Patient-environment monitoring for smart healthcare in hospitals with cooperative power-data transfer

Reliable and continuous patient-environment monitoring is of great significance for the smart healthcare in hospitals, enabling proactive care and optimized clinical environments. However, the sustainable operation of wireless sensor nodes is critically challenged by energy shortage, which can interrupt data flows and lead to the loss of vital health information upon energy depletion. To address this, this work proposes a cooperative energy and data transfer framework for healthcare sensor networks. For wireless power transfer (WPT), a power beacon (PB) is selected either based on the best channel quality or randomly from multiple available PBs. Considering the limited amount of harvested energy at the sensor node, a set of relay nodes can be employed to assist in the data transmission from the sensor node to the access point (AP). We investigate an opportunistic decode-and-forward (DF) relaying strategy using two relay selection approaches: (1) selecting the relay node that provides the highest end-to-end achievable data rate, and (2) randomly selecting a relay node. With relay assistance, the transmission distance for sensor nodes can be shortened, thereby reducing energy consumption. Specifically, we properly model the sensor node's nonlinear energy harvesting process and energy status, with channel fading characterized by the general Nakagami-m distribution. System outage probability is analyzed and minimized through numerically determining the WPT time, the sensor transmission time, and the location of relay nodes. Extensive simulations compare different PB and relay selection schemes and illustrate the impacts of key parameters on outage performance. The findings reveal that best relay selection contributes more substantially to system performance improvement than best PB selection. The proposed framework demonstrates the feasibility of building robust, energy-sustainable monitoring networks, which is a critical step toward realizing reliable and autonomous smart healthcare systems in hospital environments.