Average Localization Error (ALE) in sensor node localization process in WSNs

Donated on 8/13/2023

This data set can be used to test any regression-based machine learning algorithm. You can predict the ALE variable using four features.

Dataset Characteristics

Multivariate

Subject Area

Computer Science

Associated Tasks

Regression

Feature Type

Real

# Instances

107

# Features

4

Dataset Information

Additional Information

This data contains 6 columns (107x6). The first four columns are features, namely anchor ratio, the transmission range of a sensor, node density (here no. of sensor nodes), and iteration count. The fifth column is ALE (predictand) and the last column is the standard deviation value (you may ignore this column if not interested in the error in ALE). This data set is generated from modified Cuckoo search simulations.

Has Missing Values?

No

Introductory Paper

A Machine Learning Approach to Predict the Average Localization Error With Applications to Wireless Sensor Networks

By Abhilash Singh, Vaibhav Kotiyal, Sandeep Sharma, Jaiprakash Nagar, Cheng-Chi Lee. 2020

Published in IEEE Access

Variables Table

Variable NameRoleTypeDescriptionUnitsMissing Values
anchor_ratioFeatureIntegerno
trans_rangeFeatureIntegermetersno
node_densityFeatureIntegerno
iterationsFeatureIntegerno
aleTargetContinuousmetersno
sd_aleOtherContinuousno

0 to 6 of 6

Additional Variable Information

This data contains four features and one predictand. Features are: 1. Anchor ratio 2. Transmission range (measured in meters) 3. Node density and 4. Iteration The predictand is ALE (measured in meters)

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Creators

Abhilash Singh

abhilash.singh@ieee.org

Indian Institute of Science Education and Research Bhopal

Vaibhav Kotiyal

Sandeep Sharma

Jaiprakash Nagar

Cheng-Chi Lee

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