Sensing and Storing Less: A MARL-based Solution for Energy Saving in Edge Internet of Things

2025-10-23

AI Summary

To address the high energy consumption caused by explosive growth and redundant deployment of sensor data in edge IoT networks, this paper proposes a cooperative energy-saving optimization framework based on multi-agent reinforcement learning (MARL). The method jointly optimizes sensor-level dynamic coverage control, controller-level data partitioning, and global device load balancing to enable fine-grained identification and elimination of redundant data. Its key innovation lies in modeling sensing, storage, and transmission strategies as an integrated multi-agent collaborative decision-making process, enabling real-time resource allocation at the network edge. Experimental results demonstrate that the proposed framework reduces energy consumption of control devices by 11.37% and extends the overall network lifetime by 20%, significantly improving edge-side energy efficiency and data processing performance.

Technology Category

• Machine Learning: Learning on the Edge & Model Compression
• Multiagent Systems: Multiagent Learning
• Intelligent Robots: Learning & Optimization for ROB

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

As the number of Internet of Things (IoT) devices continuously grows and application scenarios constantly enrich, the volume of sensor data experiences an explosive increase. However, substantial data demands considerable energy during computation and transmission. Redundant deployment or mobile assistance is essential to cover the target area reliably with fault-prone sensors. Consequently, the ``butterfly effect" may appear during the IoT operation, since unreasonable data overlap could result in many duplicate data. To this end, we propose Senses, a novel online energy saving solution for edge IoT networks, with the insight of sensing and storing less at the network edge by adopting Muti-Agent Reinforcement Learning (MARL). Senses achieves data de-duplication by dynamically adjusting sensor coverage at the sensor level. For exceptional cases where sensor coverage cannot be altered, Senses conducts data partitioning and eliminates redundant data at the controller level. Furthermore, at the global level, considering the heterogeneity of IoT devices, Senses balances the operational duration among the devices to prolong the overall operational duration of edge IoT networks. We evaluate the performance of Senses through testbed experiments and simulations. The results show that Senses saves 11.37% of energy consumption on control devices and prolongs 20% overall operational duration of the IoT device network.

Problem

• Reduces energy consumption in edge IoT networks through data de-duplication
• Dynamically adjusts sensor coverage to eliminate redundant data collection
• Balances device operation duration to extend overall network lifespan

Innovation

• Uses MARL for edge IoT energy optimization
• Dynamically adjusts sensor coverage for de-duplication
• Partitions data and balances device operation durations