Automated RSSI-Based Tracking Sensor Deployment ...

Cho, C., Sakhakarmi, S., Park, J., Shrestha, P., (2017) “Automated RSSI-Based Tracking Sensor Deployment Using Electromagnetic Simulation,” 5th Seoul International Conference on Applied Science and Engineering, Seoul, South Korea, December 5-7, 2017.

Automated RSSI-Based Tracking Sensor Deployment Using Electromagnetic Simulation Chuhnee Choa, Sayan Sakhakarmib, JeeWoong Parkc,*, Pramen P. Shresthad a,b,c,d Department of Civil and Environmental Engineering and Construction, the University of Nevada, Las Vegas, USA. a E-mail address: [email protected] b E-mail address: [email protected] c

d

E-mail address: [email protected] E-mail address: [email protected]

Abstract With the recent technological advancement and its wide application in the construction industry, there has been a series of studies on the applicability of wireless sensing technology as a means of tracking. The localization of construction resources and potential applications using location information has been a major area of interests, which resulted in the development of various tracking systems for the automated monitoring of construction sites. Among several monitoring technologies, Bluetooth low-energy (BLE) technology has recently gained popularity and is now one of the most promising solutions to tracking; it can track workers’ locations and provide site information even in a complex environment such as construction sites. Since the BLE system identifies worker’s traffic line based on radio-signal-strength-indication (RSSI), minimizing unnecessary signal power loss is the key to the effective utilization of sensory resources. This study proposes a methodology to devise an optimized sensor layout of a BLE tracking system by offering minimum signal power loss during tracking. For the numerical validation, the EM energy flow from optimized sensor deployment and random sensor deployment are compared through electromagnetic simulation using the high-frequency structural simulator (HFSS). The comparison results in the case study showed that the proposed methodology is 25.68% more efficient for the sensor deployment based on the reduction in signal power loss. Hence, the effective utilization of signals significantly improves the performance of the BLE tracking system. Keywords: electromagnetic simulation, indoor localization, optimization, sensor deployment, tracking 1.

Introduction

1.1. Localization Technology in Construction Recent technological development has been combined with the demand for improved safety, 1

Cho, C., Sakhakarmi, S., Park, J., Shrestha, P., (2017) “Automated RSSI-Based Tracking Sensor Deployment Using Electromagnetic Simulation,” 5th Seoul International Conference on Applied Science and Engineering, Seoul, South Korea, December 5-7, 2017.

efficient material and asset management, and effective communication in construction. As a result, construction research has explored an active employment of information and sensing technologies. Following this effort, one of the most extensively investigated research domains is tracking – localizing construction assets including people and materials. Many researchers have investigated various algorithmic approaches by means of localization from theoretical points of view (Cai et al. 2014; Li et al. 2014; Park and Cho 2017; Taneja et al. 2016b) in the development of hybrid-tracking systems (Park et al. 2016a; Taneja et al. 2016a) and their potential applications in construction management and operation (Carbonari et al. 2011; Kim et al. 2016, 2008; Park et al. 2016b; Wang 2008). Until now, most researchers focused on methodological developments for tracking and their system-level evaluation. However, the limitations and assumptions made in previous research have resulted in impracticality of many such systems. Ultra-wideband (UWB) is one of the examples of such systems that are known for its mille-to-centimeter accuracy. Though UWB is one of the most accurate sensors, it suffers from high cost, lengthy time of calibration, and difficulty in system deployment (Shahi et al. 2012; Torrent and Caldas 2009). The use of multiple resources (e.g., wireless sensor, motion sensor and building information model) (Park et al. 2017) may require extra burden on the system manager. Previous researchers have referred to deployment as a major issue with the tracking solutions when applied to an indoor construction site. For example, Li et al. (2016) identified the potential issues as they conducted an extensive literature review on tracking systems with respect to factors that affect affecting the pragmatic and feasible application of tracking systems on construction site. However, all these factors affecting practical application of tracking systems have not been sufficiently studied and included in research despite their importance, which has impeded the wide-spread adoption of such systems in the construction industry. In other words, construction research has not adequately addressed the factors and their potential impact in both the theoretical and experimental points of view. As signals from a system that uses sensors operating on the principle of signal propagation travel through a medium, there is a loss in signal (Friis 1946), which is an indication of energy loss. Such a system with a significant signal power loss during interrogation is considered inefficient (Pozar 2009). For efficient sensor deployment, signal power loss is one of the major concerns. However, the previous experimental studies used uniform sensor distribution or specific sensor set-up for testing scenarios without addressing the complex phenomena of signal power loss. The quantification of the complex behavior of signal powers and their losses can further allow seeking an integral strategy for sensor distribution that offers a solution to minimizing energy loss (signal power loss). Park et al. (2015) reported a significant fluctuation in the radio frequency identification (RFID) signals in comparison to the signals from Bluetooth low-energy (BLE) and magnetic field sensors when used in dynamic situations. Thus, the authors used BLE technology in their proposed methodology as a RSSI-based 2

