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WSNs embedded device can be extended to a wide range of implementation in reality. Clustering is efficient way to lessen the energy utilization and improve WSN's lifespan. To improvise network lifespan numerous clustering approaches, implement various parameters for election of CH. An effective clustering algorithm depends upon the number of factors such as number of CHs, uniform cluster size, CHs distribution, energy of the CHs etc. In our research we strengthen our methodology for election of cluster head in HWSN depending on multiple node parameters such as distance, density and residual energy. This paper aims to optimize energy and improve network with Energy Balanced Cluster Technique (EBCT). In our research we reformulated for probability estimation to identify the CHs in each round characterized by node parameters: distance, density, residual energy and node dormancy mechanism. Mathematical analysis and simulations show the proposed method extends the service life by around 8% to 53% relative to the other protocols and optimizes energy utilization of HWSNs.

Researchers are increasingly using algorithms that are influenced by nature because of its ease and versatility, the key components of nature-inspired metaheuristic algorithms are investigated, involving divergence and adoption, investigation and utilization, and dissemination techniques. Grey Wolf Optimizer (GWO), a relatively recent algorithm influenced by the dominance structure and poaching deportment of grey wolves, is a very popular technique for solving realistic mechanical and optical technical challenges. Half of the recurrence in the GWO are committed to the exploration and the other half to exploitation, ignoring the importance of maintaining the correct equilibrium to ensure a precise estimate of the global optimum. To address this flaw, a Multi-tiered GWO (MGWO) is formulated, that further accomplishes an appropriate equivalence among exploration and exploitation, resulting in optimal algorithm efficiency. In comparison to familiar optimization methods, simulations relying on benchmark functions exhibit the efficacy, performance, and stabilization of MGWO.

VANETs (Vehicular Ad hoc Networks) have pulled in enormous considerations because of their real-time application and business value. Due to the limited bandwidth of the wireless interface, dynamic topology, frequently disconnected networks, the communication between vehicles is a challenging task. Clustering is seen as one of the possible solutions to achieve effective communication in VANETs; this research proposes a Clustering Adaptive Elephant Herd Optimization (CAEHO) technique for VANETs. The proposed CAEHO protocol is used to form optimized clusters for robust communication. Cluster head (CH) selection depends upon the fuzzy logic method by selecting parameters like connectivity levels, lane weighting, direction and speeds of vehicles. Based on a fitness function, these parameters are utilized to choose the optimal route between sender and receiver. The NS2 platform is used to implement the proposed work then it is contrasted with previous techniques such as Ant Colony Optimization algorithm (ACO) and Improved Whale Optimization algorithm (IWOA) respectively. Significantly, the CAEHO protocol enhances the packet delivery ratio, throughput and comparatively reduces overhead than other routing protocols.

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