Abstract—Heterogeneous Wireless Sensor Networks (HWSNs) technology is crucial for the operation of several Internet of Things (IoT), A Mobile Ad-hoc Networks (MANETs), and Vehicular Ad-hoc Networks (VANETs) focused on smart-city applications. Effective performance, particularly in energy consumption reduction and delay minimization, constitutes major challenge factors in this domain. Previous research underscores clustering’s value in enhancing network energy efficiency by significantly decreasing communication energy use. However, the dynamic and resource-constrained nature of HWSNs, coupled with issues such as improper Cluster Head (CH) selection, mobility-induced instability, and uneven energy distribution, exacerbates these challenges. Existing CH selection methods consider only energy parameters and ignore crucial parameters such as mobility, node velocity, and communication stability, leading to increased energy consumption, delay, and reduced network lifetimes. To thwart these limitations, this research proposes a novel method—Meta-Heuristic Algorithms based Collaborative Dynamically Clustered Routing Protocol (MHA-CDCRP). This protocol integrates a Collaborative Dynamically Clustered Routing Protocol (CDCRP) with a hybrid optimization technique, i.e., the Meta-Heuristic Genetic Whale Optimization (MHGWO), which blends Genetic Algorithms (GA) and the Whale Optimization Algorithm (WOA). The proposed framework guarantees stable communication, fair energy consumption, and minimum delays via adaptive Cluster Head (CH) selection based on node distance, velocity, remaining energy, and packet delivery ratio. The performance analysis with Network Simulator 2 (NS2) demonstrates considerable enhancement in energy efficiency, packet delivery ratio, throughput, and less overhead over current solutions. This holistic solution is envisioned to improve the sustainability and scalability of HWSNs for large-scale smart applications.
 
Keywords—heterogeneous wireless sensor network, vehicular adhoc networks, meta-heuristic algorithm, clustered routing protocol, genetic algorithm, whale optimization algorithm


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