A Received Signal Strength Indication Adaptive Algorithm for Wireless Sensor Network

2013 ◽  
Vol 273 ◽  
pp. 505-509 ◽  
Author(s):  
He Huang ◽  
Bin Luo
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Environmental Parameters ◽  
Signal Strength ◽  
Estimation Algorithm ◽  
Received Signal Strength ◽  
Wireless Sensor ◽  
Estimation Methods ◽  
Indoor Environments ◽  
Received Signal Strength Indication

Indoor environments are complicated and changeable, and RSSI (Received Signal Strength Indication) observations have great randomness, so the classic RSSI estimation algorithm has poor results in indoor environments. To solve this problem, a RSSI adaptive estimation algorithm (RAE-IW) based on Kalman filtering algorithm is presented in this paper, which achieves exact RSSI estimation, and fast adapts to the change of environmental parameters. Simulation results show that RAE-IW has low complexity, performs better than classic estimation methods in indoor environments, and applies to indoor wireless sensor network.

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 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>

A Localization Algorithm for Wireless Sensor Network Based on the Weighted Correction in Geometric Measurement

2015 ◽  
Vol 740 ◽  
pp. 823-829
Author(s):  
Meng Long Cao ◽  
Chong Xin Yang
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Signal Strength ◽  
Received Signal Strength ◽  
Wireless Sensor ◽  
Localization Algorithm ◽  
Received Signal Strength Indicator ◽  
Localization Algorithms ◽  
Weighted Centroid Localization ◽  
Geometric Measurement

Firstly, the characteristics of regular Zigbee localization algorithms-the received signal strength indicator algorithm (RSSI) and the weighted centroid localization algorithm are introduced. Then, the factors of the errors existing in the aforementioned algorithms are analyzed. Based on these above, the improved RSSI algorithm-correction geometric measurement based on weighted is proposed. Finally, utilizing this algorithm to design and implement the localization nodes, which have the CC2431 wireless microcontroller on them. The simulation and experimental results show that the accuracy of this localization algorithm improved about 2%, comparing with the regular algorithms.

A WSN-Based Localization Scheme Considering the Reliability of RSSI Measurements

2013 ◽  
Vol 679 ◽  
pp. 115-120
Author(s):  
In Whee Joe ◽  
Myung Oh Park
Keyword(s):  
Wireless Sensor Network ◽  
Standard Deviation ◽  
Environmental Factors ◽  
Sensor Network ◽  
Path Loss ◽  
Signal Strength ◽  
Wireless Sensor ◽  
Reference Node ◽  
Localization Scheme ◽  
Received Signal Strength Indication

In this paper, we propose a localization scheme considering the reliability of RSSI (Received Signal Strength Indication) measurements in the WSN (Wireless Sensor Network) environment. This scheme attempts to reduce location errors due to indoor obstacles or environmental factors, when location calculations are based on RSSI. The standard deviation is used to evaluate the reliability of RSSI measurements from the reference node. Also, the directional path loss exponent is calculated through learning with respect to the reference node. The experimental results show that the proposed localization scheme improves the performance significantly in terms of location accuracy, compared to the existing RSSI-based approaches.

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