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Resource Allocation for Machine-Type Communication of Energy-Harvesting Devices in Wi-Fi HaLow Networks.
Dmitry Bankov1,2, Evgeny Khorov1,2, Andrey Lyakhov1
1Institute for Information Transmission Problems, Russian Academy of Sciences, 127051 Moscow, Russia.
Sensors (Basel, Switzerland)
|April 30, 2020
Summary
Optimizing Wi-Fi HaLow Restricted Access Window (RAW) configuration for energy harvesting Internet of Things sensors significantly reduces channel resource consumption. This study presents a model for optimal grouping and timing, cutting resource use by nearly 50%.
Area of Science:
- Wireless communication networks
- Internet of Things (IoT) technologies
- Energy harvesting systems
Background:
- Wi-Fi HaLow is adapted for IoT, featuring Restricted Access Window (RAW) for efficient sensor communication.
- RAW enables access points to group sensors and assign exclusive transmission time slots.
- Energy harvesting sensors present unique challenges due to intermittent power availability.
Purpose of the Study:
- To determine optimal Restricted Access Window (RAW) configuration for energy harvesting sensor networks.
- To develop a data transmission model considering channel access and energy constraints.
- To minimize consumed channel resources while ensuring data delivery probability.
Main Methods:
- Developed a data transmission model for energy harvesting sensors within RAW intervals.
- Incorporated device grouping and channel access peculiarities into the model.
- Optimized RAW duration and number of groups based on energy and delivery probability.
Main Results:
- The proposed model effectively configures RAW for energy harvesting IoT scenarios.
- Optimal RAW configuration significantly reduces consumed channel resources.
- Numerical results demonstrate up to a 50% reduction in channel resource consumption.
Conclusions:
- Optimal RAW configuration is crucial for efficient operation of energy harvesting IoT devices.
- The developed model provides a method to balance resource usage and data delivery.
- Wi-Fi HaLow can be effectively utilized for large-scale, energy-constrained IoT deployments.
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