Optimal rendezvous points selection to reliably acquire data from wireless sensor networks using mobile sink


In rendezvous points (RPs) based data collection, the mobile sink (MS) visits a set of sensor nodes known as RPs for data gathering from wireless sensor networks to minimize the trajectory of MS. Existing RPs based methods are suitable for the scenarios where sensor nodes have uniform data generation rates along with having limited buffer capacity to store the forwarded data. However, in some situations, the sensing rate increases due to the occurrence of unusual events in the surrounding, and the RPs receive more data packets than their capacity. This creates data loss due to buffer overflow. Therefore, the selection of optimal RPs for reliable data gathering, while minimizing the trajectory of MS, is a challenging task. This paper proposes a squirrel search algorithm-based rendezvous points selection (SSA-RPS) method that chooses a set of optimal RPs for reliable data collection. The objective of the SSA-RPS is to minimize the trajectory of MS while visiting a set of optimal RPs under non-uniform data generation and limited buffer capacity constraints of sensor nodes for reliable data acquisition. The SSA-RPS applies an efficient encoding scheme to generate variable dimension squirrels that represent each possible trajectory of MS, and the dimension of squirrel presents the number of RPs. The SSA-RPS also adopts the reselection of RPs to provide a fair energy share among sensor nodes. The simulation results demonstrate that the SSA-RPS outperforms the existing state-of-the-art methods in terms of the number of dropped packets, data gathering ratio, energy consumption, and network lifetime.

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Correspondence to Anjula Mehto.

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Mehto, A., Tapaswi, S. & Pattanaik, K.K. Optimal rendezvous points selection to reliably acquire data from wireless sensor networks using mobile sink. Computing (2021). https://doi.org/10.1007/s00607-021-00917-x

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  • Wireless sensor network
  • Mobile sink
  • Rendezvous point
  • Squirrel search algorithm

Mathematics Subject Classification

  • 68M18