Content-based Wake-up for Energy-efficient and Timely Top-k IoT Sensing Data Retrieval

TL;DR

This paper proposes Content-based Wake-up (CoWu), an energy-efficient method for timely collection of top-k sensor data in IIoT, optimizing data freshness and energy use through a new metric and parameter selection.

Contribution

It introduces a novel content-based wake-up scheme for top-k data collection, along with the k-QAoI metric and parameter tuning methods for improved energy efficiency and data freshness.

Findings

CoWu outperforms round-robin scheduling in energy efficiency.

CoWu reduces top-k Query Age of Information (k-QAoI).

Effectiveness increases with larger node counts and smaller k.

Abstract

Energy efficiency and information freshness are key requirements for sensor nodes serving Industrial Internet of Things (IIoT) applications, where a sink node collects informative and fresh data before a deadline, e.g., to control an external actuator. Content-based wake-up (CoWu) activates a subset of nodes that hold data relevant for the sink's goal, thereby offering an energy-efficient way to attain objectives related to information freshness. This paper focuses on a scenario where the sink collects fresh information on top-k values, defined as data from the nodes observing the k highest readings at the deadline. We introduce a new metric called top-k Query Age of Information (k-QAoI), which allows us to characterize the performance of CoWu by considering the characteristics of the physical process. Further, we show how to select the CoWu parameters, such as its timing and threshold, to attain both information freshness and energy efficiency. The numerical results reveal the effectiveness of the CoWu approach, which is able to collect top-k data with higher energy efficiency while reducing k-QAoI when compared to round-robin scheduling, especially when the number of nodes is large and the required size of k is small.