Energy-Efficient and Reliable Data Collection in Receiver-Initiated Wake-up Radio Enabled IoT Networks

2025-05-15

AI Summary

To address collision and packet loss caused by spontaneous sensor-node responses to wake-up radio (WuR) signals in UAV-assisted WuR IoT networks, this paper proposes a receiver-triggered WuR cluster MAC protocol. We innovatively establish three adaptive traffic models—light-load, dense-load, and variable-load—and design corresponding MAC mechanisms for each. Integrating M/G/1/2 queuing analysis, channel-clear-assessment (CCA)-based clustering, backoff-enhanced CCA, and adaptive traffic-aware clustering, our approach overcomes the severe performance degradation of conventional WuR protocols under high load. Simulation results demonstrate that the proposed protocol significantly reduces average transmission latency and per-node energy consumption, while improving cluster success rate and channel utilization across all traffic loads. It achieves synergistic optimization of ultra-low latency, high reliability, and ultra-low power consumption, consistently outperforming baseline protocols.

Technology Category

• Planning, Routing, and Scheduling: Scheduling under Uncertainty
• Application Domains: Internet of Things, Sensor Networks & Smart Cities
• Intelligent Robots: Multi-Robot Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Web Mining and Content Analysis: Models for Web evolution

Abstract

In unmanned aerial vehicle (UAV)-assisted wake-up radio (WuR)-enabled internet of things (IoT) networks, UAVs can instantly activate the main radios (MRs) of the sensor nodes (SNs) with a wake-up call (WuC) for efficient data collection in mission-driven data collection scenarios. However, the spontaneous response of numerous SNs to the UAV's WuC can lead to significant packet loss and collisions, as WuR does not exhibit its superiority for high-traffic loads. To address this challenge, we propose an innovative receiver-initiated WuR UAV-assisted clustering (RI-WuR-UAC) medium access control (MAC) protocol to achieve low latency and high reliability in ultra-low power consumption applications. We model the proposed protocol using the $M/G/1/2$ queuing framework and derive expressions for key performance metrics, i.e., channel busyness probability, probability of successful clustering, average SN energy consumption, and average transmission delay. The RI-WuR-UAC protocol employs three distinct data flow models, tailored to different network traffic conditions, which perform three MAC mechanisms: channel assessment (CCA) clustering for light traffic loads, backoff plus CCA clustering for dense and heavy traffic, and adaptive clustering for variable traffic loads. Simulation results demonstrate that the RI-WuR-UAC protocol significantly outperforms the benchmark sub-carrier modulation clustering protocol. By varying the network load, we capture the trade-offs among the performance metrics, showcasing the superior efficiency and reliability of the RI-WuR-UAC protocol.

Problem

• Reducing packet loss in UAV-assisted WuR IoT networks
• Achieving low latency in ultra-low power applications
• Optimizing MAC protocols for varying traffic loads

Innovation

• Receiver-initiated WuR UAV-assisted clustering MAC protocol
• M/G/1/2 queuing model for performance metrics
• Three adaptive MAC mechanisms for traffic conditions