The Internet of Things (IoT) and cloud computing are new concepts in revolutionized communication and information technologies. Different technologies, for example, transportation and healthcare, can benefit from Cloud of Things (CloudIoT) for mobile and fixed resource applications with great promises. Fixed and mobile resources are very important items in the CloudIoT paradigm because of the need for an appropriate discovery mechanism. In the present study, a mathematical optimization model is proposed for the minimization of bandwidth, cost, and response time of CloudIoT platforms with a special focus on the role of mobile and fixed resources in resource discovery. Additionally, this study presents a heuristic resource discovery algorithm using a mathematical optimization model (RDMOM). The mixed-integer non-linear programming is used to design the discovery mechanism. Furthermore, the optimization problem is solved using the red deer algorithm. Finally, the simulation results show a significant reduction in the latency, resource efficiency, and energy consumption, as well as an improvement in availability and success ratio, compared to previous algorithms. The RDMOM algorithm significantly improves the success ratio, energy consumption, resource efficiency, availability, and latency, respectively, by 18%, 21%, 24%, 17%, and 21% in comparison to other algorithms.

中文翻译：

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基于红鹿算法的CloudIoT资源发现数学优化模型

物联网 (IoT) 和云计算是通信和信息技术革命中的新概念。不同的技术，例如交通和医疗保健，可以从物联网 (CloudIoT) 中获益，用于移动和固定资源应用程序，前景广阔。由于需要适当的发现机制，固定和移动资源是 CloudIoT 范例中非常重要的项目。在本研究中，提出了一种数学优化模型，用于最小化 CloudIoT 平台的带宽、成本和响应时间，特别关注移动和固定资源在资源发现中的作用。此外，本研究还提出了一种使用数学优化模型 (RDMOM) 的启发式资源发现算法。混合整数非线性规划用于设计发现机制。此外，使用红鹿算法解决优化问题。最后，仿真结果表明，与以前的算法相比，延迟、资源效率和能源消耗显着降低，可用性和成功率也有所提高。RDMOM 算法在成功率、能耗、资源效率、可用性和延迟方面显着提高，与其他算法相比分别提高了 18%、21%、24%、17% 和 21%。与以前的算法相比，可用性和成功率也有所提高。RDMOM 算法在成功率、能耗、资源效率、可用性和延迟方面显着提高，与其他算法相比分别提高了 18%、21%、24%、17% 和 21%。与以前的算法相比，可用性和成功率也有所提高。RDMOM 算法在成功率、能耗、资源效率、可用性和延迟方面显着提高，与其他算法相比分别提高了 18%、21%、24%、17% 和 21%。