A survey on gas leakage source detection and boundary tracking with wireless sensor networks

Gas leakage source detection and boundary tracking of continuous objects have received a significant research attention in the academic as well as the industries due to the loss and damage caused by toxic gas leakage in large-scale petrochemical plants. With the advance and rapid adoption of wireless sensor networks (WSNs) in the last decades, source localization and boundary estimation have became the priority of research works. In addition, an accurate boundary estimation is a critical issue due to the fast movement, changing shape, and invisibility of the gas leakage compared with the other single object detections. We present various gas diffusion models used in the literature that offer the effective computational approaches to measure the gas concentrations in the large area. In this paper, we compare the continuous object localization and boundary detection schemes with respect to complexity, energy consumption, and estimation accuracy. Moreover, this paper presents the research directions for existing and future gas leakage source localization and boundary estimation schemes with WSNs

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

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

Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications

Wireless sensor networks monitor dynamic environments that change rapidly over time. This dynamic behavior is either caused by external factors or initiated by the system designers themselves. To adapt to such conditions, sensor networks often adopt machine learning techniques to eliminate the need for unnecessary redesign. Machine learning also inspires many practical solutions that maximize resource utilization and prolong the lifespan of the network. In this paper, we present an extensive literature review over the period 2002-2013 of machine learning methods that were used to address common issues in wireless sensor networks (WSNs). The advantages and disadvantages of each proposed algorithm are evaluated against the corresponding problem. We also provide a comparative guide to aid WSN designers in developing suitable machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial

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

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

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

Object tracking sensor networks in smart cities: Taxonomy, architecture, applications, research challenges and future directions

The development of pervasive communication devices and the emergence of the Internet of Things (IoT) have acted as an essential part in the feasibility of smart city initiatives. Wireless sensor network (WSN) as a key enabling technology in IoT offers the potential for cities to get smatter. WSNs gained tremendous attention during the recent years because of their rising number of applications that enables remote monitoring and tracking in smart cities. One of the most exciting applications of WSNs in smart cities is detection, monitoring, and tracking which is referred to as object tracking sensor networks (OTSN). The adaptation of OTSN into urban cities brought new exciting challenges for reaching the goal of future smart cities. Such challenges focus primarily on problems related to active monitoring and tracking in smart cities. In this paper, we present the essential characteristics of OTSN, monitoring and tracking application used with the content of smart city. Moreover, we discussed the taxonomy of OTSN along with analysis and comparison. Furthermore, research challenges are investigated concerning energy reservation, object detection, object speed, accuracy in tracking, sensor node collaboration, data aggregation and object recovery position estimation. This review can serve as a benchmark for researchers for future development of smart cities in the context of OTSN. Lastly, we provide future research direction