Study of Multi-Armed Bandits for Energy Conservation in Cognitive Radio Sensor Networks

Abstract

Technological advances have led to the emergence of wireless sensor nodes in wireless networks. Sensor nodes are usually battery powered and hence have strict energy constraints. As a result, energy conservation is very important in the wireless sensor network protocol design and the limited power resources are the biggest challenge in wireless network channels. Link adaptation techniques improve the link quality by adjusting medium access control (MAC) parameters such as frame size, data rate, and sleep time, thereby improving energy efficiency. In this paper we present an adaptive packet size strategy for energy efficient wireless sensor networks. The main goal is to reduce power consumption and extend the whole network life. In order to achieve this goal, the paper introduces the concept of a bounded MAB to find the optimal packet size to transfer by formulating different packet sizes for different arms under the channel condition. At the same time, in achieve fast convergence, we consider the bandwidth evaluation according to ACK. The experiment shows that the packet size is adaptive when the channel quality changes and our algorithm can obtain the optimal packet size. We observe that the MAB packet size adaptation scheme achieves the best energy efficiency across the whole simulation duration in comparison with the fixed frame size scheme, the random packet size and the extended Kalman filter (EKF).

Cognitive routing optimization protocol based on multiple channels in wireless sensor networks

“…7 Interestingly, spectral selection may effectively improve the transmission performance of a neighbor sensor node. 4 The distributed subcarriers and power control algorithms aim to minimize the power consumption of each bit information from subcarriers, 8,9 and the simulation results demonstrated that the performance of the proposed approach is close to that of the centralized optimal solution. However, the authors only considered the data transmission rate and power consumption of a neighbor sensor node and disregarded the performance of the entire route.…”

Improvement of LoRa Communication Scalability using Machine Learning Based Adaptiveness

“…Much research has been conducted in the area of computing the optimal communication-channel parameters used by IoT end nodes and the network server [12], [13], [14], [15]. The work has been especially focused on optimizing energy requirements of end nodes [16], [17], [18], [19], [20], selecting a communication channel [21], [22], [23], reducing the number of collisions [24], [25], [26], predicting collisions [27], or making the communication generally more efficient [28], [29], [10].…”