RFID for indoor localization application basedonneuralnetwork

RFID for indoor localization applicationbased on neural network

H. Ma1, K. Wang1 & J. E. Evanger2

1Department of Production and Quality Engineering, NorwegianUniversity of Science and Technology, Trondheim, Norway2APX System AS, Oslo, Norway

Abstract

With indoor location systems becoming popular in recent years, multiple methodshave been proposed in this area. Radio frequency identification as a fast-growingtechnology is considered a viable solution to be applied to indoor location prob-lems. In this paper, we aim to test the feasibility of radio frequency identification(RFID) technology based on received signal strength value for indoor location byconducting a pilot experiment. The neural network is used to estimate the positionof an object attached to a passive RFID tag. Results show acceptable precision anddemonstrate that passive RFID is cost effective and feasible for indoor locationsensing.Keywords: radio frequency identification, RSSI, neural network, indoor location.

1 Introduction

The fast development of wireless technologies has fostered a growing interest inreal-time location systems (RTLSs) [1], which are used to automatically identifyand track the location of objects or people within a building. In most RTLSs,wireless tags are attached to objects or worn by people and fixed referencedpoints receive wireless signals from the tags to determine their location. Manyapplications need to know the exact location of an object.

Among all wireless technologies, radio frequency identification (RFID) techno-logy has fascinated society in terms of economic and scientific benefits [2]. RFIDis undergoing fast development and has widespread applications in the retail in-dustry, logistics, supply chain, transportation, etc. Generally, the RFID system iscomposed of tags, reader, antenna, and server. A tag can be attached to physical

WIT Transactions on Engineering Sciences, Vol 113, © 2016 WIT Presswww.witpress.com, ISSN 1743-3533 (on-line)

doi:10.2495/IWAMA150231

Advanced Manufacturing and Automation V 201

objects, and each tag is assigned with a unique electronic product code to realizetracking and identification. Readers can not only read information stored in the tagthrough an antenna but also write data on the tag.

Many measurements, like angle of arrival, time of arrival, and time differenceof arrival, can be obtained from an RF signal [3]. Compared with these meas-urements, received signal strength indicator (RSSI) does not require any specialhardware other than a necessary device with RSSI measurement capability [4]. AnRSSI is a useful value provided by an RFID system and is related to the distancebetween a tag and an antenna. When the distance is short, the value is large, whilewhen the distance is long, the value is small. So the RSSI value can be employedto estimate the position of an object to which an RFID tag is attached.

Though neural network is not commonly used for solving localization problems,research has demonstrated the effectiveness and accuracy of the methodology [5].Artificial neural network (ANN) is a technological discipline concerned with in-formation processing systems that autonomously develop operational capabilitiesin adaptive response to an information environment [6]. Today’s real problemsin engineering often change over time and are complex and chaotic. ANN has thepower to learn about relationships governing a problem and adapt as circumstancesand rules change.

The purpose of this study was to test the feasibility of passive RFID based on re-ceived signal strength value for indoor location by conducting a pilot experiment.The rest of the paper is organized as follows. In Section 2, relevant indoor loca-tion methods are reviewed. Section 3 presents the experimental design, includinglaboratory layout and neural network architecture. Results of the experiment arediscussed in Section 4. Finally, conclusions are drawn in Section 5.

2 Related works

Various wireless technologies, such as infrared, IEEE802.11, ultrasonic, and RFID,have been proposed for indoor localization.

The active badge location system [7] is often credited as one of the earli-est implementations of indoor sensor network developed by AT&T Cambridge.The diffused infrared technology was applied to realize indoor positioning. Ma-jor limitations of the system are line-of-sight requirement and short-range signaltransmission.

A radio-frequency (RF)-based system introduced in [8] uses a standardIEEE802.11 network adaptor to measure signal strength for locating and trackingusers inside a building.

The cricket location support system developed by AT&T Cambridge [9] andActive Bat location system proposed by MIT laboratories are two primary ex-amples where ultrasonic technology is used [11]. However, the problem in the useof ultrasonic is that a great deal of infrastructure is required in order to be highlyeffective and accurate.

