A multi city developer tutorial using open source implementation of oneM2M, developed by a French research lab, LAAS – CNRS was organised by the India EU ICT Standards Collaboration Project. …
We are happy to announce a one year, full-time Research Internship Program in IoT and Autonomous Systems.
Thank you all for visiting us on IISc Open Day 2017! Here are some impressions of our exhibits at RBCCPS: 3D printing and milling, which we use for rapid prototyping, examples …
This project proposes to develop an open, integrated and extensible IoT technology stack for Smart Management of campus utilities. The IoT stack brings together hybrid sensing, diverse networking, Big Data analytics and science-driven utility management, and will be validated through affordable and intelligent water resource management for a sustainable campus environment.
The project aims to develop a proof of concept molecular test which can differential between the viral infections of Dengue and Chikungunya. The research program will also focus on integrating a sample preparation module with the photonic sensors to produce a cartridge-based test that will operate using a single finger stick capillary blood sample and be as easy to operate as a conventional blood sugar meter.
The Indian Government has launched many national-scale ICT initiatives in the last year. Irrespective of their final mandate, these programs are ultimately driving towards a coherent plan of infrastructure development, service delivery and information transparency for the advancement and empowerment of Indian citizens. ICT will be critical for the successful execution and long-term sustainability of these projects. Among other ICT technologies, the Internet of Things (IoT) can provide a seamless inter-connect between the physical entities with the cyber world with M2M communication and closed-loop control with/without human intervention. Hence, leapfrogging to adopt IoT as the ICT “technology of choice” will be the key to success.
Globally, 1.8 billion people use drinking water sources contaminated with faeces, and this is a leading cause of diseases such as diarrhoea, cholera, typhoid, and dysentery. Fecal coliform bacteria indicate the presence of sewage contamination of a waterway and the possible presence of other pathogenic organisms. High fecal coliform counts in water indicates that it contains other possible pathogenic strains which can bring about diseases like Typhoid fever, hepatitis, gastroenteritis, dysentery and ear infections. As per WHO guidelines, no fecal coliform should be present in drinking water. Thus, early and rapid detection of fecal coliform bacteria in drinking water with high sensitivity and accuracy, by using an affordable and robust biosensor will help government bodies to take preventive and precautionary measures to avoid health hazards in a community.
The project aimed to provide personalised feedback to about 25,000 electricity consumers in the town of Aluva, Kerala. The goal of these personalised feedback inputs was to effect positive changes to consumer behaviour. The data from these 25,000 was compared with the consumption of a control group of another 25,000 from the same town.
Kumbh Mela is the largest religious festival in the world, and involves the pilgrimage of some 100 million Hindus to a sacred river. The next festival will take place in Ujjain in 2016. Unfortunately, many of the previous versions of this event have been marred by accidents and deaths. Human crowds are complex adaptive systems, making predicting their behaviour challenging. The Kumbh Mela Experiment combines geoinformatics and remote sensing with computational science. The project will develop sophisticated methods and algorithms to aid planners and event managers in managing such extremely large crowds.
Solar PV plants need to be maintained if they are to continue to work at their rated output and efficiency. But there are various failure modes observed in a PV installation. It is not practical to equip every module with sensors to detect failure modes. Therefore, the project aims to use sparsely placed, low cost sensors and a robust analytics engine to (1) Identify and localise failure modes and reduce plant downtime and repair costs, (2) Optimise plant cleaning schedules (thus reducing usage of water required to clean panels), and (3) Improve plant output and efficiency.