Automatic triangulation positioning system for wide area coverage from a fixed sensors network

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonIn a wide area that many Transmitters (TRs) operate, systems of Fixed Sensors (FS) might be used in order to detect them and find TRs position. The detection and the accurate location of a new TR entering in the area frequently can be missed if the system fails to triangulate accurately the relative readings and analyze the changes in the received data. Additionally, there are cases that a Triangulation Station Network (TSN) can detect the heading as well as the transmitter’s position wrong. This thesis presents the design of a Sensors Network (FSN) system which is able to interact with a user, and exploit the relative data of the Sensors (SRs) in real time. The system performs localization with triangulation and the SRs are detect only TRs bearing data (range free). System design and algorithms are also explained. Efficient algorithms were elaborated and the outcomes of their implementation were calculated. The system design targets to reduce system errors and increase the accuracy and the speed of detection. Synchronously and through interaction with the user and changes of relative settings and parameters will be able to offer the user accurate results on localization of TRs in the area minimizing false readings and False Triangulations (FTRNs). The system also enables the user to apply optimization techniques in order to increase the system detection rate and performance and keep the surveillance in the Field of Interest (FoI) on a high level. The optimization methodology applied for the system proves that the FSN system is able to operate with a high performance even when saturation phenomena appear. The unique outcome of the research conducted, is that this thesis paves the way to enhance the localization via Triangulation for a network of Fixed Sensors with known position. The value of this thesis is that the FSN system performs bearing only detection (Range free) with a certain accuracy and the Area of Interest (AOI) is covered efficiently

Iot-enabled smart cities: evolution and outlook

For the last decade the Smart City concept has been under development, fostered by the growing urbanization of the world’s population and the need to handle the challenges that such a scenario raises. During this time many Smart City projects have been executed–some as proof-of-concept, but a growing number resulting in permanent, production-level deployments, improving the operation of the city and the quality of life of its citizens. Thus, Smart Cities are still a highly relevant paradigm which needs further development before it reaches its full potential and provides robust and resilient solutions. In this paper, the focus is set on the Internet of Things (IoT) as an enabling technology for the Smart City. In this sense, the paper reviews the current landscape of IoT-enabled Smart Cities, surveying relevant experiences and city initiatives that have embedded IoT within their city services and how they have generated an impact. The paper discusses the key technologies that have been developed and how they are contributing to the realization of the Smart City. Moreover, it presents some challenges that remain open ahead of us and which are the initiatives and technologies that are under development to tackle them.This research was partially funded by Spain State Research Agency (AEI) by means of the project FIERCE: Future Internet Enabled Resilient CitiEs (RTI2018-093475-A-I00). Prof. Song was supported by Smart City R&D project of the Korea Agency for Infrastructure Technology Advancement (KAIA) grant funded by the Ministry of Land, Infrastructure and Transport (MOLIT), Ministry of Science and ICT (MSIT) (Grant 18NSPS-B149386-01)

Approximation Algorithm for Unrooted Prize-Collecting Forest with Multiple Components and Its Application on Prize-Collecting Sweep Coverage

In this paper, we introduce a polynomial-time 2-approximation algorithm for the Unrooted Prize-Collecting Forest with $K$ Components (URPCF$_K$) problem. URPCF$_K$ aims to find a forest with exactly $K$ connected components while minimizing both the forest's weight and the penalties incurred by unspanned vertices. Unlike the rooted version RPCF$_K$, where a 2-approximation algorithm exists, solving the unrooted version by guessing roots leads to exponential time complexity for non-constant $K$. To address this challenge, we propose a rootless growing and rootless pruning algorithm. We also apply this algorithm to improve the approximation ratio for the Prize-Collecting Min-Sensor Sweep Cover problem (PCMinSSC) from 8 to 5. Keywords: approximation algorithm, prize-collecting Steiner forest, sweep cover

