scholarly journals A Passive Tracking System Based on Geometric Constraints in Adaptive Wireless Sensor Networks

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3276 ◽  
Author(s):  
Biao Zhou ◽  
Deockhyeon Ahn ◽  
Jungpyo Lee ◽  
Chao Sun ◽  
Sabbir Ahmed ◽  
...  
Keyword(s):  
Power Consumption ◽  
Tracking System ◽  
Spatial Diversity ◽  
Geometric Constraints ◽  
Wireless Sensor ◽  
Tracking Accuracy ◽  
Passive Tracking ◽  
Save Energy ◽  
Tracking Device ◽  
Device Free

Target tracking technologies in wireless sensor network (WSNs) environments fall into two categories: active and passive schemes. Unlike with the active positioning schemes, in which the targets are required to hold cooperative devices, the research on passive tracking, i.e., tracking device-free targets, has recently showed promise. In the WSN, device-free targets can be tracked by sensing radio frequency tomography (RFT) on the line-of-sight links (LOSLs). In this paper, we propose a passive tracking scheme exploiting both adaptive-networking LOSL webs and geometric constraint methodology for tracking single targets, as well as multiple targets. Regarding fundamental knowledge, we firstly explore the spatial diversity technique for RFT detection in realistic situations. Then, we analyze the power consumption of the WSN and propose an adaptive networking scheme for the purpose of energy conservation. Instead of maintaining a fixed LOSL density, the proposed scheme can adaptively adjust the networking level to save energy while guaranteeing tracking accuracy. The effectiveness of the proposed scheme is evaluated with computer simulations. According to the results, it is observed that the proposed scheme can sufficiently reduce power consumption, while providing qualified tracking performance.

scholarly journals Efficient Node and Sensed Module Management for Multisensory Wireless Sensor Networks

Sensors ◽  
2018 ◽  
Vol 18 (7) ◽  
pp. 2328 ◽  
Author(s):  
Juan Feng ◽  
Xiaozhu Shi
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Management Strategies ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Tracking Accuracy ◽  
Node Selection ◽  
Information Utility ◽  
Save Energy ◽  
Selection Of

In target tracking wireless sensor networks, choosing a part of sensor nodes to execute tracking tasks and letting the other nodes sleep to save energy are efficient node management strategies. However, at present more and more sensor nodes carry many different types of sensed modules, and the existing researches on node selection are mainly focused on sensor nodes with a single sensed module. Few works involved the management and selection of the sensed modules for sensor nodes which have several multi-mode sensed modules. This work proposes an efficient node and sensed module management strategy, called ENSMM, for multisensory WSNs (wireless sensor networks). ENSMM considers not only node selection, but also the selection of the sensed modules for each node, and then the power management of sensor nodes is performed according to the selection results. Moreover, a joint weighted information utility measurement is proposed to estimate the information utility of the multiple sensed modules in the different nodes. Through extensive and realistic experiments, the results show that, ENSMM outperforms the state-of-the-art approaches by decreasing the energy consumption and prolonging the network lifetime. Meanwhile, it reduces the computational complexity with guaranteeing the tracking accuracy.

scholarly journals Unsupervised Learning in RSS-Based DFLT Using an EM Algorithm

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5549
Author(s):  
Ossi Kaltiokallio ◽  
Roland Hostettler ◽  
Hüseyin Yiğitler ◽  
Mikko Valkama
Keyword(s):  
Em Algorithm ◽  
Expectation Maximization ◽  
Tracking System ◽  
Model Parameters ◽  
Calibration Data ◽  
Tracking Accuracy ◽  
Time Period ◽  
The Em Algorithm ◽  
Device Free ◽  
Localization And Tracking

Received signal strength (RSS) changes of static wireless nodes can be used for device-free localization and tracking (DFLT). Most RSS-based DFLT systems require access to calibration data, either RSS measurements from a time period when the area was not occupied by people, or measurements while a person stands in known locations. Such calibration periods can be very expensive in terms of time and effort, making system deployment and maintenance challenging. This paper develops an Expectation-Maximization (EM) algorithm based on Gaussian smoothing for estimating the unknown RSS model parameters, liberating the system from supervised training and calibration periods. To fully use the EM algorithm’s potential, a novel localization-and-tracking system is presented to estimate a target’s arbitrary trajectory. To demonstrate the effectiveness of the proposed approach, it is shown that: (i) the system requires no calibration period; (ii) the EM algorithm improves the accuracy of existing DFLT methods; (iii) it is computationally very efficient; and (iv) the system outperforms a state-of-the-art adaptive DFLT system in terms of tracking accuracy.

