Programs
- M. Tech. in Automotive Engineering -
- Fellowship In Interventional Pulmonology and Pulmonary Critical care - Fellowship
The project deals with edge computing for IoT nodes to increase the energy efficiency by using context-aware energy data management and context-aware energy management.
Project Description
Real-time wireless sensor networks are an emerging technology for continuous environmental monitoring. But real-world deployments are constrained by resources, such as power, memory, and processing capabilities. In this paper, we discuss a set of techniques to maximize the lifetime of a system deployed in south India for detecting rain-fall induced landslides. In this system, the sensing subsystem consumes 77.5%, the communication subsystem consumes 22%, and the processing subsystem consumes 0.45% of total power consumption. Hence, to maximize the lifetime of the system, the sensing subsystem power consumption has to be reduced. The major challenge to address is the development of techniques that reduce the power consumption, while preserving the reliability of data collection and decision support by the system. The project proposes a wavelet-based sampling algorithm for choosing the minimum sampling rate for ensuring the data reliability. The results from the wavelet sampling algorithm along with the domain knowledge have been used to develop context aware data collection models that enhance the lifetime of the system. Two such models named context aware data management (CAD) and context aware energy management (CAE) have been devised. The results show that the CAD model extends the lifetime by six times and the CAE model does so by 20 times when compared with the continuous data collection model, which is the existing approach. In this project, we also developed mathematical modeling for CAD and CAE, which have been validated using real-time data collected in the past.
