LIPS: A Protocol Suite for Homeostatic Sensornet Management

Tate, Jonathan; Bate, Iain

doi:10.1109/iceccs.2011.33

Cited by 2 publications

(2 citation statements)

References 28 publications

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“…Having delved into the latest state-of-the-art node scheduling literature, it can be concluded that most scheduling algorithms focus on considering only two requirements and do not address all three requirements of the WSN altogether, i.e., connectivity, coverage, and network lifetime. Most works address each requirement in isolation, e.g., [ 4 , 11 , 12 , 18 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 ] do not consider network coverage; other works such as [ 6 , 29 ] consider network coverage but without ensuring a good level of network connectivity; and [ 6 ] uses a very simple WSN scenario that lacks tests on realistic and complex WSNs. To the authors’ best knowledge, there are two published works that consider addressing the three objectives collectively: the RCS algorithm [ 13 ] and clique-based node scheduling [ 13 , 30 ] have shown that partitions can occur further away from the base station as the deaths of subsequent nodes can result in live nodes attempting to compensate for the drop-in coverage and connectivity in the system (cf.…”

Section: Related Workmentioning

confidence: 99%

“…For example, a WSN can be deployed on the top of a volcano where human intervention is impossible or to monitor the radiation leakage at a nuclear plant station, which poses a risk to human lives [ 10 ]. Hence, when a WSN is designed for safety-critical applications, another level of complexity is added as rigid time/deadline requirements are to be respected [ 11 , 12 ]. Such resources need to be optimized for WSNs to function efficiently.…”

Section: Introductionmentioning

confidence: 99%

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A Novel Bio-Inspired Bat Node Scheduling Algorithm for Dependable Safety-Critical Wireless Sensor Network Systems

Al-Nader,

Lasebae,

Raheem

et al. 2024

Sensors

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The multi-objective optimization (MOO) problem in wireless sensor networks (WSNs) is concerned with optimizing the operation of the WSN across three dimensions: coverage, connectivity, and lifetime. Most works in the literature address only one or two dimensions of this problem at a time, except for the randomized coverage-based scheduling (RCS) algorithm and the clique-based scheduling algorithm. More recently, a Hidden Markov Model (HMM)-based algorithm was proposed that improves on the latter two; however, the question remains open if further improvement is possible as previous algorithms explore solutions in terms of local minima and local maxima, not in terms of the full search space globally. Therefore, the main contribution of this paper is to propose a new scheduling algorithm based on bio-inspired computation (the bat algorithm) to address this limitation. First, the algorithm defines a fitness and objective function over a search space, which returns all possible sleep and wake-up schedules for each node in the WSN. This yields a (scheduling) solution space that is then organized by the Pareto sorting algorithm, whose output coordinates are the distance of each node to the base station and the residual energy of the node. We evaluated our results by comparing the bat and HMM node scheduling algorithms implemented in MATLAB. Our results show that network lifetime has improved by 30%, coverage by 40%, and connectivity by 26.7%. In principle, the obtained solution will be the best scheduling that guarantees the best network lifetime performance as well as the best coverage and connectedness for ensuring the dependability of safety-critical WSNs.

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