Students and faculty of Signal Processing and Communication Research Center (SPCRC) and Smart City Research Center (SCRC) presented the following papers at IEEE 7th World Forum on Internet of Things, 2021, New Orleans, Louisiana, USA.
- Security Analysis of Large Scale IoT Network for Pollution Monitoring in Urban India – G V Ihita, K S Viswanadh, Y Sudhansh, Sachin Chaudhari and S Gaur – Signal Processing and Communication Research Center (SPCRC)
Research work as explained by Dr. Sachin Chaudhari and his team:
The surge in the development and adoption of Internet of Things (IoT)-enabled smart city technologies has brought with it a diverse set of critical security challenges. In this paper, protocol and network security threats pertaining to a large-scale IoT-enabled pollution monitoring sensor network, AirIoT, deployed in and around an educational campus in the Indian city of Hyderabad, have been explored. Using the STRIDE methodology, the paper assesses various threat vectors for the deployment. As solutions, the paper proposes an approach for end-to-end encryption, protocol and dashboard security, and a proof of concept deauthentication detector. This baseline threat analysis and risk assessment can provide a foundation for securing Wi-Fi and mobile network-based large-scale IoT deployments.
- Maximum Frequency based Adaptive Sensing for Particulate Matter Nodes in IoT Network – C Rajashekar Reddy, Siddharth De, Sachin Chaudhari – Signal Processing and Communication Research Center (SPCRC)
Research work as explained by Dr. Sachin Chaudhari and his team:
In most IoT-based monitoring applications, the data can vary at a slow rate but the variability pattern may not always be the same. For example, the patterns of particulate matter (PM), one of the most dominant air pollutants, often change seasonally over a year. Therefore, having a fixed predefined sensing rate is both hard to decide and energy inefficient. This paper proposes an adaptive, non-parametric method to change the sensing rate using the maximum frequency estimate based on recent historical data. The proposed algorithm has been tested on the data collected over one year from an IoT network consisting of multiple PM sensor nodes. A performance comparison of the proposed scheme with the existing approach shows the effectiveness and performance improvement in terms of Reduction Factor (RF) and Mean Absolute Error (MAE).
- Design of an IoT System for Machine Learning Calibrated TDS Measurement in Smart Campus – Sai Usha Nagasri Goparaju, S V S L N Surya Suhas Vaddhiparthy, Pradeep C, Anuradha Vattem, Deepak Gangadharan – Smart City Research Center (SCRC)
Research work as explained byDr. Deepak Gangadharan and his team:
This paper focuses on designing a low-cost and robust IoT-based TDS measurement system for the smart campus. The objective of this low-cost design problem is to find a solution that guarantees precise and uninterrupted output data. The dynamic reading of data, storage capacity, and calibration errors of sensors are the major challenges for IoT-based TDS measurement systems. These challenges are combated in the proposed design using a non-invasive mechanism for data collection, wireless connectivity to the data server, and machine learning calibration of sensor nodes. The TDS data of various water stations located inside the campus is used for the experimental study to develop a regression model for temperature compensation and calibration. The value of TDS sensor voltage variation against temperature is analyzed. The evaluation of the model was performed based on the R2 and the root mean square error.
The 7th IEEE World Forum on Internet of Things is the premier event of the Multi-Society IEEE IoT Initiative [https://iot.ieee.org/] and is held jointly with the IoT Community [https://iotcommunity.net/]. The Theme for WFIoT2021 is “The Impact of Artificial Intelligence on IoT”. IEEE WF-IoT 2021 facilitates discussion from the industry, research, and academic community on technological innovations in the field of IoT applications and solutions.
More details at: https://wfiot2021.iot.ieee.org/