SpotON is a well-known location sensing system [10] that uses RFID tech-nology. SpotON uses an aggregation algorithm for three-dimensional location


202 Advanced Manufacturing and Automation V

sensing based on radio signal strength analysis. LANDMARC [11] is another suc-cessful RFID positioning system. LANDMARC can improve the overall accuracyof locating object by utilizing reference.

A hybrid system based on Wi-Fi and Bluetooth [12] has been developed. Theformer works as the main infrastructure to enable fingerprint-based positioning,while the latter partitions the indoor space. As a result, the Wi-Fi-based onlineposition estimate has improved in a divide-and-conquer manner.

The performance of three different families of neural works, multilayer per-ceptron (MLP), radial basis function (RBF), and recurrent neural network wasquantitatively compared in Ref. [13]. The performance of neural network was alsocompared with Kalman Filter, the traditionally used method. The results showedthat RBF neural network had the best accuracy; however, it had the worst computa-tional and memory resource requirements. The MLP neural network, on the otherhand, had the best computational and memory resource requirements.

In our experimental design, based on the relationship between received signalstrength and tag location, we built a radio map, which would map the RSSI valuesto the tag position. Then, we relied on neural network to estimate the locationsbased on the new RSSI values fed into the network. In addition, different from theSpotON and LANDMARC location sensing systems, we deployed passive RFIDtag in the experiment to test whether it would provide a viable solution for indoorlocalization.

3 Experimental design

We proposed RFID technology based on the measurement of RSSI to test the feas-ibility for indoor localization in this experiment. We employed an ANN as thepositioning algorithm to train the RSSI value in order to obtain the relationshipbetween the RSSI value and physical coordinates of the object. This technique iscalled the fingerprint method [14]. The experiment was divided into two phases.In the first phase – the offline phase – the signal strength received from pointswhose locations were already known was collected to build the so-called radiomap. During the second phase – the location determination phase – the signalstrength of samples received from the access points was used to search the radiomap to estimate their locations.

3.1 Laboratory layout

A simple RFID system was built with a passive tag attached to a small Lego car,four antennas were placed at the corner of a calibration board, and one readerand middleware were employed. All the antennas were connected to the reader[Fig. 1(a)]. The reader could directly interact with the tags and read informationstored in them or data written on them. The middleware could buffer, aggregate,and filter data coming from the reader. In this experiment, the middleware wasmainly used for collecting RSSI values.



RFID tags can be classified into active tags and passive tags. Active RFID tagshave a transmitter and their own power source (typically a battery). The powersource is used to run the microchip’s circuitry and broadcast a signal to a reader, inthe same manner a cell phone transmits signals to a base station. Passive tags haveno battery. Instead, they draw power from a reader, which sends out electromag-netic waves that induce a current in the tag’s antenna. Well-known location sensingsystems, such as SpotON and LANDMARC, adopt active RFID tags. Comparedwith an active RFID tag, the passive tag is smaller and less expensive.

Figure 1: (a) Experiment layout in laboratory and (b) calibration board.

The length of the calibration board [Fig. 1(b)] is 60 cm and the width is 50 cm.Each side of a small square in the calibration board is 3 cm long. Each small squareis assigned a pair of coordinates (X , Y ). The origin of the coordinates is set in thelower left corner.

3.2 Neural network architecture

Neural networks are a network of interconnections between nodes (called neurons)with activation functions. The weights of the interconnections are modified basedon the difference between the desired output and the output of the neural network.The final weight of the MLP network is entirely dependent on initial weights. Theset of weights that results in the best performance is found through trial and error.The MATLAB neural network toolbox is applied for training and testing the neuralnetwork.

The number of inputs for the neural network is 4 because there are four antennasand four RSSI values will be obtained (Fig. 2). Moreover, the number of outputsfor the neural network is known beforehand. In the experiment, neural network willpredict the location of the object in a two-dimensional plane. So the output should



Figure 2: Neural network architecture.

be coordinates x and y. The following main work determines the hidden layer andbuilds the architecture of MLP.

After several trials and errors, which involved changing the number of hiddenlayers and neurons in every hidden layer, the final neural network architecture wasdetermined. The architecture had two hidden layers, with nine neurons in one hid-den layer and five neurons in the other hidden layer. Figure 2 presents the neuralnetwork architecture.

