Improvement of range-free localization technology by a novel DV-hop protocol in wireless sensor networks

International audienceLocalization is a fundamental issue for many applications in wireless sensor networks. Without the need of additional ranging devices, the range-free localization technology is a cost-effective solution for low-cost indoor and outdoor wireless sensor networks. Among range-free algorithms, DV-hop (Distance Vector - hop) has the advantage to localize the mobile nodes which has less than three neighbour anchors. Based on the original DV-hop algorithm, this paper presents two improved algorithms (Checkout DV-hop and Selective 3-Anchor DV-hop). Checkout DV-hop algorithm estimates the mobile node position by using the nearest anchor, while Selective 3-Anchor DV-hop algorithm chooses the best 3 anchors to improve localization accuracy. Then, in order to implement these DV-hop based algorithms in network scenarios, a novel DV-hop localization protocol is proposed. This new protocol is presented in detail in this paper, including the format of data payloads, the improved collision reduction method E-CSMA/CA, as well as parameters used in deciding the end of each DV-hop step. Finally, using our localization protocol, we investigate the performance of typical DV-hop based algorithms in terms of localization accuracy, mobility, synchronization and overhead. Simulation results prove that Selective 3-Anchor DV-hop algorithm offers the best performance compared to Checkout DV-hop and the original DV-hop algorithm

Accurate range-free localization for anisotropic wireless sensor networks

Journal ArticlePosition information plays a pivotal role in wireless sensor network (WSN) applications and protocol/ algorithm design. In recent years, range-free localization algorithms have drawn much research attention due to their low cost and applicability to large-scale WSNs. However, the application of range-free localization algorithms is restricted because of their dramatic accuracy degradation in practical anisotropic WSNs, which is mainly caused by large error of distance estimation. Distance estimation in the existing range-free algorithms usually relies on a unified per hop length (PHL) metric between nodes. But the PHL between different nodes might be greatly different in anisotropic WSNs, resulting in large error in distance estimation. We find that, although the PHL between different nodes might be greatly different, it exhibits significant locality; that is, nearby nodes share a similar PHL to anchors that know their positions in advance. Based on the locality of the PHL, a novel distance estimation approach is proposed in this article. Theoretical analyses show that the error of distance estimation in the proposed approach is only one-fourth of that in the state-of-the-art pattern-driven scheme (PDS). An anchor selection algorithm is also devised to further improve localization accuracy by mitigating the negative effects from the anchors that are poorly distributed in geometry. By combining the locality-based distance estimation and the anchor selection, a range-free localization algorithm named Selective Multilateration (SM) is proposed. Simulation results demonstrate that SM achieves localization accuracy higher than 0.3r, where r is the communication radius of nodes. Compared to the state-of-the-art solution, SM improves the distance estimation accuracy by up to 57% and improves localization accuracy by up to 52% consequently.This work is partially supported by the National Science Foundation of China (61103203, 61173169, 61332004, and 61420106009), the Hong Kong RGC General Research Fund (PolyU 5106/11E), the International Science & Technology Cooperation Program of China (2013DFB10070), and the EU FP7 QUICK project (PIRSES-GA-2013-612652)

Securing a wireless sensor network for human tracking: a review of solutions

Currently, wireless sensor networks (WSNs) are formed by devices with limited resources and limited power energy availability. Thanks to their cost effectiveness, flexibility, and ease of deployment, wireless sensor networks have been applied to many scenarios such as industrial, civil, and military applications. For many applications, security is a primary issue, but this produces an extra energy cost. Thus, in real applications, a trade-off is required between the security level and energy consumption. This paper evaluates different security schemes applied to human tracking applications, based on a real-case scenario.Junta de Andalucía P07-TIC-02476Junta de Andalucía TIC-570

