Accepted Manuscript Implementation and test of an RSSI-based indoor target localization system: human movement effects on the accuracy Apidet Booranawong, Kiattisak Sengchuai, Nattha Jindapetch PII: DOI: Reference:
S0263-2241(18)30968-0 https://doi.org/10.1016/j.measurement.2018.10.031 MEASUR 5971
To appear in:
Measurement
Received Date: Revised Date: Accepted Date:
25 May 2016 16 July 2018 10 October 2018
Please cite this article as: A. Booranawong, K. Sengchuai, N. Jindapetch, Implementation and test of an RSSI-based indoor target localization system: human movement effects on the accuracy, Measurement (2018), doi: https:// doi.org/10.1016/j.measurement.2018.10.031
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Implementation and test of an RSSI-based indoor target localization system: human movement effects on the accuracy Apidet Booranawong, Kiattisak Sengchuai, Nattha Jindapetch* Department of Electrical Engineering, Faculty of Engineering, Prince of Songkla University, 15 Kanjanavanich Road, Kho Hong, Hat Yai, Songkhla 90112, Thailand * Corresponding author: Nattha Jindapetch E-mail addresses:
[email protected] (A. Booranawong),
[email protected] (K. Sengchuai),
[email protected] (N. Jindapetch) Abstract The movement of humans in wireless networks is one of major effects leading to significant received signal strength indicator (RSSI) variation. Using fluctuated RSSI on estimating the target position in the RSSIbased indoor localization system can give large error and poor decision of the system. In this paper, how the human movement affects the accuracy of an implemented indoor target localization system is explored by experiments, and a proposed simple RSSI filtering solution as the guideline solution to directly handle such a research problem is also presented. For our purpose, firstly, the RSSI-based indoor target localization system, which consists of design communication operations for measuring the RSSI in the wireless network and selected well-known localization methods (i.e. the min-max and the trilateration methods) for estimating the target position, is implemented and tested. Secondly, selected well-known filtering methods (i.e. the moving average and the exponentially weighted moving average filters) and the span thresholding filter (i.e. the proposed solution) are applied for reducing the RSSI variation and the estimated position error caused by the human movement. Our experiments have been carried out in an indoor environment. An LPC2103F microcontroller interfacing with a 2.4 GHz CC2500 RF module is developed and employed as the wireless node. Experimental results reveal that the estimated position error determined by the min-max and the trilateration methods significantly increases during the human movement, and converts according to human movement patterns and numbers of movement people. Also, the results demonstrate that by applying the moving average filter with a high window size and the exponentially weighted moving average filter with an optimal weighting factor to raw RSSI data, the estimated position error is not much improved. In contrast, the span thresholding filter gives better results and can directly cope with the human movement problem. In average, the localization error and the standard deviation decrease 11.921% and 42.086% in the case of the min-max method, and they decrease 44.535% and 87.154% in the case of the trilateration method. Keywords: Human movement; RSSI variation; Indoor target localization; RSSI filtering; Implementation 1. Introduction In the context of indoor wireless communication networks, target localization is one of the essential subjects because the position information is useful for many applications, such as human monitoring and tracking in buildings or during emergency situations (i.e. during fires, smoke events, dark periods, and earthquakes) [1, 2], patient monitoring in hospitals and homes [3], mobile robot tracking [4], location detection of products in storehouses [5], worker monitoring and tracking in construction sites [6-8], automated control of devices [9-11], etc. To determine the target position, RSSI information is more widely used because most wireless radio devices have RSSI circuits built into them [33-35]. Consequently, no additional/special hardware is required. This reduces the cost, complexity and energy consumption of the system [12]. However, the major challenge in the RSSI-based indoor localization is that the measured RSSI is time-varying and unreliable in general: it often fluctuates over time [36]. The RSSI variation can be caused by several factors; the movement of humans obstructing the radio signal path between a transmiter and a receiver is among the
major factors that cause significant RSSI variations and large errors in position estimates [37-39]. The inaccurate estimates can also lead to poor decisions, and cannot support some specific applications at all. Based on prior studies in the research literature, the investigation of how the human movement in indoor wireless networks affects the RSSI variation and the target localization is summaried here. In [13-15], evaluations of RSSI behavior in indoor environments under various test scenarios were performed by experiments. The authors summarized factors that could cause variations in the RSSI, and also reported that when the line-of-sight (LOS) from the transmitter to the receiver was broken by the human walking between them, the radio signal was significantly affected. Thus, such effect should be taken into account if the RSSI information was utilized. The authors in [16] reported that human activities significantly influenced the performance of wireless communications, both in LOS and in non line-of-sight (NLOS) conditions. In [17], experiments demonstrated that the interference by human activities caused RSSI fluctuations and increased the standard deviation of the RSSI as well. Like the work in [18], properties of indoor received signal strength for wireless local area networks (WLANs) were studied. The authors reported that during the human blocking the radio signal path, the received RSSI fluctuated due to the multipath effects which were caused by radio reflection, diffraction and scattering. Both the mean and the standard deviation of the RSSI were significantly changed. In [19], the human movement effects on 2.4 GHz wireless sensor networks were assessed by experiments. The moving people affected the RSSI, and its variation depended on the number of people and the peoples’s movement speeds. Although in [13-19] the RSSI variation directly caused by the human movement was studied, the influence of such effect on the indoor target localization system was not included in the scope of these studies. For the work