Abstract

This paper addresses the challenge of water dis- aggregation in intermediate water supply (IWS) systems using water level data. The water level node, in conjunction with four automatic labelling nodes, was deployed to collect data on appliance usage, no activity periods, and tank filling. Data is collected and transmitted to the cloud at two-second intervals via Wi-Fi networks. The collected water level data are used to train various machine learning (ML) and deep learning (DL) models for disaggregation in filling, appliance use such as geyser, flush, washing machine, and periods of no activity. These models are then evaluated to determine the most effective approach for water disaggregation, with performance assessed using accuracy and F1 score metrics. Out of the tested models, the Long Short- Term Memory (LSTM) model emerged as the best-performing algorithm, achieving an accuracy of 94.09% and an F1 score of 0.88.

Abstract

This paper presents an Internet of Things (IoT) enabled system that utilizes machine learning (ML) techniques to estimate the water flow in pipelines based on multi-modal sensor data. Traditional methods of water flow monitoring, mainly digital water flow meters, are prohibitively expensive, making largescale deployments challenging. To address this, an alternative has been proposed that leverages the correlation between water flow rate, motor current consumption, and pipeline pressure. Current consumption data of water motors has been integrated with pipeline pressure data to develop ML-based solutions that estimate the value of water flow without directly measuring it. The proposed approach enables digital flow monitoring at a fraction of the cost of conventional digital flow meters while offering high time granularity and accuracy, making it a scalable and practical solution for smart water management in diverse settings. The system’s robustness has been demonstrated by testing the model against erasures in the data.

Abstract

Sewage Treatment Plants (STPs) are critical for public health and environmental sustainability. However, Traditional water quality monitoring in globally heavily relies on labour-intensive, lab-based methods, which pose significant challenges but in recent years, IoT-based sensors have emerged as a promising alternative. However, these sensors face challenges including sensor fouling and frequent manual maintenance, which compromise long-term accuracy and operational efficiency. This study presents an innovative IoT enabled Self-Cleaning Water Quality Monitoring System (SC-WQMS) to address these challenges through automation and precision. The proposed system integrates advanced features, including self-cleaning mechanisms. Key water quality parameter, such as Total Dissolved Solids (TDS) is analysed in real-time by using IoT-based sensors. The proposed IoT-enabled SC-WQMS demonstrates significantly better accuracy over traditional non-cleaning devices. Lab report data over a two-month period reveals that the SC-WQMS maintains an average accuracy of 95%-98.5% for TDS measurements, compared to 68%-75% accuracy observed in traditional IoT-based non cleaning devices due to issues like algae formation and particle buildup over the same period. This substantial difference highlights the SC-WQMS's effectiveness in ensuring reliable data transmission to a cloud server. By mitigating the challenges faced by traditional methods. The proposed SC-WQMS represents a scalable, efficient, and sustainable solution for modern water treatment facilities, addressing the operational challenges of traditional systems while promoting better resource management and environmental protection.

Abstract

Air pollution, primarily driven by particulate matter (PM), significantly threatens public health. India, with three cities ranking among the world's top ten most polluted and with PM concentrations exceeding WHO guidelines by almost 11 times, urgent measures are needed to address this escalating crisis. AirIoT, a densely deployed IoT-based air quality monitoring network in Hyderabad, India, is an evidence-based approach to bringing awareness and increasing public participation by alleviating data scarcity.

Abstract

This paper uses 3D stochastic geometry to analyse the coverage and meta-distribution (MD) performance of a low Earth orbit (LEO) satellite-based Internet-of-Things (IoT) network. The satellites are considered to be distributed at a fixed altitude around Earth following a Binomial point process. An IoT device broadcasts its sensed information to all the satellites with a line-of-sight within the visibility region and they act as selective decode-and-forward gateways. The ground server coherently combines the information received from multiple satellites. The analytically derived performance measures are verified through Monte Carlo simulations. The results demonstrate the impact of mega-LEO constellations on coverage and reliability, guiding 6G architecture design to improve connectivity and data offloading in smart cities and dense urban environments.