Secure Geo-location Techniques using Trusted Hyper-visor

Για πολλούς, η γεωγραφική θέση είναι μια απλή διαδικασία όπου με τη χρήση του GPS ένα άτομο μπορεί να εντοπιστεί όπου και όποτε ζητείται. Ωστόσο, ακόμη και αν η χρήση του GPS για γεωγραφική τοποθέτηση είναι ο πιο συνηθισμένος τρόπος και ταυτόχρονα ακριβής ως σύστημα, αποτελεί μια τεράστια κατανάλωση ενέργειας για να επιτευχθεί αυτή η διαδικασία και υστερεί σε μηχανισμούς και τεχνικές ασφαλείας. Σκοπός αυτής της εργασίας είναι να παρουσιάσουμε μια άλλη όψη για το πώς μπορούμε να εντοπίσουμε μια άγνωστη θέση ενός κόμβου σε ένα σύστημα και πώς θα μπορούσε να δημιουργηθεί ένα ασφαλές περιβάλλον για αυτόν τον κόμβο. Βασική μας ιδέα ήταν η δημιουργία ενός μηχανισμού όπου θα μπορούσαμε να δημιουργήσουμε ένα τρισδιάστατο πεδίο στο οποίο θα μπορούσε να εντοπιστεί άγνωστος κόμβος και στη συνέχεια θα δημιουργηθεί ένα ασφαλές περιβάλλον για τον νέο κόμβο. Μετά από μια έρευνα σε δημοσιεύσεις σχετικά με τρισδιάστατους μηχανισμούς και τεχνικές γεω-εντοπισμού, παράλληλα με την έννοια των hypervisors για τη δημιουργία ασφαλούς περιβάλλοντος με την αξιοποίηση της κρυπτογραφίας, καταλήξαμε στο συμπέρασμα της δημιουργίας ενός πλαισίου που θα ικανοποιούσε αυτά απαιτήσεις. Δημιουργήσαμε ένα τρισδιάστατο πεδίο τεσσάρων σταθμών κόμβων, όπου χρησιμοποιήσαμε δύο αλγορίθμους εντοπισμού, χωρίς GPS, για τον εντοπισμό της θέση ενός πέμπτου άγνωστου κόμβου παράλληλα με έναν hypervisor για τη δημιουργία περιβάλλοντος εμπιστοσύνης. Χρησιμοποιήσαμε ένα TPM για τη δημιουργία κρυπτογραφικών μηχανισμών και κλειδιών ασφαλείας. Σε αυτή την εργασία δημιουργήσαμε μια προσομοίωση όπου συγκρίνουμε την απόδοση αυτών των δύο αλγορίθμων γεωγραφικής τοποθέτησης από την άποψη της ταχύτητας και της ακρίβειας του υπολογισμού, παράλληλα με την απόδοση των μηχανισμών ασφαλείας του hypervisor και την ικανότητά του για ασφάλιση ακεραιότητας δεδομένων. Εκτός από τα συστατικά του προτεινόμενου μηχανισμού, παρουσιάζουμε και άλλες πληροφορίες που βρήκαμε σε σχετικά έγγραφα, όπως μια ποικιλία από hypervisors και μια ποικιλία τεχνικών εντοπισμού, για περισσότερες πληροφορίες για μελλοντικές εργασίες παράλληλα με τα βήματα υλοποίησης και εκτέλεσης.For many, geo-location is a simple process where with the utilization of GPS a person can be located wherever and whenever is requested. However, even if the utilization of GPS for geolocation is the most common way and accurate as a system, it is a huge consumption of energy in order to achieve this process and it lucks on safety mechanisms and techniques. The purpose of this paper is to present another view of how we could locate an unknown node position in a system and how a safe environment could be created for this node. Our main idea was about the creation of a framework where we could create a three-dimensional field in which an unknown node could be located and afterwards a safe environment would be created for the new node. After a research on papers relevant with three-dimensional geo-localization mechanisms and techniques, alongside with the concept of hypervisors for the creation of safe environment with the utilization of cryptography, we came to the conclusion of the creation of a framework which would satisfy those requirements. We created a 3-Dimentional field of four base nodes stations, where we utilized two localization GPS-free algorithms for the location of a fifth unknown node alongside with a hypervisor for the trust environment creation. We utilized a TPM for the cryptography mechanisms and safety keys creation. In this paper we created a simulation where we compare the performance of those two geolocation algorithms in terms of accuracy and computation speed and accuracy, alongside with the hypervisor’s security mechanisms performance and its ability for data integrity insurance. Except our proposed framework components, we present also further information that we found in relevant papers, such as a variety of hypervisors and a variety of localization techniques, for more information for future work alongside with implementation steps and guidanc

Self-organising an indoor location system using a paintable amorphous computer

This thesis investigates new methods for self-organising a precisely defined pattern of intertwined number sequences which may be used in the rapid deployment of a passive indoor positioning system's infrastructure.A future hypothetical scenario is used where computing particles are suspended in paint and covered over a ceiling. A spatial pattern is then formed over the covered ceiling. Any small portion of the spatial pattern may be decoded, by a simple camera equipped device, to provide a unique location to support location-aware pervasive computing applications.Such a pattern is established from the interactions of many thousands of locally connected computing particles that are disseminated randomly and densely over a surface, such as a ceiling. Each particle has initially no knowledge of its location or network topology and shares no synchronous clock or memory with any other particle.The challenge addressed within this thesis is how such a network of computing particles that begin in such an initial state of disarray and ignorance can, without outside intervention or expensive equipment, collaborate to create a relative coordinate system. It shows how the coordinate system can be created to be coherent, even in the face of obstacles, and closely represent the actual shape of the networked surface itself. The precision errors incurred during the propagation of the coordinate system are identified and the distributed algorithms used to avoid this error are explained and demonstrated through simulation.A new perimeter detection algorithm is proposed that discovers network edges and other obstacles without the use of any existing location knowledge. A new distributed localisation algorithm is demonstrated to propagate a relative coordinate system throughout the network and remain free of the error introduced by the network perimeter that is normally seen in non-convex networks. This localisation algorithm operates without prior configuration or calibration, allowing the coordinate system to be deployed without expert manual intervention or on networks that are otherwise inaccessible.The painted ceiling's spatial pattern, when based on the proposed localisation algorithm, is discussed in the context of an indoor positioning system

Scalable big data systems: Architectures and optimizations

Big data analytics has become not just a popular buzzword but also a strategic direction in information technology for many enterprises and government organizations. Even though many new computing and storage systems have been developed for big data analytics, scalable big data processing has become more and more challenging as a result of the huge and rapidly growing size of real-world data. Dedicated to the development of architectures and optimization techniques for scaling big data processing systems, especially in the era of cloud computing, this dissertation makes three unique contributions. First, it introduces a suite of graph partitioning algorithms that can run much faster than existing data distribution methods and inherently scale to the growth of big data. The main idea of these approaches is to partition a big graph by preserving the core computational data structure as much as possible to maximize intra-server computation and minimize inter-server communication. In addition, it proposes a distributed iterative graph computation framework that effectively utilizes secondary storage to maximize access locality and speed up distributed iterative graph computations. The framework not only considerably reduces memory requirements for iterative graph algorithms but also significantly improves the performance of iterative graph computations. Last but not the least, it establishes a suite of optimization techniques for scalable spatial data processing along with three orthogonal dimensions: (i) scalable processing of spatial alarms for mobile users traveling on road networks, (ii) scalable location tagging for improving the quality of Twitter data analytics and prediction accuracy, and (iii) lightweight spatial indexing for enhancing the performance of big spatial data queries.Ph.D