Through the Internet of Things (IoT) the internet scope is established by the integration of physical things to classify themselves into mutual things. A physical thing can be created by this inventive perception to signify itself in the digital world. Regarding the physical things that are related to the internet, it is worth noting that considering numerous theories and upcoming predictions, they mostly require protected structures, moreover, they are at risk of several attacks. IoTs are endowed with particular routing disobedience called sinkhole attack owing to their distributed features. In these attacks, a malicious node broadcasts illusive information regarding the routings to impose itself as a route towards specific nodes for the neighboring nodes and thus, attract data traffic. RPL (IP-V6 routing protocol for efficient and low-energy networks) is a standard routing protocol which is mainly employed in sensor networks and IoT. This protocol is called SoS-RPL consisting of two key sections of the sinkhole detection. In the first section rating and ranking the nodes in the RPL is carried out based on distance measurements. The second section is in charge of discovering the misbehavior sources within the IoT network through, the Average Packet Transmission RREQ (APT-RREQ). Here, the technique is assessed through wide simulations performed within the NS-3 environment. Based on the results of the simulation, it is indicated that the IoT network behavior metrics are enhanced based on the detection rate, false-negative rate, false-positive rate, packet delivery rate, maximum throughput, and packet loss rate.

通过物联网（IoT），互联网范围是通过物理事物的集成来建立的，以将自身分类为相互的事物。可以通过这种创造性的感知来创造出一种物理的东西，以在数字世界中表明自己。关于与Internet相关的物理事物，值得注意的是，考虑到众多理论和即将到来的预测，它们大多需要受保护的结构，此外，它们还具有遭受多种攻击的风险。物联网因其分布式功能而被赋予了特殊的路由违规性，被称为沉孔攻击。在这些攻击中，恶意节点广播有关路由的虚假信息，以将自身强加为通往相邻节点特定节点的路由，从而吸引数据流量。RPL（用于高效和低能耗网络的IP-V6路由协议）是一种标准路由协议，主要用于传感器网络和IoT。该协议称为SoS-RPL，由污水坑检测的两个关键部分组成。在第一部分中，基于距离测量对RPL中的节点进行评级和排名。第二部分负责通过平均分组传输RREQ（APT-RREQ）发现IoT网络中的不良行为来源。在这里，该技术是通过在NS-3环境中进行的广泛仿真来评估的。根据仿真结果表明，基于检测率，假阴性率，假阳性率，数据包传送率，最大吞吐量和数据包丢失率，IoT网络行为指标得到了增强。