3.3 Data collection

On moving the Lego car to different small squares, the distance from the fourantennas changed correspondingly. The strength of the signal received was as-sociated with the distance, so there would be a unique tuple of RSSI values forevery location, and a radio map would be generated by mapping the RSSI valuesto the locations. While moving, the orientation of the tag was consistently facingthe same direction relative to the antennas. This was crucial for the accuracy ofthe RSSI values because different orientations of the tag would affect communica-tion with the antennas. In addition, locations where the RSSI values were collectedwere evenly distributed across the calibration board so that the neural networkcould fully learn the data and make accurate prediction. There was a total of 76samples for training, 10 samples for testing, and another 10 samples for validation.The scatter plot of the locations is shown in Fig. 3.

In some positions, not all RSSI values could be collected, and only three of thefour antennas could receive radio signals because of the problem of orientationeven when the tag was in the reading range of antennas. That is, one RSSI valuewas missing in some samples. In this situation, we would replace the missing RSSIvalue with the minimum RSSI value plus 1, which meant the signal quality wasvery bad.



Figure 3: Scatter plot of data samples.

4 Results and discussion

Figure 4: (a) Performance after training and (b) error histogram.

The training process was stopped after 21 iterations; the best validation per-formance of 3.8329 was obtained at the 15th iteration [Fig. 4(a)]. Because theparameter net.trainparam.max_fial equaled 6 in our configuration of the neuralnetwork, when the performance of validation did not improve for six iterations, thetraining process was stopped.

Next, the regression of data is given in Fig. 5, which displays the neural networkoutputs with respect to targets of training and test sets. For a perfect fit, the datashould be along the diagonal line. This means all the points should fall along the45◦ line, where network outputs are equal to the targets. Although the ideal result israrely obtained in practice, regression plots in our experiment showed reasonablygood results. For the training set regression plot in Fig. 5, the R values are 0.96632



Figure 5: (a) Regression plot for training set and (b) regression plot for test set.

and 0.98239 for training set and test set, respectively. As the R value indicates therelationship between outputs and targets, they are fairly close to 1, which indicatesa good fit.

The error histogram in Fig. 4(b) can provide additional verification of the neuralnetwork’s performance. The blue bar represents training data, the green bar rep-resents validation data, and the red bar represents test data. In this case, the plotshows that most errors fall between –3 and 3.

There were outliers, such as one validation point responded to an error of 5,with a target close to 15 and output about 10. The scatter plot is helpful in showingcertain data points with poor fits. For the validation point (6, 15), its neural networkoutput is (9.8, 10.2). This is because there are not enough points that look like theoutlier point. Therefore, additional data should be collected to be used in the testset to make accurate predictions.

Figure 6(a) shows the scatter plot for the test set. As shown in the figure, loca-tions between the test targets and neural outputs were very close. Euclidean errorsof the test points were calculated [Fig. 6(b)]. The maximum Euclidean error was2.54, the minimum Euclidean error was 0.13, and the mean error was 0.9966, lessthan 1. At location (5, 13), the neural network output was (5.1, 12.9). The target

Figure 6: (a) Scatter plot for test set and (b) Euclidean error of test set.



point and output point almost overlap each other. The final test set result demon-strates that the mean error for every test point is less than one small square in thecalibration board.

5 Conclusion

In this research, we studied mainly the indoor location sensing system and conduc-ted a pilot experiment by building a prototype RFID system to predict the locationof a Lego car on the calibration board. Although many research has applied act-ive RFID technology for indoor localization with a high precision, passive RFIDcan be adopted in indoor location sensing as well; we obtained an acceptable ac-curacy in our experiment. The RSSI is easy to obtain in an RFID system and ishighly correlated with the distance between an RFID tag and an antenna. In ad-dition, the neural network’s performance is validated through the experiment eventhough there are some noises in the RSSI values, which will be fed into the inputof the neural network. Therefore, we have demonstrated that RSS-based locationfingerprinting, combined with neural network, is a promising method to estimateindoor location with satisfactory effectiveness and accuracy.

Acknowledgment

This research was financially supported by the Chinese Scholarship Council.

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RFID for indoor localization application basedonneuralnetwork