Cho, C., Sakhakarmi, S., Park, J., Shrestha, P., (2017) “Automated RSSI-Based Tracking Sensor Deployment Using Electromagnetic Simulation,” 5th Seoul International Conference on Applied Science and Engineering, Seoul, South Korea, December 5-7, 2017.

tracking system. 1.2. Objective and Scope The research objective is to address the issue of signal loss in a RSSI-based tracking system. The research presents a methodology for optimizing a sensor deployment plan with any given number of sensors, which eventually offers minimized signal loss. The high-frequency structural simulator (HFSS), a commercial software, performs computational analysis of the emitted and received signals to evaluate the ratio of energy loss. With the optimized sensor deployment plan, the proposed methodology could minimize the signal power loss, and hence maximize the use of emitted energy of a sensing/tracking system. The scope of this research is limited to the development of an optimized-sensor deployment methodology and its numerical validation. The research is based on the theory of energy loss which assumes that the most effective signal communication is the one with minimum energy loss during signal communication. By finding a deployment solution for minimized energy loss, this research can provide an efficient sensor deployment plan for a tracking system. The findings of the research will assist in designing efficient sensor layouts suitable for its application in a dynamically changing construction site. 2.

Mechanism of Automated Sensor Deployment

2.1. Theoretically Received Power Measurement for Estimating Distance In the RSSI-based BLE tracking system, each BLE beacon sensor radiates constant EM power uniformly to all azimuthal directions since omnidirectional antennas (e.g. dipole antennas) are installed. When a worker moves around deployed BLE beacon sensors, worker’s reader captures modulated EM signals from each BLE sensor and measures received power levels. As shown in Fig. 1, even when the transmitted power levels of two sensors are the same, the received power from Sensor 1 is higher than that from Sensor 2 because of electromagnetic energy dissipation in the air (wireless energy loss in the air). As a result, the tracking system predicts that the worker’s location is closer to Sensor 1 than Sensor 2. Therefore, the received power level from each BLE sensor is an important index for back-estimating the distance between the worker and BLE sensors. Transmitted Power P1

Reader Sensor 1

Received Received Pow er P1 Pow er P2

Transmitted Power P2

Distance R2 Sensor 2

R1 < R2

3

P1 = P2

Cho, C., Sakhakarmi, S., Park, J., Shrestha, P., (2017) “Automated RSSI-Based Tracking Sensor Deployment Using Electromagnetic Simulation,” 5th Seoul International Conference on Applied Science and Engineering, Seoul, South Korea, December 5-7, 2017.

Fig. 1. Illustration of an RSSI-based BLE tracking system Researchers have attempted to formulate equations by using received power levels to calculate distance (Chintalapudi et al. 2010; Li et al. 2014). Although these equations are applicable to the BLE tracking system, they are mainly empirical approaches that over-simplify the Friis transmission equation (Friis 1946). In order to more accurately consider electromagnetic performance such as antenna gain and patterns, this study adopts the modified form of the Friis transmission equation, which follows: 𝜆 2 𝑃𝑟 = 𝐺𝑟 𝐺𝑡 ( ) 𝑃𝑡 𝑤, 4𝜋𝑅

