An Iterative CrowWhale-Based Optimization Model for Energy-Aware Multicast Routing in IoT

Abstract

This paper proposes an energy-aware multicast routing protocol (MRP) based on the optimization algorithm named iterative Crow Whale-Energy Trust routing (iterative CrowWhale-ETR). The CrowWhale-ETR is developed by including the historical terms from Taylor series in the CrowWhale optimization algorithm. Initially, the effective nodes for the multicast routing process are considered by measuring the trust and energy level of nodes. Based on the fitness factor, the protected nodes are selected relies on the trust and energy level of individual nodes. Once the secure nodes are selected, route detection and route selection is performed based on iterative CrowWhale-ETR. Finally, the route maintenance is done as per the remaining energy and trust factors of the nodes in the network. The comparative analysis of developed iterative CrowWhale-ETR is performed with the evaluation metrics, like energy, delay, throughput and detection rate using 50 and 100 nodes in the presence as well as absence of attacks.

Fractional mayfly optimization algorithm‐based Infrastructure‐to‐Vehicle and Vehicle‐to‐Vehicle scheduling for service message transmission in IoV‐fog

“…However, it supports storage, computation, and application processes to end users, such as cloud computing 16‐18 . It fills the gap between IoT devices, such as smart vehicles in IoV deployment, and remote data centers 19,20 . Accordingly, it offers some other merits, like minimized latency, increased security, and reduced bandwidth.…”

“…[16][17][18] It fills the gap between IoT devices, such as smart vehicles in IoV deployment, and remote data centers. 19,20 Accordingly, it offers some other merits, like minimized latency, increased security, and reduced bandwidth. Hence, fog computing is a hopeful technology in different IoT services.…”

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