Virtual Research Symposium Series by IEEE CIS Chapter – IEEE Gujarat Section
Date: 17 Feb 2021
Time: 11:00 AM to 11:45 AM
The IEEE Computational Intelligence Society Chapter – IEEE Gujarat Section brings in Symposium series to foster exchange of ideas and experience. It provides a discussion forum for advancement of their research and gain feedback on research work in the domain of computational intelligence. This is an initiative by IEEE CIS chapter – IEEE Gujarat section to build community for intellectual exchange and support the budding researchers.
Intended Participants: Anyone interested in Computational Intelligence Domain
Details of the next Symposium talk
Speaker: Mr Jayesh Munjani, PhD Scholar, Uka Tarsadia University, Bardoli, India
Supervisor: Dr Maulin Joshi, Professor & Head, Electronics and Communication Engineering department, Sarvajanik College of Engineering &Technology, Surat
Topic: A non-conventional lightweight Auto Regressive Neural Network for accurate and energy efficient target tracking in Wireless Sensor Network
The design of an energy-efficient tracking framework is a well-investigated issue and a prominent sensor network application. The current research state shows a clear scope for developing algorithms that can work, accompanying both energy efficiency and accuracy. The prediction-based algorithms can save network energy by carefully selecting suitable nodes for continuous target tracking. However, the conventional prediction algorithms are confined to fixed motion models and generally fail in accelerated target movements. The neural networks can learn any non-linearity between input and output as they are model-free estimators. To design a lightweight neural network-based prediction algorithm for resource-constrained tiny sensor nodes is a challenging task. This research aims to develop a simpler, energy-efficient, and accurate network-based tracking scheme for linear and non-linear target movements. The proposed technique uses an autoregressive model to learn the temporal correlation between successive samples of a target trajectory. The simulation results are compared with the traditional Kalman filter (KF), Interacting Multiple models (IMM), Current Statistical model (CSM), Long Short Term Memory (LSTM), Decision Tree (DT), and Random Forest (RF) based tracking approach. It shows that the proposed algorithm can save up to 70% of network energy with improved prediction accuracy.