(1)

where 𝐺𝑡 and 𝐺𝑟 are the antenna gains of a BLE beacon sensor and a worker’s reader; 𝜆 is the wavelength (0.125m in the BLE application), which is the speed of the light (3×108 m/s) divided by the operating frequency (2.4 GHz in the BLE application); 𝑅 is the distance between a BLE beacon sensor and a worker’s reader; 𝑃𝑡 is the transmitted power level from a BLE beacon sensor; and 𝑃𝑟 is the received power; 𝑤 is the environmental factor that enables to describe EM attenuation, distortion and reflection. To estimate the distance between the reader and the BLE beacon sensor, Eq. (1) is rewritten as the following equation: 𝑃𝑡 𝑤𝐺𝑟 𝐺𝑡 𝜆 𝑅=√ . 𝑃𝑟 4𝜋

(2)

2.2. Formulation of Automated Sensor Deployment Fig. 2 presents a schematic view of a tracking approach for the RSSI-based BLE system. In the exampled construction site, eight BLE beacon sensor are randomly deployed and three worker’s locations are presented. To identify a worker’s location, the worker’s BLE reader measures received power levels from the BLE beacon sensors and classifies three (or four) sensors whose received powers are higher than others. Then, Eq. (2) converts the received power levels from the classified sensors into distances between the worker and three (or four) sensors. As shown in Fig. 2, the estimated distances are indicated as radii for each circle (2D) or sphere (3D). By calculating the intersection of the circles or spheres, worker’s location is identified.

4

Cho, C., Sakhakarmi, S., Park, J., Shrestha, P., (2017) “Automated RSSI-Based Tracking Sensor Deployment Using Electromagnetic Simulation,” 5th Seoul International Conference on Applied Science and Engineering, Seoul, South Korea, December 5-7, 2017. 4

8

2 7

3

3

1

1

6

5

: BLE sensor

2 : BLE reader (worker )

Fig. 2. Tracking method for a RSSI-based BLE tracking system To ensure optimal sensor deployment, three steps are suggested in this study. The first step calculates the received power level corresponding to sensors and the worker. To account for the locations of multiple workers and sensors, Eq. (1) is modified as: 𝑃𝑟 (𝑥𝑖 , 𝑦𝑗 ) = 𝐺𝑟 𝐺𝑡 (

2

𝜆 4𝜋𝑅(𝑥𝑖 , 𝑦𝑗 )

) 𝑃𝑡 (𝑥𝑖 )𝑤(𝑥𝑖 , 𝑦𝑗 ),

(3)

where 𝑥𝑖 is a vector in the three-dimensional Cartesian coordinate system that presents the 𝑖 th sensor location, 𝑦𝑗 is a Cartesian coordinate vector that shows the 𝑗 th location of a worker, 𝑅(𝑥𝑖 , 𝑦𝑗 ) is the distance between 𝑥𝑖 and 𝑦𝑗 , 𝑃𝑡 (𝑥𝑖 ) is the transmitted power of the 𝑖 th sensor, and 𝑤(𝑥𝑖 , 𝑦𝑗 ) is an environmental factor between 𝑥𝑖 and 𝑦𝑗 . In the second step, we identify the three highest signal strengths: 3

𝑃(𝐗, 𝑦𝑗 ) = ∑ 𝑀𝐴𝑋𝑘 {𝑃𝑟 (𝑥1 , 𝑦𝑗 ), 𝑃𝑟 (𝑥2 , 𝑦𝑗 ) … 𝑃𝑟 (𝑥𝑛 , 𝑦𝑗 )} 𝑘=1

(4)

where 𝑀𝐴𝑋𝑘 {•} is the function for calculating the 𝑘 𝑡ℎ highest value; 𝑛 is the number of sensors; 𝐗 is a matrix that contains sensor location vectors (𝐗 = [𝑥1 , 𝑥2 , … 𝑥𝑛 ]). Finally, the automated sensor deployment is formulated as: maximize ∑𝑚 𝑗=1 𝑃(𝐗, 𝑦𝑗 ) 𝐗

subject to 𝐗 L ≤ 𝐗 ≤ 𝐗 U

5

(5)

Cho, C., Sakhakarmi, S., Park, J., Shrestha, P., (2017) “Automated RSSI-Based Tracking Sensor Deployment Using Electromagnetic Simulation,” 5th Seoul International Conference on Applied Science and Engineering, Seoul, South Korea, December 5-7, 2017.

where m is the number of locations for workers; 𝐗 L and 𝐗 U are lower and upper bounds for optimization parameter 𝐗. It is noteworthy that maximizing received power is the same process as minimizing energy loss. 3.