Abstract

A system and method for implementing an experiment remotely and determining an output using a computer-vision model is provided. The system includes an image capturing device, an experiment setup, a microcontroller, a user device, and a relay unit. The microcontroller (i) receives the input of the experiment from the image capturing device,(ii) extracts one or more frames from the input data,(iii) pre-process the one or more frames to obtain a binary image,(iv) obtain a closed curve around the binary image to locate the experiment,(v) determine the coordinates of the experiment to track the experiment in each frame,(vi) determine an output of the experiment from every two consecutive frames of the one or more frames, and (vii) optimize the determined output of the experiment using a linear regression model.

Abstract

Remote labs are a groundbreaking development in the education industry, providing students with access to laboratory education anytime, anywhere. However, most remote labs are costly and difficult to scale, especially in developing countries. With this as a motivation, this paper proposes a new remote labs (RLabs) solution that includes two use case experiments: Vanishing Rod and Focal Length. The hardware experiments are built at a low-cost by retrofitting Internet of Things (IoT) components. They are also made portable by designing miniaturised and modular setups. The software architecture designed as part of the solution seamlessly supports the scalability of the experiments, offering compatibility with a wide range of hardware devices and IoT platforms. Additionally, it can live-stream remote experiments without needing dedicated server space for the stream. The software architecture also includes an automation suite that periodically checks the status of the experiments using computer vision (CV). The software architecture is further assessed for its latency and performance. RLabs is qualitatively evaluated against seven non-functional attributes - affordability, portability, scalability, compatibility, maintainability, usability, and universality. Finally, user feedback was collected from a group of students, and the scores indicate a positive response to the students’ learning and the platform’s usability.

Abstract

Indoor air pollution monitoring has been a region of interest in recent times. Multiple Internet of Things (IoT) enabled devices are available for this purpose. With the growing number of sensors in our daily environment, huge amounts of data are being collected and pushed to the servers through the Internet. This study aims to reveal that seemingly trivial indoor air pollution data containing particulate matter, carbon dioxide, and temperature can reveal complex insights about an individual’s lifestyle. Data was collected over a period of four months in a real-world environment. The study demonstrates the inference of cooking activities by using machine learning and deep learning techniques. The study further demonstrates that different food items and culinary practices have different air pollution signatures, which can be identified and distinguished with great accuracy (>90%). In the practice of inferential analysis, it is not necessary to rely on data characterised by high frequency or granularity. Less detailed data like hourly averages, can be used to make meaningful conclusions that might intrude on an individual’s privacy. With the rapid advancement in machine learning and deep learning, a proactive approach to privacy is needed to ensure that the collected data and its usage do not intentionally or unintentionally breach individual privacy.

Abstract

Particulate matter (PM) is a critical air pollutant with severe health implications, yet existing stationary monitoring networks often fail to capture its complete spatial and temporal variability in urban environments. This paper presents a novel approach to city-wide air quality monitoring using low-cost sensors mounted on mobile platforms. To validate this method, a six-month field study was conducted in Hyderabad, India, deploying IoT-enabled devices on four college buses. These mobile devices capture PM concentrations, temperature, relative humidity, GPS coordinates, and vehicle speed twice daily across different parts of the city. The primary objective was to demonstrate the necessity of mobile air pollution monitoring to identify PM variability across diverse urban areas. The data revealed significant variations in PM concentrations across different parts of the city and seasons, highlighting the impact of local activities on air quality. The study examines seasonal trends, area-specific variations, and temporal patterns of PM concentrations, identifying pollution hotspots within the city. It shows how important it is to provide up-to-date, location-specific air quality information to people with pollution-related health issues.

Abstract

Local activities have a significant impact on the air quality in every region. A study for two consecutive years (2021-2022) has been conducted in the Gachibowli region of Hyderabad, India, which employs spatially distributed low-cost IoT-based air quality monitors across residential and at traffic junctions and main roads measuring particulate matter (PM), temperature, and humidity. The study shows the observations during three seasons, aiming to establish correlations between the PM spikes and specific events that triggered these spikes. Detailed discussions focus on the variations in PM levels linked to traffic patterns over six months and the Diwali festival in 2021 and 2022. PM concentrations increased 2-3 times the normal range during Diwali and decreased post-Diwali. In the case of traffic-related pollution, 1.5 times higher PM levels were observed during morning and evening peak traffic hours, with significant reductions in afternoons across all months with variations in range depending on the season.