Scholarlink

Routing techniques enable routers to communicate with one another in a network. RPL, the standardized routing protocol for IoT networks, consists of two default objective functions: OF0 and MRHOF, routing metrics based on Hop Count and Expected Transmission Count, respectively. The performance of an objective function for optimal route selection depends upon the radio mechanism used by the network and the number of nodes in the network. This work evaluates the performance of OF0 and MRHOF objective functions in terms of energy consumption and packet delivery ratio using UDGMDL, UDGMCL, and MRM radio mechanisms with a node size of 25, 30, 40, and 50. Experimentation was performed in Contiki OS, and the Python script was used for analysis. In a dense network, the MRHOF objective function within the MRM model shows a significant enhancement of 32.39% compared to UDGMDL and an improvement of 13.25% compared to UDGMCL.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Evaluation of Objective Functions of RPL in the Contiki Simulator on Various Radio Models

  • 会议论文

  • 发表日期：

    2025年

查看全文
  • Safia Gul,
  • Bilal Ahmed Malik,
  • M. Tariq Banday

摘要

Routing techniques enable routers to communicate with one another in a network. RPL, the standardized routing protocol for IoT networks, consists of two default objective functions: OF0 and MRHOF, routing metrics based on Hop Count and Expected Transmission Count, respectively. The performance of an objective function for optimal route selection depends upon the radio mechanism used by the network and the number of nodes in the network. This work evaluates the performance of OF0 and MRHOF objective functions in terms of energy consumption and packet delivery ratio using UDGMDL, UDGMCL, and MRM radio mechanisms with a node size of 25, 30, 40, and 50. Experimentation was performed in Contiki OS, and the Python script was used for analysis. In a dense network, the MRHOF objective function within the MRM model shows a significant enhancement of 32.39% compared to UDGMDL and an improvement of 13.25% compared to UDGMCL.