Numerical Validation

3.1. Optimal Sensor Deployment For validating the automation approach for BLE sensor deployment, the research group selects a portion of a construction site as shown in Fig. The selected portion, whose dimensions are 20 m (length) by 18 m (width) by 4 m (height), consists of fifteen concrete walls. Previous research has tested BLE tracking systems with certain ranges of sensor density which are from 25 to 57 m2 per sensor (Park et al. 2017). In this study, the sensing density is set to 30 m2 per sensor. Therefore, fifteen sensors are deployed as shown in Fig. 3.

Initial sensor location Optimal sensor location Worker location

Fig. 3. Optimal sensor deployment In optimal sensor deployment with the solution of Eq. (5), the initial matrix 𝐗 0 of optimizing variables is randomly generated within a lower bound 𝐗 L and an upper bound 𝐗 U . In generating the location of workers, the system uses an equal distance of 2 m between workers. These locations correspond to the 𝑦 vectors in Eqs. (3), (4), and (5). Because the theoretical gain value of a half-wave dipole antenna is 2.15 dB (real scale=1.64), 𝐺𝑟 and 𝐺𝑡 are set as the same number. As a global optimization solver, MultiStart in MATLAB optimization toolbox is adopted to solve the optimization problem of the sensor deployment. Using MultiStart, 100 trial starting points for optimizing variable matrix 𝐗 are randomly generated. Starting from each of 100 points, the fmincon (nonlinear least-squares solver) with “sequential quadratic optimization” algorithm finds a local optimum. Finally, the optimal sensor location matrix 𝐗 ∗ 6

Cho, C., Sakhakarmi, S., Park, J., Shrestha, P., (2017) “Automated RSSI-Based Tracking Sensor Deployment Using Electromagnetic Simulation,” 5th Seoul International Conference on Applied Science and Engineering, Seoul, South Korea, December 5-7, 2017.

is decided and its objective function value is 0.1893 which are around 15% improved to be compared with initial value (0.1652). Detail values of Matrix 𝐗 0 , 𝐗 ∗ , 𝐗 L , and 𝐗 U are summarized as: 6.77, 13.56, 14.33, 17.12, 10.41, 7.21, 4.74, 5.68, 5.48, 14.57, 17.65, 11.92, 7.62, 3.68 𝐗 𝐓𝟎 = [4.08, ]m 2.83, 4.05, 4.06, 5.60, 9.56, 8.86, 7.27, 9.87, 12.20, 11.10, 13.63, 13.25, 16.30, 16.73, 16.52

(6)

𝐗 ∗𝐓 = [2.96, 4.72,

(7)

8.22, 12.58, 16.63, 17.04, 13.74, 5.50, 3.19, 2.42, 7.25, 13.04, 16.69, 9.50, 6.82, 4.56 ]m 3.78, 3.25, 2.85, 5.50, 8.61, 6.67, 8.41, 12.01, 9.50, 10.96, 9.50, 13.50, 15.58, 15.52

1.0, 5.0, 9.0, 13.0, 13.0, 9.0, 5.0, 1.0, 1.0, 5.0, 9.0, 13.0, 9.0, 5.0, 1.0 𝐗 𝐓𝐋 = [1.0, ]m 1.0, 1.0, 1.0, 5.0, 5.0, 5.0, 5.0, 9.0, 9.0, 9.0, 9.0, 13.0, 13.0, 13.0

(8)

7.0, 11.0, 15.0, 19.0, 19.0, 15.0, 11.0, 7.0, 7.0, 11.0, 15.0, 19.0, 15.0, 11.0, 7.0 𝐗 𝐓𝐔 = [7.0, ]m 7.0, 7.0, 7.0, 11.0, 11.0, 11.0, 11.0, 15.0, 15.0, 15.0, 15.0, 19.0, 19.0, 19.0

(9)

