IOT and Cloud Based Monitoring System for Coastal Water Quality and Beach Safety

Ryan Jebaraj, Loyola – ICAM College of Engineering and Technology (LICET); Nafeela Banu ,Loyola – ICAM College of Engineering and Technology (LICET); Rufina Fernandez ,Loyola – ICAM College of Engineering and Technology (LICET)

IOT, Cloud Computing

The coastal regions of India is both ecologically and economically rich and is home to 60% of the population. However the coastal waters are highly polluted due to industrial effluents, agriculture runoff and domestic waste and unlike the developed countries there is not much information on the health and status of the coastal waters. Internet of Things (IoT) is a concept that envisions all objects around us as part of internet and Cloud computing is a model for on-demand access to a shared pool of configurable resources (e.g. compute, networks, servers, storage, applications, services, and software) that can be easily provisioned as Infrastructure (IaaS), software and applications (SaaS) . A combination of data buoys installed along the major beaches of India along with cloud technology would make data on tides, rip currents and pollutant load accessible to beach goers. Data buoys with sensors would have to be deployed along the Indian coast to collect data on the oceanographic, physio-chemical and biological parameters of the coastal waters. The data collected by these buoys would then be transmitted to a public or hybrid cloud through satellite transmission protocols. Cloud apps and mobile cloud computing apps can be developed to disseminate information on beach water quality and safety to the public. In this paper we propose a novel IoT and cloud based monitoring system wherein the cloud acts as a front end to access Internet of Things that assimilates data from real time water quality data buoys and provides a mobile client with which the user can access data on the Coastal waters throughout the country, thereby reducing the health hazard and ensuring safety of the beach goers.
    [1] Qasim, S. Z. ; Sen Gupta, R. ; Kureishy, T. W. (1988) Pollution of the seas around India Proceedings of the Indian Academy of Sciences - Animal Sciences, 97 (2). pp. 117-131. [2] Ann Chervenak, Ian Foster, Carl Kesselman, Charles Salisbury, Steven Tuecke, The data grid: Towards an architecture for the distributed management and analysis of large scientific datasets, Journal of Network and Computer Applications, Volume 23, Issue 3, 2000, Pages 187-200, ISSN 1084-8045. [3] Rajkumar Buyya, Chee Shin Yeo, Srikumar Venugopal, James Broberg, Ivona Brandic, Cloud computing and emerging IT platforms: Vision, hype, and reality for delivering computing as the 5th utility, Future Generation Computer Systems, Volume 25, Issue 6, June 2009, Pages 599-616. [4] Saurabh Kumar Garg, Rajkumar Buyya, Howard Jay Siegel, Time and cost trade-off management for scheduling parallel applications on Utility Grids, Future Generation Computer Systems, Volume 26, Issue 8, October 2010, Pages 1344-1355. [5] Dan C. Marinescu, Chapter 4 - Cloud Computing: Applications and Paradigms, In Cloud Computing, Morgan Kaufmann, Boston, 2013, Pages 99-130. [6] Tomasz Jach, Ewa Magiera, Wojciech Froelich, Application of HADOOP to Store and Process Big Data Gathered from an Urban Water Distribution System, Procedia Engineering, Volume 119, 2015, Pages 1375-1380. [7] Jan G.P.W. Clevers, Fundamentals of Satellite Remote Sensing: An Environmental Approach, second edition, Emilio Chuvieco. CRC Press, Boca Raton (2016), International Journal of Applied Earth Observation and Geoinformation, Volume 51, September 2016, Pages 74-75.
Paper ID: GRDCF003019
Published in: Conference : National Conference on Computational Intelligence Systems (NCCIS - 2017)
Page(s): 90 - 94