Energy-efficient Object Tracking Research Articles

Conversion of Siamese networks to spiking neural networks for energy-efficient object tracking

Conversion of Siamese networks to spiking neural networks for energy-efficient object tracking

A PERUSAL STUDY OF METHODS FOR ENERGY EFFICIENT MULTI OBJECT TRACKING IN WIRELESS SENSOR NETWORKS

Wireless sensor networks (WSNs) has various applications in different areas such as object detection and target tracking, industrial process monitoring, environmental monitoring, wildlife monitoring, Robot control and security applications for buildings. In WSNs, Energy efficiency is one of the important research issues, as it determines the lifetime of the sensor network deployed for the specific applications. Object tracking is one of the dominant applications of wireless sensor networks and many energy-efficient multi object tracking algorithms are used for precise or exact object tracking. The main focus this paper is the perusal study of all different energy-efficient object tracking methods and algorithms for Wireless Sensor Network.

An Efficient Lagrangean Relaxation-based Object Tracking Algorithm in Wireless Sensor Networks

In this paper we propose an energy-efficient object tracking algorithm in wireless sensor networks (WSNs). Such sensor networks have to be designed to achieve energy-efficient object tracking for any given arbitrary topology. We consider in particular the bi-directional moving objects with given frequencies for each pair of sensor nodes and link transmission cost. This problem is formulated as a 0/1 integer-programming problem. A Lagrangean relaxation-based (LR-based) heuristic algorithm is proposed for solving the optimization problem. Experimental results showed that the proposed algorithm achieves near optimization in energy-efficient object tracking. Furthermore, the algorithm is very efficient and scalable in terms of the solution time.

EOATR: Energy Efficient Object Tracking by Auto Adjusting Transmission Range in Wireless Sensor Network

EOATR: Energy Efficient Object Tracking by Auto Adjusting Transmission Range in Wireless Sensor Network

A novel prediction-based strategy for object tracking in sensor networks by mining seamless temporal movement patterns

Energy saving in sensor networks has received a great deal of research attention in recent years due to its wide applications. One important research issue is energy efficient object tracking in sensor networks (OTSNs). Past studies on energy saving in OTSNs can be divided into two main directions: (1) improvement in hardware design; and (2) improvement in software approaches. Many research papers save energy in hardware design, but few discuss software approaches. The intuitive way to conserve the energy of sensor nodes is to reduce the operation time of high-powered components. Utilizing the movement patterns of objects to save energy is one software approach. However, it did not take temporal information into consideration nor did it define a suitable segmenting time unit of time interval in advance. Due to the time interval between movements is a real number, an improper segmenting time unit may not discover the useful patterns, directly resulting in the inefficient object tracking. In this paper, we propose a seamless data mining algorithm named STMP-Mine to efficiently discover the temporal movement patterns of objects in sensor networks without predefining the segmenting time unit. Moreover, we propose novel location prediction strategies that employ the discovered temporal movement patterns to reduce prediction errors to save energy. With empirical evaluation on simulated data, STMP-Mine and the proposed prediction strategies are shown to deliver excellent performance in terms of scalability and energy efficiency.