Secure Localization Topology and Methodology for a Dedicated Automated Highway System

Localization of nodes is an important aspect in a vehicular ad-hoc network (VANET). Research has been done on various localization methods. Some are more apt for a specific purpose than others. To begin with, we give an overview of a vehicular ad-hoc network, localization methods, and how they can be classified. The distance bounding and verifiable trilateration methods are explained further with their corresponding algorithms and steps used for localization. Distance bounding is a range-based distance estimation algorithm. Verifiable trilateration is a popular geometric method of localization. A dedicated automated highway infrastructure can use distance bounding and/or trilateration to localize an automated vehicle on the highway. We describe a highway infrastructure for our analysis and test how well each of the methods performs, according to a security measure defined as spoofing probability. The spoofing probability is, simply put, the probability that a given point on the highway will be successfully spoofed by an attacker that is located at any random position along the highway. Spoofing probability depends on different quantities depending on the method of localization used. We compare the distance bounding and trilateration methods to a novel method using friendly jamming for localization. Friendly jamming works by creating an interference around the region whenever communication takes place between a vehicle and a verifier (belonging to the highway infrastructure, which is involved in the localization process using a given algorithm and localization method). In case of friendly jamming, the spoofing probability depends both on the position and velocity of the attacker and those of the target vehicle (which the attacker aims to spoof). This makes the spoofing probability much less for friendly jamming. On the other hand, the distance bounding and trilateration methods have spoofing probabilities depending only on their position. The results are summarized at the end of the last chapter to give an idea about how the three localization methods, i.e. distance bounding, verifiable trilateration, and friendly jamming, compare against each other for a dedicated automated highway infrastructure. We observe that the spoofing probability of the friendly jamming infrastructure is less than 2% while the spoofing probabilities of distance bounding and trilateration are 25% and 11%, respectively. This means that the friendly jamming method is more secure for the corresponding automated transportation system (ATS) infrastructure than distance bounding and trilateration. However, one drawback of friendly jamming is that it has a high standard deviation because the range of positions that are most vulnerable is high. Even though the spoofing probability is much less, the friendly jamming method is vulnerable to an attack over a large range of distances along the highway. This can be overcome by defining a more robust infrastructure and using the infrastructure\u27s resources judiciously. This can be the future scope of our research. Infrastructures that use the radio resources in a cost effective manner to reduce the vulnerability of the friendly jamming method are a promising choice for the localization of vehicles on an ATS highway

Efficient Low Cost Range-Based Localization Algorithm for Ad-hoc Wireless Sensors Networks

Revised version submitted to Ad Hoc NetworksBuilding an efficient node localization system in wireless sensor networks is facing several challenges. For example, calculating the square root consumes computational resources and utilizing flooding techniques to broadcast nodes location wastes bandwidth and energy. Reducing computational complexity and communication overhead is essential in order to reduce power consumption, extend the life time of the battery operated nodes, and improve the performance of the limited computational resources of these sensor nodes. In this paper, we revise the mathematical model,the analysis and the simulation experiments of the Trigonometric based Ad-hoc Localiza-tion System (TALS), a range-based localization system presented previously. Furthermore, the study is extended, and a new technique to optimize the system is proposed. An analysis and an extensive simulation for the optimized TALS (OTALS) is presented showing its cost, accuracy, and efficiency, thus deducing the impact of its parameters on performance. Hence, the contribution of this work can be summarized as follows: 1) Proposing and employing a novel modified Manhattan distance norm in the TALS localization process. 2) Analyzing and simulating of OTALS showing its computational cost and accuracy and comparing them with other related work. 3) Studying the impacts of different parameters like anchor density, node density, noisy measurements, transmission range, and non-convex network areas. 4) Extending our previous joint work, TALS, to consider base anchors to be located in positions other than the origin and analyzing this work to illustrate the possibility of selecting a wrong quadrant at the first iteration and how this problem is overcome. Through mathematical analysis and intensive simulation, OTALS proved to be iterative , distributed, and computationally simple. It presented superior performance compared to other localization techniques

Distributed localisation algorithm for wireless ad hoc networks of moving nodes

Existing ad hoc network localisation solutions rely either on external location references or network-wide exchange of information and centralised processing and computation of location estimates. Without these, nodes are not able to estimate the relative locations of other nodes within their communication range. This thesis defines a new distributed localisation algorithm for ad hoc networks of moving nodes. The Relative Neighbour Localisation (RNL) algorithm works without any external localisation signal or systems and does not assume centralised information processing. The idea behind the location estimates produced by the RNL algorithm is the relationship between the relative locations of two nodes, their mobility parameters and the signal strengths measured between them. The proposed algorithm makes use of the data available to each node to produce a location estimate. The signal strength each node is capable of measuring is used as one algorithm input. The other input is the velocity vector of the neighbouring node, composed of its speed and direction of movement, which each node is assumed to periodically broadcast. The relationship between the signal strength and the mobility parameters on one, and the relative location on the other side can be analytically formulated in an ideal case. The limitations of a realistic scenario complicate this relationship, making it very difficult to formulate analytically. An empirical approach is thus used. The angle and the distance estimates are individually computed, together forming a two-dimensional location estimate. The performance of the algorithm was analysed in detail using simulation, showing a median estimate error of under 10m, and its application was tested through design and evaluation of a distributed sensing coverage algorithm, showing RNL location estimates can provide 90% of the coverage achievable with true locations being known