3.2 HFSS (numerical) Validation The proposed method for the optimal sensor deployment is formulated based on the theory of the Friis transmission which enables to estimate energy dissipation in far-field communication. However, the Friis transmission equation still has a limitation to accurately analyze EM properties such as wave reflections and distortions. Therefore, the research group adopts the HFSS simulation as a numerical validation method. HFSS is a powerful finite element method for an electromagnetic solution, which divides a 3D construction site into small elements and generates EM environments. Many microwave engineers have used the HFSS simulation tool to improve the accuracy of their antenna or radio frequency circuit designs (Kozlov and Turner 2010; Mirotznik and Prather 1997). BLE beacon sensors are modeled with dipole antennas as shown in Fig. 4. Since a main issue of this study is to investigate wireless energy flow in the construction site, dipole antenna models are sufficient to describe EM phenomena in the BLE tracking system. In other words, only antenna models are required and other components of BLE sensors are not necessary in the EM simulation. The electromagnetic radiation pattern of the dipole antenna is omnidirectional. The maximum gain of the antenna is 2.01dB which is slightly lower than the theoretical value but acceptable. Fig. 4 illustrates a 3D full wave model for the tracking system. BLE antennas are randomly deployed according to Eq. (8) and (9). The fifteen concrete walls are modeled with an exterior box—a simulation boundary—that truncates the EM domain.

7

Cho, C., Sakhakarmi, S., Park, J., Shrestha, P., (2017) “Automated RSSI-Based Tracking Sensor Deployment Using Electromagnetic Simulation,” 5th Seoul International Conference on Applied Science and Engineering, Seoul, South Korea, December 5-7, 2017. BLE sensors

Simulation boundary

Concrete wall

Worker

Fig. 4. HFSS simulation model Table 1 summarizes the comparison results between the proposed method and HFSS simulation. In the proposed method, the objective function value of the optimal deployment is 0.1893, which indicates a 14.59% improvement, as compared with that of the random deployment value, 0.1652. Since the HFSS simulation considers EM environmental reflections, the total received powers (the objective function value) are higher than those of the proposed method. The objective function values from optimal and random deployments are 0.3534 and 0.2812, respectively. The improvement effect (25.68%) in the simulation is higher than the estimation. Therefore, the accurate EM simulation validates the proposed method. Table 1. Comparison of the total received power values between the proposed method and the HFSS simulation. Optimal Deployment

Random Deployment

Improvement

Proposed method

0.1893

0.1652

14.59%

HFSS simulation

0.3534

0.2812

25.68%

4.

Summary and Discussion

This study developed a methodology for automated tracking sensor deployment to address the problem of sensor deployment, which has received little attention in the construction industry. The process of the optimal sensor deployment involved a numerical optimization and an EM analysis. Based on the Friis transmission equation, the numerical optimization formulated the objective function and minimized the EM energy loss ratio, and the proposed method was 8

Cho, C., Sakhakarmi, S., Park, J., Shrestha, P., (2017) “Automated RSSI-Based Tracking Sensor Deployment Using Electromagnetic Simulation,” 5th Seoul International Conference on Applied Science and Engineering, Seoul, South Korea, December 5-7, 2017.

validated through solutions from EM analysis. During the analysis, a technique to maximize the total received power was adopted so that the signal power loss could be minimized. The study resulted in a significant improvement in the received power by 14.59% with the optimal sensor deployment compared to the random sensor deployment. Further, the proposed method was successfully validated to be 25.68% more efficient than the random sensor deployment through EM simulation. This research was successfully conducted with BLE technology and its associated antenna characteristics. However, the proposed methodology is generic and applicable to other sensor systems with the proper inclusion of their antenna characteristics in the analysis. The proposed method of optimizing sensor deployment used EM simulation for its numerical validation. However, the methodology currently suffers from high computing demand when testing for a large construction site. At the current stage, this may limit the size of optimizing deployment area. To address this limitation of the EM simulation, the future research will be focused on devising a numerical technique to enhance the computing speed. A higher computing speed will allow the EM simulation to be an integral part of the optimization framework and the proposed method will be applicable to any construction site without limitation to its size. 5.

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Cho, C., Sakhakarmi, S., Park, J., Shrestha, P., (2017) “Automated RSSI-Based Tracking Sensor Deployment Using Electromagnetic Simulation,” 5th Seoul International Conference on Applied Science and Engineering, Seoul, South Korea, December 5-7, 2017.

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