in [20], the authors claimed that the RSSI-based localization accuracy was affected by the level of the RSSI variation which depended on the amounts of metals and other reflective materials in physical environments (causing multipath effects). Therefore, an adaptive smother designed by considering both the RSSI and link quality indicator (LQI) information for reducing the RSSI variation was proposed. The experimental results showed that the adaptive smother could reduce the RSSI variation around 25%, and the conversion of the RSSI to the actual distance was improved. However, the human movement effects on the RSSI-based localization accuracy was not integrated in [20]. The work in [21] proposed the indoor localization system for addressing the issue of the RSSI variation in fingerprinting by the simulation study. The authors mentioned that, in the fingerprinting method, the target position was estimated based on the matching data between the measured RSSI in online and offline phases. The error could occur when the RSSI data from both phases were not the same due to the RSSI variation caused by the change of conditions in environments (e.g. distribution of furniture, people movement, and opening and closing of doors). As a result, the pyroelectric infrared sensor and the fingerprinting localization were proposed for indentifying the actual target position. However, the work in [21] did not study how the people movement directly affected the RSSI and the fingerprinting accuracy. Furthermore, how well their proposed method could manage the people’s movement effect was not focused. We note that the RSSI variation in [21] was generated and simulated by varing the Gaussian random variable (with zero mean and standard deviation terms) in the lognormal shadowing path-loss model. Finally, in [22], the effects of the human in the LOS between an access point and the mobile device WLAN on the received signal strength, and making errors of the RSSI to actual distance conversion were studied. The authors claimed that, according to their experiments, human presence significantly decreased the received signal strength, approximately by -2 dBm to -5 dBm. This could lead to error from the actual distance exceeding 2 m. The authors therefore suggested that the indoor positioning system should take into account the effects of the human in order to achieve high localization precision, or minimal distance error. According to the research motivations and the research gaps as presented above, in this paper, how the human movement affects the accuracy of the RSSI-based indoor target localization system is studied. The major contributions of our paper are that a) how the human movement directly affects the RSSI variation and the localization accuracy is investigated by the experiments with the different test scenarios of human’s
movement patterns, b) the RSSI-based indoor target localization system including the developed low-cost, low-power 2.4 GHz wireless node, the design wireless communication operation and the selected wellknown localization methods (i.e. the min-max and the trilateration methods) is implemented for the test, c) how well the well-known filtering methods (i.e. the moving average and the exponentially weighted moving average filters) can reduce the RSSI variation caused by the human and improve the localization precision is presented, and d) how the proposed filtering solution, namely the span thresholding filter, can help to address such a problem is introduced. We note that the min-max and the trilateration localization methods are selected because their algorithms are simple with low computational complexity, and they can also provide accurate position estimates as reported in [12, 23, 24]. However, the evaluation of these methods, when using the RSSI variations caused by the human movement, has not been studied yet. In addition, to apply the moving average and the exponentially weighted moving average filters for smoothing the RSSI data were presented in [20, 25-27]. However, how they reduce the RSSI variation caused by the human movement and impact the min-max and the trilateration methods have still not been investigated. Our experiments have been carried out in the indoor environment. The LPC2103F microcontroller interfacing with the low-cost, low power, 2.4 GHz CC2500 RF transceiver is developed and used as the wireless node: some important implementation details, such as LPC2103F and CC2500 interfacing, nonoverlapping channel settings, RSSI reading and CC2500 packet format settings are also explained in this paper. Experimental results show that, during the human movement, the estimated position error by both the min-max and the trilateration methods significantly increases and converts according to the human movement patterns as well as the numbers of movement humans. The results also reveal that the estimated position error is not much decreased although the moving average filter and the exponentially weighted moving average filters are applied to raw RSSI data. On the other hand, the span thresholding filter provides better results to handle the human movement problem. Thus, the design and the development of an autonomous method or a localization method to directly consider the human movement problem are required for the RSSI-based target localization system. This paper is organized as follows. Section 2 explains the proposed RSSI-based indoor target localization system which consists the design communication operation and the min-max and the trilateration localization methods. In Section 3, the RSSI filtering techniques including the moving average filter, the exponentially weighted moving average filter, and the span thresholding filter are detailed. Section 4 describes experimental descriptions including experimental setups, test scenarios, and performance metrics. Section 5 provides the experimental results with discussions. Finally, we conclude this paper in Section 6. 2. An RSSI-based indoor target localization system The RSSI-based indoor target localization system presented in this work is shown in Fig. 1. There are one base station node connected to a computer (i.e. the processing center), one target node to be estimated the position, and three reference nodes (stationary at the known positions). To estimate the position of the target node in the given wireless network, there are three main processes as illustrated in Fig. 2. The first process is the RSSI measurement by the design wireless communication operation. The second process is the RSSI to distance conversion using the path-loss equation. Finally, the third process is the target position estimation by the min-max and the trilateration localization methods. All processes are described below.
