Information distribution and recharging dispatch strategy in large wireless networks

Large wireless networks are envisioned to play increasingly important roles as more and more mobile wireless devices and Internet of Things (IoT) devices are put in use. In these networks, it is often the case that some critical information needs to be readily accessible, requiring a careful design of the information distribution technique. In this work, we at first propose PeB, Periodic Broadcast, that takes advantage of periodic broadcast from the information server(s) to leave traces for nodes requesting for the information while maintaining a low overhead. Similar to swarm intelligence, PeB requires each node to keep track of traces, or past records of information flow, through itself toward information servers. We present our extensive investigation of the PeB scheme on cost and network dynamics as compared to other state-of-the-art techniques. When the devices run out of battery, they become static and need to be recharged by the wireless charging vehicles (WCVs). Often times, WCV receives a number of charging requests and form a Hamiltonian cycle and visit these nodes one-by-one. We also propose a heuristic algorithm, termed Quad, that generates a Hamiltonian cycle in a square plane. We then focus on the theoretical study of the length of the Hamiltonian cycles in such networks

Attacks and Defenses on Autonomous Vehicles: From Sensor Perception to Control Area Networks

Autonomous driving has been a focus in both industry and academia. The autonomous vehicle decision-making pipeline is typically comprised of several modules from perceiving the physical world to interacting with it. The perception module aims to understand the surrounding environment such as obstacles, lanes, traffic signals, etc. This high-level information is passed to the planning module which generates decisions such as acceleration, turning right, etc. These decisions are made also based on a prediction module that estimates the future trajectories of obstacles for precaution purposes. The control module translates the decisions into low-level instructions, transmitted on the control area network (CAN) bus in the form of CAN messages. Finally, the actuation module executes these instructions. Since autonomous vehicles normally operate at high speed and usually carry humans, their safety and security are of great importance. Recently, a number of papers raise the security concerns with various attacks. Since the perception module leverages deep learning models for object detection, traffic sign recognition, etc., it is inherently subject to adversarial examples that are specially crafted to deceive neural networks. More severely, those physically-realizable adversarial examples/patches that can be implemented externally using printed stickers or projectors, can bypass the digital protection of the autonomous systems thus being more practical, more stealthy, and more difficult to defend against. On the other hand, due to the lack of secure authentication, the CAN protocol has been demonstrated to be susceptible to ECU (electronic control unit) impersonation attacks where an attack-compromised ECU can broadcast forged CAN messages in order for dangerous actuation, such as deploying air bags on a highway even under normal driving circumstances. In this dissertation, we study the security of autonomous vehicles from sensor perception to the CAN bus. We propose attacks that nullify a broad category of state-of-the-art (SOTA) defenses, and develop our own defenses that can be generalized to defeat different attack methodologies. In particular, for the perception module we develop a visible light-based, system-aware camera attacks, termed GhostImage that can be realized physically and remotely (Chapter 3). We exploit the ghost effect of the camera system to convey adversarial noise that is not norm-bounded, thus bypassing SOTA adversarial example defenses. To detect perception attacks, we propose to adopt the idea of spatio-temporal consistency, which is demonstrated using two different methods: one is model-based (Chapter 4) for detecting ghost-based camera attacks, and the other is data-driven (Chapter 5) in that we can detect object misclassification attacks effectively and efficiently, meanwhile our algorithm is agnostic to different attack methodologies as well as different object detection and tracking systems. In Chapter 6, we investigate the planning module and enhance the adversarial robustness of the obstacle trajectory prediction. Finally in Chapter 7, to evaluate the control and actuation modules we propose a Hill-climbing-style attack that defeats SOTA CAN busintrusion detection systems that are based on multiple CAN frames

HQuad: Statistics of Hamiltonian Cycles in Wireless Rechargeable Sensor Networks

The rise of wireless rechargeable sensor networks calls for an analytical study of planned charging trips of wireless charging vehicles (WCVs). Often times, the WCV receives a number of charging requests and form a Hamiltonian cycle and visit these nodes one-by-one. Therefore, it is important to learn the statistics of such cycles. In this work, we use a heuristic algorithm, which we term HQuad, that takes O(N) to generate a Hamiltonian cycle in a 2-D network plane before we analyze its statistics. HQuad is based on a recursive approximation of dividing the region into four quadrants and the non-empty quadrants will be visited one-by-one. Our analysis is based on Poisson point distribution of nodes and models such Hamiltonian cycles surprisingly well in both expected values and the distribution functions of lengths as a function of different network parameters. Numerical results of our analysis model are compared with simulations and demonstrated to be accurate