Securing location discovery in wireless sensor networks

Providing security for wireless sensor networks in hostile environments has a significant importance. Resilience against malicious attacks during the process of location discovery has an increasing need. There are many applications that rely on sensor nodes\u27 locations to be accurate in order to function correctly. The need to provide secure, attack resistant location discovery schemes has become a challenging research topic. In this thesis, location discovery techniques are discussed and the security threats and attacks are explained. I also present current secure location discovery schemes which are developed for range-based location discovery. The thesis goal is to develop a secure range-free location discovery scheme. This is accomplished by enhancing the voting-based scheme developed in [8, 9] to be used as the bases for developing a secure range-free location discovery scheme. Both the enhancement voting-based and the secure range-free schemes are implemented on Sun SPOT wireless sensors and subjected to various levels of location discovery attacks and tested under different sensor network scales using a simulation program developed for testing purposes

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

On Localization Issues of Mobile Devices

Mobile devices, such as sensor nodes, smartphones and smartwatches, are now widely used in many applications. Localization is a highly important topic in wireless networks as well as in many Internet of Things applications. In this thesis, four novel localization schemes of mobile devices are introduced to improve the localization performance in three different areas, like the outdoor, indoor and underwater environments. Firstly, in the outdoor environment, many current localization algorithms are based on the Sequential Monte MCL, the accuracy of which is bounded by the radio range. High computational complexity in the sampling step is another issue of these approaches. Tri-MCL is presented, which significantly improves on the accuracy of the Monte Carlo Localization algorithm. To do this, three different distance measurement algorithms based on range-free approaches are leveraged. Using these, the distances between unknown nodes and anchor nodes are estimated to perform more fine-grained filtering of the particles as well as for weighting the particles in the final estimation step of the algorithm. Simulation results illustrate that the proposed algorithm achieves better accuracy than the MCL and SA-MCL algorithms. Furthermore, it also exhibits high efficiency in the sampling step. Then, in the GPS-denied indoor environment, Twi-Adaboost is proposed, which is a collaborative indoor localization algorithm with the fusion of internal sensors such as the accelerometer, gyroscope and magnetometer from multiple devices. Specifically, the datasets are collected firstly by one person wearing two devices simultaneously: a smartphone and a smartwatch, each collecting multivariate data represented by their internal parameters in a real environment. Then, the datasets from these two devices are evaluated for their strengths and weaknesses in recognizing the indoor position. Based on that, the Twi-AdaBoost algorithm, an interactive ensemble learning method, is proposed to improve the indoor localization accuracy by fusing the co-occurrence information. The performance of the proposed algorithm is assessed on a real-world dataset. The experiment results demonstrate that Twi-AdaBoost achieves a localization error about 0.39 m on average with a low deployment cost, which outperforms the state-of-the-art indoor localization algorithms. Lastly, the characteristics of mobile UWSNs, such as low communication bandwidth, large propagation delay, and sparse deployment, pose challenging issues for successful localization of sensor nodes. In addition, sensor nodes in UWSNs are usually powered by batteries whose replacements introduces high cost and complexity. Thus, the critical problem in UWSNs is to enable each sensor node to find enough anchor nodes in order to localize itself, with minimum energy costs. An Energy-Efficient Localization Algorithm (EELA) is proposed to analyze the decentralized interactions among sensor nodes and anchor nodes. A Single-Leader-Multi-Follower Stackelberg game is utilized to formulate the topology control problem of sensor nodes and anchor nodes by exploiting their available communication opportunities. In this game, the sensor node acts as a leader taking into account factors such as `two-hop' anchor nodes and energy consumption, while anchor nodes act as multiple followers, considering their ability to localize sensor nodes and their energy consumption. I prove that both players select best responses and reach a socially optimal Stackelberg Nash Equilibrium. Simulation results demonstrate that the proposed EELA improves the performance of localization in UWSNs significantly, and in particular the energy cost of sensor nodes. Compared to the baseline schemes, the energy consumption per node is about 48% lower in EELA, while providing a desirable localization coverage, under reasonable error and delay. Based on the EELA scheme, an Adaptive Energy Efficient Localization Algorithm using the Fuzzy game theoretic method (Adaptive EELA) is proposed to solve the environment adaptation problem of EELA. The adaptive neuro-fuzzy method is used as the utility function of the Single-Leader-Multi-Follower Stackelberg game to model the dynamical changes in UWSNs. The proposed Adaptive EELA scheme is able to automatically learn in the offline phase, which is required only once. Then, in the online phase, it can adapt to the environmental changes, such as the densities of nodes or topologies of nodes. Extensive numerical evaluations are conducted under different network topologies and different network node densities. The simulation results demonstrate that the proposed Adaptive EELA scheme achieves about 35% and 66% energy reduction per node on average comparing the state-of-the-art approaches, such as EELA and OLTC, while providing a desirable localization coverage, localization error and localization delay