Estimating the spatiotemporal evolution characteristics of diffusive hazards using wireless sensor networks

There is a fast growing interest in exploiting Wireless Sensor Networks (WSNs) for tracking the boundaries and predicting the evolution properties of diffusive hazardous phenomena (e.g. wildfires, oil slicks etc.) often modeled as “continuous objects”. We present a novel distributed algorithm for estimating and tracking the local evolution characteristics of continuous objects. The hazard’s front line is approximated as a set of line segments, and the spatiotemporal evolution of each segment is modeled by a small number of parameters (orientation, direction and speed of motion). As the hazard approaches, these parameters are re-estimated using adhoc clusters (triplets) of collaborating sensor nodes. Parameters updating is based on algebraic closed-form expressions resulting from the analytical solution of a Bayesian estimation problem. Therefore, it can be implemented by microprocessors of the WSN nodes, while respecting their limited processing capabilities and strict energy constraints. Extensive computer simulations demonstrate the ability of the proposed distributed algorithm to estimate accurately the evolution characteristics of complex hazard fronts under different conditions by using reasonably dense WSNs. The proposed in-network processing scheme does not require sensor node clocks synchronization and is shown to be robust to sensor node failures and communication link failures, which are expected in harsh environments

Simulation-driven emulation of collaborative algorithms to assess their requirements for a large-scale WSN implementation

Assessing how the performance of a decentralized wireless sensor network (WSN) algorithm's implementation scales, in terms of communication and energy costs, as the network size increases is an essential requirement before its field deployment. Simulations are commonly used for this purpose, especially for large-scale environmental monitoring applications. However, it is difficult to evaluate energy consumption, processing and memory requirements before the algorithm is really ported to a real WSN platform. We propose a method for emulating the operation of collaborative algorithms in large-scale WSNs by re-using a small number of available real sensor nodes. We demonstrate the potential of the proposed simulation-driven WSN emulation approach by using it to estimate how communication and energy costs scale with the network’s size when implementing a collaborative algorithm we developed in for tracking the spatiotemporal evolution of a progressing environmental hazard

Collaborative sensor network algorithm for predicting the spatiotemporal evolution of hazardous phenomena

We present a novel decentralized Wireless Sensor Network (WSN) algorithm which can estimate both the speed and direction of an evolving diffusive hazardous phenomenon (e.g. a wildfire, oil spill, etc.). In the proposed scheme we approximate a progressing hazard’s front as a set of line segments. The spatiotemporal evolution of each line segment is modeled by a modified 2D Gaussian function. As the phenomenon evolves, the parameters of this model are updated based on the analytical solution of a Kullback – Leibler (KL) divergence minimization problem. This leads to an efficient WSN distributed parameters estimation algorithm that can be implemented by dynamically formed clusters (triplets) of collaborating sensor nodes. Computer simulations show that our approach is able to track the evolving phenomenon with reasonable accuracy even if a percentage of sensors fails due to the hazard and/or the phenomenon has a time varying speed

Distributed Signal Processing and Data Fusion Methods for Large Scale Wireless Sensor Network Applications

