Proceedings of IC-NIDC 2010
ADVANCED LANDMARC WITH ADAPTIVE k-NEAREST ALGORITHM FOR RFID LOCATION SYSTEM Kyuwon Han, Sung Ho Cho Division of Electrical and Computer Engineering, Hanyang University, Seoul, Korea
[email protected],
[email protected] Significant works have been done in the area of LANDMARC for the RFID system. There are some issues in LANDMARC. The first issue is the effect of placement of reference tags and readers. In [5][6], an optimal arrangement of reference tags and readers was proposed. In [7], 3-dimentional LANDMARC algorithm technique was proposed. The second issue is the effect of received signal strength compensation. In [8], the concept of key reference tags was used for eliminating redundant reference tags of the system. In [9], a recursive data processing approach for compensation was proposed. The third issue is the effect of the number of neighbor reference tags (k value). In [4], k=4 worked the best on the average. However, because LANDMARC was based on RSS, it sometimes showed bad accuracy for fluctuation. So the analysis of using different k values was needed, but it was not fully considered in any previous work.
Abstract This paper proposed advanced LANDMARC with adaptive k-nearest neighbor algorithm for RFID location system. LANDMARC was based on Received Signal Strength (RSS). It sometimes showed bad performance for fluctuation. Therefore, adaptive k-nearest neighbor algorithm was introduced for improving accuracy and was applied to original LANDMARC. In our simulation, we showed that the proposed method can achieve a better performance for location sensing. Keywords: RFID; LANDMARC; Sensing; k-nearest algorithm
Location
1 Introduction For years, many application fields take a great interest in knowing the physical location of the objects. Especially, there have been many approaches on localization using RFID technology. Because of its technical capabilities and lower cost, RFID system has been widely used for asset tracking, industrial automation and healthcare system [1].
In this paper, we propose advanced LANDMARC with adaptive k-nearest neighbor algorithm. Performance of the original k-nearest neighbor algorithm is imbalance under various environments such as the position of the tracking tag, the distance between reference tags and so on. Therefore, by pre-applying LANDMARC to the reference tag, we can find the k value which leads the best accuracy in each environment. Simulation results using adaptive k-nearest neighbor algorithm show that it can provide better performance.
The RFID system proposes some ways for location sensing. In ToA (Time of Arrival), the principal is that the distance between RFID reader and tags is directly proportional to the propagation time [2]. Another way to use arrival time is TDoA (Time Difference of Arrival) [2], which determines the relative position of RFID reader by considering the difference in time. Recently, there is a solution using phase information of the tag signal, PDoA [3]. (Phase Difference of Arrival) However, RSS (Received Signal Strength) based method is commonly uses for some applications by reason of simple implementation. Especially, LANDMARC is the location-sensing prototype system for better accuracy [4] and prove that RSS based method is a viable cost-effective solution. The main concept of LANDMARC is that it improves the overall accuracy of locating objects using the reference tags.
The rest of this paper is organized as follows: Section 2 introduces the LANDMARC system. The adaptive k-nearest neighbor algorithm is proposed in Section 3. A log-distance path loss model and simulation results are shown in Section 4. Finally, we present our conclusions in Section 5.
2 LANDMARC System Suppose we have n RF readers along with m tags as reference tags and u tracking tags as objects being tracked. The signal strength vector for a tracking tag is defined as:
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⃗ = (1 , 2 , … , ) where S denotes the signal strength of the tracking tag perceived on reader i, and ∈ ( 1 , n ). The signal strength vector for the reference tags is defined as:
where (0 , 0 ) is the tracking tag’s real ( , ) is the computed coordinates and coordinates.
3 Proposed adaptive k-nearest neighbor algorithm
⃗ = (1 , 2 , … , ) where θ denotes the signal strength. The Euclidian distance (E) in signal strength for each tracking tag p, and p ∈ (1,u) is calculated as:
Original k-nearest neighbor algorithm is utilized by LANDMARC to estimate the location of the tracking tag [10]. To find the best k value is the one of the key issues in the algorithm. In previous works [4], the k value was applied by some experiments. They chose different k values as k=2, 3, 4, 5 and computed the location estimation error. As the results, k=4 worked best on the average and the location estimation error did not improve as the k value increasing. However, because LANDMARC was based on RSS, different k values occasionally showed better performance.
= ( − )2 =1
where j ∈ (1,m) and is the distance in signal strength between a tracking tag and reference tag . For m reference tags a tracking tag has its E vector as:
In fact, there are many factors in k-nearest neighbor algorithm to affect performance [4][5]. The distance between reference tags is the factor through some experiments. The location of target tags is also the factor. The number of readers affects performance. Therefore, by using the different k value which leads the best accuracy in each environment, we can lead to better performance.
⃗ = (1 , 2 … ) where E denotes the location relationship between the reference tags and the tracking tag, i.e., the nearer reference tag to the tracking tag has a smaller E value. k-nearest neighbor algorithm is used then to determine the number of reference tags in a reference cell that are to be used in obtaining the most accurate approximate coordinate for each unknown tracking tag. k-nearest neighbor algorithm is used with k-nearest reference tags coordinates to locate one unknown tag. The unknown tracking tag coordinate (, ) is obtained by:
Adaptive k-nearest neighbor algorithm is shown in Figure 1. The left side of the flow is the original LANDMARC. The method that finds the best k is shown in the right side of the flow. First, we can find the nearest reference tag for using the Euclidian distance between the reference tags and the tracking tag and call it the Key reference tag. Next, we apply LANDMARC to the Key reference tag using different k values. (1
