Improving RSSI based distance estimation for 802.15.4 wireless sensor networks A. Faheem*, R. Virrankoski and M. Elmusrati Department of Computer Science University of Vaasa Finland E-mail:
[email protected] Introduction Wireless sensor networks (WSNs) have been receiving a lot of attention recently due to a wide range of potential applications such as environment monitoring, warehouse inventory, object tracking etc. In many cases it is necessary to obtain accurate location information of the nodes. Many techniques based on e.g. Multilateration [1], [2], Multidimensional Scaling [3], [4] have been developed over the years to achieve localization in WSNs. Most of these techniques rely on basic node to node distance, angle or number of hops [5], [6] to achieve full scale localization. This information can be absolute or relative. There are many different parameters that have been used as indicator of distance between nodes e.g. RSSI [6], [7], [8], [9], Time Difference of Arrival (TDoA) [10], Angle of Arrival etc. Normally the nodes used in a wireless sensor network have very little resources. Therefore techniques that utilize small resources without the need for extra hardware, need to be developed. RSSI based localization present one such solution, as the recent advancements in radio hardware make it possible to achieve reliable signal strength indication [11]. In this paper an RSSI based distance estimation technique for 802.15.4 network, based on CC2420 radio core, is discussed. In this approach we use standard deviation (SD) of the RSSI value and the packet loss information as a part of model parameters estimation process. The SD and packet loss limits are optimized along with the curve parameters to achieve minimum distance error. The distance estimator uses these optimized limits as a measure of accuracy of the remote node’s estimated distance. The rest of the paper is organized as follows. In section 2 we describe the experimental setup that has been used. Section 3 gives an overview of the system that we have developed for the optimization of parameters. The results are discussed in section 4. We summarize and conclude the work in section 5. 2. Experimental setup The NanoRouter N601 from Sensinode was used as gateway node and programmed as FFD for the experiments. Sensinode devices include the radio module RC2301AT from Radiocraft. The module contains CC2431 system on chip (SoC) RF transceiver solution from Texas Instruments (TI). The operation of gateway node is controlled by a pc application using the built-in USB interface (FTDI232B). The NanoSensor N711 was programmed as a RFD. This device has battery pack support for two AA size batteries making it a suitable choice as the RFD. The RFD was displaced by one meter intervals up to 30 meter and RSSI values were collected for 50
measurements at each distance. The experimental data consists of three sets of measurements for outdoor measurements and three sets of measurements for indoor environment. 3. Developed System Log Distance Path Loss Model is a basic way of estimating path loss as a function of distance between the nodes. The model is normally expressed as following equation. P
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Where n is the path loss exponent, d is the distance between transmitter and receiver, is a Gaussian random variable with standard deviation σ and P is the received power at reference distance . Chipcon specifies the following formula [12] to compute RSSI. RSSI
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Where n is propagation exponent, d is the distance from the sender and A is the received signal strength at one meter of distance. To find the model parameters, the collected RSSI data (1—30 meter, 50 RSSI measurements at each distance) was loaded to an optimizer application developed with MATLAB. The parameter optimizer can be divided into two main parts. The first part is the error calculation function, which takes into account the provided starting point values of the model parameters (SD limit, packet loss limit, path loss exponent and the constant) and calculates the mean square error (MSE) between the original distances and the estimated distances. The second part implements the bounded minimization operation [13] on the error function and tries to minimize the average distance error by optimizing the model parameters. The optimizer returns new set of model parameters which provide the minimum error in the estimated distances. 4. Results and Discussion We implemented the new optimized model parameters to estimate the distance of a remote node. The blind node collects 50 RSSI measurements from the remote node and tries to map the collected data to a certain distance using the optimized model. Fig. 1 represents the estimated distances between a fixed blind node and a remote node for the range of 1—28 meter. In Fig. 1(a) it is noticeable that nodes at 14—28 meter are estimated to be at approximate distance of 16—20 meter. The blue line represents the true distances. The points which are far from the line are the false ones. But the blind node cannot distinguish between the false ones and the true ones without knowing the original distances. In Figure 2(b) we notice that using optimized SD and packet loss limits blind node can effectively eliminate most of the false distance estimates.