Σε αυτή τη Διδακτορική Διατριβή μελετάμε το πρόβλημα της παρακολούθησης και πρόβλεψης της εξέλιξης συνεχών αντικειμένων (π.χ. καταστροφικά περιβαλλοντικά φαινόμενα που διαχέονται) με τη χρήση Ασυρμάτων Δικτύων Αισθητήρων (ΑΔΑ) ευρείας κλίμακας. Προτείνουμε μια ευέλικτη αλλά και πρακτική προσέγγιση με δύο κύρια συστατικά: α) Ασύγχρονο συνεργατικό αλγόριθμο ΑΔΑ που εκτιμά, χρησιμοποιώντας δυναμικά σχηματιζόμενες ομάδες από τρεις συνεργαζόμενους κόμβους, τα τοπικά χαρακτηριστικά της εξέλιξης (διεύθυνση, φορά και ταχύτητα) του μετώπου, καθώς και β) Αλγόριθμο που ανακατασκευάζει το συνολικό μέτωπο του συνεχούς αντικειμένου συνδυάζοντας την πληροφορία των τοπικών εκτιμήσεων. Επιπλέον, ο αλγόριθμος ανακατασκευής, εκμεταλλευόμενος την δυνατότητα εκτίμησης της αβεβαιότητα ως προς τα τοπικά χαρακτηριστικά εξέλιξης, μπορεί να προβλέπει και την πιθανότητα το κάθε σημείο της περιοχής να έχει καλυφθεί από το συνεχές αντικείμενο σε κάθε χρονική στιγμή. Μέσω πλήθους προσομοιώσεων επικυρώσαμε την ικανότητα του συνεργατικού αλγορίθμου να εκτιμά με ακρίβεια τα τοπικά χαρακτηριστικά εξέλιξης πολύπλοκων συνεχών αντικειμένων, καθώς και την ευρωστία του σε αστοχίες των αισθητηρίων κόμβων κατά την επικοινωνία τους αλλά και λόγω της πιθανής ολοσχερούς καταστροφής τους. Τέλος, παρουσιάζουμε τη δυνατότητα του αλγορίθμου ανακατασκευής να παρακολουθεί με ακρίβεια την εξέλιξη μετώπων συνεχών αντικειμένων με πολύπλοκα σχήματα, χρησιμοποιώντας σχετικά μικρό αριθμό τοπικών εκτιμήσεων στις οποίες μπορεί να έχει υπεισέλθει και σημαντικό σφάλμα.In this Dissertation we study the problem of tracking the boundary of a continuous object (e.g. a hazardous diffusive phenomenon) and predicting its local and global spatio-temporal evolution characteristics using large-scale Wireless Sensor Networks (WSNs). We introduce a practical WSN-based approach consisting of two main components: a) An asynchronous collaborative in-network processing algorithm that estimates, using dynamically formed node triplets (clusters), local front model evolution parameters (orientation, direction and speed) of the expanding continuous object, and b) an algorithm that reconstruct the overall hazard's boundary by combining the produced local front estimates as they are becoming available to a fusion center. Based on the estimated uncertainties of local front model parameters, the reconstruction can provide for each point of the considered area the probability to be reached by the hazard’s front. Extensive computer simulations demonstrate that the proposed algorithm can estimate accurately the evolution characteristics of complex diffusive continuous objects, while it remains robust to sensor node and communication link failures. Finally, we show that it can track with accuracy the evolution of continuous objects with complex shapes, using a relatively small number of potentially distorted local front estimates

Μέθοδοι κατανεμημένης επεξεργασίας σήματος και σύντηξης δεδομένων για εφαρμογές ασυρμάτων δικτύων αισθητήρων ευρείας κλίμακας

