A Hybrid Optimization Approach for Edge Servers and Cloud Centers in Agricultural IoT.

Saved in:

Bibliographic Details
Title:	A Hybrid Optimization Approach for Edge Servers and Cloud Centers in Agricultural IoT.
Authors:	Wang, Yan¹ 2256678418@qq.com, Hu, Lijuan² 1023236794@qq.com, Liu, Chengzhi² it-rocket@163.com
Source:	IAENG International Journal of Computer Science. May2026, Vol. 53 Issue 5, p1815-1822. 8p.
Subjects:	Data centers, Optimization algorithms, Clustering algorithms, Internet of things, Greedy algorithms, Communication infrastructure, Metaheuristic algorithms
Abstract:	This paper proposes an integrated algorithm to optimize the deployment of edge servers and cloud data centers in agricultural Internet of Things (IoT) networks. First, the K-medoids algorithm is employed to cluster data collection terminals and thereby initialize edge-server locations. A greedy algorithm with local refinement is then used to supplement coverage in previously uncovered areas. Subsequently, a gravitational search algorithm optimizes global server placement. The method achieves near-optimal deployment while guaranteeing full coverage. Real-world constraints are also incorporated by modeling forbidden zones, such as lakes, roads, and restricted areas, that must be avoided during infrastructure placement. [ABSTRACT FROM AUTHOR]
	Copyright of IAENG International Journal of Computer Science is the property of International Association of Engineers (IAENG) and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database:	Engineering Source

Description
Abstract:	This paper proposes an integrated algorithm to optimize the deployment of edge servers and cloud data centers in agricultural Internet of Things (IoT) networks. First, the K-medoids algorithm is employed to cluster data collection terminals and thereby initialize edge-server locations. A greedy algorithm with local refinement is then used to supplement coverage in previously uncovered areas. Subsequently, a gravitational search algorithm optimizes global server placement. The method achieves near-optimal deployment while guaranteeing full coverage. Real-world constraints are also incorporated by modeling forbidden zones, such as lakes, roads, and restricted areas, that must be avoided during infrastructure placement. [ABSTRACT FROM AUTHOR]
ISSN:	1819656X