iLight: Device-Free Passive Tracking Using Wireless Sensor Networks

2013 ◽  
Vol 13 (10) ◽  
pp. 3785-3792 ◽  
Author(s):  
Xufei Mao ◽  
ShaoJie Tang ◽  
Jiliang Wang ◽  
Xiang Yang Li
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Wireless Sensor ◽  
Passive Tracking ◽  
Device Free

scholarly journals Pointing Sensors and Sun Tracking Techniques

2011 ◽  
Vol 2011 ◽  
pp. 1-9 ◽  
Author(s):  
D. Fontani ◽  
P. Sansoni ◽  
F. Francini ◽  
D. Jafrancesco ◽  
L. Mercatelli ◽  
...  
Keyword(s):  
Optical System ◽  
Tracking System ◽  
Field Tests ◽  
The Sun ◽  
System Configuration ◽  
The Core ◽  
Passive Tracking ◽  
Working Principle ◽  
Tracking Device ◽  
Guiding System

Every optical system for sunlight concentration requires following the sun in its movement. The sun tracking method is essentially chosen on the base of collection geometry and optical system configuration. A simple, useful, and original technique to realise sun tracking is proposed. It is based on a double guiding system using two complementary procedures. A passive tracking device performs a preliminary collector orientation. Then an active tracking system realises its fine positioning and adjustments exploiting an optical pointing sensor. The core of this active tracking device is the sun finder. Pointing sensors for fibre-coupled, CPV (Concentrating Photo voltaic), and linear collectors are presented, illustrating in detail the working principle and practical use. All sensors were optically characterised in laboratory, under controlled and reproducible conditions. Some field tests completed the experimentation evaluating the sensors performance in outdoor working conditions.

scholarly journals Tracking Mobile Robot in Indoor Wireless Sensor Networks

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Liping Zhang ◽  
Cheng-Chew Lim ◽  
Yiping Chen ◽  
Hamid Reza Karimi
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Mobile Robots ◽  
Tracking System ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Tracking Accuracy ◽  
Wireless Sensor Nodes ◽  
Localization Scheme ◽  
Indoor Wireless

This work addresses the problem of tracking mobile robots in indoor wireless sensor networks (WSNs). Our approach is based on a localization scheme with RSSI (received signal strength indication) which is used widely in WSN. The developed tracking system is designed for continuous estimation of the robot’s trajectory. A WSN, which is composed of many very simple and cheap wireless sensor nodes, is deployed at a specific region of interest. The wireless sensor nodes collect RSSI information sent by mobile robots. A range-based data fusion scheme is used to estimate the robot’s trajectory. Moreover, a Kalman filter is designed to improve tracking accuracy. Experiments are provided to assess the performance of the proposed scheme.

scholarly journals Geometric Midpoint Algorithm for Device-Free Localization in Low-Density Wireless Sensor Networks

Electronics ◽  
2021 ◽  
Vol 10 (23) ◽  
pp. 2924
Author(s):  
Chao Sun ◽  
Biao Zhou ◽  
Shangyi Yang ◽  
Youngok Kim
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Electronic Devices ◽  
Wireless Sensor ◽  
Low Density ◽  
Tracking Accuracy ◽  
Resource Limited ◽  
Geometric Objects ◽  
Device Free ◽  
Mean Square Errors

Device-free localization (DFL) is a technique used to track a target transporting no electronic devices. Radiofrequency (RF) tomography based DFL technology in wireless sensor networks has been a popular research topic in recent years. Typically, high-tracking accuracy requires a high-density wireless network which limits its application in some resource-limited scenarios. To solve this problem, a geometric midpoint (GM) algorithm based on the computations of simple geometric objects is proposed to realize effective tracking of moving targets in low-density wireless networks. First, we proposed a signal processing method for raw RSS signals collected from wireless links that can detect the fluctuations caused by a moving target effectively. Second, a geometric midpoint algorithm is proposed to estimate the location of the target. Finally, simulations and experiments were performed to validate the proposed scheme. The experimental results show that the proposed GM algorithm outperforms the geometric filter algorithm, which is a state-of-the-art DFL method that yields tracking root-mean-square errors up to 0.86 m and improvements in tracking accuracy up to 67.66%.

scholarly journals Autonomous Tracking by an Adaptable Scaled KCF Algorithm

2021 ◽  
Vol 11 (1) ◽  
pp. 48-54
Author(s):  
Din-Chang Tseng ◽  
◽  
Chien-Hung Chen ◽  
Yi-Ming Chen
Keyword(s):  
Computer Vision ◽  
Tracking System ◽  
Passive Tracking ◽  
Feature Based ◽  
Tracking Device ◽  
Tracking Devices

A multicopter is equipped by a passive tracking device to follow a specified target. However, if want to track a non-controlled target, the passive tracking device is failed. We propose a vision-based tracking system for multicopters, used computer vision method to track any target without additional tracking devices. In this study, propose scale candidate graphs and scale tables to improve KCF. There are also stable results when the scale changes. The proposed an adaptable scaled KCF algorithm, when the KCF tracking failed, a feature-based matching detector is used to re-detect the target. Several experiments on various scene based on the proposed approach were conducted and evaluated. Stable tracking results were obtain to show the feasibility of the proposed system.

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