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

SALAM: A SCALABLE ANCHOR-FREE LOCALIZATION ALGORITHM FOR WIRELESS SENSOR NETWORKS

In this dissertation, we present SALAM, a scalable anchor-free protocol for localization in wireless sensor networks. SALAM can determine the positions of sensor nodes without any infrastructure support. We assume that each node has the capability to estimate distances to its corresponding neighbors, that are within its transmission range. SALAM allows to trade the accuracy of the estimated position against node transmission range and/or computational power. The application layer can choose from a whole range of different options, to estimate the sensor node's positions with different accuracy while conserving battery power. Scalability is achieved by dividing the network into overlapping multi-hop clusters each with its own cluster head node. Each cluster head is responsible for building a local relative map corresponding to its cluster using intra-cluster node's range measurements. To obtain the global relative topology of the network, the cluster head nodes collaboratively combine their local maps using simple matrix transformations. In order for two cluster heads to perform a matrix transformation, there must be at least three boundary nodes that belongs to both clusters (i.e. the two clusters are overlapping with degree 3). We formulate the overlapping multi-hop clustering problem and present a randomized distributed heuristic algorithm for solving the problem. We evaluate the performance of the proposed algorithm through analytical analysis and simulation. A major problem with multi-hop relative location estimation is the error accumulated in the node position as it becomes multi-hop away from the cluster head node. We analyze different sources of error and develop techniques to avoid these errors. We also show how the local coordinate system (LCS) affects the accuracy and propose different heuristics to select the LCS. Simulation results show that SALAM achieves precise localization of sensors. We show that our approach is scalable in terms of communication overhead regardless of the network size. In addition, we capture the impact of different parameters on the accuracy of the estimated node's positions. The results also show that SALAM is able to achieve accuracy better than the current ad-hoc localization algorithms

Energieeffiziente und rechtzeitige Ereignismeldung mittels drahtloser Sensornetze

This thesis investigates the suitability of state-of-the-art protocols for large-scale and long-term environmental event monitoring using wireless sensor networks based on the application scenario of early forest fire detection. By suitable combination of energy-efficient protocol mechanisms a novel communication protocol, referred to as cross-layer message-merging protocol (XLMMP), is developed. Qualitative and quantitative protocol analyses are carried out to confirm that XLMMP is particularly suitable for this application area. The quantitative analysis is mainly based on finite-source retrial queues with multiple unreliable servers. While this queueing model is widely applicable in various research areas even beyond communication networks, this thesis is the first to determine the distribution of the response time in this model. The model evaluation is mainly carried out using Markovian analysis and the method of phases. The obtained quantitative results show that XLMMP is a feasible basis to design scalable wireless sensor networks that (1) may comprise hundreds of thousands of tiny sensor nodes with reduced node complexity, (2) are suitable to monitor an area of tens of square kilometers, (3) achieve a lifetime of several years. The deduced quantifiable relationships between key network parameters — e.g., node size, node density, size of the monitored area, aspired lifetime, and the maximum end-to-end communication delay — enable application-specific optimization of the protocol

Charge transport in printed silicon nanoparticle networks

Includes abstract.Includes bibliographical references.For the first time, the charge transport mechanisms in printed silicon nanoparticle networks have been comprehensively studied using variable temperature IV characteristics and Hall effect measurements, supported by microscopy studies. The conductivity can be described as hopping percolation in which activated charge transport is limited by band bending at the interface between particles and electron trapping at surface states. To probe the charge transport, two types of printed silicon nanoparticle networks based on milled silicon nanoparticles and highly doped p-type chemical vapour synthesised nanoparticles, were studied and compared

Indoor localization using place and motion signatures

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2013.This electronic version was submitted and approved by the author's academic department as part of an electronic thesis pilot project. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from department-submitted PDF version of thesis.Includes bibliographical references (p. 141-153).Most current methods for 802.11-based indoor localization depend on either simple radio propagation models or exhaustive, costly surveys conducted by skilled technicians. These methods are not satisfactory for long-term, large-scale positioning of mobile devices in practice. This thesis describes two approaches to the indoor localization problem, which we formulate as discovering user locations using place and motion signatures. The first approach, organic indoor localization, combines the idea of crowd-sourcing, encouraging end-users to contribute place signatures (location RF fingerprints) in an organic fashion. Based on prior work on organic localization systems, we study algorithmic challenges associated with structuring such organic location systems: the design of localization algorithms suitable for organic localization systems, qualitative and quantitative control of user inputs to "grow" an organic system from the very beginning, and handling the device heterogeneity problem, in which different devices have different RF characteristics. In the second approach, motion compatibility-based indoor localization, we formulate the localization problem as trajectory matching of a user motion sequence onto a prior map. Our method estimates indoor location with respect to a prior map consisting of a set of 2D floor plans linked through horizontal and vertical adjacencies. To enable the localization system, we present a motion classification algorithm that estimates user motions from the sensors available in commodity mobile devices. We also present a route network generation method, which constructs a graph representation of all user routes from legacy floor plans. Given these inputs, our HMM-based trajectory matching algorithm recovers user trajectories. The main contribution is the notion of path compatibility, in which the sequential output of a classifier of inertial data producing low-level motion estimates (standing still, walking straight, going upstairs, turning left etc.) is examined for metric/topological/semantic agreement with the prior map. We show that, using only proprioceptive data of the quality typically available on a modern smartphone, our method can recover the user's location to within several meters in one to two minutes after a "cold start."by Jun-geun Park.Ph.D