Edge server manages IoT memory to save energy and keep data fresh
Energy-Efficient Timely Relevant IoT Data Retrieval with Wireless Memory Approximation
Networking and Internet Architecture
Summary
Collecting timely and useful data from Internet of Things (IoT) devices while saving energy is tough because devices need power to keep important data ready. The authors propose a way where the edge server decides when to wake up devices and approximates stored memory, reducing energy use without losing data freshness. Their method outperforms always-on devices by balancing when and how data is refreshed and transmitted. This makes IoT data collection more energy-efficient.
What this means in practice
- •For iot system engineers: Develop IoT data collection systems that adjust device memory usage and communication timing to reduce energy consumption while maintaining timely data updates.
- •For edge network operators: Implement edge logic to remotely control IoT device memory activation and filtering to improve energy efficiency under strict data freshness constraints.
Authors
Junya Shiraishi, Zihang Song, Morten Andersen, Petar Popovski
Abstract
Collecting relevant data from Internet of Things devices under freshness and energy constraints is a key challenge in 6G edge networks. A lightweight neural network on the device filters irrelevant observations, but storing it in dynamic random access memory consumes refresh energy during standby. This letter proposes a pull-based framework where an edge server remotely activates and approximates memory and jointly selects the activation timing, refresh period, model size, and transmission threshold. We derive the expected freshness and energy over erasure channels in closed form and show that the scheme consumes less energy than an always-on baseline under the same freshness constraint.