Location-based services (LBS) are an integral part of the mobile ecosystem today, relying on the Global Positioning System (GPS) and other technologies. However, while these technologies have been highly successful at positioning outdoors, they either fail to obtain a fix or do not provide the necessary accuracy indoors. In this paper, we will describe a low-cost high-accuracy indoor positioning system that can run on mobile devices. The system has high-accuracy because it combines several sources of information, thus improving accuracy over using any one of them alone. The cost is low because the sources of information used by the system such as WiFi measurements, inertial sensor data and building maps are readily available for use on a mobile device.

This paper considers mission design strategies for mobile robots whose task is to perform spatial sampling of a static environmental field, in the framework of compressive sensing. According to this theory, we can reconstruct compressible fields using O(log n) nonadaptive measurements (where n is the number of sites of the spatial domain), in a basis that is "in coherent" to the representation basis [1]; random uncorrelated measurements satisfy this incoherence requirement. Because an autonomous vehicle is kinematically constrained and has finite energy and communication resources, it is an open question how to best design missions for CS reconstruction. We compare a two-dimensional random walk, a TSP approximation to pass through random points, and a randomized boustrophedon (lawnmower) strategy. Not unexpectedly, all three approaches can yield comparable reconstruction performance if the planning horizons are long enough; if planning occurs only over short time scales, the random walk will have an advantage.

This paper addresses the problem of coverage control of a network of mobile sensors. In the current literature, this is commonly formulated as a locational optimization problem under the assumption that sensing performance is independent of the locations of sensors. We extend this work to a more general framework where the sensor model is location-dependent. We propose a distributed control law and coordination algorithm. If the global sensing performance function is known a priori, we prove that the algorithm is guaranteed to converge. To validate this algorithm, we conduct experiments with indoor and outdoor deployments of Cyclops cameras and model its sensing performance. This model is used to simulate deployments on 1D pathways and study the coverage obtained. We also examine the coverage in the case when the global sensing function is not known and is estimated in an online fashion.

Neighbor-Every-Theta (NET) graphs are such that each node has at least one neighbor in every theta angle sector of its communication range. We show that for thetas < pi, NET graphs are guaranteed to have an edge-connectivity of at least floor (2pi)/thetas, even with an irregular communication range. Our main contribution is to show how this family of graphs can achieve tunable topology control based on a single parameter thetas. Since the required condition is purely local and geometric, it allows for distributed topology control. For a static network scenario, a power control algorithm based on the NET condition is developed for obtaining k-connected topologies and shown to be significantly efficient compared to existing schemes. In controlled deployment of a mobile network, control over positions of nodes can be leveraged for constructing NET graphs with desired levels of network connectivity and sensing coverage. To establish this, we develop a potential fields based distributed controller and present simulation results for a large network of robots. Lastly, we extend NET graphs to 3D and provide an efficient algorithm to check for the NET condition at each node. This algorithm can be used for implementing generic topology control algorithms in 3D.

Coalescence is the problem of isolated mobile robots independently searching for peers with the goal of forming a single connected network. This is important because communication is a necessary requirement for several collaborative robot tasks. In this paper, we consider a scenario where the robots do not have any information about the environment or positions of other robots and perform a random walk search. We show through probabilistic analysis that as the number of isolated robots N increases, the expected coalescence time decreases as 1/radicN. Simulations results are presented to validate this analysis.

We consider the problem of self-deployment of a mobile sensor network. We are interested in a deployment strategy that maximizes the area coverage of the network with the constraint that each of the nodes has at least K neighbors, where K is a user-specified parameter. We propose an algorithm based on artificial potential fields which is distributed, scalable and does not require a prior map of the environment. Simulations establish that the resulting networks have the required degree with a high probability, are well connected and achieve good coverage. We present analytical results for the coverage achievable by uniform random and symmetrically tiled network configurations and use these to evaluate the performance of our algorithm.