Mobile sensor networks for environmental monitoring

Vulnerability to natural disasters and the human pressure on natural resources have increased the need for environmental monitoring. Proper decisions, based on real-time information gathered from the environment, are critical to protecting human lives and natural resources. To this end, mobile sensor networks, such as wireless sensor networks, are promising sensing systems for flexible and autonomous gathering of such information. Mobile sensor networks consist of geographically deployed sensors very close to a phenomenon of interest. The sensors are autonomous, self-configured, small, lightweight and low powered, and they become mobile when they are attached to mobile objects such as robots, people or bikes. Research on mobile sensor networks has focused primarily on using sensor mobility to reduce the main sensor network limitations in terms of network topology, connectivity and energy conservation. However, how sensor mobility could improve environmental monitoring still remains largely unexplored. Addressing this requires the consideration of two main mobility aspects: sampling and mobility constraints. Sampling is about where mobile sensors should be moved, while mobility constraints are about how such movements should be handled, considering the context in which the monitoring is carried out. This thesis explores approaches for sensor mobility within a wireless sensor network for use in environmental monitoring. To achieve this goal, four sub-objectives were defined: Explore the use of metadata to describe the dynamic status of sensor networks. Develop a mobility constraint model to infer mobile sensor behaviour. Develop a method to adapt spatial sampling using mobile, constrained sensors. Extend the developed adaptive sampling method to monitoring highly dynamic environmental phenomena. Chapter 2 explores the use of metadata to describe the dynamic status of sensor networks. A context model was proposed to describe the general situation in which a sensor network is monitoring. The model consists of four types of contexts: sensor, network, sensing and organisation, where each of the contexts describes the sensor network from a different perspective. Metadata, which are descriptors of observed data, sensor configurations and functionalities, are used as parameters to describe what is happening in the different contexts. The results reveal that metadata are suitable for describing sensor network status within different contexts and reporting the status back to other components, systems or users. Chapter 3 develops a model which describes mobility constraints for inferring mobile sensor behaviour. The proposed mobility constraint model consists of three components: first, the context typology proposed in Chapter 2 to describe mobility constraints within the different contexts; second, a context graph, modelled as a Bayesian network, to encode dependencies of mobility constraints within the same or different contexts, as well as among mobility constraints and sensor behaviour; and third, contextual rules to encode how dependent mobility constraints are expected to constrain sensor behaviour. Metadata values for the monitored phenomenon and sensor properties are used to feed the context graph. They are propagated through the graph structure, and the contextual rules are used to infer the most suitable behaviour. The model was used to simulate the behaviour of a mobile sensor network to obtain a suitable spatial coverage in low and high fire risk scenarios. It was shown that the mobility constraint model successfully inferred behaviour, such as sleeping sensors, moving sensors and deploying more sensors to enhance spatial coverage. Chapter 4 develops a spatial sampling strategy for use with mobile, constrained sensors according to the expected value of information (EVoI) and mobility constraints. EVoI allows decisions to be made about the location to observe. It minimises the expected costs of wrong predictions about a phenomenon using a spatially aggregated EVoI criterion. Mobility constraints allow decisions to be made about which sensor to move. A cost-distance criterion is used to minimise unwanted effects of sensor mobility on the sensor network itself, such as energy depletion. The method was assessed by comparing it with a random selection of sample locations combined with sensor selection based on a minimum Euclidian distance criterion. The results demonstrate that EVoI enables selection of the most informative locations, while mobility constraints provide the needed context for sensor selection. Chapter 5 extends the method developed in Chapter 4 for the case of highly dynamic phenomena. It develops a method for deciding when and where to sample a dynamic phenomenon using mobile sensors. The optimisation criterion is to maximise the EVoI from a new sensor deployment at each time step. The method was demonstrated in a scenario in which a simulated fire in a chemical factory released polluted smoke into the open air. The plume varied in space and time because of variations in atmospheric conditions and could be only partially predicted by a deterministic dispersion model. In-situ observations acquired by mobile sensors were considered to improve predictions. A comparison with random sensor movements and the previous sensor deployment without performing sensor movements shows that the optimised sensor mobility successfully reduced risk caused by poor model predictions. Chapter 6 synthesises the main findings and presents my reflections on the implications of such findings. Mobile sensors for environmental monitoring are relevant to improving monitoring by selecting sampling locations that deliver the information that most improves the quality of decisions for protecting human lives and natural resources. Mobility constraints are relevant to managing sensor mobility within sampling strategies. The traditional consideration of mobility constraints within the field of computer sciences mainly leads to sensor self-protection rather than to the protection of human beings and natural resources. By contrast, when used for environmental monitoring, mobile sensors should above all improve monitoring performance, even thought this might produce negative effects on coverage, connectivity or energy consumption. Thus, mobility constraints are useful for reducing such negative effects without constraining the sampling strategy. Although sensor networks are now a mature technology, they are not yet widely used by surveyors and environmental scientists. The operational use of sensor networks in geo-information and environmental sciences therefore needs to be further stimulated. Although this thesis focuses on wireless sensor network, other types of informal sensor networks could be also relevant for environmental monitoring, such as smart phones, volunteer citizens and sensor web. Finally, the following recommendations are given for further research: extend the sampling strategy for dynamic phenomena to take account of mobility constraints; develop sampling strategies that take a decentralised approach; focus on mobility constraints related to connectivity and data transmission; elicit expert knowledge to reveal preferences for sensor mobility under mobility constraints within different types of environmental applications; and validate the proposed strategies in operational implementations. </p

Event-Driven Technologies for Reactive Motion Planning: Neuromorphic Stereo Vision and Robot Path Planning and Their Application on Parallel Hardware

