Abstract

Civic Crowdfunding (CC) uses the ``power of the crowd" to garner contributions towards public projects. As these projects are non-excludable, agents may prefer to ``free-ride," resulting in the project not being funded. Researchers introduce refunds for single project CC to incentivize agents to contribute, guaranteeing the project's funding. These funding guarantees are applicable only when agents have an unlimited budget. This paper focuses on a combinatorial setting, where multiple projects are available for CC and agents have a limited budget. We study specific conditions where funding can be guaranteed. Naturally, funding the optimal social welfare subset of projects is desirable when every available project cannot be funded due to budget restrictions. We prove the impossibility of achieving optimal welfare at equilibrium for any monotone refund scheme. Further, given the contributions of other agents, we prove that it is NP-Hard for an agent to determine its optimal strategy. That is, while profitable deviations may exist for agents instead of funding the optimal welfare subset, it is computationally hard for an agent to find its optimal deviation. Consequently, we study different heuristics agents can use to contribute to the projects in practice. We demonstrate the heuristics' performance as the average-case trade-off between the welfare obtained and an agent's utility through simulations.

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.

Abstract

The complexity and fuidity of emotions in the epic of Mahābhārata present before us an interesting case for delving into the phenomenology of emotions. In the rationalist tradition of Kant, emotions are considered as an impediment to moral discernment. The rationalist account of emotions considers it as an animal instinct which needs to be controlled through the exercise of Reason. The paper problematizes the rationalist interpretation of emotions mainly on two counts. First, it ignores the evaluative content of the emotions and considers it as a non-cognitive element. Second, it also overlooks the productive role of various emotions like guilt, shame, remorse in moral deliberation. The paper critically analyzes the episode of Yudhiṣṭhira’s grief in Śānti Parva and argues that the grief of Yudhiṣṭhira cannot be explained as personal loss. Rather, Yudhiṣṭhira’s grief should be understood as a case of moral confict where a moral agent fnds it difcult to justify his moral choices. Yudhiṣṭhira’s analysis of the futility of war and the condemnation of violence should be understood as the evaluative perspective ofered by his emotions. The phenomenological analysis of Yudhiṣṭhira’s grief allows us to understand the signifcance of emotions in constituting the moral perspective on any conficting situation. Hence, emotions cannot be relegated to the domain of irrationality rather they become the site where the truth unveils itself.

Abstract

Fairness in resource allocation is a important problem that has many real life consequences.Althoughmany algorithms that try to achieve envy free allocation,proportionality or min max share wereproposed that tries to encapsulate fairness this does not suce because it was inherently assumedthat agents are not intelligent and there is uniformity in treatment.This is vastly dierent fromreal life where there are many scenarios where agents would actively try to sabotage or reduce theallocation given to their adversaries. Therefore all agents must not be treated the same way.Asseen in economics cartels are where certain players collaborate and try to maximize their interestby undermining competition.This could lead to dangerous consequences and unfair means as seenwith real life examples of apple or google undermining competition by monopoly