Σε αυτή τη Διδακτορική Διατριβή μελετάμε το πρόβλημα της παρακολούθησης και πρόβλεψης της εξέλιξης συνεχών αντικειμένων (π.χ. καταστροφικά περιβαλλοντικά φαινόμενα που διαχέονται) με τη χρήση Ασυρμάτων Δικτύων Αισθητήρων (ΑΔΑ) ευρείας κλίμακας. Προτείνουμε μια ευέλικτη αλλά και πρακτική προσέγγιση με δύο κύρια συστατικά: α) Ασύγχρονο συνεργατικό αλγόριθμο ΑΔΑ που εκτιμά, χρησιμοποιώντας δυναμικά σχηματιζόμενες ομάδες από τρεις συνεργαζόμενους κόμβους, τα τοπικά χαρακτηριστικά της εξέλιξης (διεύθυνση, φορά και ταχύτητα) του μετώπου, καθώς και β) Αλγόριθμο που ανακατασκευάζει το συνολικό μέτωπο του συνεχούς αντικειμένου συνδυάζοντας την πληροφορία των τοπικών εκτιμήσεων. Επιπλέον, ο αλγόριθμος ανακατασκευής, εκμεταλλευόμενος την δυνατότητα εκτίμησης της αβεβαιότητα ως προς τα τοπικά χαρακτηριστικά εξέλιξης, μπορεί να προβλέπει και την πιθανότητα το κάθε σημείο της περιοχής να έχει καλυφθεί από το συνεχές αντικείμενο σε κάθε χρονική στιγμή. Μέσω πλήθους προσομοιώσεων επικυρώσαμε την ικανότητα του συνεργατικού αλγορίθμου να εκτιμά με ακρίβεια τα τοπικά χαρακτηριστικά εξέλιξης πολύπλοκων συνεχών αντικειμένων, καθώς και την ευρωστία του σε αστοχίες των αισθητηρίων κόμβων κατά την επικοινωνία τους αλλά και λόγω της πιθανής ολοσχερούς καταστροφής τους. Τέλος, παρουσιάζουμε τη δυνατότητα του αλγορίθμου ανακατασκευής να παρακολουθεί με ακρίβεια την εξέλιξη μετώπων συνεχών αντικειμένων με πολύπλοκα σχήματα, χρησιμοποιώντας σχετικά μικρό αριθμό τοπικών εκτιμήσεων στις οποίες μπορεί να έχει υπεισέλθει και σημαντικό σφάλμα. In this Dissertation we study the problem of tracking the boundary of a continuous object (e.g. a hazardous diffusive phenomenon) and predicting its local and global spatio-temporal evolution characteristics using large-scale Wireless Sensor Networks (WSNs). We introduce a practical WSN-based approach consisting of two main components: a) An asynchronous collaborative in-network processing algorithm that estimates, using dynamically formed node triplets (clusters), local front model evolution parameters (orientation, direction and speed) of the expanding continuous object, and b) an algorithm that reconstruct the overall hazard's boundary by combining the produced local front estimates as they are becoming available to a fusion center. Based on the estimated uncertainties of local front model parameters, the reconstruction can provide for each point of the considered area the probability to be reached by the hazard’s front. Extensive computer simulations demonstrate that the proposed algorithm can estimate accurately the evolution characteristics of complex diffusive continuous objects, while it remains robust to sensor node and communication link failures. Finally, we show that it can track with accuracy the evolution of continuous objects with complex shapes, using a relatively small number of potentially distorted local front estimates

Predictive modeling of the spatiotemporal evolution of an environmental hazard and its sensor network implementation

Predicting accurately the spatiotemporal evolution of a diffusive environmental hazard is of paramount importance for its effective containment. We approximate the front line of a hazard with a set of line segments (local front models). We model the progression characteristics of these front segments by appropriately modified 2D Gaussian functions. The modified Gaussian model parameters are adjusted based on the solution of a Kullback-Leibler (KL) divergence minimization problem. The whole scheme can be realized by a wireless sensor network by forming dynamically triplets of cooperating sensor nodes along the path of the hazard. It is shown that the algorithm can track effectively the front characteristics (in terms of direction and speed) even in the presence of faulty sensor nodes

Urban Informatics

This open access book is the first to systematically introduce the principles of urban informatics and its application to every aspect of the city that involves its functioning, control, management, and future planning. It introduces new models and tools being developed to understand and implement these technologies that enable cities to function more efficiently – to become ‘smart’ and ‘sustainable’. The smart city has quickly emerged as computers have become ever smaller to the point where they can be embedded into the very fabric of the city, as well as being central to new ways in which the population can communicate and act. When cities are wired in this way, they have the potential to become sentient and responsive, generating massive streams of ‘big’ data in real time as well as providing immense opportunities for extracting new forms of urban data through crowdsourcing. This book offers a comprehensive review of the methods that form the core of urban informatics from various kinds of urban remote sensing to new approaches to machine learning and statistical modelling. It provides a detailed technical introduction to the wide array of tools information scientists need to develop the key urban analytics that are fundamental to learning about the smart city, and it outlines ways in which these tools can be used to inform design and policy so that cities can become more efficient with a greater concern for environment and equity

