DEDF: lightweight WSN distance estimation using RSSI data distribution-based fingerprinting. Issue 6 (August 2016)

• Record Type: Journal Article
• Authors: Luo, Qinghua; Yan, Xiaozhen; Li, Junbao; Peng, Yu; Tang, Yumei; Wang, Jiaqi; Wang, Dan
• Is Part Of: Neural computing & applications. Volume 27:Issue 6(2016)
• Page Start: 1567
• Page End: 1575
• Publication Date: 2016-08
• Subjects: Wireless sensor network -- Distance estimation -- Fingerprinting -- Distribution; Neural networks (Computer science) -- Periodicals; Neural circuitry -- Periodicals; Artificial intelligence -- Periodicals; Neural Networks (Computer) -- Periodicals; Réseaux neuronaux (Informatique) -- Périodiques; Réseaux nerveux -- Périodiques; Intelligence artificielle -- Périodiques; 006.32
• DOI: 10.1007/s00521-015-1956-2
• Languages: English
• ISSNs: 0941-0643

Abstract

Abstract When estimating the distance for wireless sensor networks (WSNs), we always suppose that a fixed curve model exists between the received signal strength indicator (RSSI) and communication distance. But there exist some negative factors in practice, which makes this assumption to contradict with the situation in real communication environment. It results in large distance estimation error with low efficiency. Thus, a lightweight WSN communication distance estimation method is presented, which is called distance estimation using distribution-based fingerprinting. First, we considered the uncertainty in RSSI values, and got the fingerprinting relationship in terms of RSSI data distribution, which is gained through a statistical calculation. Then, a data matching algorithm is implemented to estimate the communication distance. Finally, RSSI values in different conditions are utilized to validate this method. Experimental results demonstrated that the new method can obtain better results with high efficiency than other related methods, and can be applied in WSN localization system.