Study of Received Signal Strength Indication in

Study of Received Signal Strength Indication in ZigBee Location Cluster for Indoor Localization Kamol Kaemarungsi#, Rachasak Ranron*, Prasit Pongsoon##National Electronics and Computer Technology Center (NECTEC)Pathumthani, Thailand1kamol.kaemarungsi@nectec.or.th3prasit.pongsoon@nectec.or.th*King Mongkut’s University of Technology North Bangkok (KMUTNB)Bangkok, Thailand2ratchasak.ranron@Abstract— This work focuses on the properties of received signal strength indication (RSSI) of ZigBee wireless sensor network, which can be used to implement indoor location system. The study collects a set of measurement samples in a closed room from an implementation of indoor localization application based on ZigBee Cluster Library (ZCL) framework. The measurement data are analyzed for their statistical parameters. The results are compared with the known properties of RSSI of wireless local area network reported in the literature. The insight of RSSI’s properties obtained in this work could be used to improve the localization application using ZigBee wireless sensor network. Keywords— Indoor localization, location fingerprint, received signal strength indication, wireless sensor network, ZigBeeI.I NTRODUCTIONWireless sensor network (WSN) such as ZigBee WSN can be deployed with localization capability, which can be applied for localization and tracking of object or people in health care applications [1]. That is the wireless sensor network can be used to relay location information and radio frequency (RF) channel parameters in order to estimate a location of mobile wireless sensor node [2]. The underlying framework for localization mechanism is outlined by the ZigBee Alliance in ZigBee Cluster Library Specification [2]. The crucial RF channel parameter used for locating a mobile node is the received signal strength indication (RSSI) reported in decibel milliwatt (dBm), which can be derived from Link Quality Indication (LQI) defined by the IEEE 802.15.4 standard [3]. Typically, the LQI associates with a received IEEE 802.15.4 packet and is used to provide strength and/or quality of the packet. It can be obtained from the hardware of IEEE 802.15.4/ZigBee’s RF transceiver such as MC1322x system-on-chip (SoC) platform from Freescale semiconductor [4]. In the literature, indoor positioning systems using wireless sensor network and their implementations have been presented in a number of research works since 2006. For instance, an implementation of ZigBee indoor location system was presented in [5] using Chipcon (now Texas Instrument)’s CC2420 RF transceiver and a ZigBee stack on ATmega128 microcontroller. The user’s position in [5] was estimated using both ultrasound and time-of-arrival (ToA) of RF signal. However, the authors did not report any measurement of the RSSI. Measurements of LQI versus transmitter-receiver distance using Jennic’s JN5121 and Texas Instrument’s CC2430 SoC were reported in [6] in which the authors used the measurement data to create path loss models in two different environments. The authors in [6] reported that they implemented a centralized localization algorithm that combines multidimensional scaling (MDS) and maximum likelihood estimator (MLE) methods to estimate distance between transmitter and receiver on a laptop.A study on variation of received signal strength in WSN due to various environmental factors using CC2430 was presented in [7]. The authors investigated on the influence of the temperature, the height of node’s position, the type of antenna, and the electromagnetic interference of human body on the RSSI. An interesting finding was pointed out that the RSSI could change according to the temperature. The larger attenuation of RF signal was observed when the surrounding temperature was higher. The effects of the rest of the factors on RSSI were similar to common understandings of RF propagation. Another experimental study on the variation of RSSI due to the effects of obstacles in indoor environment was reported in [8]. The measurement experiments were performed using RF230 and CC2420 RF transceivers. The authors in [8] investigated the attenuation of RSSI over different distances in both line-of-sight (LoS) and non line-of-sight (NLoS) environments. In the NLoS case, there were three types of obstacles between transmitter and receiver, which were glass (glazed door), glass and wood (compressed wood), and partial wall (brick and gypsum board). The authors concluded that the RSSI alone could not be used to accurately locate mobile nodes in an indoor environment with obstacles [8].Based on our literature survey, none of the existing research works has implemented the ZigBee’s RSSI location cluster in their systems. Moreover, none of them has considered the statistical properties of the RSSI such as its probability distribution and skewness. Therefore, in this work we implemented a ZigBee localization system using the framework of ZigBee Cluster Library (ZCL) and performed a data collection of RSSI over a small and closed