### Related Work
However, the deployment of these sensors and mutual information exchange is another important issue to be explored for reliable operation for customers' satisfaction. Furthermore, a recently developed GWO has been demonstrated for 29 well-known objective functions, and the experimental results show that GWO outperformed the existing computational intelligence techniques, such as, GSA, PSO, HSA, differential evolution (DE) and evolutionary programming (EP) [1]. Recent trends reveal that the intelligent computational methods are usually adopted for multi-objective discrete problems such as clustering in WSNs [31; 32; 33]. The dual gray wolf optimization (GWO) algorithm has been refined, and binary and dogmatic components have been proposed in [34]. However, it has been observed that out of 29 objectives, GWO is not used for the load-balanced clustering of WSNs. Researchers, manufacturers, and application developers have recently shown a lot of interest in the development of new digital industrial technology known as Industry 4.0 [35; 36]. Industry 4.0 aims to connect and integrate all elements of the conventional manufacturing environment in order to facilitate quicker, more adaptable, and more efficient production processes that result in higher-quality products at lower costs. For use in smart grid applications, the authors in [37] suggested a unique dynamic clustering-based energy-efficient and QoS-aware routing protocol that was inspired by several aspects of bird mating optimization (BMO). In both sparse and dense network deployments, the suggested distributed approach increases network stability and reduces wasteful packet retransmissions [37]. A multi-objective tournament harmony search-based load-balanced clustering technique was presented by [PERSON] and [PERSON] [38]. By changing the status of the on-duty sensors to off-duty and the off-duty sensors to on-duty, respectively, the coverage conscious memory consideration, random selection, and tournament selection-based pitch modification to eliminate clumps and filling voids is enforced during harmony improvisation. In addition, an AI-based drone routing strategy is modified for data collection to get around the hot spot issue in [39]. The suggested method is contrasted with currently used clustering techniques for fixed WSNs, such as LEACH, LEACH-C, and LEACH-B [39]. The authors of [40] presented a unique routing system for wireless ad hoc and sensor networks for supervised device data transfer from smart grid generators to the command-and-control center. Due to each generator's complex mechanical design, the protocol expects that numerous sensor devices, i.e., temperature sensors, oil level sensors, turbine status sensors, etc., will be installed. In order to study and extract network behaviors in cellular networks for Industry 4.0 applications from a big data perspective, [PERSON] et al. [41] presented a big data-based analytical framework employing Hadoop, Hive, and HBase. To analyze the performance of long-lived C-TCP flows across Industry 4.0 WiFi infrastructure while accounting for all losses, [PERSON] and [PERSON] [42] created a thorough model. In order to investigate the effects of WiFi system parameters on transport-layer performance and fairness, the proposedmathematical model incorporates WiFi system factors including the retransmissions cap and the AP buffer size. Industrial wireless sensor networks have more stringent standards for energy homogeneity, real-time data transmission, and energy consumption. In order to meet these requirements, a novel energy-efficient clustering method known as quantum elite gray wolf optimization, which is inspired by quantum-related bio-inspired optimization, is suggested in [43]. According to the pertinent research, Industry 4.0 load-balanced clustering can indeed be accomplished using advanced optimization approaches. The proposed work, however, offers an enhanced GWO for load-balanced clustering that is energy-efficient.