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Energy and delay efficient data acquisition in wireless sensor networks by selecting optimal visiting points for mobile sink

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Abstract

Data acquisition using mobile sink in wireless sensor networks (WSNs) has shown significant advantages, especially in large-scale networks. Unlike the acquisition of sensor data by static sink through multi-hop forwarding, mobile sink travels across the sensing field to acquire data, which significantly reduces the energy consumption of sensor nodes. However, deciding sojourning locations for mobile sink in sensing field and designing a delay efficient trajectory path for its movement are very challenging. This paper takes up this issue and proposes an energy and delay efficient data acquisition technique, named as EDEDA. It divides the sensor field into virtual grids and identifies a certain number of grid cells, termed as visiting points (VPs), in such a way that mobile sink sojourns in them and acquires data from nine adjacent grid cell heads in single-hop. Furthermore, the mobility pattern of mobile sink is modelled as Hamiltonian cycle starting and ending at the base station (BS) after visiting all the VPs. Mobile sink offloads collected data at BS after each cycle. Simulations are performed on NS-2 to evaluate the performance of EDEDA at varying number of sensor nodes and found that it outperforms existing routing protocols in terms of energy consumption and throughput. Moreover, EDEDA results in 25.72%, 25.72%, 19.54%, 14.57% improvement in data acquisition latency for varying number of sensor nodes when compared with TCBDGA, PSOBS, RkM, VGRSS, respectively.

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Authors and Affiliations

  1. Department of Computer Science, Indian Institute of Information Technology, Lucknow, India

    Rahul Kumar Verma

  2. Thapar Institute of Engineering & Technology, Patiala, India

    Shubhra Jain

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  1. Rahul Kumar Verma
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  2. Shubhra Jain
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Verma, R.K., Jain, S. Energy and delay efficient data acquisition in wireless sensor networks by selecting optimal visiting points for mobile sink. J Ambient Intell Human Comput 14, 11671–11684 (2023). https://doi.org/10.1007/s12652-022-03729-9

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  • DOI: https://doi.org/10.1007/s12652-022-03729-9

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