Fig. 1. The proposed RSSI-based indoor target localization system.
Fig. 2. The proposed RSSI-based indoor target localization process.
In the first process, when the computer which is connected to the base station node wants to determine the target positions, it first commands the base station node to generate and send a packet, namely the command packet, to each reference node sequentially (begin with the reference node 1). The corresponding reference node then generates a packet, namely the beacon packet, and sends such packet to the target node. Upon receiving the beacon packet from the reference node, the target node reads the RSSI value which is provided by its radio circuit and then transfers the RSSI reading as encapsulated in a packet, namely the data packet, to the base station node. The base station node also forwards the data packet to the computer. The configuration and setting of the command, the beacon and the data packets will be further described in the Section 4. We note that to reduce the signal interference from other radio devices which use the same radio frequency that would cause packet losses, and to study the human movement effect without such kind of the interference, we intend to control the base station node to negotiate with each reference node sequentially. As illustrated in Fig. 1, the base station node is programmed to transmit the command packet to the reference nodes 1, 2, and 3, respectively. After transmitting the command packet to each reference node, the base station node also waits with a small period of time for receiving the data packet and then transmits the next command packet to the next reference node. These procedures are illustrated by notations (a) to (i) as presented in Fig. 1. At the computer, after the RSSI value are inserted by the base station node, the second process then begins. The RSSI value is converted to the distance value using the path-loss equation which describes the relationship between the measured RSSI and the corresponding distance in the test environment. The path-
loss equation is expressed by (1) and (2) [6, 12], where is the mean RSSI value (in dBm) at distance (between the transmitter and the receiver), is the mean RSSI value (in dBm) at the reference distance from the transmitter ( ), and is called the path-loss exponent; it is the rate at which the received signal strength decreases along with distance. The parameters and can be determined in the test field by collecting the RSSI data in which the distances from the transmitter to the receiver is known. They are again described in Section 4.
(1)
(2) Finally, after the RSSI is transformed to the distance, and the computer gets all distance values from all reference nodes, the third process immediately performs. As mentioned before, the min-max and the trilateration localization methods are used to determine the target position. We intently select them because their algorithms are quite simple and low computational complexity. Hence, they are easily implemented on hardware platforms. Furthermore, they accurately give the estimated position as well. The min-max and the trilateration methods determine the unknown target position using (3) – (11), where three reference nodes are employed. In the min-max method [28], the target node determines an intersection area which is the boundary within , , , and positions, calculated from (3) to (6). The intersection boundaries are given by the maximum of all coordinate minimums, and the minimum of all maximums. The center of the intersection area is assumed as the estimated target position ( and ), determined by (7) and (8). (3) (4) (5) (6)
(7)
(8) For the trilateration method [29], the target node determines an intersection point of the three circles by solving (9) to (11). The solution to these equations is the estimated target position ( and ). The trilateration method solves the intersection point from (9) to (11) by using a system of linear equations: it requires more arithmetic operations than the min-max method. Therefore, the min-max method is more suitable to be integrated into hardware platforms with limited resources as recommended by the works in [12, 23].
(9) (10) (11) 3. RSSI Filtering 3.1 Moving average and exponentially weighted moving average filters As mentioned before, we also apply the moving average filter and the exponentially weighted moving average filter to raw RSSI signals which are measured by the design communication operations as presented in Section 2. By this way, how well both filters can reduce the RSSI variation caused by the human movement and improve the accuarcy of the min-max and the trilateration method can be explored. The smoothen RSSI output values after applying the moving average and the exponentially weighted moving average filters are shown in (12) and (13), respectively. Where and are the smoothen RSSI values at the sample number by the moving average and the exponentially weighted moving average filters, is the raw RSSI input value beginning with time , is the window size which indicates the number of RSSI input samples to be averaged, and is the weighting factor. For the moving average filter in (12), the smoothen RSSI is the mean of the last RSSI imput values. The larger value of will have a greater smoothing effect, but it requires more computation operations. For the exponentially weighted moving average filter in (13), the smoothen RSSI depends on the previous smoothen RSSI value and the recent raw RSSI input value multiplied by the weighting factor . The weighting factor close to 1 gives high priority to recent changes in the RSSI input value, while the weighting factor close to 0 indicates that the previous smoothen RSSI plays a role in the calculation. In this work, both and are varied in the experiments to see their response. Note that derivation of (13) can be found in Appendix A.
