A number of trends, such as an aging population, an increasing number of people with Chronic Diseases (CD) (860 million says World Health Organization), rising costs, percentage of death caused by CD, are creating a major impetus to developing scalable healthcare and disease management systems. The aim of the proposed research is to develop an energy efficient framework for wireless body area sensor networks (WBASN) with scalable signal processing capability. The framework will be specifically, but not exclusively, designed for applications in healthcare and biomedical research, and deals with signals such as Electrocardiogram (ECG), Electroencephalogram (EEG), and more. As intensive processing of such signals poses significant challenges to power efficiency and delay sensitive communication, traditional signal processing techniques may not be suitable. Thus to achieve our goals, we plan to pursue the following research objectives: 1) Develop robust and fairly generic signal processing techniques using discrete transforms and compressive sensing techniques that can provide scalable traffic for optimal energy efficient communication, 2) Develop a WSN framework for energy-efficient and scalable communication using cross layer and protocol optimization., 3) Demonstrate the robustness of the framework by developing a testbed and 4) implementing two applications: respiration disorder classification and detection of patient’s voluntary movements via a brain computer interface.