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Average Localization Error (ALE) in sensor node localization process in WSNs Data Set
Download: Data Folder, Data Set Description

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

Data Set Characteristics:  

Multivariate

Number of Instances:

107

Area:

Computer

Attribute Characteristics:

Real

Number of Attributes:

6

Date Donated

2021-06-05

Associated Tasks:

Regression

Missing Values?

N/A

Number of Web Hits:

39019

Source:

Abhilash Singh
Indian Institute of Science Education and Research Bhopal
Email: abhilash.singh '@' ieee.org
Webpage: https://www.abhilashsingh.net/?source=data_ale


Data Set 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.
Anyone can use this data with proper acknowledgment/citation.


Attribute 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)


Relevant Papers:

Singh, A., Kotiyal, V., Sharma, S., Nagar, J. and Lee, C.C., 2020. A Machine Learning Approach to Predict the Average Localization Error With Applications to Wireless Sensor Networks. IEEE Access, 8, pp.208253-208263.



Citation Request:

If you are using this data then please cite the following paper;
1. Singh, A., Kotiyal, V., Sharma, S., Nagar, J. and Lee, C.C., 2020. A Machine Learning Approach to Predict the Average Localization Error With Applications to Wireless Sensor Networks. IEEE Access, 8, pp.208253-208263.

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