Project

Context-Aware Edge Computing in IoT

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.

Publication Details

• Aswathy, A., Rekha Prabha, Lakshmi S. Gopal, Divya Pullarkatt, and Maneesha Vinodini Ramesh. “An efficient twitter data collection and analytics framework for effective disaster management.” In 2022 IEEE Delhi Section Conference (DELCON), pp. 1-6. IEEE, 2022.
• Gopal, Lakshmi S., Rekha Prabha, Divya Pullarkatt, and Maneesha Vinodini Ramesh. “Machine learning based classification of online news data for disaster management.” In 2020 IEEE Global Humanitarian Technology Conference (GHTC), pp. 1-8. IEEE, 2020.
• Ramesh, Maneesha Vinodini, Rekha Prabha, Hemalatha Thirugnanam, Aryadevi Remanidevi Devidas, Dhanesh Raj, Sruthy Anand, and Rahul Krishnan Pathinarupothi. “Achieving sustainability through smart city applications: protocols, systems and solutions using IoT and wireless sensor network.” CSI Transactions on ICT 8 (2020): 213-230.
• Prabha, Rekha, Maneesha Vinodini Ramesh, Venkat P. Rangan, P. V. Ushakumari, and T. Hemalatha. “Energy efficient data acquisition techniques using context aware sensing for landslide monitoring systems.” IEEE Sensors Journal 17, no. 18 (2017): 6006-6018.
• Hariharan, Balaji, Venkat P. Rangan, Simi Surendran, P. Rekha, RD Arya Devi, and Maneesha Vinodini Ramesh. “Delay and energy optimization in multilevel balanced WSNs for landslide monitoring.” In 2016 IEEE Global Humanitarian Technology Conference (GHTC), pp. 266-271. IEEE, 2016.
• Lekshmy, V. M., P. Rekha, and Maneesha Vinodhini Ramesh. “Impact of algorithm complexity on energy utilization of wireless sensor nodes.” In 2016 International Conference on Advances in Computing, Communications and Informatics (ICACCI), pp. 2592-2596. IEEE, 2016.