Urban Informatics

This open access book is the first to systematically introduce the principles of urban informatics and its application to every aspect of the city that involves its functioning, control, management, and future planning. It introduces new models and tools being developed to understand and implement these technologies that enable cities to function more efficiently – to become ‘smart’ and ‘sustainable’. The smart city has quickly emerged as computers have become ever smaller to the point where they can be embedded into the very fabric of the city, as well as being central to new ways in which the population can communicate and act. When cities are wired in this way, they have the potential to become sentient and responsive, generating massive streams of ‘big’ data in real time as well as providing immense opportunities for extracting new forms of urban data through crowdsourcing. This book offers a comprehensive review of the methods that form the core of urban informatics from various kinds of urban remote sensing to new approaches to machine learning and statistical modelling. It provides a detailed technical introduction to the wide array of tools information scientists need to develop the key urban analytics that are fundamental to learning about the smart city, and it outlines ways in which these tools can be used to inform design and policy so that cities can become more efficient with a greater concern for environment and equity

How often should dead-reckoned animal movement paths be corrected for drift?

Abstract: Background: Understanding what animals do in time and space is important for a range of ecological questions, however accurate estimates of how animals use space is challenging. Within the use of animal-attached tags, radio telemetry (including the Global Positioning System, ‘GPS’) is typically used to verify an animal’s location periodically. Straight lines are typically drawn between these ‘Verified Positions’ (‘VPs’) so the interpolation of space-use is limited by the temporal and spatial resolution of the system’s measurement. As such, parameters such as route-taken and distance travelled can be poorly represented when using VP systems alone. Dead-reckoning has been suggested as a technique to improve the accuracy and resolution of reconstructed movement paths, whilst maximising battery life of VP systems. This typically involves deriving travel vectors from motion sensor systems and periodically correcting path dimensions for drift with simultaneously deployed VP systems. How often paths should be corrected for drift, however, has remained unclear. Methods and results: Here, we review the utility of dead-reckoning across four contrasting model species using different forms of locomotion (the African lion Panthera leo, the red-tailed tropicbird Phaethon rubricauda, the Magellanic penguin Spheniscus magellanicus, and the imperial cormorant Leucocarbo atriceps). Simulations were performed to examine the extent of dead-reckoning error, relative to VPs, as a function of Verified Position correction (VP correction) rate and the effect of this on estimates of distance moved. Dead-reckoning error was greatest for animals travelling within air and water. We demonstrate how sources of measurement error can arise within VP-corrected dead-reckoned tracks and propose advancements to this procedure to maximise dead-reckoning accuracy. Conclusions: We review the utility of VP-corrected dead-reckoning according to movement type and consider a range of ecological questions that would benefit from dead-reckoning, primarily concerning animal–barrier interactions and foraging strategies

Urban Informatics

This open access book is the first to systematically introduce the principles of urban informatics and its application to every aspect of the city that involves its functioning, control, management, and future planning. It introduces new models and tools being developed to understand and implement these technologies that enable cities to function more efficiently – to become ‘smart’ and ‘sustainable’. The smart city has quickly emerged as computers have become ever smaller to the point where they can be embedded into the very fabric of the city, as well as being central to new ways in which the population can communicate and act. When cities are wired in this way, they have the potential to become sentient and responsive, generating massive streams of ‘big’ data in real time as well as providing immense opportunities for extracting new forms of urban data through crowdsourcing. This book offers a comprehensive review of the methods that form the core of urban informatics from various kinds of urban remote sensing to new approaches to machine learning and statistical modelling. It provides a detailed technical introduction to the wide array of tools information scientists need to develop the key urban analytics that are fundamental to learning about the smart city, and it outlines ways in which these tools can be used to inform design and policy so that cities can become more efficient with a greater concern for environment and equity