(12) (13) 3.2 Span thresholding filter The span thresholding filter designed for directly reducing the RSSI variation caused by the human movemnt is also proposed in this work. The design concept of the proposed solution is that if nobody is present in the wireless network, the raw RSSI input which is measured at that time can be utilized for the postition estimation. On the other hand, if the human movement can be detected, that RSSI input is not allowed for the estimation. The human movement is detected by considering the level of the RSSI variation as determined from the test environment. The corresponding proposed operations are presented by (14) to (17) and described here. To detect the human movement in the wireless network, a number of measured RSSI samples in each predefined window size ( ) are first examined. In each predefined window, if the absolute difference between the maximum RSSI ( ) and the minimum RSSI ( ) is lower than the predefined threshold ( ), this indicates that no human is blocking the radio signal
path, so the system accepts the measured RSSI input ( ) as the RSSI output ( ) for the position estimation. However, if the absolute difference exceeds the predefined threshold, indicating movement of the human, the proposed solution uses the RSSI output value from the previous sample ( ) on estimating the position. The optimal values of and are manually determined by the experiments which will be described in Section 4, and represents the maximum of the RSSI variation level during no human presence in the test filed. It is determined in an offline phase by finding the maximum value of the absolute subtraction between the maximum RSSI and the minimum RSSI (in each window) of the measured RSSI, where is the order of the window, and is the number of windows. (14) (15)
(16)
(17) 4. Experimental descriptions 4.1 Experimental setup The experiments have been carried out in a laboratory room at the Department of Electrical Engineering, Prince of Songkla University, Thailand, as illustrated in Fig. 3. The dimension of this test filed is 4.00 m × 2.80 m. Three reference nodes 1, 2, and 3 are fixed at the right positions ( = 0.0 m, = 0.0 m), ( = 4.0 m, = 0.0 m), and ( = 0.0 m, = 2.80 m), respectively, while the target node to be estimated its position is at the center of the test field ( = 2.0 m, = 1.4 m). All nodes are placed at the same level from the ground, at one meter height. In this experiment, the target node can wirelessly transfer the measured RSSI data to the base station node via one-hop communications. The base station node communicates with the computer via an RS232 serial port interface, and the real-time RSSI signals and the estimated positions by the min-max and the trilateration methods can be displayed on the LabVIEW, as illustrated in Fig. 4 (a) and (b). For the process which is implemented on LabVIEW, it has five tasks including device scaning, RSSI data reading, RSSI data saperation (from each device), target position estimation, and data display and recording, as shwon in Fig. 5.
Fig. 3. The test field.
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(b) Fig. 4. The LabVIEW front panel.
Fig. 5. The process on LabVIEW.
The LPC2103F microcontroller interfacing with the CC2500 RF transceiver [30] designed and developed by our research team is employed as the wireless node, as shown in Fig. 6. The CC2500 RF transceiver is connected to and communicated with the LPC2103F microcontroller via a serial peripheral interface (SPI), which has four pins, namely master out slave in (MOSI), master in slave out (MISO), slave select (SS), and serial clock (SCK). Here, the CC2500 is the slave, and the LPC2103F microcontroller is the master. Implementation details of the SPI can be further found in [31, 32]. The CC2500 is a low-cost 2.4 GHz radio module designed for very low power wireless applications. It has the maximum data rate of 500 kbps. The CC2500 can also support various radio channels as operating in the frequency range of 2.4−2.4835 GHz. Therefore, we aim to configure the radio channels of our wireless nodes differently from the radio channels of WLAN devices to avoid radio signal interference. In addition, in the CC2500, the RSSI value in dBm ( ) for the selected channel can be read using the procedure as shown in Fig. 7, where the is the offset value corresponding to the data rate. The is 72 in this work, since the data rate is set to 500 kbps. Finally, as mentioned in Section 2, for the RSSI measurement, the command, the beacon, and the data packets are employed. According to the CC2500 configuration, the CC2500 packet format consists of a preamble bit (32 bits), a synchonization word (32 bits), a packet length (8 bits), an address field (8 bits), a data payload (upon the packet types), and an optional cyclic redundancy check (CRC) (16 bits). The data payload sizes of the command, the beacon, and the data packets are set to 24, 16, and 72 bits, respectively, as shown in Tables. 1 and 2. The data payload of the command packet contains the base station node ID (8 bits), the target node ID (8 bits), and the reference node ID (8 bits). The data payload of beacon packet contains the referenc node ID (8 bits), and the target node ID (8 bits). For the data payload of data packet contains the target node ID (8 bits), the the reference node ID (8 bits), the base station node ID (8 bits), the RSSI information (40 bits), and the data packet number (8 bits). Note that the packet’s maximum size by the CC2500 is 256 bytes.
