scholarly journals A Research Cluster based Scheme for Node Positioning in Indoor Environment

2019 ◽  
Vol 8 (6S) ◽  
pp. 79-83
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Indoor Environment ◽  
Cluster Head ◽  
Signal Strength ◽  
Received Signal Strength ◽  
Location Estimation ◽  
Wireless Sensor ◽  
Localization Error ◽  
Network Efficiency

Location estimation in Wireless Sensor Network (WSN) is mandatory to achieve high network efficiency. Identifying the positions of sensors is an uphill task as monitoring nodes are involved in estimation and localization. Clustered Positioning for Indoor Environment (CPIE) is proposed for estimating the position of the sensors using a Cluster Head (CH) based mechanism. The CH estimates the number of neighbor nodes in each floor of the indoor environment. It sends the requests to the cluster members and the positions are estimated based on the Received Signal Strength Indicators (RSSIs) from the members of the cluster. The performance of the proposed scheme is analyzed for both stable and mobile conditions by varying the number of floors. Experimental results show that the propounded scheme offers better network efficiency and reduces delay and localization error

scholarly journals LON Technology in Wireless Sensor Networking Applications

Sensors ◽  
2006 ◽  
Vol 6 (1) ◽  
pp. 30-48 ◽  
Author(s):  
Marek Miskowicz ◽  
Ryszard Golanski
Keyword(s):  
Wireless Sensor ◽  
Sensor Networking ◽  
Wireless Sensor Networking

scholarly journals Target Localization with Unknown Transmit Power and Path-Loss Exponent Using a Kalman Filter in WSNs

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6582
Author(s):  
SeYoung Kang ◽  
TaeHyun Kim ◽  
WonZoo Chung
Keyword(s):  
Kalman Filter ◽  
Path Loss ◽  
Target Position ◽  
Signal Strength ◽  
Received Signal Strength ◽  
Target Localization ◽  
Wireless Sensor ◽  
Transmit Power ◽  
Path Loss Exponent ◽  
Localization Algorithms

We present a novel hybrid localization algorithm for wireless sensor networks in the absence of knowledge regarding the transmit power and path-loss exponent. Transmit power and the path-loss exponent are critical parameters for target localization algorithms in wireless sensor networks, which help extract target position information from the received signal strength. In the absence of information on transmit power and path-loss exponent, it is critical to estimate them for reliable deployment of conventional target localization algorithms. In this paper, we propose a simultaneous estimation of transmit power and path-loss exponent based on Kalman filter. The unknown transmit power and path-loss exponent are estimated using a Kalman filter with the tentatively estimated target position based solely on angle information. Subsequently, the target position is refined using a hybrid method incorporating received signal strength measurements based on the estimated transmit power and path-loss exponent. Our proposed algorithm accurately estimates transmit power and path-loss exponent and yields almost the same target position accuracy as the simulation results confirm, as the hybrid target localization algorithms with known transmit power and path-loss exponent. Simulation results confirm the proposed algorithm achieves 99.7% accuracy of the target localization performance with known transmit power and path-loss exponent, even in the presence of severe received signal strength measurement noise.

Environmental Monitoring Based on the Wireless Sensor Networking Technology

2014 ◽  
Vol 5 (4) ◽  
pp. 1-39 ◽  
Author(s):  
Eirini Karapistoli ◽  
Ioanna Mampentzidou ◽  
Anastasios A. Economides
Keyword(s):  
Environmental Monitoring ◽  
Real Life ◽  
Pollution Monitoring ◽  
Wireless Sensor ◽  
General View ◽  
Monitoring Applications ◽  
Sensor Networking ◽  
Wireless Sensor Networking ◽  
Networking Technology ◽  
Critical Overview

This paper investigates real-life environmental monitoring applications based on Wireless Sensor Networks (WSNs). Wireless sensor networking is an emerging technology, which through the research in the labs and the real deployments has proved to be a significant and valuable tool for scientists in their effort to explore various environmental phenomena. During the last decades, this wireless networking technology has been adopted by many scientific fields in order to accurately and effectively monitor climate phenomena such as air pollution, destruction phenomena (i.e., landslides), etc. It has also been widely used in agriculture as well as in horticulture for field monitoring. This paper provides a critical overview of the basic components existing WSN deployments use. It also categorizes these deployments, 111 in total, into five different field categories, namely agricultural monitoring, environmental monitoring, air-water pollution monitoring, monitoring of destruction phenomena, as well as monitoring of livestock, and wild animal, in order to provide a general view of the technologies used, the conditions under which the deployments were conducted, and much more. Then, five easy-to-use guides are provided discussing basic considerations for deploying WSNs in each of these fields. These guides cover various issues, such as sensor node platforms, operating systems (OSs), topologies, installation and maintenance issues, and much more. In order to showcase the usefulness of consulting the resulted guides, this work considers representative application scenarios for each of these field deployments.

scholarly journals An RSSI-based Wireless Sensor Network Localization Algorithm with Error Checking and Correction

2017 ◽  
Vol 13 (12) ◽  
pp. 52 ◽  
Author(s):  
Bo Guan ◽  
Xin Li
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Signal Strength ◽  
Received Signal Strength ◽  
Wireless Sensor ◽  
Localization Algorithm ◽  
Sensor Network Localization ◽  
Anchor Node ◽  
Network Localization ◽  
Error Checking

<p style="margin: 1em 0px;"><span style="font-family: Times New Roman; font-size: medium;">This paper studies the wireless sensor network localization algorithm based on the received signal strength indicator (RSSI) in detail. Considering the large errors in ranging and localization of nodes made by the algorithm, this paper corrects and compensates the errors of the algorithm to improve the coordinate accuracy of the node. The improved node localization algorithm performs error checking and correction on the anchor node and the node to be measured, respectively so as to make the received signal strength value of the node to be measured closer to the real value. It corrects the weighting factor by using the measured distance between communication nodes to make the coordinate of the node to be measured more accurate. Then, it calculates the mean deviation of localization based on the anchor node close to the node to be measured and compensates the coordinate error. Through the simulation experiment, it is found that the new localization algorithm with error checking and correction proposed in this paper improves the localization accuracy by 5%-6% compared with the weighted centroid algorithm based on RSSI.</span></p>

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