Correlation-based wireless sensor networks performance: the compressed sensing paradigm

In this paper, the performance of Wireless Sensor Networks (WSNs) operating for environmental monitoring is investigated. The performance metrics considered are normalized reconstruction error and energy estimation error. The temporal, spatial and spatiotemporal correlations are separately considered for the above metrics. The independent case and correlated cases for dense measurement cases along with the Compressed Sensing (CS) compressibility rule by selecting a subset of measurements for metric evaluation are thoroughly examined with extensive simulations and technical interpretations. Finally, applications of the proposed scheme are formulated in terms of topology and routing in fifth generation sensor networks and Internet of Things (IoT) deployment scenarios.

Automated summary

This paper investigates the performance of Wireless Sensor Networks in environmental monitoring by examining metrics such as normalized reconstruction error and energy estimation error, considering temporal, spatial, and spatiotemporal correlations. The study thoroughly examines both independent and correlated cases for dense measurement scenarios, and proposes applications in topology and routing for fifth generation sensor networks and IoT deployment scenarios.