International Journal of Science, Engineering and TechnologyAuthors: Kunal Vireksha
Abstract: The proliferation of Internet of Things (IoT) devices has created dynamic wireless networks that generate massive volumes of data requiring efficient and reliable management. Integrating these networks with cloud platforms enables scalable storage, processing, and advanced analytics, but also introduces challenges such as latency, energy constraints, and variable connectivity. This article explores adaptive connectivity and control models as solutions for optimizing performance, reliability, and energy efficiency in wireless IoT cloud environments. Adaptive connectivity models dynamically adjust communication pathways, protocols, and resource allocation to maintain seamless operation, while adaptive control models utilize real-time monitoring, predictive analytics, and feedback mechanisms to optimize network and device behavior. Cloud integration provides centralized orchestration, AI-driven decision-making, and scalable analytics to enhance these adaptive strategies. The article highlights applications across smart cities, industrial IoT, healthcare, and agriculture, demonstrating improved latency, throughput, reliability, and energy efficiency. Challenges such as security, interoperability, scalability, and technical complexity are discussed, along with emerging trends including edge AI, 5G/6G networks, digital twins, and autonomous network management. The study emphasizes that combining adaptive models with cloud orchestration is critical for creating intelligent, resilient, and efficient IoT ecosystems capable of supporting large-scale, real-time applications.
DOI: https://doi.org/10.5281/zenodo.18161066
