Frequency-hopping scheduling algorithm for energy-efficient IoT, long-range, wide-area networks

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Abstract

A long-range, wide-area network is a cost-effective, energy-efficient technology for wide-area sensor networks. But the massive Internet of Things (IoT) brings challenges such as increased traffic and energy consumption. Thus, there is a pressing need to design a scheduling strategy to improve network energy efficiency without compensating for its reliability. We have proposed a deep deterministic policy gradient-based scheduling algorithm with a frequency-hopping spread spectrum that avoids repeated collisions and retransmissions. Frequency-hopping divides frequency channels into subchannels, allowing multiple devices to operate simultaneously. This makes it a favorable scheduling strategy for dense networks, as it reduces collisions and energy consumption. Scheduling in a long-range, wide-area network involves selecting transmission parameters for each device, which can be cumbersome. We used the deep deterministic policy gradient algorithm to optimize schedule generation for high-density networks, enhancing energy efficiency. In this paper, we compared the performance of the frequency-hopping spread spectrum with other heuristic and machine learning-based algorithms using the LoRaSim simulator. We observed a 42% increase in the packet delivery ratio and a 17% improvement in energy efficiency with our solution, along with detailed results on the transmission time and collision reduction.

Keywords

deep deterministic policy gradient / energy efficiency / frequency hopping spread spectrum / LoRaWAN / massive IoT / scheduling

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