Haseeb Khalid, Almogren Ahmad, Ud Din Ikram, Islam Naveed, Altameem Ayman
Department of Computer Science, Islamia College Peshawar, Peshawar 25000, Pakistan.
Chair of Cyber Security, Department of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh 11633, Saudi Arabia.
Sensors (Basel). 2020 Apr 27;20(9):2468. doi: 10.3390/s20092468.
Nowadays, the integration of Wireless Sensor Networks (WSN) and the Internet of Things (IoT) provides a great concern for the research community for enabling advanced services. An IoT network may comprise a large number of heterogeneous smart devices for gathering and forwarding huge data. Such diverse networks raise several research questions, such as processing, storage, and management of massive data. Furthermore, IoT devices have restricted constraints and expose to a variety of malicious network attacks. This paper presents a Secure Sensor Cloud Architecture (SASC) for IoT applications to improve network scalability with efficient data processing and security. The proposed architecture comprises two main phases. Firstly, network nodes are grouped using unsupervised machine learning and exploit weighted-based centroid vectors for the development of intelligent systems. Secondly, the proposed architecture makes the use of sensor-cloud infrastructure for boundless storage and consistent service delivery. Furthermore, the sensor-cloud infrastructure is protected against malicious nodes by using a mathematically unbreakable one-time pad (OTP) encryption scheme to provide data security. To evaluate the performance of the proposed architecture, different simulation experiments are conducted using Network Simulator (NS3). It has been observed through experimental results that the proposed architecture outperforms other state-of-the-art approaches in terms of network lifetime, packet drop ratio, energy consumption, and transmission overhead.
如今，无线传感器网络（WSN）与物联网（IoT）的集成引起了研究界对实现先进服务的极大关注。物联网网络可能包含大量异构智能设备，用于收集和转发海量数据。如此多样化的网络引发了若干研究问题，例如海量数据的处理、存储和管理。此外，物联网设备存在诸多限制，并面临各种恶意网络攻击。本文提出了一种用于物联网应用的安全传感器云架构（SASC），以通过高效的数据处理和安全性来提高网络可扩展性。所提出的架构包括两个主要阶段。首先，使用无监督机器学习对网络节点进行分组，并利用基于加权的质心向量来开发智能系统。其次，所提出的架构利用传感器云基础设施进行无限存储和持续的服务交付。此外，通过使用数学上不可破解的一次性密码本（OTP）加密方案来保护传感器云基础设施免受恶意节点攻击，以提供数据安全性。为了评估所提出架构的性能，使用网络模拟器（NS3）进行了不同的模拟实验。通过实验结果观察到，所提出的架构在网络寿命、数据包丢弃率、能耗和传输开销方面优于其他现有方法。
