Street Light Monitoring Using Smartphones
Rajiv Kumar
Rajiv Kumar, Associate Professor, CSE- AIT, Chandigarh University, Gharuan, Mohali, Punjab, India.
Manuscript received on September 17, 2019. | Revised Manuscript received on October 05, 2019. | Manuscript published on October 30, 2019. | PP: 3193-3199 | Volume-9 Issue-1, October 2019 | Retrieval Number: F7916088619/2019©BEIESP | DOI: 10.35940/ijeat.F7961.109119
Open Access | Ethics and Policies | Cite | Mendeley
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Rapid urbanization poses challenge to the maintenance agencies in monitoring and maintaining city-wide services. Traditional ways, to monitor the services, involve huge manpower and time. Technical revolutions opens up smarter ways of monitoring the civic services with the use of miniature sensors embedded in smart devices and potentially covers wider area. Over the past decade, the smart city concept has emerged as a potential research area where many devices work in a network and has been applied as city-wide smart monitoring technique. In this paper, author has highlighted the use of mobile crowd sensing though smart phones to devise civic infrastructural monitoring application specifically for monitoring street lights conditions and generating heat maps accordingly. A client-server based mobile crowd sourcing framework has been proposed where the client represents smart phones equipped with devised phone application to crowd sense data and the open-source server is used for data analysis and distribution of results. The framework works in sequential manner with three key modules i) data harvesting, ii) analyze and iii) visual reporting module. Smartphones are used to harvest city-wide contextual data and transfer it to the server. Effective measures to protect privacy of the user have been applied during data harvesting. Server analyzes the harvested data and retrieves useful metrics as computed luminous index which are communicated back to clients (phones) as heat maps visualized on Google® maps. The proposed mobile crowd sourcing framework helps in quick data sensing and spotting the poor street light conditions. The resulted maps potentially disseminate the information to the city residents and the administration to respond accordingly. Whether there is need to install more street lamp posts or to repair the malfunctioning lamp post. A better lighting condition in the streets enhances the visibility and thus the safety of the residents, as the dark areas are prone to accidents or promotes crime.
Keywords: Community participation, Data visualization, Google Maps.