Efficient Data Collection from Wireless Nodes under Two-Ring Communication Model

We introduce a new geometric routing problem which arises in data muling applications where a mobile robot is charged with collecting data from stationary sensors. The objective is to compute the robot's trajectory and download sequence so as to minimize the time to collect the data from all sensors. The total data collection time has two components: the robot's travel time and the download time. The time to download data from a sensor $s$ is a function of the locations of the robot and $s$: If the robot is a distance $r_{in}$ away from $s$, it can download the sensor's data in $T_{in}$ units of time. If the distance is greater than $r_{in}$ but less than $r_{out}$, the download time is T_{out} > T_{in}. Otherwise, the robot can not download the data from $s$. Here, $r_{in}$, $r_{out}$, $T_{in}$ and $T_{out}$ are input parameters. We refer to this model, which is based on recently developed experimental models for sensor network deployments, as the two ring model, and the problem of downloading data from a given set of sensors in minimum amount of time under this model as the Two-Ring Tour (TRT) problem. We present approximation algorithms for the general case which uses solutions to the Traveling Salesperson with Neighborhoods (TSPN) Problem as subroutines. We also present efficient solutions to special but practically important versions of the problem such as uniform and sparse deployment

Maintaining Connectivity in Environments with Obstacles

Robotic routers (mobile robots with wireless communication capabilities) can create an adaptive wireless network and provide communication services for mobile users on-demand. Robotic routers are especially appealing for applications in which there is a single user whose connectivity to a base station must be maintained in an environment that is large compared to the wireless range. In this paper, we study the problem of computing motion strategies for robotic routers in such scenarios, as well as the minimum number of robotic routers necessary to enact our motion strategies. Assuming that the routers are as fast as the user, we present an optimal solution for cases where the environment is a simply-connected polygon, a constant factor approximation for cases where the environment has a single obstacle, and an O(h) approximation for cases where the environment has h circular obstacles. The O(h) approximation also holds for cases where the environment has h arbitrary polygonal obstacles, provided they satisfy certain geometric constraints - e.g. when the set of their minimum bounding circles is disjoint