A Comparative Study of k-Shortest Path Algorithms

Brander, A. W.; Sinclair, Mark

doi:10.1007/978-1-4471-1007-1_25

Cited by 73 publications

(40 citation statements)

References 7 publications

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“…Algorithms for discovering the k shortest paths between a source and all the nodes in a weighted graph are well known and can be computed in O(|E|+|V | log |V |+k|V |) time [36,37,38]. However, as we noted above, the energy costs in this problem are associated with the nodes themselves rather than with the edges; see (3).…”

Section: K-shortest Path Pruningmentioning

confidence: 99%

Evolutionary multi-path routing for network lifetime and robustness in wireless sensor networks

2016

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Wireless sensor networks frequently use multi-path routing schemes between nodes and a base station. Multi-path routing confers additional robustness against link failure, but in batterypowered networks it is desirable to choose paths which maximise the overall network lifetime -the time at which a battery is first exhausted. We introduce multi-objective evolutionary algorithms to find the routings which approximate the optimal trade-off between network lifetime and robustness. A novel measure of network robustness, the fragility, is introduced. We show that the distribution of traffic between paths in a given multi-path scheme that optimises lifetime or fragility may be found by solving the appropriate linear program. A multi-objective evolutionary algorithm is used to solve the combinatorial optimisation problem of choosing routings and traffic distributions that give the optimal trade-off between network lifetime and robustness. Efficiency is achieved by pruning the search space using k-shortest paths, braided and edge disjoint paths. The method is demonstrated on synthetic networks and a real network deployed at the Victoria & Albert Museum, London. For these networks, using only two paths per node, we locate routings with lifetimes within 3% of those obtained with unlimited paths per node. In addition, routings which halve the network fragility are located. We also show that the evolutionary multi-path routing can achieve significant improvement in performance over a braided multi-path scheme.

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Section: K-shortest Path Pruningmentioning

confidence: 99%

Evolutionary multi-path routing for network lifetime and robustness in wireless sensor networks

2016

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“…We select from among several shortest path routes for each node because, if each node were to utilise its shortest path, nodes that occur in many of the 1-shortest paths would be disproportionately burdened. Algorithms for discovering the shortest path between two nodes in a weighted graph are well known and the shortest path can be found in O(n log n) time (Yen, 1971;Eppstein, 1998;Brander and Sinclair, 1995). However, as we noted above, the energy costs in this problem are associated with the nodes themselves rather than with the edges.…”

Section: K-shortest Path Pruningmentioning

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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“…A WSN is represented as a network graph, G = {V, E}, where V is a finite set of n sensor nodes vi plus a base station node, vB, and E is the finite set of m edges [2,4]. Each node must communicate with the base station, perhaps by relaying a message through one or more other nodes.…”

Section: System Model and Pareto Opti-malitymentioning

confidence: 99%

“…Then in one reporting cycle, the energy expended at vi in sending its own data to its downstream node d = Si [2] and relaying messages received from nodes with indices in the set I and sending them on to nodes with indices O is…”

Section: System Model and Pareto Opti-malitymentioning

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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A Comparative Study of k-Shortest Path Algorithms

Cited by 73 publications

References 7 publications

Evolutionary multi-path routing for network lifetime and robustness in wireless sensor networks

Evolutionary multi-path routing for network lifetime and robustness in wireless sensor networks

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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