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Optimization of energy and delay on interval data based graph model of wireless sensor networks

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Abstract

Emerging applications of wireless sensor networks (WSNs) in various domain of real-life require establishment of such routing topology for wireless sensors which can balance energy consumption with delay minimization. Despite a plethora research on energy and delay optimization in this field, work on developing delay minimum and energy efficient connected routing topology on heterogenous bi-directional WSNs where each node is assigned with a power interval, remain untouched. Considering this problem, we have introduced ‘Interval data based graph model (IDGM)’ and ‘Sorted-interval data based graph model (S-IDGM)’ of WSNs to explicitly deal with nodes’ power interval and proposed ‘Energy and Delay Optimization (EDO)’ algorithm to optimize S-IDGM such that the maximum topology delay, total topology delay and maximum node’s power interval become minimum in polynomial time complexity. A new function is formulated to estimate topology delay based on link distance and link interference after showing dependency analysis between link distance and link interference on large number of WSNs towards achieving optimal solutions. Extensive simulation work, graphical and statistical t-test analysis have been carried out to show the performance of EDO algorithm in minimizing topology delay and nodes’ power consumption, better than the existing algorithms from similar grounds. t-test analysis shows that the proposed EDO algorithm achieves optimal energy saving of nodes at 5% level of significance along with optimal minimization of max and total topology delay at 2% level of significance on S-IDGMs.

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Authors and Affiliations

  1. Department of Applied Mathematics, Delhi Technological University, Bawana Road, Rohini, Delhi, India

    Radhika Kavra, Anjana Gupta & Sangita Kansal

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  1. Radhika Kavra
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  2. Anjana Gupta
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Kavra, R., Gupta, A. & Kansal, S. Optimization of energy and delay on interval data based graph model of wireless sensor networks. Wireless Netw 29, 2293–2311 (2023). https://doi.org/10.1007/s11276-023-03292-x

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