A Synergetic Trust Model Based on SVM in Underwater Acoustic Sensor Networks
A Synergetic Trust Model Based on SVM in Underwater Acoustic Sensor Networks
复制标题
水声传感器网络中基于SVM的协同信任模型
DOI：
10.1109/tvt.2019.2939179
复制
发表时间：
2019-11-01
影响因子：
6.8
通讯作者：
Ansere, James Adu
中科院分区：
文献类型：
--
作者：
Han, Guangjie;He, Yu;Ansere, James Adu
水下声学传感器网络（UASN）已广泛应用于水下场景，其中众多锚定或浮动传感器节点协作执行某些特定任务，例如信息收集或数据传输。近年来，信任模型已被认为是应对网络内部攻击的重要工具。虽然有一些信任模型最近被建议作为陆地网络的有效方法，它是不可行的，直接将这些信任模型应用于UASNs由于复杂的水下环境和不可靠的水声通信。为了实现对用户自组织网络的准确和鲁棒的信任评估，提出了一种基于支持向量机的协同信任模型（STMS）。该网络被划分为一定数量的相互连接的簇，簇头和簇成员（CM）协同执行功能。培训计划主要由三部分组成。在第一部分中，三种信任证据，这是精心提炼，以反映大部分的攻击结果，由CM产生。第二部分采用支持向量机（SVM）技术训练信任预测模型，以评估准确的信任值。第三部分提出了双簇头机制来提高网络的安全性和生存时间。仿真结果表明，在稀疏部署环境下，STMS在恶意节点检测准确率、通信成功率和网络生命周期等方面均优于其他相关工作。
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.