Abstract

Monitoring water flow helps to identify leaks and wastage, leading to better management of water resources and conservation of this precious resource. To address this challenge, there is a need for an efficient and sustainable water management system. This paper presents an Internet of Things (IoT) based solution that involves retrofitting existing analog water meters using readily available off-the-shelf electronic components. Real-time data collection and analysis are performed through edge computation, which locally processes water meter images captured by the camera and extracts water meter readings. These readings are transmitted to the cloud for storage and further analysis. Various strategies have been implemented to optimize supply-current usage, preserving charge-discharge cycles of solar-powered batteries even in adverse environmental conditions. To streamline the firmware update process for multiple connected devices, a broadcasting technique is employed, offering the benefits of reduced manual labor and time savings. To assess the reliability and performance of developed solution, field deployment is conducted over several months, enabling the characterization of water usage patterns across different locations. Integrating energy harvesting capabilities into system reduces maintenance costs and promotes eco-friendly energy practices. Overall, this solution offers an effective and comprehensive approach towards achieving efficient and sustainable water management.

Abstract

This paper presents a star-of-star topology for internet-of-things (IoT) networks using mega low-Earth-orbit constellations. The proposed topology enables IoT users to broadcast their sensed data to multiple satellites simultaneously over a shared channel, which is then relayed to the ground station (GS) using amplify-and-forward relaying. The GS coherently combines the signals from multiple satellites using maximal ratio combining. To analyze the performance of the proposed topology in the presence of interference, a comprehensive outage probabil- ity (OP) analysis is performed, assuming imperfect channel state information at the GS. The paper employs stochastic geometry to model the random locations of satellites, making the analysis gen- eral and independent of any specific constellation. Furthermore, the paper examines successive interference cancellation (SIC) and capture model (CM)-based decoding schemes at the GS to mitigate interference. The average OP for the CM-based scheme and the OP of the best user for the SIC scheme are derived analytically. The paper also presents simplified expressions for the OP under a high signal-to-noise ratio (SNR) assumption, which are utilized to optimize the system parameters for achieving a tar- get OP. The simulation results are consistent with the analytical expressions and provide insights into the impact of various system parameters, such as mask angle, altitude, number of satellites, and decoding order. The findings of this study demonstrate that the proposed topology can effectively leverage the benefits of multiple satellites to achieve the desired OP and enable burst transmissions without coordination among IoT users, making it an attractive choice for satellite-based IoT networks. Index Terms—Amplify-and-forward, LEO satellites, outage probability, satellite-based IoT, stochastic geometry

Abstract

Robust spectrum sensing is crucial for facilitating opportunistic spectrum utilization for secondary users (SU) in the absense of primary users (PU). However, propagation environment factors such as multi-path fading, shadowing, and lack of line of sight (LoS) often adversely affect detection performance. To deal with these issues, this paper focuses on utilizing reconfig- urable intelligent surfaces (RIS) to improve spectrum sensing in the scenario wherein both the multi-path fading and noise are correlated. In particular, to leverage the spatially correlated fading, we propose to use maximum eigenvalue detection (MED) for spectrum sensing. We first derive exact distributions of test statistics, i.e., the largest eigenvalue of the sample covariance matrix, observed under the null and signal present hypothesis. Next, utilizing these results, we present the exact closed-form expressions for the false alarm and detection probabilities. In addition, we also optimally configure the phase shift matrix of RIS such that the mean of the test statistics is maximized, thus improving the detection performance. Our numerical analysis demonstrates that the MED’s receiving operating characteristic (ROC) curve improves with increased RIS elements, SNR, and the utilization of statistically optimal configured RIS. Index Terms—Reconfigurable Intelligent Surfaces, Spectrum Sensing, Maximum Eigenvalue Detector, Correlated Fading, e

Abstract

This paper aims to address the limitations of current water level monitoring systems, which are bulky, expensive, and difficult to maintain, resulting in limited deployment. To overcome these challenges, the study utilises Internet of Things (IoT)-enabled low-cost sensor nodes for water level monitoring. In this study, an ultrasonic sensor-based water level node is developed to send data to the cloud through GPRS (2G). Five such nodes were deployed to monitor water levels in overhead tanks and sumps on the campus of IIIT Hyderabad, India. The collected water level data were analysed for behavioural patterns and detecting faulty float switches. In addition, a deep learning algorithm was employed, which can predict future water needs. Thus, the proposed IoT-based smart water level meter offers a more accessible and cost-effective approach to water level monitoring.

