In this paper, we consider a hybrid solution to the sensor network position inference problem, which combines a real-time filtering system with information from a more expensive, global inference procedure to improve accuracy and prevent divergence. Many online solutions for this problem make use of simplifying assumptions, such as Gaussian noise models and linear system behaviour and also adopt a filtering strategy which may not use available information optimally. These assumptions allow near real-time inference, while also limiting accuracy and introducing the potential for ill-conditioning and divergence. We consider augmenting a particular realtime estimation method, the extended Kalman filter (EKF), with a more complex, but more highly accurate, inference technique based on Markov Chain Monte Carlo (MCMC) methodology. Conventional MCMC techniques applied to this problem can entail significant and time consuming computation to achieve convergence. To address this, we propose an intelligent bootstrapping process and the use of parallel, communicative chains of different temperatures, commonly referred to as parallel tempering. The combined approach is shown to provide substantial improvement in a realistic simulated mapping environment and when applied to a complex physical system involving a robotic platform moving in an office environment instrumented with a camera sensor network.
A new sensor network architecture called active sensor network (ASN) is proposed in this paper. By integrating multiple small, sensor network-friendly mobile robots into a traditional sensor network, a closed-loop, dynamic, adaptive sensor network is formed. Such sensor networks have the following merits: adaptivity, self healing, responsiveness and longer lifetime. This paper focuses on the distributed sensor node localization using multiple mobile robots. A potentialbased robot area partition algorithm and a localization algorithm are developed. Simulation results verify the proposed algorithms.
Sensors that know their location, from microphones to vibration sensors, can support a wider arena of applications than their location unaware counterparts. We offer a method for sensors to determine their own location relative to one another by using only exogenous sounds and the differences in the arrivals of these sounds at different sensors. We present a distributed and computationally efficient solution that offers accuracy on par with more active and computationally intense methods.
This paper briefly sketches a pair of algorithms for localizing and deploying a mobile sensor network. We use the term'mobile sensor network' to describe a dis ibuted collection of nodes, each of which has sensing, computation, communication and locomotion capabilities. It is the latter capability that distinguishes a mobile sensor network from its more conventional static cousins.
Monte Carlo localization (MCL) is a Bayesian algorithm for mobile robot localization based on particle filters, which has enjoyed great practical success. This paper points out a limitation of MCL which is counterintuitive, namely that better sensors can yield worse results. An analysis of this problem leads to the formulation of a new proposal distribution for the Monte Carlo sampling step. Extensive experimental results with physical robots suggest that the new algorithm is significantly more robust and accurate than plain MCL. Obviously, these results transcend beyond mobile robot localization and apply to a range of particle filter applications.