Localization for Wireless Sensor Networks: A Neural Network Approach

02/07/2016
by   Shiu Kumar, et al.
0

As Wireless Sensor Networks are penetrating into the industrial domain, many research opportunities are emerging. One such essential and challenging application is that of node localization. A feed-forward neural network based methodology is adopted in this paper. The Received Signal Strength Indicator (RSSI) values of the anchor node beacons are used. The number of anchor nodes and their configurations has an impact on the accuracy of the localization system, which is also addressed in this paper. Five different training algorithms are evaluated to find the training algorithm that gives the best result. The multi-layer Perceptron (MLP) neural network model was trained using Matlab. In order to evaluate the performance of the proposed method in real time, the model obtained was then implemented on the Arduino microcontroller. With four anchor nodes, an average 2D localization error of 0.2953 m has been achieved with a 12-12-2 neural network structure. The proposed method can also be implemented on any other embedded microcontroller system.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
04/19/2021

Anchor Nodes Positioning for Self-localization in Wireless Sensor Networks using Belief Propagation and Evolutionary Algorithms

Locating each node in a wireless sensor network is essential for startin...
research
12/13/2019

RSSI-based Secure Localization in the Presence of Malicious Nodes in Sensor Networks

The ability of a sensor node to determine its location in a sensor netwo...
research
05/31/2018

Topology Control for Energy-Efficient Localization in Mobile Underwater Sensor Networks using Stackelberg Game

The characteristics of mobile Underwater Sensor Networks (UWSNs), such a...
research
08/09/2019

Received Signal Strength Based Wireless Source Localization with Inexact Anchor Position

Received signal strength(RSS)-based approach of wireless localization is...
research
04/30/2021

RSSI-Based Machine Learning with Pre- and Post-Processing for Cell-Localization in IWSNs

Industrial wireless sensor networks are becoming crucial for modern manu...
research
11/02/2022

Optical Channel Impulse Response-Based Localization Using An Artificial Neural Network

Visible light positioning has the potential to yield sub-centimeter accu...
research
12/09/2022

A Grid-based Sensor Floor Platform for Robot Localization using Machine Learning

Wireless Sensor Network (WSN) applications reshape the trend of warehous...

Please sign up or login with your details

Forgot password? Click here to reset