Abstract
In this paper, we propose techniques for load reduction in Wireless Sensor Networks (WSNs) that work by understanding the nature of data. These techniques analyse time series data to understand patterns in the data and create simple or complex prediction models. These models are adaptive in nature, and are present both at the sensor nodes and the sink node. These models predict data whenever the sensor nodes measure data. Data transmission from sensor nodes to sink node occurs only when the measurements do not agree with model predictions. This reduces the amount of data transmitted across the network, leading to reduced communication and energy consumption. The prediction models are developed before sensor node deployment, and once steady-state operation starts, the prediction models are recreated at the sink node whenever their performance degrades. The sink node then sends updated model parameters to the corresponding sensor node whose model performance has degraded. Simulation results indicate a reduction of up to 88% in data transmitted from sensor nodes across the network, proving the efficacy of predictive models in reducing the amount of data sent, thereby saving transmission energy and improving network lifetime.
Keywords
- Load reduction
- Wireless sensor networks
- Predictive modelling
- Machine learning
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Chauhan, A.A., Udgata, S.K. (2022). Predictive Models for Load Reduction in Wireless Sensor Networks. In: Udgata, S.K., Sethi, S., Gao, XZ. (eds) Intelligent Systems. Lecture Notes in Networks and Systems, vol 431. Springer, Singapore. https://doi.org/10.1007/978-981-19-0901-6_39
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DOI: https://doi.org/10.1007/978-981-19-0901-6_39
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