### Related Work  
For sustainable cities, the deployment of sensors may be employed for several purposes such as smart energy management for electricity, water and gas, climate change due to temperature, humidity and air quality, smart governance for economy and trade, smart transportation and traffic control, and the smart education system. Smart sensors are usually operating wirelessly, and these wireless sensor networks are built with many sensors deployed in inaccessible locations [2]. These sensors' size is minimal and has limited computational and processing resources [3,4]. In addition, during the mobility of sensor nodes, re-authenticating and locating the node movement is a security issue in WSNs [5]. The sensors are deployed at several locations and operate in clusters. These clusters have their cluster heads [4] and work as gateways that aggregate the information and forward it to the base station [6]. In practice, the sensors assigned to a particular cluster can be reassigned to another cluster head depending on the type and priority of information exchanged during operation [7]. Here, it can be noticed that clustering is a discrete problem, and hence, it is required to develop some heuristic or metaheuristic approaches to achieve an energy-efficient load-balanced clustering of WSNs. In a smart city environment, the performance of the sustainable WSNs needs to be analyzed in terms of the network lifetime, coverage area, and energy consumption during information exchange. In the literature, the researcher has explored several nature-inspired approaches with different objectives under different operating conditions and constraints. The authors in [8, 9, 10] have presented the harmony search algorithm, genetic algorithm and the least-distance-based load-balanced clustering of WSNs for the objective of the number of active sensor nodes, cluster heads, and convergence rate. Similarly, a heuristic approach has also appeared in [11], where clustering is performed based on lingering and residual energy. However, this approach is suitable for small networks. The authors in [12] divided the network geographically into two parts, and the cells are allowed to communicate in two categories where the operator predefines the operating rules. Hence, it may not yield a good solution under the change in operating constraints for different objectives. In [13], the authors have presented fog computing for the efficient clustering of large WSNs, and in [14], a fuzzy C-means algorithm has appeared for the objective of active sensors and scalability. In [15], a routing algorithm is developed for IoT-based modules to reduce end-to-end delay and an improved lifetime of nodes in a wireless mesh network. [PERSON] et al. [16] realized the application of smart sensors in a smart grid application for energy management. Several other computational techniques, such as genetic algorithm (GA) in [17], simulated annealing (SA) in [18], particle swarm optimization (PSO) in [19], harmony search algorithm (HSA) in [20; 21; 22], ant colony optimization (ACO) in [23], artificial bee colony (ABC) in [24], and gravitational search algorithm (GSA) in [25], have also appeared in the literature. Here, the PSO is good at small and medium-size problems, while HSA is good at big-size problems, because PSO has prematurity due to global-best-oriented searching [26]. However, the authors in [27] presented big data grouping into a cluster and proposed automated clustering using hybridized PSO. In these works, the authors' motive is to compare the convergence characteristics of their approaches. In contrast, a performance matrix needs to be developed to evaluate the WSNs for sustainable smart cities comprehensively. The authors in [28; 29; 30] conferred that the wireless sensor networks are elemental to build smart systems in smart cities and proposed IoT-based infrastructures for effective communication among sensors.