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

TL;DR

This paper introduces Senses, a MARL-based approach that reduces energy consumption in edge IoT networks by minimizing redundant sensing and data storage, thereby extending device operational lifetime.

Contribution

The paper presents a novel MARL-based framework for dynamic sensor coverage adjustment and data de-duplication in edge IoT, improving energy efficiency and operational duration.

Findings

Senses saves 11.37% energy on control devices.

Senses extends overall IoT network operational time by 20%.

Effective data de-duplication reduces unnecessary data transmission.

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.