A Synergetic Trust Model Based on SVM in Underwater Acoustic Sensor Networks

Underwater acoustic sensor networks (UASNs) have been widely applied in underwater scenarios where numerous anchoring or floating sensor nodes collaborate in performing some specific assignments, such as information collection or data transmission. In recent years, the trust model has been known as an important tool in responding to attackers inside the network. Although there are a number of trust models recently been suggested as effective methods for terrestrial networks, it is infeasible to directly apply these trust models in UASNs due to the complex underwater environment and the unreliable underwater acoustic communication. For the purpose of achieving accurate and robust trust evaluation for UASNs, a synergetic trust model based on SVM (STMS) is proposed in this paper. The network is divided into a certain number of interconnected clusters in which cluster heads and cluster members (CMs) are synergetic to perform functions. The STMS is mainly comprised of three parts. In the first part, three kinds of trust evidences, which are refined elaborately to reflect most of the attack results, are generated by CMs. In the second part, the support vector machine (SVM) technology is adopted in training a trust prediction model for evaluating accurate trust value. Furthermore, the mechanism of double cluster heads is presented to improve network security and lifetime in the third part. Simulation results demonstrate that STMS performs better than other related works in the sparse deployment environment, which is reflected in the aspect of detect accuracy of malicious nodes, success rate of communication and network lifetime.