Exploiting Temporal Correlation Mechanism for Energy Efficient Data Collection in Intrabody Nanonetworks

Nanosensors outstanding capabilities of detecting fine-grained information are enormously increasing their scope for diverse healthcare applications. Due to characteristics of monitored phenomena, detected information can be highly time correlated, that can be exploited to addresses the primary challenge of energy consumption in Intrabody nanonetworks. In this work, temporal correlation is employed for proposing an energy efficient data collection solution for obtaining real-time physiological information. Furthermore, the proposed scheme also takes advantage of exponential weighting moving average for combining past trends in making data transmission decisions. Simulations are carried out using the Nano-SIM tool for evaluating the performance of our presented scheme. Results show that using temporal correlation for data collection decreases communication and data exchange that leads to low consumption of energy resources and minimum end-to-end delay.