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<p>The advancement and incorporation of next-generation Internet of Things applications are introducing an additional complication into sixth-generation (6G) mobile communication systems. Key challenges include the necessity for widespread connectivity, a substantial increase in network capacity, and the requirement for ultra-low-latency communications. Ultra-dense networking refers to the deployment of a large number of base stations located in proximity, which is recognized as a viable solution to these challenges. However, the practical and economical feasibility of establishing such a dense network of base stations is often questionable. In this context, drone-based stations (DBSs) have emerged as an alternative, attracting significant research interest. These aerial units are highly effective in rapidly expanding network coverage, especially in catering to the specific needs of next-generation Internet-of-Things applications due to their quick deployment capabilities, which provide immediate connectivity during unexpected traffic surges in network demand. A critical challenge with DBSs is determining their optimal positioning in the sky, considering their limited energy resources and the potential reduction in signal quality during air-to-ground communication. Addressing the complexities of optimal drone placement has led to an increased focus on swarm-based algorithms. This paper presents a new and sophisticated quasi-opposition-based Dandelion optimization algorithm to assess its effectiveness and competence in solving the DBS placement problem. The study thoroughly examines the algorithm’s performance in various scenarios, comparing it with those of competitive algorithms. This detailed investigation highlights the significance of drone-based solutions in the 6G network landscape.</p>

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Enhanced Base Station Deployment in Smart Cities: A Drone-Based Internet-of-Things as a Service Framework Utilizing Quasi-Opposition-Based Dandelion Optimizer

  • 期刊论文

  • 发表日期：

    2026年6月1日

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  • Vigneash Loganathan,
  • Permalsami Ananthakumar Prassath,
  • Sowmya Ravichandran,
  • Premkumar Manoharan

摘要

The advancement and incorporation of next-generation Internet of Things applications are introducing an additional complication into sixth-generation (6G) mobile communication systems. Key challenges include the necessity for widespread connectivity, a substantial increase in network capacity, and the requirement for ultra-low-latency communications. Ultra-dense networking refers to the deployment of a large number of base stations located in proximity, which is recognized as a viable solution to these challenges. However, the practical and economical feasibility of establishing such a dense network of base stations is often questionable. In this context, drone-based stations (DBSs) have emerged as an alternative, attracting significant research interest. These aerial units are highly effective in rapidly expanding network coverage, especially in catering to the specific needs of next-generation Internet-of-Things applications due to their quick deployment capabilities, which provide immediate connectivity during unexpected traffic surges in network demand. A critical challenge with DBSs is determining their optimal positioning in the sky, considering their limited energy resources and the potential reduction in signal quality during air-to-ground communication. Addressing the complexities of optimal drone placement has led to an increased focus on swarm-based algorithms. This paper presents a new and sophisticated quasi-opposition-based Dandelion optimization algorithm to assess its effectiveness and competence in solving the DBS placement problem. The study thoroughly examines the algorithm’s performance in various scenarios, comparing it with those of competitive algorithms. This detailed investigation highlights the significance of drone-based solutions in the 6G network landscape.