iMuDS: An internet of multimodal data acquisition and analysis systems for monitoring urban waterways

Publication Type:
Conference Proceeding
Citation:
Proceedings - 25th International Conference on Systems Engineering, ICSEng 2017, 2017, 2017-January pp. 431 - 437
Issue Date:
2017-11-27
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© 2017 IEEE. Freshwater monitoring is becoming an essential activity due to limited availability of drinking water and an increasing presence of various pollutants. Tons of toxic waste added to water sources everyday contributes to the decrease in the planet's biodiversity and even an extinction of many species of animals and marine life. Many millions of birds perish each year due to waterway pollution. New technologies such as the Internet of Things (IoT), Wireless Sensor Networks and computer vision allow us to monitor fresh water sources in a continuous mode. To minimize the effects of pollution, various monitoring activities can be planned and executed for very large areas and geographical regions. This work presents a system architecture for the IoT-based multimodal data acquisition and analysis system. The idea is to deploy sensor clusters in various locations of a waterway to create a network of sensing and measuring smart devices. Every cluster of such devices can be perceived as a 'thing'. Such a 'thing' or a node has camera sensing modalities for a macro level pollution detection with analog sensors to measure microlevel water parameters. Our solution involves a low power microprocessor devices provisioned to capture raw data, extract features from the raw data and then transmit these data to the Cloud for further analysis and reporting. A 5G mobile network communication is used for data transmission. The Cloud server runs a software framework that supports a sophisticated analysis and trending of various environmental parameters such as surface density of water, salinity, temperature, etc. The proposed software framework has a set of computational algorithms to process features supplied by each node. These algorithms can classify features into various classes like floating objects, water salinity level, etc. An experiment to simulate the 'IoT' data acquisition is conducted to validate the proposed solution. Based on a case study, this solution can be used in a real-life scenario representing as a feasible and viable solution to track pollution in urban waterways.
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