Artificial Bee Colony Reinforced Extended Kalman Filter Localization Algorithm in Internet of Things with Big Data Blending Technique for Finding the Accurate Position of Reference Nodes-Reference-Cited by-同舟云学术

Artificial Bee Colony Reinforced Extended Kalman Filter Localization Algorithm in Internet of Things with Big Data Blending Technique for Finding the Accurate Position of Reference Nodes

Published:2021-11-05 Issue: Volume: Page:
ISSN:2167-6461
Container-title:Big Data
language:en
Short-container-title:Big Data

Author:

Raghav R. Srinivasa¹,Thirugnanasambandam Kalaipriyan²,Varadarajan Vijayakumar³,Vairavasundaram Subramaniyaswamy¹^ORCID,Ravi Logesh⁴

Affiliation:

1. School of Computing, SASTRA Deemed University, Thanjavur, India.

2. School of Computer Science and Engineering, VIT University, Chennai Campus, Tamil Nadu, India.

3. School of Computer Science and Engineering, University of New South Wales, Sydney, Australia.

4. Department of Computer Science and Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Avadi, India.

Publisher

Mary Ann Liebert Inc

Subject

Information Systems and Management,Computer Science Applications,Information Systems

Link

https://www.liebertpub.com/doi/pdf/10.1089/big.2020.0203

Reference35 articles.

1. A localization method for the Internet of Things

2. Improving Accuracy of the Kalman Filter Algorithm in Dynamic Conditions Using ANN-Based Learning Module

3. BeeSensor: An energy-efficient and scalable routing protocol for wireless sensor networks

4. OKPS: A Reactive/Cooperative Multi-Sensors Data Fusion Approach Designed for Robust Vehicle Localization

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