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The Los Angeles County Department of Parks and Recreation oversees over 70,000 acres and manages 182 parks across the County. The Department uses over 600 vehicles to cover this vast area. Currently, the Department uses a paper-based system to manage these vehicles. However, the paper-based system cannot effectively schedule a regular maintenance. This can result in safety hazards for drivers as well as poor management of the County resources. In addition, detecting unauthorized use of vehicles is very difficult. Current proprietary industry solutions are too costly for the Department; yet the system in use by the Department is unable to serve the County’s needs.
In this paper we propose FMWare, a low cost, fully automated IoT-based fleet management solution. The Edge portion of FMWare is composed of a Raspberry Pi 3 B+ , a vehicle on-board diagnostics (OBDs) adapter, a GPS module, an RFID reader, and a custom power supply unit (PSU). To use a vehicle, an employee must scan their I.D. badge to the RFID reader. This ensures that the driver is identified for each trip. Unauthorized use of County vehicles is detected as well. When a vehicle is moving, FMWare collects the vehicle’s diagnostics and GPS data. The collected data is saved in the on-board memory of the Raspberry Pi. Upon returning to a base station, the collected data from the vehicle is uploaded to the County Cloud server through secure wireless gateways available at the County parking lots.
We use Apache NiFi and MiNiF, open-source data transmission control software, to upload the collected data. Given the volume of data, the overhead of a traditional RESTFul Web Service would overwhelm the server. Experiments demonstrate the feasibility of the FMWare data collection method and the overall design of the Edge data collection platform.
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Due to space limitation in an instrument panel, we opted to install the Raspberry Pi inside the passenger side dashboard. For a vehicle manufactured after 1996, an OBD-II DLC is usually located under the instrument panel on the driver side.
Apache NiFi Architecture. (2018, May 3). Retrieved from https://nifi.apache.org/docs/nifi-docs
County of Los Angeles Annual Report 2009–2010. Public Affairs, Chief Executive Office (p. 23).
Dhall, R., & Solanki, V. (2017). An IoT based predictive connected car maintenance approach. International Journal of Interactive Multimedia and Artificial Intelligence, 4(3), 16–22. CrossRef
Fleet Genius: Fleet Management Software. (2018, May 3). Retrieved May 3, 2018, from urlhttps://www.fleet-genius.com
Goodwin, H. B. (1910). The haversine in nautical astronomy. Naval Institute Proceedings, 36(3), 735746
Istrefi, D., & Cico, B. (2013). Fleet Management Solution Fuel consumption and collision prevention system modules. IRACST-International Journal of Computer Science and Information Technology and Security, 3(3), 2249–9555.
Mastrack: GPS Tracking The Way You Need It. (2018, May 3). Retrieved from https://www.mastrack.com
Penna, M., Shivashankar, Arjun, B., Goutham, K. R., Madhaw, L. N., & Sanjay, K. G. (2017). Smart fleet monitoring system using internet of things (IoT). In 2nd IEEE International Conference On Recent Trends in Electronics Information and Communication Technology (RTEICT). Piscataway, NJ: IEEE.
RASPBERRY PI. (2018, May 3). Retrieved from https://www.raspberrypi.org
Saghaei, H. (2016). Design and implement of a fleet management system using novel GPS/GLONASS tracker and web-based software. In 1st International Conference on New Research Achievements in Electrical and Computer Engineering.
Sodeyama, K., Takada, M., Hane, S., Furuno, Y., & Watanabe, H. (2015). Cloud-based fleet management system proof of concept. Featured Articles, Hitachi Review, 64(1), 29–34
State of California Air Resource Board, Workshop Report. Technical Status and Revisions to Malfunction and Diagnostic System Requirements for 2010 and Subsequent Model Year Heavy-Duty Engines (HD OBD). March 2012, CA. (2018, May 3). Retrieved from https://www.epa.gov/state-and-local-transportation/vehicle-emissions-board-diagnostics-obd
US Fleet Tracking Co. (2018, May 3). Retrieved from https://www.usfleettracking.com
- FMWare: IoT-Based Fleet Management System
Mohammed Al Rawi