Arpit Mittal, Vimlesh Shukla, Prem Kumar Sivakumar, Liang Huang
ABSTRACT
This paper describes the development of an indoor wayfinding system based on the IEEE 802.15.4 and ZigBee standards using the Jennic Wireless Microcontroller. Indoor wayfinding is a problem for many individuals with cognitive impairments. The prototype system was implemented using a wireless sensor network (WSN) that used ten reference nodes, one coordinator node, and a blind node. An embedded system controls the network and a software algorithm estimates the blind node to within 1 meter. A building floor plan is overlaid with the location. The system has been tested on janitorial carts in an office wing of the Jewish Vocational Services (JVS), Southfield, Michigan. JVS is a NISH affiliated agency that provides vocational training for workers with cognitive impairments. The system would enable JVS to effectively train its clients and improve on-site worker efficiencies by allowing a supervisor to monitor his/her own team and provide individualized task prompting to workers.
BACKGROUND
While there are many applications for indoor location and wayfinding system, this project will target wayfinding as a work aid for individuals with cognitive impairments. JVS exemplifies an agency that can greatly benefit from this application. JVS is a CARF certified, NISH affiliated, non-profit organization that offers programs and services throughout Metropolitan Detroit. JVS supports individuals, challenged with disabilities, in the workplace and provides on-site, supervision and coaching at community-based employers through their janitorial operations. Job Coaches have expressed interest in finding a way to remotely monitor their employees and provide task prompting.
Current wireless technology standards such as Wi-Fi 802.11x protocols are not used for indoor localization because of their high cost. Technologies such as Bluetooth, RFID, and Infrared provide solutions for indoor wireless networks. However, most of these technologies are susceptible to noise or have range limitations [1]. This project utilizes the IEEE 802.15.4 and ZigBee standard to provide a low-cost and easily implemented solution for developing a wireless network. Smart Buildings that use ZigBee ready nodes for lighting and environmental control are rapidly increasing [2]. Therefore, the emerging building infrastructure can implement a ZigBee network for wayfinding. The ZigBee protocol uses the received signal strength indication (RSSI) and link quality indicator (LQI) to correlate signal strength to location [3].
PROBLEM STATEMENT
The Enabling Technologies Laboratory (ETL) has a long-range goal of developing an indoor wayfinding and localization system that can support the employment of individuals with cognitive impairments. This system has two distinct customers: 1) the service providers (Job Coaches and trainers) and 2) the workers with cognitive impairments. The service providers need to monitor, supervise, and train their clients. The clients need assistance in navigating their environment and focusing and remembering assigned tasks. This project addresses the core requirements for both customer groups using indoor localization and wayfinding.
Prior to this project another ETL student design team conducted a study with Texas Instrument’s CC2431 ZigBee module, however, these modules showed poor results due to problems with wall reflection noise and unreliable RSSI measurements. Similar results were seen from groups when conducting blind node estimation [4]. We then proceeded with the Jennic IEEE 802.15.4 Wireless Microcontroller that displayed promising results.
Therefore, the objective of this project is to design and develop a WSN with a graphical map interface to provide tracking information to Job Coaches.
METHODS
A customer-centered approach has driven our design process with an emphasis on low-cost, portability, and system level robustness. Specifically, the system should account for reference node failures. Our design strategy was to first prototype a single node platform using the Jennic Microcontroller, then perform testing to determine a distance to LQI model. Using this model, the task was to implement a network suitable for an office building, and then develop an algorithm for localization. Finally, a set of testing procedures for measuring system performance was established.
EMBEDDED DESIGN
One of our goals for the WSN implementation was to provide a portable solution, requiring minimal effort to setup. By selecting the Jennic 5139 Wireless Microcontroller, we minimized design size by taking advantage of the integrated RISC microcontroller and IEEE 802.15.4 transceiver. Unlike the TI CC2431, the Jennic system does not have a prototyping platform. All wireless modules were custom designed using Altium Designer: from circuit and schematic design, to printed circuit board (PCB) layout. Additionally, a 3D CAD representation was generated prior to PCB fabrication. Figure 1 shows a photo of the prototyped Jennic device both in a 3D model and actual fabrication. Poor localization results from initial tests suggested that the blind node design include an accelerometer to improve the localization algorithm. This conclusion has been reached by other groups as well [5].
