.png){kind=icon}
.png){kind=icon}
Return
A modified ant colony optimization algorithm (mACO) for energy efficient wireless sensor networks
.png){kind=icon}
PRE
AI
DOI：10.1016/j.ijleo.2015.11.117.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
The primary limiting factor of a wireless sensor network is the limited battery power that leads to suboptimal utilization of the network potential. This happens because of non-uniform generation of signals by the sensor nodes through time and space that leads to over utilization of the battery power. This causes the non-functionality of the network, even though a number of nodes with near-maximal battery levels are still operational. To combat with this problem, various groups have proposed various routing strategies. In this work, an ant colony optimization algorithm is modified and is applied to the problem of finding the optimal energy efficient path for the recruitment of sensor nodes for signal transmission to prolong the network lifetime. The proposed method combines the intra-node spatial distance with the rate of battery drain out/recovery with respect to signal transmission. The results indicate the applicability of the mACO approach. (C) 2015 Elsevier GmbH. All rights reserved.
Keywords:
Wireless sensor network
Genetic algorithm
Ant colony optimization
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.