Wireless healthcare systems are hierarchical and heterogeneous in nature with components that have different energy and performance capabilities. Ensuring the optimal energy consumption across all these components while meeting performance requirements is a critical issue. In such systems with processing, sensing, and communication tasks, allocation of tasks to devices of the system affects the system battery lifetime and energy consumption.

This thesis presents a number of static and dynamic task assignment strategies to save energy and extend system lifetime. The problem of optimal task assignment with objectives related to minimizing the total energy consumption and maximizing the system lifetime are formulated using Integer Linear Program (ILP) -based solutions. The ILP based solutions are able to improve the battery lifetime by up to 1.4 times compared to performing all of the processing tasks on the backend server.

Given the dynamic nature of wireless systems, three dynamic algorithms are proposed. These algorithms are computationally efficient and are able to adapt to changing system conditions in real-time unlike ILP based solutions. DynAGreen algorithm is a graph-based task assignment algorithm with the objective of minimizing total system energy consumption. DynALife algorithm is a heuristic task assignment strategy that extends system battery lifetime. DynAGreenLife balances both system energy and system lifetime in wireless healthcare systems. Our dynamic scheduling techniques are able to improve system lifetime by up to 88% and on an average 30% in comparison to the static task assignment given by the ILP in dynamically changing urban conditions that represent real life scenarios.