Task allocation for event-aware spatiotemporal sampling of environmental variables

Abstract

Abstract-Monitoring of environmental phenomena with embedded networked sensing confronts the challenges of both unpredictable variability in the spatial distribution of phenomena, coupled with demands for a high spatial sampling rate in three dimensions. For example, low distortion mapping of critical solar radiation properties in forest environments may require two-dimensional spatial sampling rates of greater than 10 samples/m 2 over transects exceeding 1000 m 2 . Clearly, adequate sampling coverage of such a transect requires an impractically large number of sensing nodes. A new approach, Networked Infomechanical System (NIMS), has been introduced to combine autonomous-articulated and static sensor nodes enabling sufficient spatiotemporal sampling density over large transects to meet a general set of environmental mapping demands. This paper describes our work on a critical parts of NIMS, the Task Allocation module. We present our methodologies and the two basic greedy Task Allocation policies -based on time of the task arrival (Time policy) and distance from the robot to the task (Distance policy). We present results from NIMS deployed in a forest reserve and from a lab testbed. The results show that both policies are adequate for the task of spatiotemporal sampling, but also complement each other. Finally, we suggest the future direction of research that would both help us better quantify the performance of our system and create more complex policies combining time, distance, information gain, etc.

Multiscale Sensing: A new paradigm for actuated sensing of high frequency dynamic phenomena

“…Following the methodology developed in [5], OMRTA consists of assigning available robots to sampling tasks according to an online greedy heuristic that will maximize the utility in a given time epoch. We have implemented two heuristics -Sampling Area and Service Time.…”

“…Note that the physical delay in image acquisition dictates the length of the decision epoch [5] to also equal 15 seconds. A down-looking imager captured snapshots (768x480 pixels) of the understory of a forest canopy covering an area approximately 6 meters in length by 4 meters in width.…”

“…Another approach to reduce sampling latency is to use a combination of static sensing elements and mobile robots [4], [5]. In [4], [5] a set of n static sensors is deployed such that the environment is discretized into n regions (one sensor in each region).…”

“…In [4], [5] a set of n static sensors is deployed such that the environment is discretized into n regions (one sensor in each region). Next, these sensors monitor corresponding regions for events of interest (such as high concentration of a phenomenon).…”

Mobile Element Scheduling with Dynamic Deadlines

“…For instance, in the NIMS system [2] mentioned in the beginning, the mobile NIMS node is equipped with sensing capability and moves to build a spatio-temporal map of the sensed phenomenon. Scheduling is essential when an event-aware [19] sampling is desired instead of a raster scan. Another example is the Cyclops-based [20] sensor network, where the nodes have a smart vision sensor.…”