An Intelligent Multi-hop Routing for Wireless Sensor Networks

Islam, Obidul; Hussain, Sajid

doi:10.1109/wi-iatw.2006.42

Cited by 16 publications

(11 citation statements)

References 4 publications

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“…3) Evolutionary Algorithms: A GA based multi-hop routing technique named GA-Routing is proposed in [91] for maximizing network longevity in terms of time to first node death. The challenge here is to come up with a routing algorithm that maximizes the number of rounds before the first node dies.…”

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

View full text Add to dashboard Cite

show abstract

“…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. In comparison, centralised approaches, which mostly incorporate variants of heuristic algorithms (Chang and Tassiulas, 2004;Xue et al, 2006;Islam and Hussain, 2006;Yetgin et al, 2012) require lower computational power and storage at the nodes, as most of the computation and storage is conducted 2 Evolutionary Computation Volume x, Number x by the central base station. Nonetheless, there is a system-wide overhead incurred in gathering connectivity information and broadcasting routing information.…”

Section: Introductionmentioning

confidence: 99%

“…We therefore seek to optimise the lifetime of network nodes by modelling the charge held in their batteries and the energy expenditure at each node. Islam and Hussain (2006) and Kamath and Nasipuri (2011) have considered maximising only the minimum remaining lifetime among nodes. Such approaches can improve the individual node specific energy state, but can be sub-optimal from system-wide perspective.…”

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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“…Distributed approaches, in which responsibility for routing is distributed across the constituent nodes, involve techniques like reinforcement learning [7] and swarm intelligence [1], etc. On the other hand centralised systems, in which a centrally calculated route is broadcast to participating nodes, mostly incorporate variants of evolutionary algorithms (EAs) [11,13,8]. In the distributed approach, nodes need to have sufficient computational power to decide on the best path for sending data and sufficient storage for storing information about local connectivity.…”

Section: Introductionmentioning

confidence: 99%

“…We therefore seek to optimise the lifetime of network nodes by modelling the charge held in their batteries and the energy expenditure at each node. Islam et al [8], and Kamath et al [9] have considered maximising only the minimum remaining lifetime among nodes. Such approaches can improve individual node specific energy state, but can be sub-optimal from system-wide perspective.…”

Section: Introductionmentioning

confidence: 99%

Multi-objective routing optimisation for battery-powered wireless sensor mesh networks

Rahat

Everson

Fieldsend

2014

Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation

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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 and resilience against routing failures. However, multi-hop mesh networks suffer from higher energy costs, and the routing strategy directly affects the lifetime of nodes with limited energy sources. Hence while planning routes there are trade-offs to be considered between individual and system-wide battery lifetimes. We present a novel multi-objective routing optimisation approach using 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 and thus permit dynamic optimisation for self-healing networks, our approach uses novel k-shortest paths based search space pruning in conjunction with a new edge metric, which associates the energy cost at a pair of nodes with the link between them. We demonstrate our solution on a real network, deployed in the Victoria & Albert Museum, London. We show that this approach provides better trade-off solutions in comparison to the minimum energy option, and how a combination of solutions over the lifetime of the network can enhance the overall minimum lifetime.

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An Intelligent Multi-hop Routing for Wireless Sensor Networks

Cited by 16 publications

References 4 publications

Computational Intelligence in Wireless Sensor Networks: A Survey

Computational Intelligence in Wireless Sensor Networks: A Survey

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

Multi-objective routing optimisation for battery-powered wireless sensor mesh networks

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