On-line distance estimation: During the RSSI-D estimation procedure, the RSSI value is measured by a wireless sensor node (e.g., CC2530 WSN node), and we can estimate the communication distance using uncertain data clustering.In the on-line distance estimation module, considering different levels of uncertainty in RSSI values, we adopt RSSI-D estimation methods using both hard and soft uncertain data clustering methods to improve the estimation accuracy.The contributions of this paper are as follows:(1)We propose DEUDC, a RSSI-based communication estimation method, which uses a mapping strategy and an uncertain data clustering method. Unlike sample-based mapping in RADAR  and ARIADNE  systems, we resort to distribution-based mapping to overcome the uncertainty in RSSI readings.
(2)To address the uncertainty in RSSI values, we adopt interval data and statistical information to represent the RSSI distribution characteristic of each distance. In comparison to sample-based mapping, by exploiting distribution-based statistics, our approach can potentially obtain greater improvement in estimation accuracy and efficiency.(3)We propose an RSSI-D estimation method in which uncertain data soft and hard clustering algorithms are implemented in order to obtain better estimation accuracy with respect to different levels of uncertainty in RSSI.(4)We have evaluated DEUDC using real data sets from representative wireless environment. Experimental results show that DEUDC out-performs state-of-art estimation methods.
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 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.2.?Related WorksRSSI provides an inexpensive and practical way  of estimating communication distances during the operation of range-based localization systems or other range-based service systems used for wireless communications. Many uncertain factors exist during the measurement of RSSI , and the uncertainty in RSSI values leads to very low accuracy when estimating communication distances.
For the RSSI-based communication distance estimation problem, many studies have been performed to improve the estimation accuracy. These studies Entinostat can be divided into two categories: those dedicated to model-based methods, and those dedicated to mapping-based methods.2.1. Model-Based Estimation MethodsShang et al. adopted empirical models of radio propagation to estimate communication distance . However, the estimation accuracy of this method is sensitive to many uncertain factors. Li et al.