Die Robotik wird immer mehr zu einem Schlüsselfaktor des technischen Aufschwungs. Trotz beeindruckender Fortschritte in den letzten Jahrzehnten, übertreffen Gehirne von Säugetieren in den Bereichen Sehen und Bewegungsplanung noch immer selbst die leistungsfähigsten Maschinen. Industrieroboter sind sehr schnell und präzise, aber ihre Planungsalgorithmen sind in hochdynamischen Umgebungen, wie sie für die Mensch-Roboter-Kollaboration (MRK) erforderlich sind, nicht leistungsfähig genug. Ohne schnelle und adaptive Bewegungsplanung kann sichere MRK nicht garantiert werden. Neuromorphe Technologien, einschließlich visueller Sensoren und Hardware-Chips, arbeiten asynchron und verarbeiten so raum-zeitliche Informationen sehr effizient. Insbesondere ereignisbasierte visuelle Sensoren sind konventionellen, synchronen Kameras bei vielen Anwendungen bereits überlegen. Daher haben ereignisbasierte Methoden ein großes Potenzial, schnellere und energieeffizientere Algorithmen zur Bewegungssteuerung in der MRK zu ermöglichen. In dieser Arbeit wird ein Ansatz zur flexiblen reaktiven Bewegungssteuerung eines Roboterarms vorgestellt. Dabei wird die Exterozeption durch ereignisbasiertes Stereosehen erreicht und die Pfadplanung ist in einer neuronalen Repräsentation des Konfigurationsraums implementiert. Die Multiview-3D-Rekonstruktion wird durch eine qualitative Analyse in Simulation evaluiert und auf ein Stereo-System ereignisbasierter Kameras übertragen. Zur Evaluierung der reaktiven kollisionsfreien Online-Planung wird ein Demonstrator mit einem industriellen Roboter genutzt. Dieser wird auch für eine vergleichende Studie zu sample-basierten Planern verwendet. Ergänzt wird dies durch einen Benchmark von parallelen Hardwarelösungen wozu als Testszenario Bahnplanung in der Robotik gewählt wurde. Die Ergebnisse zeigen, dass die vorgeschlagenen neuronalen Lösungen einen effektiven Weg zur Realisierung einer Robotersteuerung für dynamische Szenarien darstellen. Diese Arbeit schafft eine Grundlage für neuronale Lösungen bei adaptiven Fertigungsprozesse, auch in Zusammenarbeit mit dem Menschen, ohne Einbußen bei Geschwindigkeit und Sicherheit. Damit ebnet sie den Weg für die Integration von dem Gehirn nachempfundener Hardware und Algorithmen in die Industrierobotik und MRK

Cooperative Navigation for Low-bandwidth Mobile Acoustic Networks.

This thesis reports on the design and validation of estimation and planning algorithms for underwater vehicle cooperative localization. While attitude and depth are easily instrumented with bounded-error, autonomous underwater vehicles (AUVs) have no internal sensor that directly observes XY position. The global positioning system (GPS) and other radio-based navigation techniques are not available because of the strong attenuation of electromagnetic signals in seawater. The navigation algorithms presented herein fuse local body-frame rate and attitude measurements with range observations between vehicles within a decentralized architecture. The acoustic communication channel is both unreliable and low bandwidth, precluding many state-of-the-art terrestrial cooperative navigation algorithms. We exploit the underlying structure of a post-process centralized estimator in order to derive two real-time decentralized estimation frameworks. First, the origin state method enables a client vehicle to exactly reproduce the corresponding centralized estimate within a server-to-client vehicle network. Second, a graph-based navigation framework produces an approximate reconstruction of the centralized estimate onboard each vehicle. Finally, we present a method to plan a locally optimal server path to localize a client vehicle along a desired nominal trajectory. The planning algorithm introduces a probabilistic channel model into prior Gaussian belief space planning frameworks. In summary, cooperative localization reduces XY position error growth within underwater vehicle networks. Moreover, these methods remove the reliance on static beacon networks, which do not scale to large vehicle networks and limit the range of operations. Each proposed localization algorithm was validated in full-scale AUV field trials. The planning framework was evaluated through numerical simulation.PhDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/113428/1/jmwalls_1.pd

Integration of RFID and Industrial WSNs to Create A Smart Industrial Environment

A smart environment is a physical space that is seamlessly embedded with sensors, actuators, displays, and computing devices, connected through communication networks for data collection, to enable various pervasive applications. Radio frequency identification (RFID) and Wireless Sensor Networks (WSNs) can be used to create such smart environments, performing sensing, data acquisition, and communication functions, and thus connecting physical devices together to form a smart environment. This thesis first examines the features and requirements a smart industrial environment. It then focuses on the realization of such an environment by integrating RFID and industrial WSNs. ISA100.11a protocol is considered in particular for WSNs, while High Frequency RFID is considered for this thesis. This thesis describes designs and implementation of the hardware and software architecture necessary for proper integration of RFID and WSN systems. The hardware architecture focuses on communication interface and AI/AO interface circuit design; while the driver of the interface is implemented through embedded software. Through Web-based Human Machine Interface (HMI), the industrial users can monitor the process parameters, as well as send any necessary alarm information. In addition, a standard Mongo database is designed, allowing access to historical and current data to gain a more in-depth understanding of the environment being created. The information can therefore be uploaded to an IoT Cloud platform for easy access and storage. Four scenarios for smart industrial environments are mimicked and tested in a laboratory to demonstrate the proposed integrated system. The experimental results have showed that the communication from RFID reader to WSN node and the real-time wireless transmission of the integrated system meet design requirements. In addition, compared to a traditional wired PLC system where measurement error of the integrated system is less than 1%. The experimental results are thus satisfactory, and the design specifications have been achieved