EDGE COMPUTING TERMINAL DEPLOYMENT IN POWER INTERNET OF THINGS BASED ON CULTURAL ANT COLONY ALGORITHM.

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Bibliographic Details
Title:	EDGE COMPUTING TERMINAL DEPLOYMENT IN POWER INTERNET OF THINGS BASED ON CULTURAL ANT COLONY ALGORITHM.
Authors:	Wu, Zhengrong¹, Li, Hua¹ lihua0298@igit.ac.cn, Yin, Furong², Liu, Zilong², Chen, Wenqin²
Source:	International Journal of Industrial Engineering. 2026, Vol. 33 Issue 4, p1096-1111. 16p.
Subjects:	Edge computing, Ant algorithms, Internet of things, Energy consumption, Swarm intelligence, Resource allocation, Telecommunication systems
Abstract:	This paper addresses the dynamic deployment of edge computing terminals in the heterogeneous Power Internet of Things (PIoT), where large-scale devices, fluctuating concurrency, and spatial heterogeneity challenge the balance among coverage, resource efficiency, and cost. We propose a dynamic deployment strategy based on the Cultural Ant Colony Algorithm (CACA). A multi-dimensional terminal model is constructed, covering candidate locations and hardware parameters. With minimization of annual average deployment cost as the objective, constraints including communication uniqueness, hardware adaptability, and delay are incorporated. CACA integrates swarm intelligence and cultural evolution via pheromone-guided search, elite collaboration, and global update to yield near-optimal deployment plans. Experiments show that our method achieves full coverage under varying states, saves over RMB 110,000, reduces response time by >22%, and lowers energy consumption and failure rate by >15% and >18%, respectively. It demonstrates superior engineering applicability and optimization efficiency. [ABSTRACT FROM AUTHOR]
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Database:	Engineering Source

Description
Abstract:	This paper addresses the dynamic deployment of edge computing terminals in the heterogeneous Power Internet of Things (PIoT), where large-scale devices, fluctuating concurrency, and spatial heterogeneity challenge the balance among coverage, resource efficiency, and cost. We propose a dynamic deployment strategy based on the Cultural Ant Colony Algorithm (CACA). A multi-dimensional terminal model is constructed, covering candidate locations and hardware parameters. With minimization of annual average deployment cost as the objective, constraints including communication uniqueness, hardware adaptability, and delay are incorporated. CACA integrates swarm intelligence and cultural evolution via pheromone-guided search, elite collaboration, and global update to yield near-optimal deployment plans. Experiments show that our method achieves full coverage under varying states, saves over RMB 110,000, reduces response time by >22%, and lowers energy consumption and failure rate by >15% and >18%, respectively. It demonstrates superior engineering applicability and optimization efficiency. [ABSTRACT FROM AUTHOR]
ISSN:	10724761
DOI:	10.23055/ijietap.2026.33.4.11759