The results show the effectiveness of EACCO to design reliable, energy neutral and sustainable indoor IoT swarm robotic systems.
Abstract
In this paper, we propose an Energy-Aware Cross-Layer Communication and Computation Optimization (EACCO) framework to maximize energy efficiency and operational lifetime of indoor Internet of Things (IoT) swarm robotic networks. The method takes into account energy harvesting, adaptive scheduling, communication and computation altogether in a single cross-layer optimization framework. Results of simulation show that EACCO reduced the energy consumed per day to 2.0-2.3 Wh/day and obtained the energy neutral ratio (ENR) of 1.05-1.18, increased the packet delivery ratio (PDR) to 95-97% and increased mission lifetime to 30-36 hours compared to other energy-only, EDF and FIFO schedulers under the same operating conditions. The results show the effectiveness of EACCO to design reliable, energy neutral and sustainable indoor IoT swarm robotic systems.
This work presents a multi-mode energy harvesting-assisted edge computing architecture, integrated with a joint optimization of energy consumption and communication behaviour, aimed at enhancing the sustainability, reliability and autonomy of operation in an industrial IoT context.
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