Abstract
For communication distance estimations in Wireless Sensor Networks (WSNs), the RSSI (Received Signal Strength Indicator) value is usually assumed to have a linear relationship with the logarithm of the communication distance. However, this is not always true in reality because there are always uncertainties in RSSI readings due to obstacles, wireless interferences, etc. In this paper, we specifically propose a novel RSSI-based communication distance estimation method based on the idea of interval data clustering. We first use interval data, combined with statistical information of RSSI values, to interpret the distribution characteristics of RSSI. We then use interval data hard clustering and soft clustering to overcome different levels of RSSI uncertainties, respectively. We have used real RSSI measurements to evaluate our communication distance estimation method in three representative wireless environments. Extensive experimental results show that our communication distance estimation method can effectively achieve promising estimation accuracy with high efficiency when compared to other state-of-art approaches.
Highlights
Wireless Sensor Networks (WSNs) have attracted tremendous attention in both the research community and industry [1,2,3]
The remainder of this paper is organized as follows: we present related work in Section 2; Section 3 introduces the uncertain data expression, including related definitions and the distance computation method used to handle interval data; Section 4 describes the Received Signal Strength Indicator (RSSI)-D estimation method using uncertain data clustering and its implementation; we evaluate the performance of this RSSI-D estimation method in Section 5; Section 6 concludes the paper
Targeted for communication distance estimation in real WSN applications, we have proposed a RSSI-D estimation method, Distance Estimation using Uncertain Data Clustering (DEUDC), which utilizes uncertain data clustering algorithms
Summary
Wireless Sensor Networks (WSNs) have attracted tremendous attention in both the research community and industry [1,2,3]. Ultrasonic distance measurement methods have been proposed for accurate distance measurement [7,8,9], and Received Signal Strength Indicator (RSSI), Time of Arrival (TOA), Time. Difference of Arrival (TDOA), and Angle of Arrival (AOA) techniques can be used to estimate the communication distance [10]. In many WSN applications, the sensor node is sensitive to cost and power consumption, so by taking practicability, energy and cost into consideration, WSNs often adopt the low-cost Received Signal Strength Indicator (RSSI) method. In RSSI-based distance (referred to as “RSSI-D”) estimation, as is known from the ideal propagation model of radio signals, the relationship between the communication distance (D) and the radio signal strength is expressed by Equation (1) [11]: P(D) [dBm]=P(D0) [dBm] −10nlg(D/D0) −Xr (1)
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