Fig. 6. The developed wireless node with SPI interfacing; where the CC2500 RF transceiver (i.e. the slave) is connected with the LPC2103F microcontroller (the master) via an SPI interface which has four pins: MOSI, MISO, SS, and SCK. RSSI reading by the CC2500 RF transceiver Begin 1: Read the RSSI status register 2: Convert the reading from a hexadecimal number to a decimal number (
)
3: Set 4: Set End Fig. 7. RSSI reading by the CC2500 RF transceiver. Table 1. CC2500 packet format configuration. Packet type Preamble Synchonization bit (bits) word (bits) Command packet 32 32 Beacon packet 32 32 Data packet 32 32 Table 2. Data payload information. Packet type Command packet
Beacon packet Data packet
Packet length (bits) 8 8 8
Address field (bits) 8 8 8
Data payload (bits) 24 16 72
Data payload information - Base station node ID (8 bits) - Target node ID (8 bits) - Reference node ID (8 bits) - Referenc node ID (8 bits) - Target node ID (8 bits) - Target node ID (8 bits) - Reference node ID (8 bits) - Base station node ID (8 bits) - RSSI information (40 bits) - Data packet number (8 bits)
CRC (bits) 16 16 16
Total (bits) 120 112 168
To find the path-loss equation of the test field in Fig. 3, one transmitter node and one receiver node are used to collect the RSSI data at five different distances: 1 m, 2 m, 3 m, 4 m, and 5 m, respectively. At each distance, the receiver node continuously collects 10,000 RSSI samples. By applying the linear curve fitting to the plot of the average RSSI value in dBm versus the distance in meters (logarithmic scale), the path-loss equation can be determined. In our experiment, the parameters and are -47.130 and 2.161, respectively. 4.2 Test scenarios and performance metrics There are four different test scenarios of the human movement patterns in the given test field which are illustrated in Fig. 8 (a) to (d). In the first scenario, there is no human in the test field. In the second scenario, one man continuously walks in the test field passing the reference nodes 2, 1, and 3, respectively. Note that the arrow line in the figure indicates the moving direction of the man. In the third scenario, two people continuously walk in the test field passing the reference nodes 2, 1, and 3, respectively (i.e. single file movement). Finally, in the fourth scenario, two people continuously walk in the opposite direction in the test filed; one walks passing the reference nodes 2, 1, and 3 respectively, and at the same time another walks passing the reference nodes 3, 1, and 2, respectively. We intend to present these four scenarios for the investigation because they are simple and easy to track their effects on the RSSI variation, and also such scenarios are generally possible in the real world. For examples, people walk inside buildings and elderly people move from one room to another room in their houses. In addition, we intend to deploy the target node at the center of the test field because, at that point, if the actual distances between the target and the reference nodes (i.e. , , and are 2.4413 m) are directly applied to calculate the target position, both the min-max and the trilateration methods give the same answer ( = 2.0 m, = 1.4 m). This means that the min-max and the trilateration method presented by (3) to (11) can correctly estimate the position at the given target point. Therefore, if there is any estimated error during the experiment, it is due to the variation of the RSSI data presented in the test field.
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(d) Fig. 8. The test scenarios; (a), (b), (c), and (d) are the movement patterns 1, 2, 3, and 4, respectively.
As mentioned in Section 3, we apply the moving average, the exponentially weighted moving average, and the span thresholding filters to raw RSSI signals to investigate their response to the accuracy of the localization. To do this, all filters are applied to raw RSSI signals which will be measured from the test scenarios 2, 3, and 4, where the window size for the moving average filter is varied in five different levels: 5, 10, 15, 20, and 25, respectively, the weighting factor for the exponentially weighted moving average filter is also varied in five different levels: 0.1, 0.3, 0.5, 0.7, and 0.9, respectively, and finally the window size and for the span thresholding filters are set to 10 and 2 dBm which are manually determined by the test and described in Section 5. We note that the window size for the moving average filter can be set greater than 25. However, since represents the number of RSSI input samples to be averaged, a large window size requires more computational complexity. For the weighting factor , 0.1, 0.3, 0.5, 0.7, and 0.9 are set to cover the range between 0 and 1 . To evaluate all test scenarios as presented above, an error distance (ED) is selected as the performance metric. It is the different distance between the actual target position and the estimated target position which is defined by (18), where ( , ) is the actual target position, and ( , ) is the estimated target position at each iteration of the calculation ( ) as determined by the localization methods. Moreover, an average error distance (AED) defined in (19) is also employed for the evaluation, where is the number of RSSI samples.
