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Autonomous, wireless sensor network-assisted target search and mapping

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The requirements of wireless sensor networks for localization applications are largely dictated by the need to estimate node positions and to establish routes to dedicated gateways for user communication and control. These requirements add significantly to the cost and complexity of such networks. In some applications, such as autonomous exploration or search and rescue, which may benefit greatly from the capabilities of wireless sensor networks, it is necessary to guide an autonomous sensor and actuator platform to a target, for example to acquire a large data payload from a sensor node, or to retrieve the target outright. We consider the scenario of a mobile platform capable of directly interrogating individual, nearby sensor nodes. Assuming that a broadcast message originates from a source node and propagates through the network by flooding, we study applications of autonomous target search and mapping, using observations of the message hop count alone. Complex computational and communication tasks are offloaded from the sensor nodes, leading to significant simplifications of the node hardware and software. This introduces the need to model the hop count observations made by the mobile platform to infer node locations. Using results from first-passage percolation theory and a maximum entropy argument, we formulate a stochastic jump process which approximates the message hop count at distance r from the source. We show that the marginal distribution of this process has a simple analytic form whose parameters can be learned by maximum likelihood estimation. Target search involving an autonomous mobile platform is modeled as a stochastic planning problem, solved approximately through policy rollout. The cost-to-go at the rollout horizon is approximated by an open-loop search plan in which path constraints and assumptions about future information gains are relaxed. It is shown that the performance is improved over typical information-driven approaches. Finally, the hop count observation model is applied to an autonomous mapping problem. The platform is guided under a myopic utility function which quantifies the expected information gain of the inferred map. Utility function parameters are adapted heuristically such that map inference improves, without the cost penalty of true non-myopic planning.
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  Autonomous, Wireless Sensor Network-AssistedTarget Search and Mapping bySteffen Beyme Dipl.-Ing. Electrical Engineering, Humboldt-Universität zu Berlin, 1991 A THESIS SUBMITTED IN PARTIAL FULFILLMENTOF THE REQUIREMENTS FOR THE DEGREE OF Doctor of Philosophy in THE FACULTY OF GRADUATE AND POSTDOCTORALSTUDIES (Electrical and Computer Engineering) The University Of British Columbia(Vancouver)October 2014c  Steffen Beyme, 2014  Abstract The requirements of wireless sensor networks for localization applications arelargely dictated by the need to estimate node positions and to establish routes todedicated gateways for user communication and control. These requirements addsignificantly to the cost and complexity of such networks.In some applications, such as autonomous exploration or search and rescue,which may benefit greatly from the capabilities of wireless sensor networks, itis necessary to guide an autonomous sensor and actuator platform to a target, forexample to acquire a large data payload from a sensor node, or to retrieve the targetoutright.We consider the scenario of a mobile platform capable of directly interrogatingindividual, nearby sensor nodes. Assuming that a broadcast message srcinatesfrom a source node and propagates through the network by flooding, we studyapplications of autonomous target search and mapping, using observations of themessage hop count alone. Complex computational and communication tasks areoffloaded from the sensor nodes, leading to significant simplifications of the nodehardware and software.This introduces the need to model the hop count observations made by the mo-bile platform to infer node locations. Using results from first-passage percolationtheory and a maximum entropy argument, we formulate a stochastic jump processwhich approximates the message hop count at distance  r   from the source. We showthat the marginal distribution of this process has a simple analytic form whose pa-rameters can be learned by maximum likelihood estimation.Targetsearchinvolvinganautonomousmobileplatformismodeledasastochas-tic planning problem, solved approximately through policy rollout. The cost-to-goii  at the rollout horizon is approximated by an open-loop search plan in which pathconstraints and assumptions about future information gains are relaxed. It is shownthat the performance is improved over typical information-driven approaches.Finally, the hop count observation model is applied to an autonomous mappingproblem. The platform is guided under a myopic utility function which quantifiesthe expected information gain of the inferred map. Utility function parameters areadapted heuristically such that map inference improves, without the cost penalty of true non-myopic planning.iii  Preface Chapters 2 to 4 are based on manuscripts that to date have either been published, oraccepted or submitted for publication, in peer-reviewed journals and conferences.All manuscripts were co-authored by the candidate as the first author, with revi-sions and comments by Dr. Cyril Leung. In all these works, the candidate had theprimary responsibility for conducting the research, the design and performance of simulations, results analysis and preparation of the manuscripts, under the supervi-sion of Dr. Cyril Leung. The following list summarizes the publications resultingfrom the candidate’s PhD work: ã  S. Beyme and C. Leung, “Modeling the hop count distribution in wirelesssensor networks”,  Proc. of the 26th IEEE Canadian Conference on Electri-cal and Computer Engineering (CCECE) , pages 1–6, May 2013. ã  S. Beyme and C. Leung, “A stochastic process model of the hop count dis-tribution in wireless sensor networks”,  Elsevier Ad Hoc Networks , vol. 17,pages 60–70, June 2014. ã  S. Beyme and C. Leung, “Rollout algorithm for target search in a wirelesssensor network”,  Proc. of the IEEE 80th Vehicular Technology Conference ,Sept. 2014. Accepted. ã  S. Beyme and C. Leung, “Wireless sensor network-assisted, autonomousmapping with information-theoretic utility”,  6th IEEE International Sym- posium on Wireless Vehicular Communications , Sept. 2014, Accepted.iv
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