Collaborative Routing Algorithm for Wireless Sensor Network Longevity

Bashyal, Shristy; Venayagamoorthy, G.K.

doi:10.1109/issnip.2007.4496896

Cited by 20 publications

(11 citation statements)

References 15 publications

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“…For example, a network that has lost a quarter of its nodes from one quadrant is less effective than the network that has lost a quarter of its nodes from the whole area uniformly. Article [84] defines a new parameter for evaluating longevity of wireless sensor networks. The parameter is called Effectiveness, and [84] defines it as in (23).…”

Section: Collaborative Clustering Algorithmmentioning

confidence: 99%

See 1 more Smart Citation

Computational Intelligence in Wireless Sensor Networks: A Survey

Kulkarni

Förster

Venayagamoorthy

2011

IEEE Commun. Surv. Tutorials

Self Cite

610

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: Collaborative Clustering Algorithmmentioning

confidence: 99%

“…Article [84] defines a new parameter for evaluating longevity of wireless sensor networks. The parameter is called Effectiveness, and [84] defines it as in (23). The network longevity is defined as the time for which the network Effectiveness is equal to or greater than 70%.…”

Section: Collaborative Clustering Algorithmmentioning

confidence: 99%

Computational Intelligence in Wireless Sensor Networks: A Survey

Kulkarni

Förster

Venayagamoorthy

2011

IEEE Commun. Surv. Tutorials

Self Cite

610

270

View full text Add to dashboard Cite

show abstract

“…Bashyal et al [7] have proposed the Collaborative Routing Algorithm for Wireless Sensor Networks (CRAWL). Assuming that the nodes have a random non-uniform distribution of residual energy levels, CRAWL forms clusters by assigning the role of cluster heads to the nodes with larger residual energy.…”

Section: Other Protocolsmentioning

confidence: 99%

Swarm intelligence based routing protocol for wireless sensor networks: Survey and future directions

Saleem¹,

Farooq

2011

Information Sciences

332

158

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“…In addition, Madan and Lall (2006) described a distributed approach that can locate the optimal routing scheme using a sub-gradient algorithm to solve the convex optimisation problem presented by maximum lifetime routing. Also, some heuristic approaches, such as reinforcement learning (Förster, 2007) and swarm intelligence (Bashyal and Venayagamoorthy, 2007), can be applied in a distributed fashion, although these only approximate the optimal solutions. An important consideration with distributed approaches is that nodes require sufficient computational power and storage to collect and store information regarding local connectivity and compute the best routes based on available information.…”

Section: Introductionmentioning

confidence: 99%

Hybrid evolutionary approaches to maximum lifetime routing and energy efficiency in sensor mesh networks

Rahat¹,

Everson²,

Fieldsend³

2015

SIGEVOlution

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Mesh network topologies are becoming increasingly popular in battery powered wireless sensor networks, primarily due to the extension of network range. However, multi-hop mesh networks suffer from higher energy costs, and the routing strategy employed directly affects the lifetime of nodes with limited energy resources. Hence when planning routes there are trade-offs to be considered between individual and system-wide battery lifetimes. We present a multi-objective routing optimisation approach using hybrid evolutionary algorithms to approximate the optimal trade-off between minimum lifetime and the average lifetime of nodes in the network. In order to accomplish this combinatorial optimisation rapidly, our approach prunes the search space using k-shortest path pruning and a graph reduction method which finds candidate routes promoting long minimum lifetimes. When arbitrarily many routes from a node to the base station are permitted, optimal routes may be found as the solution to a well-known linear program. We present an evolutionary algorithm that finds good routes when each node is allowed only a small number of paths to the base station. On a real network deployed in the Victoria & Albert Museum, London, these solutions, using only three paths per node, are able to achieve minimum lifetimes of over 99% of the optimum linear program solution's time to first sensor battery failure.

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Collaborative Routing Algorithm for Wireless Sensor Network Longevity

Cited by 20 publications

References 15 publications

Computational Intelligence in Wireless Sensor Networks: A Survey

Computational Intelligence in Wireless Sensor Networks: A Survey

Swarm intelligence based routing protocol for wireless sensor networks: Survey and future directions

Hybrid evolutionary approaches to maximum lifetime routing and energy efficiency in sensor mesh networks

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