Data Mules

From RSN

Some sensor network applications (e.g. habitat monitoring) require collecting data from sensors sparsely deployed over a large area. In these scenarios, robots can act as data mules and gather the data from the sensors. We have been working on such systems for a few years now. Here are some highlights:

In [1], we built a proof-of-concept system and showed experimentally that using robots can yield significant energy savings. More recently, we studied the following problems that arise in systems where mobile robots periodically collect data from (static) wireless sensor network nodes. Suppose we are given approximate locations of the static nodes:

• In what order should the robots visit the nodes? This sounds like TSP, but the problem, which we call the Data Gathering Problem (DGP), differs from TSP in the following aspects: In DGP, the objective is to compute a tour for each robot in such a way that minimizes the time to collect data from all devices. In order to download the data from a device, a robot must visit a point within the communication range of the device (not necessarily the point itself). Then, it spends a fixed amount of time to download the data. Thus, the time to complete a tour depends on not only the travel time but also the time to download the data, and the number of devices visited along the tour (in TSP, it depends only on the distance).
• From the static node's perspective: given the stochastic nature of the robot's arrival, what is an energy-efficient strategy to wake up and send/receive beacon messages? Such a strategy must simultaneously minimize the robot's waiting time and the number of beacon messages. For this problem, we presented an optimal algorithm.
• From the robot's perspective: given the stochastic nature of the wireless link quality, what is an energy-efficient motion strategy to find a good pose (location and orientation) from where the data can be downloaded efficiently? The robot must be able to find such a location quickly but without taking too many measurements so as to conserve the static node's energy. When the signal strength function is arbitrary, it is easy to show that there is no competitive online strategy for this problem. We present an efficient, data-driven heuristic based on experiments.

For the first problem, we present an approximation algorithm in [2]. The last two results appear in [3], along with a system implementation for an indoor data collection application.

Related Papers

1. O. Tekdas, D. Bhadauria, V. Isler. Efficient Data Collection from Wireless Nodes under the Two-Ring Communication Model, International Journal of Robotics Research, 2012. pdf bib
2. D. Bhadauria, O. Tekdas, V. Isler. Robotic Data Mules for Collecting Data over Sparse Sensor Fields, Journal of Field Robotics, 28(3): 388--404, 2011. pdf bib
3. O. Tekdas, N. Karnad, V. Isler. Efficient Strategies for Collecting Data from Wireless Sensor Network Nodes using Mobile Robots, International Symposium on Robotics Research, August, 2009. pdf bibtex
4. D. Bhadauria, V. Isler. Data Gathering Tours for Mobile Robots, IEEE International Conference on Intelligent Robots and Systems (IROS), 2009. pdfbibtex
5. O. Tekdas, J. H. Lim, A. Terzis, V. Isler. Using Mobile Robots to Harvest Data from Sensor Fields, IEEE Wireless Communications, Special Issue on Wireless Communications in Networked Robotics, February 2009. pdf bibtex