Abstract

This paper presents a novel approach to estimate water usage without directly interfacing with the water line. Instead, it utilizes a device that indirectly gauges water consumption by monitoring the position of the tap handle after it is rotated or moved. The setup process for this device is straightforward, as it doesn’t require any modifications to the existing water infrastructure. The system, known as ”Sensible Flow,” employs a sensor node consisting of an inertial Measurement Unit (IMU) to track and analyze the movement and position of the tap handle. To ensure accurate readings, the device is initially calibrated using a flow rate sensor or through manual measurement with a known-volume beaker. Once the flow rate is inferred based on the tap handle position, the data is transmitted via Bluetooth Low Energy (BLE) to a base gateway unit. This base unit provides real-time updates on water consumption and can also be configured to send the collected data to the cloud via WiFi, enabling further analysis and monitoring. Extensive testing has been conducted to evaluate the device’s accuracy and performance. The Sensible Flow system provides a cost-effective and non-intrusive solution for estimating water usage, with potential applications in water conservation and management.

Abstract

Air pollution due to industrial activity, vehicles, and construction has shown immediate and long-term effects on human health. While respiratory condition manifests directly, long-term conditions like heart diseases, cancer, lung damage, and the impact on fetal health have been established. This study is aimed to explore the effects of air pollution on the physiological health of humans using a smart wearable watch. A total of 8 healthy security guards posted at the institute gate close to a very busy traffic junction for 12 hours a day were recruited. The physiological indicators such as heart rate (HR), body temperature (BT), and SPO2 levels were recorded by the wearable device for changes during the day/night for two seasons (summer and winter). Air pollution monitoring sensors were also deployed at the same site to record PM2.5, PM10, relative humidity (RH), and environmental temperature (ENT). A survey of 43 questions under three different sections: demographic, medical condition, and quality of life (QoL) were also filled by each participant. Descriptive statistics and inferential statistical analysis (Wilcoxon, Correlation) have been presented based on the collected data. The research shows an association between short-term fluctuations in physiological parameters due to air pollution. Still, the findings need to be weighted with the accuracy and consistency of the wearable devices.

Abstract

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Abstract

The high rate of groundwater usage has resulted in a rapid decline in groundwater levels, which has necessitated its monitoring. This paper focuses on estimating the groundwater level inside borewells remotely and in real-time using the Internet of Things (IoT). For this, a solution is proposed which is based on string tension and does not require any electrical components to be lowered into the borewell. The solution provides a completely automated, real-time, reliable, and IoT-enabled alternative to existing methods for borewell water monitoring. The proposed approach is compared with an ultrasonic sensor-based approach in a controlled environment as well as in a tank. Additionally, four nodes were deployed in a small educational campus in the Indian city of Hyderabad to ascertain its usability and reliability in practical situations. The observations from the data collected over a month show that the proposed low-cost solution is reliable and has a good performance in the field. Index Terms—Borewell, Real-time, IoT, String tension, Water-level.

Abstract

This study presents an Internet of Things (IoT)- based system that utilises machine learning (ML) techniques to estimate water flow through pipes based on pressure. The system incorporates an ESP-32 microcontroller, a Danfoss MBS 3000 pressure sensor, and a flow meter deployed at three locations to collect data for three months. To model the relationship between pressure and flow rate, ML algorithms such as linear regression (LR), support vector regression (SVR), and convolutional neural network (CNN) were trained, analysed, and compared. By establishing a model to estimate the flow rate based on pressure, the need for a flow meter in the setup can be eliminated. The system’s low-cost, easy-to-implement, and non-invasive nature makes it suitable for widespread adoption in residential areas offering a promising solution for optimising water distribution and reducing water wastage.

Abstract

This paper focuses on the direction of arrival (DoA) estimation for two coherent sources using a uniform linear array (ULA). It is demonstrated that the DoA estimation error increases as the angle of the incoming signal moves away from the center in the range (-90°, 90°) for existing schemes. In addition, this paper also focuses on improving the DoA estimation performance in low signal to noise ratio (SNR). Therefore a cascaded neural network (CaNN) is proposed to improve the DoA estimation consisting of two stages of neural networks (NNs). The first NN is the enhanced SNR (ESNR) classifier, which is used to improve performance for different SNRs. The second NN is the angle estimator, which improves the performance for different angle ranges. For overcoming the issue of coherent signals, a spatially smoothened auto covariance matrix is fed to the SNR classifier and angle estimator blocks. A performance comparison with an existing scheme such as spatial smoothing- multiple signal classification (SS-MUSIC) and ESNR CaNN shows that the proposed CaNN for different angles and SNR ranges performs better than the existing schemes.