Learning nodes: machine learning-based energy and data management strategy

Abstract

The efficient use of resources in wireless communications has always been a major issue. In the Internet of Things (IoT), the energy resource becomes more critical. The transmission policy with the aid of a coordinator is not a viable solution in an IoT network, since a node should report its state to the coordinator for scheduling and it causes serious signaling overhead. Machine learning algorithms can provide the optimal distributed transmission mechanism with little overhead. A node can learn by itself by utilizing the machine learning algorithm and make the optimal transmission decision on its own. In this paper, we propose a novel learning Medium Access Control (MAC) protocol with learning nodes. Nodes learn the optimal transmission policy, i.e., minimizing the data and energy queue levels, using the Q-learning algorithm. The performance evaluation shows that the proposed scheme enhances the queue states and throughput.

PPAASS: Practical Power-Aware Duty Cycle Algorithm for Solar Energy Harvesting Sensors

“…These works aim to have the same battery backup level at the beginning and end of a period. Others, such as [17], [18], [22], [25], [27], aim to keep the duty cycle stable in order to avoid abrupt variations. Finally, some works try to optimize the performance of the network by individually taking advantage of the device's energy-neutral operation.…”

“…Some works omit energy predictions while adapting the device's duty cycle in order to decrease the computational complexity of such algorithms. These include algorithms using machine learning methods such as those shown in [15], [22] and [27]. They mostly use reinforcement learning methods or a combination with deep learning.…”