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. 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 work describes our progress on a critical part of NIMS, the Task Allocation module. We present our algorithm 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.