Study of Multi-Armed Bandits for Energy Conservation in Cognitive Radio Sensor Networks
AbstractTechnological 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). View Full-Text
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Zhang, J.; Jiang, H.; Huang, Z.; Chen, C.; Jiang, H. Study of Multi-Armed Bandits for Energy Conservation in Cognitive Radio Sensor Networks. Sensors 2015, 15, 9360-9387.
Zhang J, Jiang H, Huang Z, Chen C, Jiang H. Study of Multi-Armed Bandits for Energy Conservation in Cognitive Radio Sensor Networks. Sensors. 2015; 15(4):9360-9387.Chicago/Turabian Style
Zhang, Juan; Jiang, Hong; Huang, Zhenhua; Chen, Chunmei; Jiang, Hesong. 2015. "Study of Multi-Armed Bandits for Energy Conservation in Cognitive Radio Sensor Networks." Sensors 15, no. 4: 9360-9387.