A distributed energy-efficient clustering algorithm with improved coverage in wireless sensor networks

Liu, Zhixin; Zheng, Qingchao; Xue, Liang; Guan, Xinping

doi:10.1016/j.future.2011.04.019

Cited by 101 publications

(40 citation statements)

References 20 publications

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“…We propose two different detection techniques for a possible Sybil attack followed by a centralized cluster head selection approach. Similar to Distributed Energy-Efficient Clustering with Improved Coverage (DEECIC) algorithm [26], the residual energy of each node plays a crucial role in cluster head selection. Apart from the residual energy, our cluster head selection technique relies on the previous history of selection and geographical location of each node.…”

Section: Related Workmentioning

confidence: 99%

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A Sybil attack detection scheme for a forest wildfire monitoring application

Jan

Nanda

et al. 2018

Future Generation Computer Systems

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Wireless Sensor Networks (WSNs) have experienced phenomenal growth over the past decade. They are typically deployed in human-inaccessible terrains to monitor and collect time-critical and delay-sensitive events. There have been several studies on the use of WSN in different applications. All such studies have mainly focused on Quality of Service (QoS) parameters such as delay, loss, jitter etc. of the sensed data. Security provisioning is also an important and challenging task lacking in all previous studies. In this paper, we propose a Sybil attack detection scheme for a cluster-based hierarchical network mainly deployed to monitor forest wildfire. We propose a two-tier detection scheme. Initially, Sybil nodes and their forged identities are detected by high-energy nodes. However, if one or more identities of a Sybil node sneak through the detection process, they are ultimately detected by the two base stations. After Sybil attack detection, an optimal percentage of cluster heads are elected and each one is informed using nomination packets. Each nomination packet contains the identity of an elected cluster head and an end user's specific query for data collection within a cluster. These queries are user-centric, on-demand and adaptive to an end user requirement. The undetected identities of Sybil nodes reside in one or more clusters. Their goal is to transmit high false-negative alerts to an end user for diverting attention to those geographical regions which are less vulnerable to * Corresponding author Email addresses: Priyadarsi.Nanda@uts.edu.au (Priyadarsi Nanda), Xiangjian.He@uts.edu.au (Xiangjian He) Preprint submitted to ElsevierMay 24, 2016 a wildfire. Our proposed approach has better network lifetime due to efficient sleep-awake scheduling, higher detection rate and low false-negative rate.

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Section: Related Workmentioning

confidence: 99%

“…This parameter determines the efficiency of an algorithm in terms of data aggregation and geographical coverage [26]. The average size of a cluster, C average , is defined as the ratio of number of alive nodes to the number of clusters within a network and is calculated using Equation 14.…”

Section: Average Size Of the Clustersmentioning

confidence: 99%

A Sybil attack detection scheme for a forest wildfire monitoring application

Jan

Nanda

et al. 2018

Future Generation Computer Systems

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“…The related technique-efficient issues, such as clustering routing, topology control, and multihop transmission, are widely used to improve energy efficiency for WSNs [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20]. On the other hand, it is very important to note that hybridization combination of various approaches may affect total performance.…”

Section: Introductionmentioning

confidence: 99%

Evidence-Efficient Multihop Clustering Routing Scheme for Large-Scale Wireless Sensor Networks

Xin

2017

Wireless Communications and Mobile Computing

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Energy consumption and transmission reliability are the most common issues in wireless sensor networks (WSNs). By studying the broadcast nature of data transmission in WSNs, the mechanism of guaranteeing reliable transmission is abstracted as propagation of responsibility and availability. The responsibility and availability represent the accumulated evidence of nodes to support reliable transmission. Based on the developed mechanism, an evidence-efficient cluster head rotation strategy and algorithm are presented. Furthermore, backbone construction algorithm is studied to generate the minimum aggregation tree inside the candidate cluster heads. A minimum aggregation tree-based multihop routing scheme is also investigated, which allows the elected cluster heads to choose the optimally main path to forward data locally and dynamically. As a hybridization of the above, an evidence-efficient multihop clustering routing (EEMCR) method is proposed. The EEMCR method is simulated, validated, and compared with some previous algorithms. The experimental results show that EEMCR outperforms them in terms of prolonging network lifetime, improving transmission reliability, postponing emergence of death nodes, enhancing coverage preservation, and degrading energy consumption.

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“…How to find useful knowledge from high dimensional, massive and distributed data has become a key issue of data mining in wireless sensor networks [8,9]. algorithm with improved coverage by analyzing communication energy consumption of the clusters and the impact of node failures on coverage with different densities in wireless sensor networks [11]. In WSNs, the stream nature of the data, the limited resources, and the distributed nature of sensor networks bring new challenges for the mining techniques.…”

Section: Introductionmentioning

confidence: 99%

Distributed Global Function Model Finding for Wireless Sensor Network Data

Deng

Yang

et al. 2016

Applied Sciences

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Abstract:Function model finding has become an important tool for analysis of data collected from wireless sensor networks (WSNs). With the development of WSNs, a large number of sensors have been widely deployed so that the collected data show the characteristics of distribution and mass. For distributed and massive sensor data, traditional centralized function model finding algorithms would lead to a significant decrease in performance. To solve this problem, this paper proposes a distributed global function model finding algorithm for wireless sensor network data (DGFMF-WSND). In DGFMF-WSND, on the basis of gene expression programming (GEP), an adaptive population generation strategy based on sub-population associated evolution is applied to improve the convergence speed of GEP. Secondly, to solve the generation of global function model in distributed wireless sensor networks data, this paper provides a global model generation algorithm based on unconstrained nonlinear least squares. Four representative datasets are used to evaluate the performance of the proposed algorithm. The comparative results show that the improved GEP with adaptive population generation strategy outperforms all other algorithms on the average convergence speed, time-consumption, value of R-square, and prediction accuracy. Meanwhile, experimental results also show that DGFMF-WSND has a clear advantage in terms of time-consumption and error of fitting. Moreover, with increasing of dataset size, DGFMF-WSND also demonstrates good speed-up ratio and scale-up ratio.

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A distributed energy-efficient clustering algorithm with improved coverage in wireless sensor networks

Cited by 101 publications

References 20 publications

A Sybil attack detection scheme for a forest wildfire monitoring application

A Sybil attack detection scheme for a forest wildfire monitoring application

Evidence-Efficient Multihop Clustering Routing Scheme for Large-Scale Wireless Sensor Networks

Distributed Global Function Model Finding for Wireless Sensor Network Data

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