Multi-objective hybrid green anaconda skill optimization enabled energy and cache based QoS aware routing in delay tolerant–IoT network

• Published in: Sustainable computing informatics and systems Vol. 47; p. 101158
• Main Authors: Bhavani, Ashapu, Venkataramana, Attada, Chakravarthy, A.S.N.
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 01.09.2025
• Subjects: Anaconda optimization; And skill optimization algorithm; Cluster head selection; Delay tolerant network; Internet of things; Internet of things; Delay tolerant network; Cluster head selection; Anaconda optimization; And skill optimization algorithm
• ISSN: 2210-5379
• DOI: 10.1016/j.suscom.2025.101158

Delay-Tolerant Network (DTN) is developed to overcome the challenges of environments where classical networking models fail due to unstable connectivity and high latency. The DTN offers stable connections between nodes and operates effectively in scenarios where nodes frequently experience disruptions or only sporadic communication opportunities. However, the classical techniques allowed limited data communication and did not apply to the network with reduced resources and which had low delivery rates and high delays. Therefore, this research aims to develop a Green Anaconda Skill Optimization (GASO) for an eQoS-aware routing solution for a DTN-IoT network. Initially, the DTN-IoT network is simulated by considering energy and mobility models. Then, for predicting the energy, Recurrent Radial Basis Function Networks (RRBFN) is used. After that, Cluster Head (CH) selection is executed by GASO, considering multiple objectives, like cache ratio, residual energy, predicted energy, throughput, distance, trust factors, and delay. Finally, GASO is employed for routing, and the above-mentioned multi-objectives are considered. Here, the GASO is established through the fusion of Green Anaconda Optimization (GAO) and Skill Optimization Algorithm (SOA). The evaluation results highlight that the GASO accomplished a reduced distance of 0.253 m, low energy consumption of 0.783 J, and minimal delay of 0.270 sec, with an increased throughput of 0.313 Mbps. •To model dynamic systems and implement control strategies, Recurrent Radial Basis Function Networks (RRBFN) is considered.•Green Anaconda Optimization (GAO) is taken to optimize sensor placements, routing protocols, and energy management strategies.•To aid resource leveling, the Skill Optimization Algorithm (SOA) is used to maximize success and minimize cost and delay.