Data analytics methods for attack detection and localization in wireless networks

Wireless ad hoc network operates without any fixed infrastructure and centralized administration. It is a group of wirelessly connected nodes having the capability to work as host and router. Due to its features of open communication medium, dynamic changing topology, and cooperative algorithm, security is the primary concern when designing wireless networks. Compared to the traditional wired network, a clean division of layers may be sacrificed for performance in wireless ad hoc networks. As a result, they are vulnerable to various types of attacks at different layers of the protocol stack. In this paper, I present real-time series data analysis solutions to detect various attacks including in- band wormholes attack in the network layer, various MAC layer misbehaviors, and jamming attack in the physical layer. And, I also investigate the problem of node localization in wireless and sensor networks, where a total of n anchor nodes are used to determine the locations of other nodes based on the received signal strengths. A range-based machine learning algorithm is developed to tackle the challenges --Abstract, page iii

Accurate real-time relative localization using single-frequency GPS

For outdoor navigation, GPS provides the most widely-used means of node localization; however, the level of accu-racy provided by low-cost receivers is typically insufficient for use in high-precision applications. Additionally, many of these applications do not require precise absolute Earth coordinates, but rather rely on relative positioning to infer information about the geometric configuration of the con-stituent nodes in a system. This paper presents a novel ap-proach that uses GPS to derive relative location information for a scalable network of single-frequency receivers. Net-worked nodes share their raw satellite observations, enabling each node to localize its neighbors in a pairwise fashion as opposed to computing its own standalone position. Random and systematic errors are mitigated in novel ways, challeng-ing long-standing beliefs that precision GPS systems require extensive stationary calibration times or complex equipment configurations. In addition to presenting the mathematical ba-sis for our technique, a working prototype is developed, en-abling experimental evaluation of several real-world test sce-narios. The results of these experiments indicate sub-meter relative positioning accuracy under various conditions and in varying environments. This represents up to order of magni-tude increase in precision over existing absolute positioning techniques or other unimodal GPS-based solutions

Wireless sensing: Material identification and localization

Wireless signals are everywhere around us, and they have truly revolutionized the world by all standards. When one thinks of this revolution, one envisions the advances in wireless communication—TV broadcasts, FM radios, WiFi, Bluetooth, cellular mobile phones, and even wireless chips inside the human body. What gets less appreciated, however, is that wireless signals can also be a powerful sensor. The fact that wireless signals touch and penetrate all objects in our environment, and bounce back, make them a powerful lens to view our world through. This thesis focuses on using wireless signals as sensors. We will explore how modifications to wireless signal propagation can reveal the physical properties of the materials that these signals have passed through. This enables identification of materials without touching them or performing any chemical analysis on them. We will show the ability to distinguish between closely related liquids, such as Pepsi and Coca-Cola, or distilled water and mineral water, by simply passing wireless signals through the liquids, and analyzing the signals that emerge on the other side. The propagation delay of wireless signals when passing through air can reveal the distance between a transmitter and a receiver. We show how this primitive can be extended for localization with applications to sports, battlefields, and emergency response. Through modifications to the distance measurement mechanisms, we show how localization is possible even when wireless devices are constantly under motion. We end by discussing future directions in which both of these sensing techniques can be extended. Under the right conditions, it might be possible to localize an object to 5mm precision with applications in robotic machines, augmented reality, and virtual reality. We then discuss the possibility of using reflections of wireless signals, for example, to determine soil moisture content in agricultural fields

Motion tracking problems in Internet of Things (IoT) and wireless networking

The dissertation focuses on inferring various motion patterns of internet-of-things (IoT) devices, by leveraging inertial sensors embedded in these objects, as well as wireless signals emitted (or reflected) from them. For instance, we use a combination of GPS signals and inertial sensors on drones to precisely track its 3D orientation over time, ultimately improving safety against failures and crashes. In another application in sports analytics, we embed sensors and radios inside baseballs and cricket balls and compute their 3D trajectory and spin patterns, even when they move at extremely high speeds. In a third application for wireless networks, we explore the possibility of physically moving wireless infrastructure like Access Points and basestations on robots and drones for enhancing the network performance. While these are diverse applications in drones, sports analytics, and wireless networks, the common theme underlying the research is in the development of the core motion-related building blocks. Specifically, we emphasize the philosophy of "fusion of multi modal sensor data with application specific model” as the design principle for building the next generation of diverse IoT applications. To this end, we draw on theoretical techniques in wireless communication, signal processing, and statistics, but translate them to completely functional systems on real-world platforms