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

Climate classification plays a significant role in the development of building codes and standards. It guides the design of buildings’ envelope and systems by considering their location’s climate conditions. Various methods, such as ASHRAE Standard 169, Köppen, Trewartha utilize climate parameters such as temperature, humidity, solar radiation, precipitation, etc., to classify climates. When establishing requirements in building codes and standards, it is crucial to validate the classification based on the building’s thermal loads. This paper introduces a novel methodology for classifying cities based on the number of similar days between them. It calculates similarity using daily mean temperature, relative humidity, and solar radiation by applying threshold values. A matrix of similar days is analyzed through agglomerative hierarchical clustering with different thresholds. A scoring system based on building thermal load, where lower scores signify better classification, is employed to select the best method. The method was tested using U.S. weather data, yielding a lower score of 54.5 compared to ASHRAE Standard 169’s score of 63.09. This suggests that the new approach results in less

Abstract

The performance of high-power laser systems based on incoherent beam combining (IBC) depends on laser beam propagation properties in the atmosphere and their orientation on the target. We have designed an IBC system based on three different beam directing mechanisms, viz., collimated, inclined, and focused-inclined. We have mathematically modeled the laser beam propagation in the atmosphere, including turbulence, wandering, jitter, Strehl ratio, and thermal blooming. The beam propagation efficiency is evaluated for the above combinations to evaluate the IBC system performance in the turbulent atmosphere. We report the simulation results of an IBC system of 44 individual laser sources, each of 1 kW combined incoherently at a target. A comparative study of 3 beam-directing mechanisms is performed, and it is observed that the collimated beam IBC system is most affected by the atmospheric parameters due to the maximum wandering and spreading of the beam at the target compared to inclined and focused-inclined IBC. Whereas a focused-inclined IBC system is found to be the most effective for high-power energy applications, with an average intensity of more than 250 W/cm^2 even at the 10 km target location.

Abstract

Residential District Cooling Systems (DCS) are crucial for maintaining thermal comfort in urban areas, making it imperative to understand occupant cooling behavior as it significantly influences DCS operation. While several studies have investigated cooling behavior within small user groups through on-site measurements or surveys, these often fall short in representing the broader population. In this study we considered 387 homes in Hyderabad, which has a DCS connection for chilled water supply. Operational data spanning three months across different seasons were meticulously selected to assess the residents’ behavior for the cooling demand. In order to better facilitate the operation of the residential DCS, user load profile and system performance were analyzed using real world data for different seasons in the year. Our findings revealed notable disparities in DCS electrical consumption, with summer usage being 1.9 times higher than the monsoon period and 2.7 times higher than the winter period. Furthermore, a strong positive correlation emerged between outdoor temperature and thermal energy usage by the residents. On average, the daily thermal consumption per residence during winter, summer and monsoon is 3.3 kWhth, 35.1 kWhth and 10.4 kWhth respectively. Interestingly, the probability of a residence using AC during the day for the winter, summer and monsoon seasons are 0.07, 0.41 and 0.18 respectively.

Abstract

This study used IoT-based smart plugs and energy meters to collect device-level electricity data from homes for understanding the seasonal load patterns. The electrical energy consumption of five air conditioners (ACs) and five geysers was monitored using smart plugs during summer and winter to understand the power demand for cooling and hot water consumption. The study offered insights into the seasonal power usage patterns of these appliances. Further, a detailed analysis of daily electrical energy consumption in two homes was conducted to understand disaggregated electricity consumption. Five common household appliances (refrigerator, washing machine, television, water purifier, and microwave oven) were monitored using smart plugs in these homes. Along with smart plugs, smart energy meters were installed in these homes to monitor the total household electricity consumption. Their daily electricity consumption in summer was twice that of winter.

