed Hesitant Language Preference Relationships (HLPR) to improve the credibility of WSNs by fusing uncertain information and putting forward exact opinion about different WSN schemes. Wang et al [19] proposed a novel data fusion algorithm inspired by hesitant fuzzy entropy to reduce the redundant sensory data transfer in WSN clustering.…”

Section: Introductionmentioning

confidence: 99%

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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Performance Evaluation of Wireless Sensor Networks Based on Hesitant Fuzzy Linguistic Preference Relations

Cited by 4 publications

References 4 publications

Thermal entropy based hesitant fuzzy linguistic term set analysis in energy efficient opportunistic clustering

Thermal entropy based hesitant fuzzy linguistic term set analysis in energy efficient opportunistic clustering

A New Data Fusion Algorithm for Wireless Sensor Networks Inspired by Hesitant Fuzzy Entropy

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

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