Figure 1: Photo of the prototyped Jennic device both in a 3D model from Altium Designer and actual fabrication.
LQI DETERMINATION
A crucial step toward reference node placement was to determine how far apart each node should be placed from one another. Since our current Jennic model contained a directional antenna, distance calibration was performed using line-of-sight positioning with two nodes, one transmitter and one receiver. Starting from the receiver location, the transmitter traversed a linear path moving away from the receiver node.
The observed LQI measurements were inversely proportional to distance moved. Studies from [5, 6] expressed the log-linear model used to quantify this relationship and how to empirically determine A , the received power and γ the loss parameter. Ten tests were conducted and the average A and γ were -7.41 and 159.23 respectively. The automatic gain control (AGC) on the Jennic activates beyond 3 meters producing unreliable LQI readings. This indicates that reference nodes should be placed 3 meters apart. This constraint is acceptable for office spaces and the newer Smart Buildings that have high densities of ZigBee modules in the infrastructure. Figure 2a shows this curve generated from the line-of-sight tests and Figure 2b shows the LQI measurements beyond 3 meters.
Figure 2a: Log-linear relationship between distance and LQI. Best-fit line determined from raw data set.
Figure 2b: Raw data set showing from line-of-sight test when the Jennic's AGC activates at 3 meters.
NETWORK TOPOLOGY
The star network infrastructure is used in the design because of its network robustness. If one reference node is removed from the network, the others are not affected. Figure 3 illustrates the basic network topology. The localization of a blind node is estimated from the LQI measured from each reference-blind node pair.
Figure 3: Illustrates the star network topology with the blind node, reference node, and coordinator.
From the network topology, the firmware was developed for the Jennic Wireless Microcontroller modules to act as the coordinator node, reference node, and blind node. The blind node broadcasts its acceleration signal to all nodes listening to its address. The reference nodes continually receive data from the blind node until there is a request from the coordinator. The coordinator sends requests to each reference node to retrieve the accelerometer and the link quality data. The software algorithm performs the localization when it obtains the coordinator data.
LOCALIZATION ALGORITHM
For prototype development, the reference nodes are positioned in a zigzag layout across a hallway such that at any given point three reference nodes are in close proximity (3 meters) to the blind node. This placement is consistent with the results from initial module testing and parameter estimation (A,γ) studies. Figure 4.1a shows the node placement.
The localization algorithm has evolved from methods using signal strength measurements, to a more robust triangulation method. The key in localizing the blind node is to select the suitable set of reference nodes forming a triangle. Filtering the LQI from the past data reduces the environmental noise. The blind node’s onboard accelerometer provides information about its movement and trajectory direction. The algorithm executes in real-time on a MATLAB platform.
Figure 4: a) The reference node placement in a zigzag pattern spaced 3 meters apart. b) A zoomed in view of a set of reference nodes.
Figure 4a shows the top view of the hall where the reference nodes are placed in a triangular pattern. Figure 4b shows close view of three reference nodes forming a triangle.
We used the log-linear model to estimate the distance from each of the reference nodes to the blind node. Acceleration data from the blind node determined its movement pattern; when stationary the LQI was unreliable due to the Jennic’s AGC. From the weighted centroid algorithm [7, 8], weights are assigned to each reference node based upon its calculated blind node distance. The circles in Figure 4b display the estimated uncertainty in localization from each node. Larger circles correspond to higher uncertainty and lower LQI measurements, and conversely for the smaller circle. The final step estimates the location from the new weighted points. This method is a variant of the adaptive weighted centroid algorithm [8].
TESTING PROCEDURE
The network was implemented primarily for an office hallway with maximum width distance of 3 meters. The test setup is performed as follows:
- Reference nodes are placed in a zigzag pattern along the hallway.
- The blind node is mounted to the janitorial cart.
- Predefined marker locations are positioned.
- Once the network is initialized, the software begins localizing from a preloaded building map.
- As the cart passes each predefined marker location, the estimated location is recorded.