(18)
(19) 5. Experimental results and discussions Fig. 9 (a) to (d) demonstrate the raw RSSI signals which are measured by the target node from the test scenarios 1, 2, 3, and 4, respectively. We can observe that for the test scenario 1 the RSSI signals received from the reference nodes 1, 2, and 3 are likely the same, as shown in Fig. 9 (a). There is no RSSI signal fluctuation caused by the human movement. For the RSSI signals in Fig. 9 (b) to (d), they are immediately and significantly fluctuate when the people walk in the test filed. The experimental results reveal that the trend of the RSSI fluctuation strongly depends on the human movement patterns and the number of movement humans. As seen in Fig. 9 (b), there are three main peaks of the RSSI signals corresponding to the man walking passing the reference nodes 2, 1, and 3, respectively. In Fig. 9 (c), at least six main peaks of the RSSI signals are presented. This RSSI fluctuation is due to two people walking in a single file movement manner passing the reference nodes 2, 1, and 3, respectively. Finally, in Fig. 9 (d), there are at least five main peaks of the RSSI signals which are corresponding to two people walking in the opposite direction and at the same time. The histograms of the RSSI signals from all test scenarios presented in Fig. 9 (a) to (d) are also illustrated in Fig. 10 (a) to (d). The results show that when the human is present, like the test scenarios 2, 3, and 4, the distribution of the RSSI signal is left skewed (or negatively skewed). The RSSI values are more spread out on the left side. These experimental results are agree with the report from the pioneer work in [18]. We note that we also measure the ratio of the total number of the data packets (contains the RSSI information as seen in Fig. 9) received at the base station node to the total number of data packets sent by the target node which indicates the successful level of the delivery data to the base station node, as shown in
Table 3. The results show that the ratios are 100%, 99.917%, 99.917%, and 99.958% for the test scenarios 1, 2, 3, and 4, respectively. These results confirm that the design communication operation presented in Section 2 can help to increase in the successful data transmission. Table 3. Delivery ratio of the data packets. Test The total num. of the The total num. of the scenario data packets sent by data packets received the target node at the base station node For For For For For For ref. 1 ref. 2 ref. 3 ref. 1 ref. 2 ref. 3 1 800 800 800 800 800 800 2 800 800 800 798 800 800 3 800 800 800 800 798 800 4 800 800 800 799 800 800
% Delivery ratio
For ref. 1 100 99.75 100 99.875
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(d) Fig. 9. The RSSI signals; (a), (b), (c), and (d) are the signals from the test scenarios 1, 2, 3, and 4, respectively.
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(d) Fig. 10. The histograms of the RSSI signals; (a), (b), (c), and (d) are the histograms of the test scenarios 1, 2, 3, and 4, respectively.
Fig. 11 (a) to (d) show the error distances of the test scenarios 1, 2, 3, and 4, respectively, which are determined by the min-max and the trilateration methods, and Table 4 also shows the average error distances of all test scenarios. The experimental results indicate that the trend of error distance directly depends on the raw RSSI input signal as presented in Fig. 9 (a) to (d) before, and the average error distance and its standard deviation significantly increase in the case of the human movement. The experimental results also demonstrate that, as the man moves, the min-max method which determines the target position within the bounding box provides significantly smaller error distances than the trilateration method which determines the target position from the intersection point of the three circles. Furthermore, the min-max method better tolerates high RSSI variations than the trilateration method; it gives the smaller error distances in the presence of humans. As described in Section 4.2, if the actual distances from the target to the reference nodes are directly used in calculating the target position, the error distances for the min-max and the trilateration methods are 0 m. However, when the distances as converted from the raw RSSI values from the test scenarios 1, 2, 3, and 4 are used in calculating the target position instead, the maximum error distance can reach 0.845 m, 2.105 m, 2.441 m, and 2.441 m in the case of the min-max method and 1.064 m, 89.393 m, 170 m, and 170 m in the case of the trilateration method. These errors stem from the radio interference effects of the human movement (major effect), walls, floors, and nearby objects in test field (minor effect).
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(d) Fig. 11. The error distances of the test scenarios (a) 1, (b) 2, (c) 3, and (d) 4. Table 4. The average error distances of all test scenarios. Test scenario Min-max method AED (m) SD 1 0.526 0.087 2 1.187 0.459 3 1.337 0.410 4 1.313 0.494
Trilateration method AED (m) SD 0.616 0.126 2.872 5.520 3.483 7.816 4.086 9.723
Tables 5, 6, 7, and 8 show the average error distancs of the test secenarios 2, 3, and 4 after applying the moving average filter with the window size of 5, 10, 15, 20, and 25 and the exponentially weighted
