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Energy-Aware IoT Deployment Planning
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AI
DOI：10.1145/3649153.3649864.png)
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Increasingly, the Internet of Things (IoT) is evolving toward an architecture consisting of sensing and actuation devices communicating with edge computers and storage systems. These edge deployments localize communication, computation, and storage for security, increased efficiencies (e.g. lower latency response), and reliability. In settings where electrical power infrastructure is lacking, however, these deployments typically rely on renewable energy and battery storage for power. In this paper, we investigate power-optimizing scheduling for IoT communication in edge deployments that compose battery-powered sensors. We focus on radio communication since it is often the largest component of the sensor battery budget in edge deployments. We model radio communication between sensors and co-located base stations using their distance, bandwidth availability, and duty cycle for data transmission. We present three heuristic approaches that allocate radio bandwidth to minimize sensor power consumption. We consider both shared and decentralized battery infrastructure. We empirically analyze these approaches in terms of performance and computational efficiency and present a methodology for using these techniques to inform configuration and management of energy-efficient edge deployments.
Keyword:
communication scheduling
numerical optimization
IoT deployment planning
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