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IoT-Driven Scheduling of Residential HVAC and Virtual Bus Lanes for Energy Savings.

Record Type:	Electronic resources : Monograph/item
Title/Author:	IoT-Driven Scheduling of Residential HVAC and Virtual Bus Lanes for Energy Savings./
Author:	Petrov, Daniel.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
Description:	172 p.
Notes:	Source: Dissertations Abstracts International, Volume: 83-06, Section: B.
Contained By:	Dissertations Abstracts International83-06B.
Subject:	Scheduling. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28845141
ISBN:	9798759924357

IoT-Driven Scheduling of Residential HVAC and Virtual Bus Lanes for Energy Savings.
Petrov, Daniel.

IoT-Driven Scheduling of Residential HVAC and Virtual Bus Lanes for Energy Savings. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 172 p.

Source: Dissertations Abstracts International, Volume: 83-06, Section: B.

Thesis (Ph.D.)--University of Pittsburgh, 2021.

This item must not be sold to any third party vendors.

The availability of commodity Internet connection and the decrease in price and form factor of consumer electronics led to the emergence of Internet of Things (IoT), with which our world becomes more connected and instrumented. IoT is a great vehicle for enabling solutions to problems in the connected environment that surrounds us (i.e., smart homes and smart cities). An example is the use of sensors and IoT to address issues related to energy efficiency, the broad area of this dissertation.Our hypothesis is that data processing and decision making need to be carried out at the network edge, specifically as close to the physical system as possible, where data are generated and used, to produce results in real-time and make sure the data is not exposed to privacy and security risks. To this end, we propose to leverage scheduling principles and statistical techniques in the context of two applications, namely aiming to reduce duty cycle of HVAC (Heating, Ventilation, and Air Conditioning) systems in smart homes and to mitigate road congestion in smart cities. The common goal in these two aims is the reduction of energy consumption and the reduction of atmospheric pollution.To achieve our first aim we propose intelligent scheduling of the duty cycles of HVAC systems in residential buildings. Our solution combines linear and polynomial regression enabled estimator that drives the calculations about the amounts of time thermally conditioned air should be supplied to each room. The output from our estimator is fed into our scheduler based on integer linear programming to decrease the duty cycle of the home's HVAC systems. We evaluate the effectiveness and efficiency of our HVAC solution with a dataset collected from several residential houses in the state of Pennsylvania. To achieve the second aim, we propose the concept of virtual bus lanes, that combines on-demand creation of bus lanes with dynamic control of traffic lights. Moreover, we propose to guide drivers through less congested routes using light boards that provide to drivers information in real-time for such routes. Our methods are anchored to priority scheduling, incremental windowed-based aggregation, and shortest path first Dijkstra's algorithm. We evaluate the effectiveness and efficiency of our virtual bus lanes solution with a real dataset from the city of Beijing, China, and a synthetic traffic scenario from the city of Luxembourg.

ISBN: 9798759924357Subjects--Topical Terms:

750729
Scheduling.

IoT-Driven Scheduling of Residential HVAC and Virtual Bus Lanes for Energy Savings.
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500 $a Source: Dissertations Abstracts International, Volume: 83-06, Section: B.
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502 $a Thesis (Ph.D.)--University of Pittsburgh, 2021.
506 $a This item must not be sold to any third party vendors.
520 $a The availability of commodity Internet connection and the decrease in price and form factor of consumer electronics led to the emergence of Internet of Things (IoT), with which our world becomes more connected and instrumented. IoT is a great vehicle for enabling solutions to problems in the connected environment that surrounds us (i.e., smart homes and smart cities). An example is the use of sensors and IoT to address issues related to energy efficiency, the broad area of this dissertation.Our hypothesis is that data processing and decision making need to be carried out at the network edge, specifically as close to the physical system as possible, where data are generated and used, to produce results in real-time and make sure the data is not exposed to privacy and security risks. To this end, we propose to leverage scheduling principles and statistical techniques in the context of two applications, namely aiming to reduce duty cycle of HVAC (Heating, Ventilation, and Air Conditioning) systems in smart homes and to mitigate road congestion in smart cities. The common goal in these two aims is the reduction of energy consumption and the reduction of atmospheric pollution.To achieve our first aim we propose intelligent scheduling of the duty cycles of HVAC systems in residential buildings. Our solution combines linear and polynomial regression enabled estimator that drives the calculations about the amounts of time thermally conditioned air should be supplied to each room. The output from our estimator is fed into our scheduler based on integer linear programming to decrease the duty cycle of the home's HVAC systems. We evaluate the effectiveness and efficiency of our HVAC solution with a dataset collected from several residential houses in the state of Pennsylvania. To achieve the second aim, we propose the concept of virtual bus lanes, that combines on-demand creation of bus lanes with dynamic control of traffic lights. Moreover, we propose to guide drivers through less congested routes using light boards that provide to drivers information in real-time for such routes. Our methods are anchored to priority scheduling, incremental windowed-based aggregation, and shortest path first Dijkstra's algorithm. We evaluate the effectiveness and efficiency of our virtual bus lanes solution with a real dataset from the city of Beijing, China, and a synthetic traffic scenario from the city of Luxembourg.
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