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Dynamic Collaboration Between Networked Robots and Clouds in Resource-Constrained Environments
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AI
DOI：10.1109/TASE.2015.2406115.png)
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摘要
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Underwater mobile sensor networks such as Autonomous Underwater Vehicles (AUVs) or robots are envisioned to enable applications for oceanographic data collection, environmental and pollution monitoring, offshore exploration, and distributed tactical surveillance. These applications require running compute-and data-intensive algorithms that go beyond the capabilities of the individual AUVs that are involved in a mission. To execute these task-parallel algorithms in resource-and time-constrained environments, dynamic and reliable collaboration between local networked robots (e.g., AUVs) and remote public Clouds is needed. To this end, the heterogeneous sensing, computing, communication, and storage capabilities of local and remote resources are exploited to form a loosely coupled mobile Cloud, and a novel resource provisioning engine that dynamically takes decisions on what and where the tasks should be executed in the mobile Cloud is introduced. Comparison of benefits of collaboration between local and Cloud resources with purely local and centralized approaches are presented through exhaustive computer simulations. Note to Practitioners-The mission length and operations of any underwater application such as data collection, environmental monitoring, and undersea exploration are severely limited by battery capacity of AUVs. During the course of a mission, many computation-intensive tasks have to be executed, along with establishing communication with other vehicles in the team, which leads to further consumption of battery capacity. In this paper, a communication framework is introduced that expands the resources (computation and data resources) available to the team of AUVs by including public Clouds. A public Cloud consists in a set of networked computers that provide a range of computation and storage resources on demand and at a nominal price. A resource provisioning engine is designed to share the workload between local and Cloud resources based on communication cost, computation cost, and battery capacity. Such a framework enables increasing the lifetime of vehicles, execution of tasks with higher accuracy, and exploitation of any external information in the Cloud that is not available to a team of AUVs in the field.
Keyword:
Amazon EC2
autonomous underwater vehicles
cloud computing
robot coordination
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