Details of Research Outputs

Emerging revolution in the healthcare has caught the attention of both the industry and academia due to the rapid proliferation in the wearable devices and innovative techniques. In the mean-time, body sensor networks (BSNs) have become the potential candidate in transforming the entire landscape of the medical world. However, large battery lifetime and less power drain are very vital for these resource-constrained sensor devices while collecting the bio signals. Hence, minimizing their charge and energy depletions are still very challenging tasks. It is examined through large real-time data sets that due to the dynamic nature of the wireless channel, the traditional predictive transmission power control (TPC) and a constant transmission power techniques are no more supportive and potential candidates for BSNs. Thus, this paper first proposes a novel joint TPC and duty-cycle adaptation-based framework for pervasive healthcare. Second, an adaptive energy-efficient transmission power control algorithm is developed by adapting the temporal variation in the on-body wireless channel amid static (i.e., standing and walking at a constant speed) and dynamic (i.e., running) body postures. Third, a feedback control-based duty-cycle algorithm is proposed for adjusting the execution period of tasks (i.e., sensing and transmission). Fourth, system-level battery and energy harvesting models are proposed for body sensor nodes by examining the energy depletion of sensing and transmission tasks. It is validated through Monte Carlo experimental analysis that the proposed algorithm saves more energy of 11.5% with reasonable packet loss ratio by adjusting both the transmission power and the duty cycle unlike the conventional constant TPC and PTPC methods.