Secret Key Generation In WSN Via Received Signal Strength Post Processing

Recently, the security of Wireless Sensor Network(WSN) have been paid much attentions. Generating secret keys is one of the most important things in WSN’s security. Wireless channel characteristic based secret key generation method has been rapidly developed. Wireless channel has the characters of reciprocity, uniqueness-based-region, rapidly-change, and unpredictable. The secret key extracting from wireless channel characteristic is changing constantly, with the change of communication environment and spatial location. So it can achieve the target of “once a secret” in cryptography. Due to the fact that attackers and legitimate nodes are in different positions, the channel characteristic between attackers and legitimate nodes is different. So, it is very hard for attackers to generation the same key with legitimate nodes.It usually uses receive signal strength(RSS) and Multipath information as the channel characteristics in the research of extracting secret keys based on wireless channel characteristics in physical layer. This paper introduces the character electromagnetic propagation, channel reciprocity, and other theoretical foundation of extracting from channel characteristics, then raises the following content.1. The paper will discuss the randomness of accumulated RSS and improve the Level Crossing Algorithm. The Level Crossing Algorithm designed by Mathur in literature[1] is based on the quantization of the directly measuring RSS value. This paper will discuss the randomness of the accumulated RSS and show that quantifying the accumulated RSS value to get secret keys in the Level Crossing Algorithm can raise t he key generation rate in theory.2. The paper will design a novel secret key generation method based on accumulated RSS’s randomness. The method quantifies the accumulated RSS to get secret key, and designs the four level cycle rounds in the quantization stage. This secret key generation method is deployed in four different kinds of typical real environments. At last, this paper evaluates the key generation rate and the randomness of secret key. The result ofthe evaluation shows that this method can well improve the key generation rate.