Sumathi Appasamy C, Akila Muthuramalingam, Pérez de Prado Rocío, Wozniak Marcin, Divakarachari Parameshachari Bidare
Department of CSE, KPR Institute of Engineering and Technology, Coimbatore, Tamil Nadu 641407, India.
Telecommunication Engineering Department, University of Jaén, 23071 Linares, Spain.
Sensors (Basel). 2021 Nov 16;21(22):7611. doi: 10.3390/s21227611.
Smart home and smart building systems based on the Internet of Things (IoT) in smart cities currently suffer from security issues. In particular, data trustworthiness and efficiency are two major concerns in Internet of Things (IoT)-based Wireless Sensor Networks (WSN). Various approaches, such as routing methods, intrusion detection, and path selection, have been applied to improve the security and efficiency of real-time networks. Path selection and malicious node discovery provide better solutions in terms of security and efficiency. This study proposed the Dynamic Bargaining Game (DBG) method for node selection and data transfer, to increase the data trustworthiness and efficiency. The data trustworthiness and efficiency are considered in the Pareto optimal solution to select the node, and the bargaining method assigns the disagreement measure to the nodes to eliminate the malicious nodes from the node selection. The DBG method performs the search process in a distributed manner that helps to find an effective solution for the dynamic networks. In this study, the data trustworthiness was measured based on the node used for data transmission and throughput was measured to analyze the efficiency. An SF attack was simulated in the network and the packet delivery ratio was measured to test the resilience of the DBG and existing methods. The results of the packet delivery ratio showed that the DBG method has higher resilience than the existing methods in a dynamic network. Moreover, for 100 nodes, the DBG method has higher data trustworthiness of 98% and throughput of 398 Mbps, whereas the existing fuzzy cross entropy method has data trustworthiness of 94% and a throughput of 334 Mbps.
智慧城市中基于物联网（IoT）的智能家居和智能建筑系统目前存在安全问题。特别是，数据可信度和效率是基于物联网（IoT）的无线传感器网络（WSN）中的两个主要问题。已经应用了各种方法，如路由方法、入侵检测和路径选择，以提高实时网络的安全性和效率。路径选择和恶意节点发现从安全性和效率方面提供了更好的解决方案。本研究提出了用于节点选择和数据传输的动态讨价还价博弈（DBG）方法，以提高数据可信度和效率。在帕累托最优解中考虑数据可信度和效率来选择节点，并且讨价还价方法为节点分配分歧度量以从节点选择中消除恶意节点。DBG方法以分布式方式执行搜索过程，这有助于为动态网络找到有效的解决方案。在本研究中，基于用于数据传输的节点来测量数据可信度，并测量吞吐量以分析效率。在网络中模拟了SF攻击，并测量了数据包交付率以测试DBG方法和现有方法的弹性。数据包交付率的结果表明，在动态网络中，DBG方法比现有方法具有更高的弹性。此外，对于100个节点，DBG方法具有98%的更高数据可信度和398 Mbps的吞吐量，而现有的模糊交叉熵方法具有94%的数据可信度和334 Mbps的吞吐量。
