Coverage performance of nonlinear energy harvesting based wireless sensor networks for the healthcare in hospitals

Wireless sensors are important nowadays to collect real-time data in hospital wards. Small sensors can be deployed either on/in patients' bodies or at fixed locations in wards. We propose an autonomous wireless system, where sensors can harvest energy from radio frequency signals, monitor and transmit data using the harvested energy. By properly modeling energy statuses and stochastic distributions of sensors, we analyze the coverage performance using Campbell's theorem, probability generating functional (PGFL), and Gil-Pelaez inversion theorem. The nonlinear energy harvesting model and the general Nakagami-m fading channels are considered in the analysis. Numerical results show that the coverage performance degrades when sensors are distributed more densely, the energy and data links are prolonged, or the transmission rate gets larger, while it improves when higher power is used for the energy transfer, or the energy and data links undergo lighter fading. Our proposed scheme can well facilitate the long-time monitoring tasks in hospital wards, which can greatly alleviate the burdens of medical staff.