We focus on three application areas of societal benefit: safety, security, and privacy. We apply Belief Propagation (BP) algorithms to sensor networks, and describe our modular framework for the more general Reweighted-BP formulation. To improve safety, we track pallets in warehouses. We apply Particle Filtering and model the cardiod-shaped response pattern of ultrasound between static beacons and mobile sensors to improve tracking accuracy by 11%. We also show using inter-distance sensor readings, we can improve accuracy by 3-4x over the recent SMCL+R formulation, while being more likely to converge. We use a generalization of Particle Filtering, Nonparametric-BP (NBP), which can model multi-modal and ring-shaped distributions found in inter-distance tracking problems. We develop a novel tracking algorithm based on NBP to fuse dynamics and multi-hop inter-distance information that increases accuracy, reduces computation, and improves convergence. For security, we present a novel approach for intruder surveillance using a robotic camera, controlled by binary motion sensors and use Particle Filtering to model intruder dynamics and environment geometry. We also present a localization-free approach to robot navigation using a distributed set of beacons, which emit a sequence of signals to direct a robot to the goal, modeling the robot's dynamics uncertainty, with up to 93.4% success rate. We introduce an approach to privacy for visual surveillance, "Respectful Cameras," that uses probabilistic Adaptive Boosting to learn an environment-specific 9-dimensional color model to track colored markers, which act as a proxy for each face. We integrate probabilistic Adaptive Boosting with Particle Filtering to improve robustness, and demonstrate a 2% false-negative rate.