Distance estimation identifies the distance between two machines in wireless network. The Received Signal Strength Indication (RSSI) of Bluetooth can be used to estimate distance between smart devices. The characteristic of Bluetooth RSSI value is different as environments. So, we have tested the relation between distance and Bluetooth RSSI value in several environments, such as indoor hall, meeting room, and ElectroMagnetic Compatibility (EMC) chamber environment. This paper shows the distance characteristic of Bluetooth RSSI from these experiment results. There are a lot of measurement errors at Bluetooth RSSI raw data. The minimum RSSI value is -88 dBm and the maximum RSSI value is -66 dBm at 11m of the indoor hall environment. The difference between maximum value and minimum value is 22 dBm. So, it is hard to estimate the distance using Bluetooth RSSI raw data. Therefore, we use the Low Pass Filter (LPF) for reducing the measurement errors. The minimum RSSI value is -80.6 dBm and the maximum RSSI value is -71 dBm in the same environment. The difference between maximum value and minimum value is just 8.4 dBm. The measurement error is significantly reduced. We compare the distance estimation between the Bluetooth RSSI raw data and LPF data at the EMC environment. This paper shows that the distance estimation is possible with small error rates using Bluetooth RSSI LPF data. Keywords-Distance Estimation; Bluetooth; RSSI I. INTRODUCTION Wireless Sensor Networks (WSNs) are one of the essential research domains. There are many applications for WSNs in military and civil applications . The Machine to Machine (M2M) distance estimation is a fundamental issue for a lot of applications of indoor WSNs, such as a Bluetooth and Zigbee. Distance estimation identifies the distance between two machines in wireless network. Such estimates are an important component of systems’ localization, because they are used by the position computation and localization algorithm components. Different methods, such as RSSI, Time of Arrival (ToA), and Time Difference of Arrival (TDoA), can be used to estimate a M2M distance. Nowadays, lots of location systems have tried to estimate M2M distance using different models in wireless networks. For example, the Active Badge System used an infrared signal . Cricket, developed at MIT, uses TDoA method . Global Positioning System (GPS) uses ToA . RADAR, developed at Microsoft, uses RSSI to estimate M2M distance . SpotON is a RSSI-based ad-hoc localization system . In this paper, we discuss the M2M distance estimation using Bluetooth RSSI. The rest of the paper is organized as follows. Section II describes related work. In Section III, we describe distance characteristic of Bluetooth RSSI. In Section IV, we describe Bluetooth RSSI using a low pass filter. Section V provides the experimental results, and some concluding remarks are finally given in Section VI. II. RELATED WORK A. RSSI RSSI can be used to estimate the M2M distance based on the received signal strength from another machine. The longer the distance to the receiver machine, the lesser the signal strength at received machine. Theoretically, the signal strength is inversely proportional to squared distance, and there is a known radio propagation model that is used to convert the signal strength into distance. However, in real environments, it is hard to measure distance using RSSI because of noises, obstacles, and the type of antenna. In these cases, it is common to make a system calibration , where values of RSSI and distances are evaluated ahead of time in a controlled environment. The advantage of this method is its low cost, because most receivers can estimate the received signal strength. The disadvantage is that it is affected by noise and interference. So, distance estimation may have inaccuracies. Some experiments  show errors from 2 to 3 m in some scenarios. Distance estimation using RSSI in real-world applications is still questionable because of inaccuracy . However, RSSI could become the most used technology of distance estimation from the cost/precision viewpoint because of low cost . A. Awad et al.  discuss and analyze intensively some approaches relying on the received signal strength indicator. The most important factor for proper distance estimation is to choose a transmission power according to the relevant distances. It was showed that even for noisy indoor environments an average positioning error of 50cm on an area of 3.5 x 4.5 m is possible by choosing the RF and algorithm parameters carefully based on empirical studies. S. Feldmann et al.  also presented an indoor positioning system based on signal strength measurements, which were approximated by the received RSSI in a mobile device. The functional dependence between the received RSSI and the distance was achieved by a well fitted polynomial approximation.
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