Evaluating alternative approaches to mobile object localization in wireless sensor networks with passive architecture

2012 ◽  
Vol 63 (9) ◽  
pp. 941-947 ◽  
Author(s):  
Mohammad Gholami ◽  
Ningxu Cai ◽  
Robert W. Brennan
Author(s):  
Maryam Sadat Mirsadeghi ◽  
Ali Mahani

Mobile target tracking is one of the most important applications of wireless sensor networks (WSNs). But, the use of sensor networks for object tracking faces a number of issues in which the limited energy supply is the most important. So in target tracking problem, using methods to decrease the energy consumption as well as high accuracy and quality of tracking is the main goal. Hence, reducing the number of participant nodes in tracking phase, increasing the sleep duration of noninvolved nodes and decreasing the number of transmitted packets to the sink are the most referred methods. In this chapter the authors introduce the most suitable methods for energy efficient mobile object tracking.


Author(s):  
CHI-LU YANG ◽  
YEIM-KUAN CHANG ◽  
YU-TSO CHEN ◽  
CHIH-PING CHU ◽  
CHI-CHANG CHEN

Service systems used for various applications in home automation and security require estimating the locations precisely using certain sensors. Serving a mobile user automatically by sensing his/her locations in an indoor environment is considered as a challenge. However, indoor localization cannot be carried out effectively using the Global Positioning System (GPS). In recent years, the use of Wireless Sensor Networks (WSNs) in locating a mobile object in an indoor environment has become popular. Some physical features have also been discussed to solve localization in WSNs. In this paper, we inquire into received signal strength indication (RSSI)-based solutions and propose a new localization scheme called the closer tracking algorithm (CTA) for indoor localization. Under the proposed CTA, a mechanism on mode-change is designed to switch automatically between the optimal approximately closer approach (ACA) and the real-time tracking (RTT) method according to pre-tuned thresholds. Furthermore, we design a mechanism to move reference nodes dynamically to reduce the uncovered area of the ACA for increasing the estimation accuracy. We evaluate the proposed CTA using ZigBee CC2431 modules. The experimental results show that the proposed CTA can determine the position accurately with an error distance less than 0.9 m. At the same time, the CTA scheme has at least 87% precision when the distance is less than 0.9 m. The proposed CTA can select an adaptive mode properly to improve the localization accuracy with high confidence. Moreover, the experimental results also show that the accuracy can be improved by the deployment and movement of reference nodes.


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