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

TL;DR

This work tackles congestion and reliability challenges in UAV-assisted wake-up radio IoT networks by introducing RI-WuR-UAC, a receiver-initiated wake-up MAC that supports three data-flow models (CCA clustering for light loads, CSMA-CA clustering for dense loads, and ADP clustering for variable loads) to manage high traffic during UAV-triggered MR activation. An $M/G/1/2$ queuing framework is developed to derive key metrics such as the channel busyness probability $\alpha$, JReq loss $P_{Loss}$, average HoL delay $E[D_{HoL}]$, and average transmissions per round $E[\tau]$, along with energy consumption $E_R$. The UAV acts as a cluster head (CH) forming independent, non-overlapping clusters, and data are collected in TDMA slots assigned during a setup phase, followed by data transmission in the steady-state phase. Simulation results show substantial improvements in energy efficiency and reliability over the SCM-WuR benchmark, with a trade-off of increased transmission delay, and reveal that CCA clustering provides the best energy-delay balance while ADP clustering often yields favorable reliability under higher loads. The proposed framework advances UAV-based data collection by enabling scalable, energy-aware, reliable operation in mission-driven IoT deployments.

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.

Figures (10)

• Figure 1: The proposed system model highlighting the single cluster formation.
• Figure 2: Illustration of a single round of the RI-WUR-UAC protocol's working principle, highlighting the processes occurring at the UAV and WuR-enabled SNs in the right and left vertical big blocks, respectively. The setup phase and steady-state phase of the protocol are highlighted in the top and bottom horizontal big blocks, respectively.
• Figure 3: Illustration of a single round of a UAV for the proposed protocol.
• Figure 4: Working procedure of the (a) CCA clustering, (b) CSMA-CA clustering, and (c) ADP clustering for reliable transmission of JReq frame.
• Figure 5: Structure of JReq frame showing the assignment of bits to the frame control field.