SummarySource location privacy is a developing research topic in the social Internet of Things. Source location privacy holds paramount importance in security critical wireless sensor network applications like tracking and monitoring. Several methods have been proposed for source location privacy in the social Internet of Things, but the existing methods have some issues such as improper path selection, the transmission of duplicate messages, and low network lifetime. To overcome these issues, a source location privacy protocol based on energy‐efficient and link‐reliable multi‐scale bifurcated deep Capsnet routing in the social Internet of Things is proposed in this manuscript. At first, the optimal route for the source is selected with the help of energy‐efficient and link‐reliable routing, this method helps to avoid improper path selection. To estimate the quality of the selected optimal path, the multi‐scale bifurcated deep Capsnet is applied. The introduced method is executed in MATLAB. The introduced method's performance is estimated with the aid of several performances evaluating metrics like sensitivity, energy consumption, network lifetime, safety period, and delay.

中文翻译：

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社交物联网中基于源位置隐私协议的节能且链路可靠的多尺度分叉深度 Capsnet 路由

摘要源位置隐私是社交物联网中一个正在发展的研究课题。源位置隐私在跟踪和监控等安全关键无线传感器网络应用中至关重要。针对社交物联网中的源位置隐私，人们提出了多种方法，但现有方法存在路径选择不当、重复消息传输和网络寿命低等问题。为了克服这些问题，本手稿提出了一种基于社交物联网中节能且链路可靠的多尺度分叉深度 Capsnet 路由的源位置隐私协议。首先，借助节能和链路可靠的路由选择源的最佳路由，该方法有助于避免不当的路径选择。为了估计所选最佳路径的质量，应用了多尺度分叉深度 Capsnet。所介绍的方法在MATLAB中执行。所引入方法的性能是借助灵敏度、能耗、网络寿命、安全期和延迟等多种性能评估指标来估计的。