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With the development of low-cost, durable unmanned aerial vehicles (UAVs), it is now practical to perform persistent
sensing and target tracking autonomously over broad surveillance areas. These vehicles can sense the environment
directly through onboard active sensors, or indirectly when aimed toward ground targets in a mission environment by
ground-based passive sensors operating wirelessly as an ad hoc network in the environment. The combination of the
swarm intelligence of the airborne infrastructure comprised of UAVs with the ant-like collaborative behavior of the
unattended ground sensors creates a system capable of both persistent and pervasive sensing of mission environment,
such that, the continuous collection, analysis and tracking of targets from sensor data received from the ground can be
achieved. Mobile software agents are used to implement intelligent algorithms for the communications, formation
control and sensor data processing in this composite configuration. The enabling mobile agents are organized in a
hierarchy for the three stages of processing in the distributed system: target detection, location and recognition from the
collaborative data processing among active ground-sensor nodes; transfer of the target information processed on the
ground to the UAV swarm overhead; and formation control and sensor activation of the UAV swarm for sustained
ground-target surveillance and tracking. Intelligent algorithms are presented that can adapt to the operation of the
composite system to target dynamics and system resources. Established routines, appropriate to the processing needs of
each stage, are selected as preferred based on their published use in similar scenarios, ability to be distributively
implemented over the set of processors at system nodes, and ability to conserve the limited resources at the ground
nodes to extend the lifetime of the pervasive network.
In this paper, the performance of this distributed, collaborative system concept for persistent-pervasive sensing of a
ground environment is assessed via simulation of the selected adaptive algorithms using parameter values planned for
ground sensors and UAVs and mission scenarios found in published studies.
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