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首页> 外文期刊>International journal of communication systems >A lightweight IoT-based security framework for inventory automation using wireless sensor network
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A lightweight IoT-based security framework for inventory automation using wireless sensor network

机译:基于物联网的轻量级安全框架,用于使用无线传感器网络进行库存自动化

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摘要

Internet of things (IoT) has evolved as an innovation of next generation in this world of smart devices. IoT tends to provide services for data collection, data management, and data and device security required for application development. Things or devices in IoT communicate and compute to make our lives comfortable and safe. In inventory automation, real-time check on items, their information management, and status management, monitoring can be carried out using IoT. The huge amount of data that flows among the devices in the network demands for a security framework that ensures authentication, authorization, integrity, and confidentiality of data. The existing security solutions like SIMON or SPECK offer lightweight security solutions but are vulnerable to differential attack because of their simplicity. Moreover, existing solutions do not offer inbuilt authentication. Therefore, this research work contributes a secure and lightweight IoT-based framework using wireless sensor network (WSN) as a technology. The existing security solutions SPECK and SIMON are compared with the proposed security approach using COOJA simulator. The results show that proposed approach outstands others by 2% reduction in number of CPU cycles, 10% less execution time, 4% less memory requirements of security approach, and with minimum 10% more security impact.
机译:物联网(IoT)已经发展成为智能设备世界中的下一代创新。物联网倾向于为数据收集,数据管理以及应用程序开发所需的数据和设备安全性提供服务。物联网中的事物或设备进行通信和计算,以使我们的生活舒适安全。在库存自动化,项目的实时检查,其信息管理和状态管理中,可以使用IoT进行监视。在网络中的设备之间流动的大量数据需要安全框架,以确保数据的身份验证,授权,完整性和机密性。现有的安全解决方案(如SIMON或SPECK)提供了轻量级的安全解决方案,但由于其简单性而容易受到差异攻击。此外,现有解决方案不提供内置身份验证。因此,这项研究工作使用无线传感器网络(WSN)作为技术,为基于IoT的安全轻量级框架做出了贡献。将现有的安全解决方案SPECK和SIMON与使用COOJA模拟器提出的安全方法进行了比较。结果表明,所提出的方法在CPU周期数上减少了2%,执行时间减少了10%,安全方法的内存需求减少了4%,并且对安全性的影响至少减少了10%,从而比其他方法更具优势。

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