laboratory room. The main objective of this work is to comparethestatistical properties of RSSI in ZigBee WSN with the known properties of RSSI in wireless local area network (WLAN) or Wi-Fi network reported in the literature.The organization of this paper is as follows. Section II describes the architecture of the indoor localization system, which consists of both hardware and software components. Section III explains the experimental design. Section IV reports on the measurement results. Section V discusses our findings on the properties of the RSSI from the ZigBee localization system. Finally, Section VI concludes our work and outlines the future work.II.S YSTEM A RCHITECTUREAn indoor localization system used in this study is implemented on a set of ZigBee wireless sensor nodes developed in our research laboratory. The network comprises of five ZigBee nodes in which there are four fixed nodes and one mobile node. One of the fixed nodes is connected to a laptop computer via a RS232-to-USB serial interface and the node is the ZigBee coordinator (ZC). The rest of the nodes in the network are implemented as ZigBee routers (ZR). The implemented localization system can be categorized as a centralized architecture as described in ZigBee Telecom Application Profile Specification [9]. The two-dimensional locations in this system are identified using Cartesian coordinate system or pair of numerical coordinates. Generally, ZigBee localization system can be implemented using four different techniques as outlined by ZigBee Cluster Library Specification [2], which are the lateration, the proximity or the signposting, the RF fingerprinting, and out-of-band localization device. First three of these techniques rely on the measurement of RSSI, while the last technique depends on non-ZigBee based device connected to each node to provide location information such as infrared or ultrasound localization device. In this work, the implemented system utilizes the RF fingerprinting technique that can be deployed in two phases: the offline phase and the online phase [10]. During the offline phase, the system must collect the RSSI patterns at each location and create a database of RF fingerprints, which will be used during the online phase to estimate a location based on the closest matching of RF fingerprint with the current RSSI pattern at a location.The rest of this section described three important parts of our ZigBee localization system. First, a brief description of ZigBee Location Cluster, which is implemented in a commercial ZigBee protocol stack, is explained. Second, the hardware and its characteristics are summarized. Third, a personal computer (PC) based localization application developed for controlling and visualizing the localization system is presented.A.ZigBee Location ClusterZigBee specification defines a cluster as standardized communication interface between ZigBee client and server devices. Inside each cluster, there is a collection of attributes and commands used by specific ZigBee application. For instance, ZigBee’s RSSI Location Cluster is defined in ZigBee Cluster Library (ZCL) and given a unique Cluster ID of 0x000b. The location cluster enables ZigBee devices to exchange relevant location information and channel parameters for localization application and optionally report collected data such as RSSI from networked devices to a centralized device which estimates locations of mobile nodes [2]. In this work, we utilize all these mechanisms inside the ZigBee protocol stack.Examples of related attributes, which are arranged into different sets, in the RSSI Location Cluster are location type, location method, location age, quality measure, number of devices, coordinates, power, path loss exponent, reporting period, calculation period, and number of RSSI measurements [2]. Some of these attributes are mandatory. Examples of related commands in this cluster are Set Absolute Location, Set Device Configuration, Get Device Configuration, Get Location Data, RSSI response, Send Pings, and Anchor Node Announce [2]. The cluster also defines payload format of each command and action upon receiving a command such as setting relevant attributes. Some of these attributes and commands are implemented in this work.Fig. 1 illustrates the ZigBee localization system deployed in this study. The nomenclature of each ZigBee node is defined in ZigBee Telecom Application Profile Specification [9]. The fixed node with a known location is called the anchor node, while the mobile node is called the location node. The centralized node that collects the RSSI data from all other nodes and relays the data to a computer is called the locationgateway.Fig. 1 ZigBee Localization System Architecture.The usage of ZigBee’s RSSI Location Cluster is summarized in Fig. 2 and can be explained as follows [2]. Initially, the location gateway, which is the centralized device, receives commands called “Anchor Node Announce” from all anchor nodes after they joined the network. If the localization system wants to locate the mobile location