Flood and Traffic Wireless Monitoring System for Smart Cities

Handle URI:
http://hdl.handle.net/10754/621154
Title:
Flood and Traffic Wireless Monitoring System for Smart Cities
Authors:
Moussa, Mustafa ( 0000-0001-9355-1343 )
Abstract:
The convergence of computation, communication and sensing has led to the emergence of Wireless Sensor Networks (WSNs), which allow distributed monitoring of physical phenomena over extended areas. In this thesis, we focus on a dual flood and traffic flow WSN applicable to urban environments. This fixed sensing system is based on the combination of ultrasonic range-finding with remote temperature sensing, and can sense both phenomena with a high degree of accuracy. This enables the monitoring of urban areas to lessen the impact of catastrophic flood events, by monitoring flood parameters and traffic flow to enable public evacuation and early warning, allocate the resources efficiently or control the traffic to make cities more productive and smarter. We present an implementation of the device, and illustrate its performance in water level estimation and rain detection using a novel combination of L1 regularized reconstruction and machine learning algorithms on a 6-month dataset involving four different sensors. Our results show that water level can be estimated with an uncertainty of 1 cm using a combination of thermal sensing and ultrasonic distance measurements. The demonstration of the performance included the detection of an actual flash flood event using two sensors located in Umm Al Qura University (Mecca). Finally, we show that Lagrangian (mobile) sensors can be used to inexpensively increase the performance of the system with respect to traffic sensing. These sensors are based on Inertial Measurement Units (IMUs), which have never been investigated in the context of traffic ow monitoring before. We investigate the divergence of the speed estimation process, the lack of the calibration parameters of the system, and the problem of reconstructing vehicle trajectories evolving in a given transportation network. To address these problems, we propose an automatic calibration algorithm applicable to IMU-equipped ground vehicles, and an L1 regularized least squares formulation for vehicle speed estimation. Results show that this system can be used to generate accurate traffic monitoring data, and significantly outperforms GPS sensors (traditionally used as traffic flow sensors) in terms of cost, accuracy and reliability.
Advisors:
Claudel, Christian G. ( 0000-0003-0702-6548 )
Committee Member:
Alouini, Mohamed-Slim ( 0000-0003-4827-1793 ) ; Shamma, Jeff S ( 0000-0001-5638-9551 ) ; Zhang, Xiangliang ( 0000-0002-3574-5665 ) ; Younes, Mohammed; Prasad, Ranga Venkatsha
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Program:
Electrical Engineering
Issue Date:
Oct-2016
Type:
Dissertation
Appears in Collections:
Dissertations

Full metadata record

DC FieldValue Language
dc.contributor.advisorClaudel, Christian G.en
dc.contributor.authorMoussa, Mustafaen
dc.date.accessioned2016-10-23T09:00:53Z-
dc.date.available2016-10-23T09:00:53Z-
dc.date.issued2016-10-
dc.identifier.urihttp://hdl.handle.net/10754/621154-
dc.description.abstractThe convergence of computation, communication and sensing has led to the emergence of Wireless Sensor Networks (WSNs), which allow distributed monitoring of physical phenomena over extended areas. In this thesis, we focus on a dual flood and traffic flow WSN applicable to urban environments. This fixed sensing system is based on the combination of ultrasonic range-finding with remote temperature sensing, and can sense both phenomena with a high degree of accuracy. This enables the monitoring of urban areas to lessen the impact of catastrophic flood events, by monitoring flood parameters and traffic flow to enable public evacuation and early warning, allocate the resources efficiently or control the traffic to make cities more productive and smarter. We present an implementation of the device, and illustrate its performance in water level estimation and rain detection using a novel combination of L1 regularized reconstruction and machine learning algorithms on a 6-month dataset involving four different sensors. Our results show that water level can be estimated with an uncertainty of 1 cm using a combination of thermal sensing and ultrasonic distance measurements. The demonstration of the performance included the detection of an actual flash flood event using two sensors located in Umm Al Qura University (Mecca). Finally, we show that Lagrangian (mobile) sensors can be used to inexpensively increase the performance of the system with respect to traffic sensing. These sensors are based on Inertial Measurement Units (IMUs), which have never been investigated in the context of traffic ow monitoring before. We investigate the divergence of the speed estimation process, the lack of the calibration parameters of the system, and the problem of reconstructing vehicle trajectories evolving in a given transportation network. To address these problems, we propose an automatic calibration algorithm applicable to IMU-equipped ground vehicles, and an L1 regularized least squares formulation for vehicle speed estimation. Results show that this system can be used to generate accurate traffic monitoring data, and significantly outperforms GPS sensors (traditionally used as traffic flow sensors) in terms of cost, accuracy and reliability.en
dc.language.isoenen
dc.subjectflood detectionen
dc.subjectWireless Sensor Networksen
dc.subjectmobile sensorsen
dc.subjectOptimizationen
dc.subjectMachine Learningen
dc.subjectAlgorithmsen
dc.titleFlood and Traffic Wireless Monitoring System for Smart Citiesen
dc.typeDissertationen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
thesis.degree.grantorKing Abdullah University of Science and Technologyen_GB
dc.contributor.committeememberAlouini, Mohamed-Slimen
dc.contributor.committeememberShamma, Jeff Sen
dc.contributor.committeememberZhang, Xiangliangen
dc.contributor.committeememberYounes, Mohammeden
dc.contributor.committeememberPrasad, Ranga Venkatshaen
thesis.degree.disciplineElectrical Engineeringen
thesis.degree.nameDoctor of Philosophyen
dc.person.id113073en
All Items in KAUST are protected by copyright, with all rights reserved, unless otherwise indicated.