With the goal of developing low-cost and innovative perception and localization techniques for autonomous vehicles, this work explores a system that solely relies on a LoRa receiver and a magnetometer for agent localization within urban environments. Using the received signal strength from LoRa beacons distributed across a test area of 16,000 square meters, a model of expected RSSI values per beacon is estimated using Gaussian Process (GP) regression. Motion is estimated using a probabilistic signal similarity classifier, and localization is obtained via a particle filter. Our experiments demonstrate that our proposed system is able to estimate our location to within three meters RMSE when the agent is within the convex hull of prior data. In real-world scenarios, characterized by signal interference and environmental complexities, our approach highlights the potential of leveraging affordable technology such as LoRa receivers and magnetometers for robust and accurate location estimation in complex urban environments. The integration of low-cost LoRa devices, Gaussian Process regression, particle filtering and our novel signal similarity motion estimator offers a promising avenue for achieving cost-effective localization solutions without compromising accuracy or reliability.

IEEE 802.11 (WiFi) only has two modes of trust---complete trust or complete untrust. The lack of nuance leaves no room for sensors that a user does not fully trust but wants to connect to their network, such as a WiFi sensor. Solutions exist, but they require advanced knowledge of network administration. We solve this problem by introducing a new way of modulating data in the latency of the network, called Latency Shift Keying. We use specific characteristics of the WiFi protocol to carefully control the latency of just one device on the network. We build a transmitter, receiver, and modulation scheme that is designed to encode data in the latency of a network. We develop an application, Wicket, that solves the WiFi trust issue using Latency Shift Keying to create a new security association between an untrusted WiFi sensor and a wired device on the trusted network. We evaluate its performance and show that it works in many network conditions and environments.