node, the location gateway will issue a “Send Pings” command to the location node. Upon receiving the “Send Pings” command, the location node will periodically send multiple “RSSI Ping” commands to all its single-hop anchor nodes in our system. Each anchor node that received “RSSI Ping” command will measure the RSSI of each “RSSI Ping” command and calculate an averagevalue of multiple RSSIs for the sending location node. Next, the location node will issue the “RSSI Request” command to all single-hop anchor nodes to ask for its averaged RSSI values. Each anchor node will reply to the requesting location node with “RSSI Response” command that contains coordinate data of the anchor node, the averaged RSSI value, and the number of RSSI measurements. Finally, the location node will send “Report RSSI Measurements” command to the location gateway to provide its measurement for localization.Fig. 2 RSSI Location Cluster Usage.B.ZigBee Wireless Sensor NodesFig. 3 shows the set of actual ZigBee wireless sensor nodesused in our experiment. There are two types of printed circuitboards (PCBs): the large board and the smaller board which isthe one in the middle of the figure. The different between thetwo types of the boards are the number of peripheralinput/output (I/O) interfaces in which the smaller board has noI/O connectors and is suitable for equipping as a mobiledevice and operating on small battery. The larger board hastwo RS232 serial interfaces, which can be used to connect to alaptop computer.Fig. 3 Actual ZigBee Wireless Sensor Nodes.The main component of each node is the ZFSM-201-1module from California Eastern Laboratories (CEL) [11]. Thisis an integrated transceiver module for ZigBee/IEEE 802.15.4that is based on Freescale’s MC13224V SoC platform [4] andan additional RF power amplifier front end. The MC13224Vitself is actually contained both IEEE 802.15.4 RF transceiverand a low power 32-bit ARM7TDMI-S microcontroller. TheMC13224V possesses the ability to report LQI for a receivedpacket. The LQI is an 8-bit hexadecimal value from 0x00 to0xFF defined in IEEE 802.15.4 standard [3]. The value 0xFFis approximately equal to -15dBm, while the value 0x00 isapproximately equal to -100dBm [4]. The reference manual ofthe MC13224V defines the conversion expression between theRSSI or the power in dBm and the LQI in decimal as [4]:1003)()(−=decimalLQIdBmRSSI(1)The embedded software of each ZigBee node is developedbased on proprietary software templates and a proprietaryZigBee Stack called Freescale BeeStack [12], which is writtenin C programming language. Note that the ZigBeecoordinator’s and ZigBee router’s codes are different and haveto be modified independently. The provided softwaretemplates did not implement the RSSI Location Cluster andwe implemented additional codes to enable the localizationability in the system. The communication interface betweenthe location gateway and the laptop computer in Fig. 1 isbased on the Freescale’s BeeStack BlackBox ZigBee TestClient (ZTC) application programming interface (API) [13].The API defines the frame format of message exchangingbetween the location gateway and the laptop computer.The ZigBee network parameters are set as followings. TheZigBee stack profile is selected as Stack Profile 0x02, whichis the ZigBee PRO 2007 specification. The network topologyis formed as a mesh network without enabling securitymechanism. The maximum transmit power of each node is setto 20dBm. The RF channel is chosen as channel number 14 asdefined by IEEE 802.15.4 specification in 2.4GHz ISM band.The personal area network identification (PANID) is set to0x1aff.C.ZigBee Location ApplicationA PC software application is developed in this work usingJAVA programming language to provide graphical userinterface (GUI) for both ZigBee localization systemmanagement and visualization. After connecting the gatewaynode through the USB port of the laptop, the user can start theconnection to begin a communication with the gateway nodevia a play command icon. The application can automaticallydetect all nodes that have joined the ZigBee network. Then, itallows the user to perform each detected node’s placement ona map and to collect RF fingerprint of the location node ondifferent locations during the offline phase. The applicationutilizes proprietary Freescale’s ZTC API to communicate withthe location gateway node in which it allows the user to sendcommands to and gather the RSSI data from the ZigBeenetwork. Moreover, the application maintains a database ofRF fingerprints, which are collected through the locationgateway node. It also performs location estimation algorithm and shows the estimated location of mobile node on the map during the online phase. Initially, we implemented a simple algorithm using the single nearest neighbour pattern classification based on the Euclidean distance between the RF fingerprints in the database and the new RSSI pattern collected during the online phase.Fig. 4 is a screen capture of the developed PC application. There is a map on the right hand side of the screen