Department of Electronics and Communication, Chhotubhai Gopalbhai Institute of Technology, Uka Tarsadia University, Maliba Campus, Bardoli-Mahuva Road, Tarsadi, Gopal Vidyanagar, Barodli - 394350, Gujarat, India.
Department of Electronics and Communication, Sarvajanik College of Engineering and Technology, Dr. R.K. Desai Marg, Opp. Mission Hospital, Athwalines, Surat - 395001, Gujarat, India.
ISA Trans. 2021 Sep;115:12-31. doi: 10.1016/j.isatra.2021.01.021. Epub 2021 Jan 13.
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.
节能跟踪框架的设计是一个研究充分的问题，也是传感器网络的一个重要应用。当前的研究现状表明，开发既能提高效率又能保证准确性的算法具有很大的发展空间。基于预测的算法可以通过仔细选择适合的节点来为连续目标跟踪节省网络能源。然而，传统的预测算法受到固定运动模型的限制，通常无法处理目标的加速运动。神经网络作为无模型估计器，可以学习输入和输出之间的任何非线性关系。为资源受限的微型传感器节点设计轻量级基于神经网络的预测算法是一项具有挑战性的任务。本研究旨在为线性和非线性目标运动开发一种更简单、节能且准确的基于网络的跟踪方案。所提出的技术使用自回归模型来学习目标轨迹的连续样本之间的时间相关性。将仿真结果与传统的卡尔曼滤波器 (KF)、交互多模型 (IMM)、当前统计模型 (CSM)、长短时记忆 (LSTM)、决策树 (DT) 和随机森林 (RF) 跟踪方法进行了比较。结果表明，所提出的算法可以在提高预测精度的同时节省高达 70%的网络能源。