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(a) (b) Figure 1: Distance estimates w with (a) Curve fitting parameters; (b) Optim mized limits and curve parameters.
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In figure 2 the percentage distannce error with the percentage of nodes that bellong to that error margin are displayed along withh the percentage of SD and packet loss limits which w have been used to achieve these results, aree displayed. For example the first four bars in th he graph indicate one set of experiment where the error is reduced to 80 percent of the maximum error e and 10 % of d RSSI and about nodes were excluded by using (optimized) 60 % of maximum SD of collected 18% of maximum packet loss vaalues as filtering parameters.
Three experiiments, each with two different sets of parameters
Figure 2: Percentage of averaage error and corresponding number of nod des using certain percentage of STD and packet loss limits.
5. Conclusion We proposed a very simple method of eliminating faulty distance estimates by filtering the measured RSSI through optimized SD and packet loss limits. We conducted experiments with CC2431 radio nodes to collect enough RSSI measurements for model parameter estimation. We devised an optimizer to find optimized model parameters and limits. Then we used the experimental data to verify the optimized model and found that our method does help reducing the average distance error by effectively identifying and eliminating those estimates which introduce the most error. References: [1] [2] [3] [4] [5] [6] [7]
[8] [9]
[10] [11] [12] [13]
Z. Yang and Y. Liu, "Quality of trilateration: Confidence based iterative localization," In Proc. IEEE ICDCS 2008, Beijing,China, June 17-20, pp.446-453. F. Thomas and L. Ros, “Revisiting trilateration for robot localization,” IEEE Trans. Robot., vol. 1, no. 1, pp. 93–101, Feb. 2005. V. Vijayanth , W. Vincent W.S.,"Ordinal MDS-based localisation for wireless sensor networks, International Journal of Sensor Networks, v.1 n.3/4, pp.169-178, January 2006. P. Drineas, M. Magdon-Ismail, G. Pandurangan, R. Virrankoski, and A. Savvides, “Distance matrix reconstruction from incomplete distance information for sensor network localization,” Yale University Electrical Engineering, Tech. Rep., 2005. H.Chen, K.Sezaki, P.Deng and H.C.So, "An improved DV-hop localization algorithm for wireless sensor networks," Proc. IEEE Conference on Industrial Electronics and Applications (ICIEA 2008), Singapore pp.1557-1561, June 2008, S. Tian, X. Zhang, P. Liu, P. Sun, and X. Wang, "A RSSI-Based DV-Hop Algorithm for Wireless Sensor Networks," in Wireless Communications, Networking and Mobile computing, 2007. WiCom 2007. International Conference, 2007, pp. 2555-2558. A. Abdalkarim, F. Thorsten and D. Falko, "Adaptive Distance Estimation and Localization in WSN using RSSI Measures,” In 10th EUROMICRO Conference on Digital System Design - Architectures, Methods and Tools (DSD 2007), L¨ubeck, Germany, August 2007. pp 471–478 P. Chuan-Chin and C. Wan-Young, "Mitigation of Multipath Fading Effects to Improve Indoor RSSI Performance," IEEE Sensors Journal, vol. 8, NO. 11, pp. 1884—1886 Nov. 2008. K. Benkic, M. Malajner, P. Planinsic and Z. Cucej, “Using RSSI value for distance estimation in wireless sensor networks based on ZigBee,” in Proceedings of 15th International Conference on Systems, Signals and Image Processing - IWSSIP 2008, pp. 303 – 306, 2008. S. Schwarzer, M. Vossiek, M. Pichler, and A. Stelzer, "Precise distance measurement with IEEE 802.15.4 (ZigBee) devices," in Radio and Wireless Symposium, 2008 IEEE, pp. 779—782, 2008. K. Srinivasan and P. Levis, “RSSI is under appreciated,” In Proceedings of the Third Workshop on Embedded Networked Sensors, 2006. K. Aamodt, Chipcon Products from Texas Instruments, Application Note AN042 (Rev. 1.0). J. D'Errico, “MATLAB Central,” Sep. 28, 2006. [Online]. Available: http://www.mathworks.com/matlabcentral/fileexchange/8277. [Accessed: Mar. 28, 2010].