moving average with the weighting factor of 0.1, 0.3, 0.5, 0.7, and 0.9. The experimental results reveal that the average error distance and the standard deviation after applying both filters are not significantly improved in the case of the min-max method. On the other hand, they are consistently reduced in the case of the trilateration method, where and give the best performance. In this case, the average error distance at is reduced 0.590 m, 1.123 m, and 1.568 m for the test secenarios 2, 3, and 4, respectively, while the average error distance at is reduced 0.636 m, 1.186 m, and 1.476 m, respectively. However, although the moving average filter with and the exponentially weighted moving average filter with provide the same results approximately, the moving average filter with high window size setting requires more computational complexity. Consequently, it may not appropriately support the real-time application and the mobile target node. Table 5. The average error distances determined by the min-max method. Test scenario 2 Test scenario 3 AED (m) SD AED (m) SD 5 1.178 0.443 1.340 0.378 10 1.174 0.439 1.343 0.357 15 1.174 0.442 1.343 0.347 20 1.175 0.442 1.339 0.332 25 1.176 0.440 1.337 0.320
Test scenario 4 AED (m) SD 1.315 0.460 1.316 0.433 1.319 0.420 1.321 0.411 1.318 0.396
Table 6. The average error distances determined by the min-max method. Test scenario 2 Test scenario 3 AED (m) SD AED (m) SD 0.1 1.177 0.433 1.342 0.324 0.3 1.175 0.439 1.342 0.367 0.5 1.179 0.446 1.338 0.384 0.7 1.183 0.452 1.338 0.397 0.9 1.185 0.456 1.338 0.407
Test scenario 4 AED (m) SD 1.327 0.407 1.319 0.449 1.316 0.471 1.315 0.483 1.313 0.491
Table 7. The average error distances determined by the trilateration method. Test scenario 2 Test scenario 3 AED (m) SD AED (m) SD 5 2.693 3.991 2.839 2.974 10 2.534 3.038 2.589 2.000 15 2.436 2.653 2.453 1.625 20 2.355 2.333 2.353 1.376 25 2.282 2.043 2.270 1.172
Test scenario 4 AED (m) SD 3.294 3.895 2.950 2.524 2.754 1.926 2.619 1.600 2.518 1.383
Table 8. The average error distances determined by the trilateration method. Test scenario 2 Test scenario 3 AED (m) SD AED (m) SD 0.1 2.236 1.972 2.297 1.174 0.3 2.583 3.399 2.709 2.462 0.5 2.719 4.266 2.936 3.379 0.7 2.799 4.917 3.128 4.356 0.9 2.852 5.362 3.343 6.132
Test scenario 4 AED (m) SD 2.610 1.544 3.121 3.221 3.454 4.995 3.728 6.879 3.972 8.749
An example of the error distances of the test scenario 2 determined by the min-max and the trilateration methods is presented in Fig. 12 (a) and (b), where MA-25 and EMA-0.1 are the moving average filter with and the exponentially weighted moving average filter with , respectively. This example shows that although both selected filters can be used to smoothen the fluctuated RSSI data, they are not intentionally designed for handling the effect of the human movement: as seen in Fig. 12, during the human
movement, both the peak and the width peak of the error are not much reduced.
Error distance (m)
5 Original MA-25 EMA-0.1
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(a)
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(b) Fig. 12. The error distances of the test scenario 2 determined by (a) the min-max and (b) the trilateration methods.
The average error distances and the standard deviations by the span thresholding filter with and of 10 samples and 2 dBm are shown in Table 9. These results shows that the average error distance and its standard deviation are more decreased compared with the original result as shown in Table 4. In average, the error distance and the standard deviation decrease 11.921% and 42.086% in the case of the min-max method, and they decrease 44.535 % and 87.154 % in the case of the trilateration method. Also, the span thresholding filter gives better results than the moving average filter with and the exponentially weighted moving average filter with . In the span thresholding filter, and are manually set as mentioned before. is the maximum of the RSSI variation level measured from the test scenario 1 (during no human movement). It is determined in an offline phase by finding the maximum value of the absolute subtraction between the maximum RSSI and the minimum RSSI of the measured RSSI data (as shown in Fig. 9 (a)). In our test, when is 10, the maximum value of the absolute subtraction is 2 dBm as shown in Fig. 13 (a). Note that we also illustrate the absolute subtraction results in the case of using the measured RSSI data from the test scenario 2, which is shown in Fig. 13 (b), to see the difference. In Fig. 14, an example of the error distances of the test scenario 2 determined by the span thresholding filter is illustrated. The result confirms that the span thresholding filter can reduce the peak and the width peak of the error. Table 9. The average error distances of the test scenarios 2, 3, and 4 after applying the span thresholding filter.
Test scenario
ABS (RSSI max - RSSI min)
2 3 4 Average
Min-max method
Trilateration method
AED (m) 1.196 1.172 1.162
AED (m) 1.972 1.752 1.938
SD 0.453 0.245 0.277
SD 1.155 0.785 0.736
% Decrease Min-max method
% Decrease Trilateration method
Compared with Table 4
Compared with Table 4
AED (m)
SD
No difference
No difference
12.341 11.500 11.921
40.244 43.927 42.086
AED (m) 31.337 49.699 52.570 44.535
SD 79.076 89.957 92.430 87.154
5 From the ref. node 1 From the ref. node 2 From the ref. node 3
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(b) Fig. 13. The absolute subtraction between the maximum RSSI and the minimun RSSI with results for the test scenarios 1 and 2, respectively.
Error distance (m)
50 Min-max Trilataretion Min-max with the span thresholding filter Trilateration with the span thresholding filter
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Fig. 14. The error distances of the test scenario 2 determined by the span thresholding filter.
