Resource-aware IoT Control: Saving Communication through Predictive Triggering

TL;DR

Novel predictive and self triggering protocols are proposed for feedback control of resource-aware IoT control allowing agents to decide online whether communication with other agents is needed, or not, from a unified Bayesian decision framework.

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.

Figures (15)

• Figure 1: Abstraction of an IoT control system. Each Thing is composed of Dynamics representing its physical entity and an Agent representing its algorithm unit. Dynamics and Agent are interconnected via sensors (S) and actuators (A). The Network connects all things to the IoT.
• Figure 2: Algorithmic components implemented on each agent $i=1,\dots,N$ of the IoT control system in Fig. \ref{['fig:iotControlSchematic']}. Agent $i$'s control decision is based on local information (State Estimation) and predictions of all (or a subset of) other things (Prediction Thing 1 to $N$). Each agent sends an update (Event Trigger) to all other agents whenever the prediction of its own state (Prediction Thing $i$) deviates too far from the truth, so that predictions can be reset (R).
• Figure 3: Predictive triggering problem. The sensor agent $i$ runs a local State Estimator and transmits its estimate $\hat{x}_k^i$ to the remote agent $j$ in case of a positive triggering decision ($\gamma_k^i = 1$). The predictive trigger computes the triggering decisions ($\gamma_{k+M}^i \in \{0,1\}$) $M$ steps ahead of time. This information can be used by the network to allocate resources. Local control (cf. Fig. \ref{['fig:agentSchematic']}) is omitted here for clarity, but treated in the analysis.
• Figure 4: Example \ref{['ex:ex1']} with self trigger (ST). TOP: KF estimation error $\hat{e}=x-\hat{x}$ ( blue) and remote error $e=x-\hat{x}$ ( orange). MIDDLE: components of the triggering signal $\bar{E}^\text{mean}$\ref{['eq:trigSigMean']} ( blue), $\bar{E}^\text{var}$\ref{['eq:trigSigVar']} ( black, hidden), the triggering signal $\bar{E} = \bar{E}^\text{mean} + \bar{E}^\text{var}$ ( orange), and the threshold $C_k = 0.6$ ( dashed). BOTTOM: triggering decisions $\gamma$.
• Figure 5: Example \ref{['ex:ex1']} with predictive trigger (PT) and $C_k = 0.6$. Coloring of the signals is the same as in Fig. \ref{['fig:example1_1']}. The triggering behavior is stochastic.

Theorems & Definitions (14)

• Remark 1
• Remark 2
• Remark 3
• Lemma 1
• proof
• Lemma 2
• proof
• Corollary 1
• proof
• Proposition 1