that shows all location fingerprints (denoted by fingerprint icons) and thepositions of the gateway (denoted by small computer icon) and anchor nodes (denoted by base station icons). On the left hand side of the screen, there is a device list that itemizes all nodes with their IEEE 802.15.4 medium access control (MAC) addresses under their corresponding type of node. Note that the list of all RF fingerprints and their corresponding three dimensional coordinates (denoted by X, Y, Z) are also displayed. Note that all Z-axis values are zeros because we only use two-dimensional localization system in this work.Fig. 4 Screen Capture of ZigBee Localization Application.III.E XPERIMENTAL S ETUPThis section describes the preparation of the ZigBee wireless sensor network inside a small laboratory room for our measurement experiment. Fig. 5 illustrates a localization area which is defined as a square grid of locations with grid spacing of 1.4m×1.4m inside a small laboratory room. The positions of all ZigBee nodes are also labelled as L1 to L24 in the figure. There were 24 locations inside this room which were used to collect RF fingerprints. The anchor nodes are labelled with A1 to A4. There is no large obstacle that block in between any transmitters and receivers. This can be considered as an environment with clear line-of-sight (LoS). To form the ZigBee wireless sensor network, the location gateway was powered on first at the lower right corner of the Fig. 4 and connected to the laptop that ran our localization application as shown in Fig. 6. Then, we placed the rest of the anchor nodes at the other three corners as shown in the figure. After all nodes were detected, we sent a command via PCapplication to set their Cartesian coordinates.Fig. 5 Grid of locations with the positions of all nodes.Fig. 6 A photograph of the gateway node in the test room.The user collected the RF fingerprint during the offline phase at each location through the application’s command after placing the actual location node at that location. The process was repeated until RF fingerprints were collected for all locations. In this work, each sample of RF fingerprint consists of four different RSSI samples from the location gateway and the three anchor nodes. Note that the location gateway node also acted as an anchor node. We collected 30 RSSI samples from each anchor node at each location and averaged them out to obtain one of the components in the RF fingerprint. The sampling rate was one sample every nine seconds. Thus the measurement period per location was 9×30 = 270 seconds or 4.5 minutes. Note that only the integer value of the averaged data is kept in the database. There were a total of 30×24 = 720 samples. This set of RSSI samples will be used in our statistical data analysis in Section V.To measure the localization performance of the system, the software can be switched to operate in the online phase in which the user can locate or track the current location of the mobile location node. Note that we only test the system by placing the mobile location node on the same grid locations as those locations that were collected the RSSI. The localization application will use the location estimation algorithm to provide an estimated location. We compared the estimated location with the actual location and calculated the error distance for each user’s request as illustrated in Fig. 5. The online experiments were performed for 50 times at each location. Consequently, there are a total of 50×24 = 1,200 test results. The measurement results will be reported in the next section.IV.P ERFORMANCE M EASUREMENT R ESULTSThe localization performance measurement results are reported in Table I. The first column is the error distance which is the coordinate’s difference between the correct location and the estimated location returned by our application. The second column, which is labelled as probability density, is counted and normalized from the frequency of test results with corresponding error distance. The last column is the cumulative value of the probability density given in the second column. Note that these results were reported previously as a plot of cumulative distribution function in our two-page short paper [14]. The numerical data are included here for completeness of our study. In the literature, the localization performance is usually reported in terms of location accuracy and location precision. The location accuracy is defined as an error distance (in meters) that the estimated location is deviated from the actual location [15]. On the other hand, the location precision is defined as a percentage or a cumulative probability density value that the system returns all correct locations within a given distance of accuracy [15]. Based on the results in the table, our system achieved 2.8 meters of accuracy with approximately 92 percent of precision. The mean accuracy of the system can be calculated by summing all products of error distances and their corresponding probability densities, which is approximately 0.77m.TABLE IL