Abstract

This study aims to understand residential electricity consumption patterns using appliance load profiles of significant appliances in five residential units. To create load profiles, empirical electricity data from households and appliances was collected every second between May 1, 2023, and June 30, 2023, across five households in Hyderabad, India using device-level monitoring. A total of 48 appliances representing 12 distinct categories were monitored, and load profiles were created for four major appliances, namely air conditioner, geyser, refrigerator and washing machine for peak summer. It was found that these four appliances contribute to approximately 65% of the daily energy consumption. Air conditioners (AC) consumed most of the total daily energy and were primarily operated during solar off-peak hours, while geysers and washing machines were used close to solar peak hours. Hence, shifting geyser and washing machine usage can redirect 1.21 kWh per home in daily energy to solar peak hours. Moreover, improving the efficiency of AC by 10% and using heat pumps in place of geysers could lead to a 0.79 kWh reduction in daily energy consumption. Additionally, including thermal storage can entirely shift the AC energy usage to the daytime.

Abstract

Load diversity plays a pivotal role in accurately estimating cooling loads across various regions, which is essential for assessing the feasibility and impact of a District Cooling System (DCS). This study focuses on a typical residential DCS, serving a five-towered apartment complex with 387 residences, providing chilled water for space cooling. Our research delves into the analyze of residential air conditioning (AC) usage within the DCS during peak summer conditions in Hyderabad, India, in May 2022. By examining cooling data of residences and electricity data of DCS alongside system details, this study delineates the load profile and average daily AC consumption for May amounting to 34.3 kWhth (9.7 TRh) per residence. During peak usage, the thermal loading on the chiller reaches 75.8% of its total capacity, and while an average day, it varies between 21% to 61% of the chiller capacity.

Abstract

Modern System on Chip (SoC) seeks multiple LDOs to provide regulated supply to its different peripherals. However, distributed LDOs across the chip increase power consumption and at the same time, each driver requires a separate transient detection block to reduce the undershoot/overshoot, increasing the area. The work presents a viable solution to tackle such issues by proposing a Multi-loop LDO that uses distributed Flipped Voltage Follower (FVF) drivers. The topology presents one central unit common to every distributed FVF driver across the chip that acts as a global loop, whereas each FVF loop serves as a fast local loop. The proposed power grid structure reduces the overshoot/undershoot voltage without adding additional transient detection schemes. The work depicts design and simulation results to validate the proposed idea with four distributed drivers in 180 nm TSMC node. Simulation results show that the proposed technique helps reduce the droop voltage by more than 40% compared to conventional structures without increasing the design complexity.

Abstract

Personal Health Records (PHRs) allow patients to have full control over their health data. However, storage and sharing of PHRs still remains a difficult but necessary task, especially when health data is one of the major targets of cyber attacks worldwide. Searchable Encryption (SE) is a feasible solution for this problem and can be augmented by Blockchain to address some of its issues, such as verifiability. Therefore, SE using blockchain is a promising technologies to tackle the challenge of PHR storage and sharing. In this survey, we have explored the research works that use SE and blockchain technology for the same. The work starts with an introduction of cloud, searchable encryption and blockchain. Subsequently, we present a literature survey of the corresponding technologies. We then describe SE in detail and how it fits with blockchain. This is followed by description of noteworthy existing solutions for secure storage and sharing of PHRs. Even though there have been a number of surveys related to SE, none of them have surveyed the use of blockchain with SE or use of SE and blockchain in PHR sharing. The work concludes with a comparative study of these existing solutions and future scope in this direction. KEYWORDS: Searchable encryption, Blockchain, Cloud computing, Personal Health Records (PHRs), Storage, Security

Abstract

Secure storage and sharing of Personal Health Records (PHRs) in Internet of Medical Things (IoMT) is one of the significant challenges in the healthcare ecosystem. Due to the high value of personal health information, PHRs are one of the favourite targets of cyber attackers worldwide. Over the years, many solutions have been proposed; however, most solutions are inefficient for practical applications. For instance, several existing schemes rely on the bilinear pairings, which incur high computational costs. To mitigate these issues, we propose a novel PHR-sharing scheme that is dynamic, efficient, and practical. Specifically, we combine searchable symmetric encryption, blockchain technology and a decentralized storage system, known as Inter-Planetary File System (IPFS) to guarantee confidentiality of PHRs, verifiability of search results, and forward security. Moreover, we provide formal security proofs for the proposed scheme. Finally, we have conducted extensive test-bed experiments and the results demonstrate that the proposed scheme can be used in practical scenarios related to IoMT environment.