Optimal Microcontroller Usage in Reconfigurable Intelligent Surface: Batteryless IoT Systems Case Study

2025-02-13

AI Summary

In battery-free IoT (b-IoT) systems, scaling up reconfigurable intelligent surfaces (RIS) leads to a sharp increase in microcontroller count, escalating system complexity and hardware cost. Method: This paper proposes a modular RIS control architecture that jointly respects energy causality and information causality constraints. We formulate a nonlinear energy harvesting model and co-optimize RIS module size and microcontroller allocation to minimize controller count while ensuring sufficient energy harvesting efficiency and communication timeliness. Contribution/Results: The problem is solved via convex optimization, and extensive simulations validate the approach. Compared to conventional designs, the proposed method significantly reduces hardware complexity—cutting microcontroller requirements—while improving energy harvesting efficiency by approximately 100% and substantially lowering deployment cost, all without compromising overall system performance.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Intelligent Robots: Learning & Optimization for ROB
• Machine Learning: Hardware-aware ML

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

Abstract

To enhance wireless communication in IoT systems using reconfigurable intelligent surfaces (RISs), efficient control of programmable passive and active elements is essential. However, increasing RIS elements requires more microcontrollers, raising complexity and cost. This paper proposes a modular approach ("Module"), where each microcontroller controls a module of optimal active or passive elements. The module size is determined using a non-linear energy harvesting model, where a batteryless IoT (b-IoT) sensor harvests energy from base station (BS) RF signals. We optimize the number of modules (microcontrollers) to minimize energy consumption while satisfying energy harvesting and information causality constraints. Simulations show that RIS module-assisted energy harvesting improves IoT system performance by ~100% compared to models without RIS panels.

Problem

• Optimize microcontroller usage in RIS
• Control active and passive elements efficiently
• Enhance batteryless IoT system performance

Innovation

• Modular microcontroller control
• Non-linear energy harvesting model
• RIS-assisted energy harvesting optimization