OCALIZATION P ERFORMANCE R ESULTSError Distance(m.) ProbabilityDensityCumulative ProbabilityDensity (CDF)0 0.7025 0.70251.4 0.1050 0.80751.98 0.0592 0.86672.8 0.0517 0.91843.13 0.0108 0.92924.43 0.0633 0.99255.77 0.0075 1V.S TATISTICAL D ATA A NALYSISIn the literature, there is a comprehensive research work in [16] that analyses statisticalparameters of RSSI measured from a number of Wi-Fi interfaces. The authors of that work pointed out that there is a need to understand characteristics of RSSI beyond typical RF propagation in order to better design and implement an indoor localization system. An interesting finding in that work is that most of the RSSI data collected from various indoor environments,if it is considered as a random variable, possesses a unique probability distribution function. Most of RSSI’s distributions were not Gaussian but rather had left-skew distributions [16]. Minority of the distributions was found to have symmetry shape which is similar to Gaussian distribution. The skewness parameters were calculated for various RSSI data sets from a number of WLAN access points and most of them were found to be negative. The standard deviation of the distribution was observed to be larger when the RSSI samples were collected at a location that was closer to the WLAN access points.In this work, we performed similar statistical data analysis in term of the shape of RSSI distribution, the standard deviation, and the skewness. Fig. 7 compares the normalized histograms of RSSI from all locations where each sub-graph represents data from different anchor nodes. We observe that most of the histograms are symmetry which could be modelled by Gaussian distribution. This is different from the results reported in LoS environment of Wi-Fi in [16].Fig. 7 Normalized Histograms of RSSI from All Locations Fig. 8 plots the mean RSSI values of RSSI collected at each anchor node (from anchor 1 to 4) against each location (from location 1 to 24). The combination of mean RSSI values from all four anchor nodes forms a RF fingerprint at a location. A visual inspection of the pattern of mean RSSI values at each location is quite different from other locations. This enables the localization algorithm to separate one location from another through the unique pattern of RSSIs.Fig. 8 Mean of RSSI from All LocationsFig. 9 considers standard deviations of the RSSI samples collected by all anchor nodes at each location. We found that most of the standard deviations were below 2 and were not larger than 4. This is different from the results reported by small area scenario with LoS propagation of [16] where Wi-Fitends to have larger standard deviation than 4. We observed that when mobile location node is at the same location as anchor node, the standard deviation of RSSI is zero. For example, when mobile node is at location 1, the standard deviation of RSSI measured at Anchor 1 is 0. In other words, Wi-Fi signal is more fluctuate than ZigBee signal under theLoS environment.Fig. 9 Standard Deviation of RSSI from All Locations.Fig. 10 compares the skewness values of all RSSI distributions from different locations and anchor nodes. Skewness is a measure of symmetry of distribution. A probability density function (PDF) is said to be left-skewed (has a long tail on the left) when it has its mean less than its median which is less than its mode [17]. Note that the left-skewed distribution will have a large negative skewness value. However, in this figure we observed that most of the distributions were not left-skewed. This is also different fromthe Wi-Fi results reported in [16].Fig. 10 Skewness of RSSI from All Locations.VI. C ONCLUSIONS AND F UTURE W ORK An implementation of ZigBee localization system, whichconsists of both hardware and software components, is described in this work. Preliminary localization performancein term of location accuracy and location precision is also summarized where the average accuracy was 0.77m. Atapproximately 92% of precision, the system can achieve the accuracy of 2.8m. The main contribution of this paper is the study of statistical data analysis on the RSSI in RF fingerprint database. We found that for small area with LoS RF propagation most of the distributions of RSSI in ZigBee WSN are symmetry which is different from Wi-Fi results in [16]. Moreover, the standard deviation was also different in which ZigBee RSSI signal is less fluctuate in this work. The majority left-skew distributions were not observed in this work, while it is prevalent in the Wi-Fi system. However, in term of statistics the total number of measured samples in this work is still small. Our preliminary findings reported in this work may not be valid in all environments and scenarios because we only collected the data from a small room environment. We are planning to deploy the ZigBee localization system in a larger area and perform more extensive measurement experiments, which will include NLoS environment and low signal strength condition.R EFERENCES[1]L. 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