원문정보
보안공학연구지원센터(IJFGCN)
International Journal of Future Generation Communication and Networking
Vol.9 No.5
2016.05
pp.19-32
피인용수 : 0건 (자료제공 : 네이버학술정보)
초록
영어
Localization is one of the most important research topics in the wireless sensor network applications. To improve the indoor localization accuracy, the centroid localization algorithm based on Mamdani fuzzy system has been adopted to attain the weight between sensor node and anchor node. This paper proposes a novel optimized input membership function by bat algorithm in fuzzy inference system using the data of received signal strength in real indoor condition. The author has realized the algorithm on Zigbee platform and the experimental comparison on other different centroid localization algorithms indicates that Mamdani fuzzy inference adopting the membership function optimized by bat algorithm renders smaller mean localization errors.
목차
Abstract
1. Introduction
2. Localization Algorithm Model
2.1. RSS Features and Ranging Model Analysis
2.2. Weighted Centroid Localization Algorithm
2.3. Bat Algorithm
3. Fuzzy Inference System
3.1. Input Membership Function
3.2. Output Membership Function
3.3. Fuzzy Rules
3.4 Fuzzy Inference Process
4. Experiment and Analysis
4.1. Experimental Environment Setting
4.2. Hardware of Localization System
4.3. Average Localization Error
4.4. Experiment and Analysis
5. Conclusions
References
1. Introduction
2. Localization Algorithm Model
2.1. RSS Features and Ranging Model Analysis
2.2. Weighted Centroid Localization Algorithm
2.3. Bat Algorithm
3. Fuzzy Inference System
3.1. Input Membership Function
3.2. Output Membership Function
3.3. Fuzzy Rules
3.4 Fuzzy Inference Process
4. Experiment and Analysis
4.1. Experimental Environment Setting
4.2. Hardware of Localization System
4.3. Average Localization Error
4.4. Experiment and Analysis
5. Conclusions
References
키워드
저자정보
참고문헌
자료제공 : 네이버학술정보