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@article{196143,
        author = {R.Yanitha and Dr.M.Logambal},
        title = {Analysis of QoS in Cluster-Based IoT Routing Using Ant Colony Optimization and Krill Herd Algorithm},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {2285-2293},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=196143},
        abstract = {Efficient routing in Internet of Things (IoT) networks is essential for achieving high Quality of Service (QoS) and addressing challenges such as energy constraints, scalability, and latency. This paper presents a comparative performance evaluation of two swarm intelligence-based approaches—Ant Colony Optimization (ACO) and the Krill Herd Algorithm (KHA)—for optimizing IoT routing. ACO, inspired by the pheromone-based path finding of ants, and KHA, modeled on the collective motion and foraging strategies of krill swarms, are applied to dynamic IoT environments. The analysis evaluates both algorithms across multiple performance metrics, including end-to-end delay, packet delivery ratio (PDR), routing overhead, throughput, and energy consumption. Experimental results reveal distinct trade-offs: ACO demonstrates superior packet delivery and lower routing overhead, while KHA achieves better energy efficiency and reduced delay in certain scenarios. The findings provide insights into the applicability of each algorithm, offering guidelines for selecting suitable routing strategies in diverse IoT deployments.},
        keywords = {IoT Routing; Ant Colony Optimization (ACO); Krill Herd Algorithm (KHA); Swarm Intelligence; Quality of Service (QoS); Network Optimization},
        month = {April},
        }