; (a) and (b) are the
Finally, we provide discussions for system improvement and development here. In wireless networks, due to the shared wireless medium, jamming attacks can be launched and result in a great damage to the network [47]. The jammer can emit signals to disrupt the communication between the transmitter node and the target node. For the wireless network with the current version of the communication operation as proposed in Section 2, jamming attacks are not yet considered. Thus, an optimal algorithm for jammer detection and localization should be implemented in our future work. Here, as illustrated by the works in [47-49], jammers can be properly detected by considering related factors during jamming attacks, such as radio signal strength, carrier sensing time, packet delivery ratio, and energy consumption amount. Applying such factors with appropriate jammer localization mechanisms [50] can be a solution for addressing the jamming attack problem. In this work, all wireless nodes used for the experiment (as presented in Section 4) have enough energy levels for testing. However, since batteries are the main source of power supply for wireless nodes, the limited energy is one of the concerned issues which will be taken into consideration in our future works. Here, as presented by the pioneer works in [40-46], energy levels of wireless nodes could be estimated by using appropriate energy consumption models. For example, as proposed by [41, 43, 44, 46], the total consumed energy of wireless nodes was calculated from the consumed energy during transmission, reception, idle, and sleep states, where the power and the time consumed during each state were determined based on the specific information of the RF transceiver, the packet size, and the data rate. Note that this concept is under the assumption that the wireless node consumes energy for sensing, communicating, and data processing, where more energy is required for data communication than other processes. Therefore, when the initial energy (estimated based on the battery type) and the total energy consumption can be estimated, the remaining energy can also be determined. In addition, the energy information can also be included in the packet format, like the case of the RSSI information as described in Section 4.1. Such an energy metric can be useful for decision making and management. By this described solution, we can monitor wireless nodes with weak energy levels (it can also affect the radio signal). The maintenance team can also be informed to replace RF device’s batteries. In our experiment, the area of the test filed is 4.00 m × 2.80 m, and an indoor transmission range of the developed wireless node provided by the CC2500 RF transceiver (with a specific output power) can cover the test field; it is greater than the maximum distance of the test field. Therefore, using three reference nodes is enough for the test. However, to support bigger test areas in real-world applications, or to increase the localization accuracy, employing more reference nodes in the test field can help to address such requirements, as demonstrated by the experiment in [12]. However, using more reference nodes, the cost and complexity are significantly increased [12, 23]. Thus, increasing the accuracy, reducing the complexity, and using small numbers of reference nodes are required for the design of the localization method. As presented by the prior work in [24], limitations of the minmax method were investigated by the experiment. Here, we are going to continue to study the impact of the number of reference nodes to the accuracy and complexity of the localization method. We are also going to develop a more efficient localization method. To first understand how the human movement affects the RSSI variation and the accuracy of the minmax and trilateration methods, in our experiment, the room and subjects for the test are fixed. The laboratory room with obstacles (i.e., chairs, desks, boxes, books, instruments, walls, and floors) is tested, and there are two subjects (i.e. two adults). However, in realistic situations, different environments and subjects (with different sizes and volumes) have a different effect on radio signals. In addition, the movement of children and pets or the movement of pets along with human can also influence RSSI signals. Therefore, the understanding on these mentioned situations should be further investigated and tested. Then, the design and development of more efficient localization and filtering methods to cope with such situations are required. Finally, although the span thresholding filter presented in Section 3.2 is illustrated as a guideline solution to handle the human movement effect, it should be further extended to improve its accuracy.
Since the span thresholding filter uses the RSSI output value from the previous sample on estimating the position when the human is blocking the radio signal path, its performance can be reduced when the room is full of humans in long periods of time. 6. Conclusions In this paper, the RSSI-based indoor target localization system has been implemented and tested for exploring how the human movement affects the RSSI variation and the accuracy of the target localization. In addition, the span thresholding filter which is directly designed for reducing the RSSI variation affected by the human movement is illustrated as a guideline. The experimental results by our test scenarios demonstrate that the localization error significantly increases during the human movement and depends on the human movement patterns and numbers of movement humans. The span thresholding filter can properly handle such introduced research problems. By applying the span thresholding filter, the localization error significantly decreases. Based on this study, we believe that to further design and develop an autonomous and efficient method to cope with the human movement problem in various scenarios, environments, and applications are required for improving the accuracy and the reliability of the RSSI-based indoor target localization system. Acknowledgments This research was fully supported by the Postdoctoral Fellowship from Prince of Songkla University, and partially supported by Center of Excellence in Wireless Sensor Networks (CoE-WSN), Faculty of Engineering, Prince of Songkla University, Thailand. The authors also thank the anonymous reviewers for their valuable comments and suggestions. Appendix A Derivation of (13) is presented here. By substituting , ,... into (13), the general form of the exponentially weighted moving average filter can be expressed by (20). (13)
. . . , where
(20)
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Highlights - An RSSI-based indoor target localization system is implemented and tested. -
How the human movement affects the RSSI and localization accuracy is studied.
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Results show that the localization error increases during the human movement.
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A span thresholding filter reduces the RSSI variation caused by the human movement.
