Effective-SNR estimation for wireless sensor network using Kalman filter

• Published in: Ad hoc networks Vol. 11; no. 3; pp. 944 - 958
• Main Authors: Qin, Fei, Dai, Xuewu, Mitchell, John E.
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier B.V 01.05.2013; Elsevier
• Subjects: Access methods and protocols, osi model; Applied sciences; Computer science; control theory; systems; Computer systems and distributed systems. User interface; Dynamical systems; Dynamics; Estimation; Exact sciences and technology; Indicators; Kalman filter; Kalman filters; Link quality; Links; Networks; Radiocommunications; Sensor networks; Sensors; SNR; Software; Telecommunications; Telecommunications and information theory; Teleprocessing networks. Isdn; Wireless communication; Link quality; Sensor networks; SNR; Estimation; Kalman filter; Availability; Energy consumption; Redundancy; Empirical method; Transmission protocol; Dynamical system; Radiofrequency; Local network; Quality assurance; Experimental result; Wireless network; Sensor array; Signal to noise ratio
• ISSN: 1570-8705; 1570-8713
• DOI: 10.1016/j.adhoc.2012.11.002

In many Wireless Sensor Network (WSN) applications, the availability of a simple yet accurate estimation of the RF channel quality is vital. However, due to measurement noise and fading effects, it is usually estimated through probe or learning based methods, which result in high energy consumption or high overheads. We propose to make use of information redundancy among indicators provided by the IEEE 802.15.4 system to improve the estimation of the link quality. A Kalman filter based solution is used due to its ability to give an accurate estimate of the un-measurable states of a dynamic system subject to observation noise. In this paper we present an empirical study showing that an improved indicator, termed Effective-SNR, can be produced by combining Signal to Noise Ratio (SNR) and Link Quality Indicator (LQI) with minimal additional overhead. The estimation accuracy is further improved through the use of Kalman filtering techniques. Finally, experimental results demonstrate that the proposed algorithm can be implemented on resource constraints devices typical in WSNs.