# Resource-aware IoT Control: Saving Communication through Predictive Triggering

## TL;DR

This paper introduces resource-aware IoT control methods using predictive and self-triggering protocols to reduce communication load, demonstrated through hardware experiments and simulations.

## Contribution

It proposes novel predictive and self-triggering schemes based on Bayesian decision theory for efficient IoT communication management.

## Key findings

- Significant reduction in network traffic achieved.
- Effective control performance demonstrated in hardware experiments.
- Scalability discussed with multi-vehicle simulation.

## Abstract

The Internet of Things (IoT) interconnects multiple physical devices in large-scale networks. When the 'things' coordinate decisions and act collectively on shared information, feedback is introduced between them. Multiple feedback loops are thus closed over a shared, general-purpose network. Traditional feedback control is unsuitable for design of IoT control because it relies on high-rate periodic communication and is ignorant of the shared network resource. Therefore, recent event-based estimation methods are applied herein for resource-aware IoT control allowing agents to decide online whether communication with other agents is needed, or not. While this can reduce network traffic significantly, a severe limitation of typical event-based approaches is the need for instantaneous triggering decisions that leave no time to reallocate freed resources (e.g., communication slots), which hence remain unused. To address this problem, novel predictive and self triggering protocols are proposed herein. From a unified Bayesian decision framework, two schemes are developed: self triggers that predict, at the current triggering instant, the next one; and predictive triggers that check at every time step, whether communication will be needed at a given prediction horizon. The suitability of these triggers for feedback control is demonstrated in hardware experiments on a cart-pole, and scalability is discussed with a multi-vehicle simulation.