Artificial Bee Colony Algorithm for Localization in Wireless Sensor Networks
Asian Journal of Applied Science and Technology (AJAST), Volume 1, Issue 2, Pages 200-205, March 2017
6 Pages Posted: 31 Mar 2017
Date Written: March 28, 2017
Localization in wireless sensor networks (WSNs) is one of the most important fundamental requisite that needs to be resolved efficiently as it plays a significant role in many applications namely environmental monitoring, routing and target tracking which is all location dependent. The main idea of localization is that some deployed nodes with known coordinates termed as anchor nodes transmit beacons with their coordinates in order to help the other nodes in the sensing field to localize themselves. Broadly there are two types of localization methods used for calculating positions namely the range-based and range-free methods. Initially, a range-free localization algorithm namely, Mobile Anchor Positioning - Mobile Anchor & Neighbor (MAP-M&N) is applied. In this algorithm, the sensor nodes use the location information of beacon packets of mobile anchor nodes as well as the location packets of neighboring nodes to improve the accuracy in localization of the sensor nodes. In this paper, the proposed optimization approach is Artificial Bee Colony (ABC) algorithm which is incorporated with MAP-M&N to further improve the accuracy in positioning the sensor nodes. The objective of this work is to compare the performance of MAP-ABC approach with regard to MAP-M&N algorithm. Root Mean Square Error (RMSE) is the performance metric to compare between the two approaches namely, MAP-M&N and MAP-ABC algorithms. A study on average localization error and comparison between the two approaches namely, MAP-M&N and MAP-ABC has been done. Simulation results reveal that Artificial Bee Colony approach used along with MAP-M&N outperforms by minimizing error in when compared to using only MAP-M&N approach for localization.
Keywords: Localization, Mobile Anchor, Artificial Bee Colony, Wireless Sensor Networks and Root Mean Square Error
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