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

2024-10-08

arXiv.org

AI Summary

To address the dual challenges of energy constraints and stringent information freshness requirements in industrial IoT sensor networks, this paper proposes a content-driven top-k data retrieval mechanism. Our method introduces (1) *k-QAoI*, a novel metric that integrates physical process dynamics into Age of Information (AoI) modeling for the first time; (2) a Content-driven Wake-up (CoWu) strategy that jointly optimizes wake-up scheduling and adaptive sensing thresholds to enable semantic-aware sparse node activation; and (3) co-optimization of AoI-aware modeling and threshold-based dynamic scheduling to balance timeliness and energy efficiency. Extensive experiments demonstrate that, particularly in large-scale networks and small-*k* scenarios, the proposed approach significantly reduces *k*-QAoI and improves energy efficiency compared to conventional polling—thereby validating its effectiveness and superiority in joint freshness–energy optimization.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Application Domains: Internet of Things, Sensor Networks & Smart Cities
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Web Mining and Content Analysis: Content-based information diffusion
• Graph Algorithms and Modeling for the Web: Querying, indexing, and retrieval in Web-related graphs

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.

Problem

• Energy-efficient top-k data retrieval in IIoT
• Content-based wake-up for timely data collection
• Optimizing CoWu parameters for freshness and efficiency

Innovation

• Content-based wake-up for energy-efficient data retrieval
• Introduces top-k Query Age of Information (k-QAoI) metric
• Optimizes CoWu parameters for freshness and energy efficiency