Routing Protocol for Low-Power and Lossy Networks (RPL) is considered as one of the essential information forwarding options for the Internet of Thing appliances, which possesses a flexible design. However, RPL suffers from the lack of an applicable load balancing mechanism in massive traffic scenarios. Uneven traffic distribution leads to congestion and deteriorates packet losses, delay periods, power consumption handling, and finally, reduces network lifetime. The present study proposed a scheme named “Lightweight Load Balancing and Route Minimizing solution for RPL” (L²RMR), compromising a novel Objective Function (OF) and a new routing metric based on the minimization of path routes. Also, a Probability Function was introduced, which prevents from creating the general Herd Decampment Phenomenon (HDP) problem. The proposed solution enables delayed parent joining for the nodes in order to achieve a lower rank instead of a greedy thundering, leading to multiple instabilities in the network topology. The present study evaluated L²RMR performance using the Contiki-Cooja simulator. To this end, L²RMR was examined under scenarios, including variable network sizes, transmission rates, and density. The simulation results revealed that this mechanism could enhance the average Packet Loss Ratio, End-to-End Delay, and energy consumption criteria in an environment incorporated with RPL and other comparative approaches.

低功耗有损网络的路由协议（RPL）被视为Thing设备Internet的基本信息转发选项之一，它具有灵活的设计。但是，RPL在大规模流量情况下缺乏适用的负载平衡机制。流量分布不均会导致拥塞，并使数据包丢失，延迟时间，功耗处理问题恶化，并最终缩短网络寿命。本研究提出了一个名为“ RPL的轻量级负载平衡和路由最小化解决方案”的方案（L ²RMR），从而基于路径路由的最小化而损害了新颖的目标函数（OF）和新的路由度量。此外，还引入了一个概率函数，以防止产生一般的畜群减贫现象（HDP）问题。所提出的解决方案使得节点的延迟父级加入能够实现较低的等级，而不是贪婪的雷声，从而导致网络拓扑中的多个不稳定性。本研究使用Contiki-Cooja模拟器评估了L ² RMR性能。为此，L ²在各种情况下检查了RMR，包括可变的网络大小，传输速率和密度。仿真结果表明，在与RPL和其他比较方法结合的环境中，该机制可以提高平均数据包丢失率，端到端延迟和能耗标准。