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FiCloud-2021

Dr. Sachin Chaudhari and his students presented the following papers virtually at IEEE 8th International Conference on Future Internet of Things and Cloud (FiCloud), Rome from 23 – 25 August:

  • Making Analog Water Meter Smart using ML and IoT-based Low-Cost Retrofitting – Ayush Kumar Lall (ECD-3rd year), Ansh Khandelwal, Rishikesh Bose, Nilesh Bawankar, Ayush Kumar Dwivedi, Nitin Nilesh and Dr. Sachin Chaudhari. Research work as explained by the authors: 

 This paper introduces an internet-of-things (IoT) based economic retrofitting setup for digitising the analog water meters to make them smart. The setup contains a Raspberry-Pi microcontroller and a Pi-camera mounted on top of the analog water meter to take its images. The captured images are then pre-processed to estimate readings using a machine learning (ML) model. The employed ML algorithm is trained on a rich dataset that includes digits from the images of water meters captured by the hardware setup for ten days. The readings are posted on a cloud server in real-time using Raspberry-Pi. High temporal resolution plots of flow rate and volume are generated to derive inferences. The collected data can be used for deriving water consumption patterns and fault detection for efficient water management.

 

  • Hierarchical Clustering based Spatial Sampling of Particulate Matter Nodes in IoT Network – C Rajashekar Reddy, Dr. Sachin Chaudhari. Research work as explained by the authors: 

The number and locations of nodes required in an IoT Network for optimally understanding an environmental variable in a geographical space is not always straightforward.  This paper proposes a framework based on Hierarchical Agglomerative Clustering for deciding an optimal number of nodes and which nodes in an IoT network of sensor nodes. The approach proposed is an end to end framework to obtain the required number of nodes and their location based on an error threshold. The framework’s performance has been compared with the brute force approach, which entirely relies on comparing all the possible combinations. The paper finally helps to understand the trade-off between the sensor nodes’ spatial density and the error in the spatial reconstruction for an optimal spatial understanding of PM values.

  • Comparative Evaluation of New Low-Cost Particulate Matter Sensors – Ishan Patwardhan, Spanddhana Sara and  Dr. Sachin Chaudhari. Research work as explained by the authors: 

In recent times, a few new low-cost sensors have been introduced to the global market for monitoring particulate matter (PM). In this paper, the performance of three such low-cost PM sensors, namely SDS011, Prana Air, and SPS30, for measuring PM 2.5 and PM 10 levels is evaluated against a standard reference Aeroqual Series-500. The test setup was exposed to PM concentrations ranging from 30 μg/cm 3 to 600 μg/cm 3 . The results were based on 1 min, 15 min, 30 min, and 1 hr average readings. The experiments were carried out in indoor as well as outdoor environments. The comparative evaluation was performed before and after calibration. The performance of these sensors is evaluated in terms of coefficient of determination (R 2 ), coefficient of variation (C v ) and root mean square error (RMSE). Evaluation results show that these low-cost sensors have good performance after calibration with a reference sensor.

The theme of this conference was to promote the state-of-the-art in scientific and practical research of the IoT and cloud computing. It provides a forum for bringing together researchers and practitioners from academia, industry, and the public sector in an effort to present their research work and share research and development ideas in the area of IoT and cloud computing.

Conference link :- http://www.ficloud.org/2021/