Figure 5 shows the network layout with X and Y coordinates and node positions on the Engineering Building floor plan.
Figure 5: The estimated location overlaid on the building floor plan of the Engineering Building. The blue oval marks blind node location.
RESULTS
During development, 50 tests were conducted in the Engineering Building hallways. The data presented here are from field-tests conducted at the JVS facility down an office hallway length of 30 meters and width of 1.5 meters. The blind node was mounted on a janitorial cart at a height of 1 meter and 10 reference nodes were positioned. Three trials were performed where the user would traverse the hallway with the cart passing each reference node. To measure position accuracy, locations estimated from the algorithm were compared to predefined points on the map. Table 1 shows the actual and estimated locations from the algorithm.
Table 1: Experimental results from trials performed at JVS. Similar results were seen with 50 trials at the Engineering Building.
The average estimated location was taken from the three trials at the JVS facility during the testing. Similar results were seen from 50 trials performed in the Engineering Building. From the table, the average X and Y coordinate estimations were within 1 meter of the predefined locations. On average, the percent error for the X coordinate was much higher than the Y coordinate; however, the absolute difference was lower for the X coordinate. This is because the user motion was mostly restricted in the X direction. The Y coordinate in most situations will vary greatly because the user will be traversing down the hall in the Y direction; therefore, this parameter determines the accuracy of the localization. As shown in the table, the average estimated percent error for the Y coordinate is 5%, and the difference measurement was 0.4 meter, less than 1 meter. This measured accuracy is comparable to TI’s claim of 3 meter accuracy of their CC2431 ZigBee devices.
DISCUSSION
The wayfinding system has been successfully demonstrated at the JVS facility. The system met our objectives for providing a solution that is low-cost, portable, easy to use, and is robust. From the test results, we have been able to localize a janitorial cart to within 1 meter accuracy. The network can be setup in most office hallways with 3 meter spaced node placement. Total Jennic prototype boards cost about $52/board (15 boards made), this includes the onetime PCB setup charges. The Jennic module alone costs about $23 and an additional $15 for components. The blind node is more expensive because it contains a $20 accelerometer.
An issue with the current design is that using a directional antenna requires the nodes to be placed at the same height for line-of-sight positioning. In addition, the Jennic’s AGC limits the range to 3 meters for reliable data. In the future design, the blind node will use an omnidirectional antenna with higher gain instead of the directional antenna. This modification will alleviate the height-positioning problem and improve the range of the transceiver. Additional plans are to convert the existing MATLAB interface to a web application allowing the Job Coaches access to the interface on their smart phone or computer.
JVS’s response has been very positive toward the system and expressed great interests in the improvements in the software. Our direct representative from JVS, Derek Finely, stated, “This service is a great application for indoor wayfinding by providing our Job Coaches the flexibility in being able to remotely monitor the location of our workers in most facilities”.
REFERENCES
[1] A. L. Liu, H. Hile, H. Kautz, G. Borriello, P. A. Brown, M. Harniss, and K. Johnson, “Indoor wayfinding:: developing a functional interface for individuals with cognitive impairments,” presented at the Proceedings of the 8th international ACM SIGACCESS conference on Computers and accessibility, Portland, Oregon, USA, 2006.
[2] G. Wenqi, W. M. Healy, and Z. Mengchu, “ZigBee-wireless mesh networks for building automation and control,” in Networking, Sensing and Control (ICNSC), 2010 International Conference on, 2010, pp. 731-736.
[3] M. Sugano, T. Kawazoe, Y. Ohta, and M. Murata, “Indoor localization system using rssi measurement of wireless sensor network based on zigbee standard,” From Proceeding (538) Wireless Sensor Networks, vol. 7, pp. 54–69, 2006.
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[7] J. Blumenthal, R. Grossmann, F. Golatowski, and D. Timmermann, “Weighted centroid localization in zigbee-based sensor networks,” Intelligent Signal Processing, pp. 1–6, 2007.
[8] R. Grossmann, J. Blumenthal, F. Golatowski, D. Timmermann, and R. Str, “Localization in Zigbee-based Sensor Networks,” 2007.
CONTACT INFORMATION
Arpit Mittal
arpit.mittal2@gmail.com