An Energy-aware Approach with Spectrum Detection in Wireless Sensor Networks

Chengyi Yang, Feng Liu, Siyuan Shen, Jiayin Qi

Abstract: Spectrum detection plays an important role in 5G communication and Internet of things (IoT) networks which is one of the core technologies in wireless communication network. The process of spectrum detection largely depends on energy sensing as this process needs to detect the energy of different frequency bands in the information transmission. In this paper, we propose an energy-aware scheme based on spectrum detection in wireless sensor networks applications, which can save the cost of data collection and computing power of DDoS attack de-tection without collecting a large number of traffic characteris-tics and large-scale deep learning model training. By detecting the information transmission energy in different frequency channels, the busy and idle state of the channel is judged. On the basis of the busy channel detection, the scope of DDoS attack is further judged by the topology of wireless sensor networks. Sim-ulation results show that the proposed energy-aware algorithm can achieve the expected efficiency and accuracy, and is suitable for low load and high security requirements of wireless sensor networks.