Research on Clustering Routing Algorithms in Wireless Sensor Networks

Bian, Qing; Zhou, Yan; Zhao, Yanjuan

doi:10.1109/icicta.2010.343

Cited by 18 publications

(6 citation statements)

References 9 publications

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“…Where: p is the desired percentage of cluster heads in the sensor area and r is the prevailing round number, where G is the set of SNs that have not been chosen as cluster heads in the last 1/p rounds [2] [8].…”

Section: Pure Leachmentioning

confidence: 99%

An Energy Balanced Algorithm for data communication in WSN

Lagraini¹,

Ouldzira²,

Chhiba³

et al. 2019

Proceedings of the Third International Conference on Computing and Wireless Communication Systems, ICCWCS 2019, April 24-25, 20

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Wireless sensor networks are seeming as an emergent requisite for mankind. Such networks are still in research stage though, they have high prospective to be useful in practically every arena of life. A lot of study is done and lots of other is a waiting to be standardized. In this paper, clustering in WSNs is deliberate precisely. Furthermore, we improve one of the utmost popular WSN's routing algorithms "LEACH" as enhanced LEACH. Our improved algorithms, in comparison with basic algorithm overtake it by means of lifetime of cluster head and network lifespan. Subsequently, new thresholds are employed on improved algorithms that claim the performance even more. To finish a brief performance test of basic protocol, with ES-LEACH and ESD-LEACH is assumed bearing in mind energy leftover and distance metrics.

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Section: Pure Leachmentioning

confidence: 99%

An Energy Balanced Algorithm for data communication in WSN

Lagraini¹,

Ouldzira²,

Chhiba³

et al. 2019

Proceedings of the Third International Conference on Computing and Wireless Communication Systems, ICCWCS 2019, April 24-25, 20

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“…Koloniari and Pitoura (2012) proposed a game theoretic approach to cluster the social network's users. Zheng et al (2012) proposed the Bayesian games for effective routing and energy consumption in wireless sensor networks. Another work which proposed game theory for clustering is Li et al (2010) in which automatic formation of clusters is considered.…”

Section: Related Workmentioning

confidence: 99%

“…Game theory is a multifaceted approach for learning, which is able to give an explanation for the behavioural characteristics of a wide range of existing learning algorithms (Li, Chen, He, & Jiang, 2010). Ideal clustering needs to guarantee an internal condition which is minimizing the within cluster distances and an external condition which is maximum between cluster distances (Zheng, Liu, & Qi, 2012). It has been claimed that game theory is a good means for achieving the mentioned conditions and forming good quality clusters (Li et al, 2010;Heileman, Caudell, Hush, Krause, & Verzi, 2015).Unlike classic games, where players show quite logical behaviour and always choose the optimal strategy in complex environments, coalitional game theory combines the traditional ones with the idea of evolutionary game theory and assumes rationalities such as limited information and cognitive environment.…”

mentioning

confidence: 99%

A geodesic distance‐based approach for shape‐independent data clustering using coalitional game

2018

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Different clustering approaches consider different aspects of quality including accuracy of cluster formation, speed of execution, and resource requirements. A major issue in data clustering is to consider the density of clusters and their structure. A challenge is to relax any assumption on the clusters shape such as sphericity, which is common in most partitioning approaches such as K‐means. In this article, with the help of the coalitional game theory and the concept of geodesic distance calculation, a density‐based shape‐independent clustering approach is proposed. In addition to the emphasis on the application of game theory, we also pay attention to the relative neighbourhood of data, which is depicted using geodesic distance. Geodesic distance is a well‐known measure for finding the manifold of data. The new idea supposes to discover any embedded structure of data and avoid finding only spherical clusters. The proposed approach is evaluated on a number of standard University of California, Irvine (UCI) datasets, and the results show the effectiveness of the proposed approach in comparison with some other approaches.

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“…If the number is less than a threshold, the node turns into a cluster head for the present round. The threshold is set as shown in formula (1) [3]:…”

Section: A Leach Clustering Protocol: Advantages and Problemsmentioning

confidence: 99%

Energy Efficient Cluster-Based Intrusion Detection System for Wireless Sensor Networks

Abdullah¹,

Alsanee²,

Alseheymi³

2014

ijacsa

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Abstract-Wireless sensor networks (WSNs) are network type where sensors are used to collect physical measurements. It has many application areas such as healthcare, weather monitoring and even military applications. Security in this kind of networks is a big concern especially in the applications that required confidentiality and privacy. Therefore, providing a WSN with an intrusion detection system is essential to protect its security from different types of intrusions, cyber-attacks and random faults. Clustering has proven its efficiency in prolong the node as well as the whole WSN lifetime. In this paper we have designed an Intrusion Detection (ID) system based on Stable Election Protocol (SEP) for clustered heterogeneous WSNs. The benefit of using SEP is that it is a heterogeneous-aware protocol to prolong the time interval before the death of the first node. KDD Cup'99 data set is used as the training data and test data. After normalizing our dataset, we trained the system to detect four types of attacks which are Probe, Dos, U2R and R2L, using 18 features out of the 42 features available in KDD Cup'99 dataset. The research used the K-nearest neighbour (KNN) classifier for anomaly detection. The experiments determine K = 5 for best classification and this reveals recognition rate of attacks as 75%. Results are compared with KNN classifier for anomaly detection without using a clustering algorithm.

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Research on Clustering Routing Algorithms in Wireless Sensor Networks

Cited by 18 publications

References 9 publications

An Energy Balanced Algorithm for data communication in WSN

An Energy Balanced Algorithm for data communication in WSN

A geodesic distance‐based approach for shape‐independent data clustering using coalitional game

Energy Efficient Cluster-Based Intrusion Detection System for Wireless Sensor Networks

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