Finding the physical location of a specific network node is a prototypical task for navigation inside a wireless network. In this paper, we consider in depth the implications of wireless communication as a measurement input of gradient-based taxis algorithms. We discuss how gradients can be measured and determine the errors of this estimation. We then introduce a gradient-based taxis algorithm as an example of a family of gradient-based, convergent algorithms and discuss its convergence in the context of network robotics. We also conduct an exemplary experiment to show how to overcome some of the specific problems related to network robotics. Finally, we show how to adapt this framework to more complex objectives.
In this paper we explore methods for the online mapping of received radio signal strength with mobile robots and localizing the source of the radio signal. By utilizing Gaussian processes, we are able to build an online model of the signal-strength map that can, in turn, be used to provide the current maximum likelihood estimate of the source location. Furthermore, using the estimate of the source location, the Gaussian process model allows for prediction of received signal strength with confidence bounds in regions of the environment that have not been explored. Finally, we develop a control law for collecting samples of the signal strength with mobile robots that allows for online estimation of the radio signal source. Relative bearing between robots is important in applications like pursuit-evasion [11] and SLAM [7]. This is also true in sensor networks, where the bearing of one sensor node relative to another has been used for localization [5], [18], [20] and topology control [14], [21], [6]. Most systems use dedicated sensors like an IR array or a camera to obtain relative bearing. We study the use of radio signal strength (RSS) in commodity radios for obtaining relative bearing. We show that by using the robot's mobility, commodity radios can be used to obtain coarse relative bearing. This measurement can be used for a suite of applications that do not require very precise bearing measurement. We analyze signal strength variations in simulation and experiment and also show an algorithm that uses this coarse bearing computation in a practical setting. Radio signal strength (RSS) is a reasonable proxy for link quality, but its accurate estimation requires frequency and spatial diversity due to fluctuation caused by fading. We consider a Rayleigh Rician fading model, and gather RSS measurements during motion in a complex environment to enable gradient estimation. Using the RSS gradient, we develop control laws to track active sources. These may be used to establish and preserve connectivity among collaborative autonomous agents, to locate and approach radio sources, as well as deploying agents to assist mobile ad hoc networks (MANETs).  Many previous studies have examined the placement of access points (APs) to improve the community's understanding of the deployment and behavioral characteristics of wireless networks. A key implicit assumption in these studies is that one can estimate the AP location accurately from wardriving-like measurements. However, existing localization algorithms exhibit high error because they over-simplify the complex nature of signal propagation. In this work, we propose a novel approach that localizes APs using directional information derived from local signal strength variations. Our algorithm only uses signal strength information, and improves localization accuracy over existing techniques. Furthermore, the algorithm is robust to the sampling biases and non-uniform shadowing, which are common in wardriving measurements.