Genetic Algorithm Based Approach for Extending the Lifetime of Two-Tiered Sensor Networks

Wazed, S.; Bari, Ataul; Jaekel, Arunita; Bandyopadhyay, Subir

doi:10.1109/iswpc.2007.342578

Cited by 18 publications

(10 citation statements)

References 22 publications

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“…Two-Tier GA: Another application of GA for maximum lifetime routing for a two-tier network is presented in [77]. In a two tier sensor network, a few sensor nodes having higher power levels act as cluster heads.…”

Section: Securitymentioning

confidence: 99%

Computational Intelligence in Wireless Sensor Networks: A Survey

Kulkarni

Förster

Venayagamoorthy

2011

IEEE Commun. Surv. Tutorials

617

270

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Wireless sensor networks (WSNs) are networks of distributed autonomous devices that can sense or monitor physical or environmental conditions cooperatively. WSNs face many challenges, mainly caused by communication failures, storage and computational constraints and limited power supply. Paradigms of computational intelligence (CI) have been successfully used in recent years to address various challenges such as data aggregation and fusion, energy aware routing, task scheduling, security, optimal deployment and localization. CI provides adaptive mechanisms that exhibit intelligent behavior in complex and dynamic environments like WSNs. CI brings about flexibility, autonomous behavior, and robustness against topology changes, communication failures and scenario changes. However, WSN developers are usually not or not completely aware of the potential CI algorithms offer. On the other side, CI researchers are not familiar with all real problems and subtle requirements of WSNs. This mismatch makes collaboration and development difficult. This paper intends to close this gap and foster collaboration by offering a detailed introduction to WSNs and their properties. An extensive survey of CI applications to various problems in WSNs from various research areasand publication venues is presented in the paper. Besides, a discussion on advantages and disadvantages of CI algorithms over traditional WSN solutions is offered. In addition, a general evaluation of CI algorithms is presented, which will serve as a guide for using CI algorithms for WSNs.

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Section: Securitymentioning

confidence: 99%

Computational Intelligence in Wireless Sensor Networks: A Survey

Kulkarni

Förster

Venayagamoorthy

2011

IEEE Commun. Surv. Tutorials

617

270

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“…For our experiments, we have used first order radio model for communication energy dissipation of [12] which has been widely used by many researchers as in [23,[48][49][50][51][52] …”

Section: Resultsmentioning

confidence: 99%

Synthesized Cluster Head Selection and Routing for Two Tier Wireless Sensor Network

Rana

Zaveri

2013

Journal of Computer Networks and Communications

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Large scale sensor networks can be efficiently managed by dividing them into several clusters. With the help of cluster heads, each cluster communicates using some routing schedule. It is essential to rotate the role of cluster heads in a cluster to distribute energy consumption if we do not have dedicated high energy cluster heads. Usually routing and cluster head selection for such networks have been separately solved. If cluster heads are selected with the consideration of routing and routing schedule is prepared with the consideration of selected cluster heads, it can help each other. We have proposed an integrated approach of cluster head selection and routing in two tier wireless sensor network (WSN) using Genetic Algorithm based cluster head selection with A-Star algorithm based routing method to extend life of WSN. This approach can lead to significant improvements in the network lifetime over other techniques.

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“…Another extreme behavior we can see is in the trapezoidal MF, which generates 2 CHs for 33 nodes and the remaining 31 nodes are CMs. 2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,19,20,21,23,24,25,26,27,28 3,4,5,6,7,8,9,10 ,11,12,13,14,15,16 ,17,19,20,21,23,24 ,25,26,27,28,29 The graphs are obtained through experimental runs, and an in-depth study of them further justifies the selection of a triangular MF. To make the experiments factual, we proceeded with clustering using fuzzy logic alone.…”

Section: Fuzzy Logic Systemmentioning

confidence: 90%