Scholarlink

<p>IoT sensors for smart agriculture have huge potential, but effectiveness depends on what lies beneath in terms of data processing infrastructure. This paper compares and evaluates a cloud computing model with a fog computing model for real-time soil moisture monitoring and rainfall prediction for an agricultural IoT environment. The system was simulated using the iFogSim toolkit to determine data latency, data transfer efficiency, and speed of actionable intelligence. The results show that the fog computing model that stores data locally for processing reduced the average application delay by 94%, from 200&#xa0;ms to only 12&#xa0;ms, for time-sensitive or critical tasks compared to the centralized cloud model. In addition, by supporting local computation, the fog architecture significantly decreased network usage by 93% (from 161,520 to 10,669 MB per farm for four devices), which substantially lowered bandwidth consumption. The results confirm that fog computing is a more responsive, reliable, and resource-saving architecture for time-sensitive agricultural applications, offering a stable solution for areas with intermittent or sparse internet connectivity. This paper emphasizes the need for a decentralized computing strategy to leverage the optimum potential of IoT for sustainable and resilient agriculture.</p>

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

Latency-aware and energy-efficient fog computing framework for IoT-based smart irrigation control in precision agriculture

  • 期刊论文

  • 发表日期：

    2025年12月17日

查看全文
  • Rishika Agarwal,
  • Parth Kapur,
  • Prabh Deep Singh,
  • Narpinder Singh

摘要

IoT sensors for smart agriculture have huge potential, but effectiveness depends on what lies beneath in terms of data processing infrastructure. This paper compares and evaluates a cloud computing model with a fog computing model for real-time soil moisture monitoring and rainfall prediction for an agricultural IoT environment. The system was simulated using the iFogSim toolkit to determine data latency, data transfer efficiency, and speed of actionable intelligence. The results show that the fog computing model that stores data locally for processing reduced the average application delay by 94%, from 200 ms to only 12 ms, for time-sensitive or critical tasks compared to the centralized cloud model. In addition, by supporting local computation, the fog architecture significantly decreased network usage by 93% (from 161,520 to 10,669 MB per farm for four devices), which substantially lowered bandwidth consumption. The results confirm that fog computing is a more responsive, reliable, and resource-saving architecture for time-sensitive agricultural applications, offering a stable solution for areas with intermittent or sparse internet connectivity. This paper emphasizes the need for a decentralized computing strategy to leverage the optimum potential of IoT for sustainable and resilient agriculture.