An improved approach for iterative nodes localization by using artificial bee colony

Shenkai Gu; Cheng Li; Jing Wang; Xianglong Li

doi:10.1117/12.2615329

15 October 2021 An improved approach for iterative nodes localization by using artificial bee colony

Shenkai Gu, Cheng Li, Jing Wang, Xianglong Li

Proceedings Volume 11933, 2021 International Conference on Neural Networks, Information and Communication Engineering; 1193320 (2021) https://doi.org/10.1117/12.2615329
Event: 2021 International Conference on Neural Networks, Information and Communication Engineering, 2021, Qingdao, China

Abstract

Recently, many artificial bee colony-based localization algorithms have been used to obtain more accurate location information. However, the situation of co-linear anchor nodes and measurement errors lead to the problem of flip ambiguities. In the current researches on the localization algorithm of artificial bee colony, the lack of detection and correction mechanism for flipped nodes causes large errors. In this paper, we propose an improved artificial bee colony localization algorithm. We firstly qualify the initial search area based on the measured distance. Then, we introduce a robustness criterion to evaluate the localization nodes, which will avoid using the possible flipped nodes as assistant anchor nodes and thus improve the localization accuracy. Simulation results show that our algorithm can effectively avoid the flip ambiguities and achieve higher accuracy than existing ABC-based and other metaheuristic-based localization algorithms.

Citation Download Citation

Shenkai Gu, Cheng Li, Jing Wang, and Xianglong Li "An improved approach for iterative nodes localization by using artificial bee colony", Proc. SPIE 11933, 2021 International Conference on Neural Networks, Information and Communication Engineering, 1193320 (15 October 2021); https://doi.org/10.1117/12.2615329

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available