# Green Communication in Internet of Things: A Hybrid Bio-Inspired Intelligent Approach
## 1 Introduction  
Over the years, revolutionary development in IoT devices has opened the paradigm for dynamic sensing technology that provides seamless communication over the Internet [1]. Wireless sensor networks (WSNs) are prominently used for the collection of data and communication over the fifth generation (5G) and beyond 5G IoT network envision as sixth generation (6G) technology [2]. Moreover, the combination of IoT networks with WSNs has many potentials in various applications, such as precision agriculture, intelligent transport systems, health care, smart cities, military, environment and habitat monitoring, environment anomalies and human intrusion detection [3, 4]. However, with all these remarkable properties, the fallout in the unbalanced energy consumption and lower lifetimeof battery-enabled IoT devices limits the seamless communication of intelligent devices over the IoT network. Therefore, energy-efficient communication over 5G and beyond 5G (6G) enabled IoT devices is the utmost concern in IoT network use cases. Clustering is a robust and scalable approach to lower energy consumption with better network throughput [4; 5]. It is widely studied as probability-based [6; 7; 8; 9], weight-based [10; 11; 12] and heuristic-based approaches [13; 14; 15; 16; 17; 18] for conservation of network energy. Moreover, local decision and uncertainties of the network dynamics have a huge impact on energy consumption in the optimal cluster head (CH) selection as designated data forwarder. Further, the problem of CH selection is non-deterministic polynomial hard (NP) since optimal data aggregation cannot be efficiently solved in polynomial time to ensure balanced energy consumption in each round using a probability- and weight-based approach [19]. Recent studies have shown that meta-heuristics approaches are more suitable for approximately solving NP problems for CH selection [20]. Consequently, proper optimization methods such as fuzzy logic inference [13; 14], bat algorithm [15], particle swarm optimization [16; 17; 18], differential evolutionary and harmony search [19; 20], genetic algorithm (GA) [21], and bio-inspired chicken swarm optimization (CSO) have the potential to be effectively used for finding the optimal number of CHs [22; 23; 24]. A critical investigation of CSO techniques concluded that it has better ability for IoT network-centric feature selection with faster convergence rate over fuzzy logic and genetic algorithm due to the effective balance between network uncertainty and finding the parameter optima [17]. In fuzzy logic, the output depends upon only the knowledge base rule, which is robust in nature, but fuzzy logic may not be applicable in frequent network environment change scenarios, whereas GA has the ability to adapt the environment precisely and CSO has better hierarchal classification and speed reduction design in size for optimization problems with maximum accuracy [25]. Therefore, in this paper, integrating the above three features of the mentioned technique, we propose a novel Hybrid Intelligent Optimization Algorithm (HIOA) to optimize the overall energy consumption in the network by rotating the role of CHs. The presented HIOA integrates Fuzzy logic (FL) and chicken-swarm genetic optimization (CSGO) algorithms that inherently address the problem of repeatedly choosing the same IoT nodes as CH during transmission rounds. CSGO simulates foraging activity by dividing the chicken into smaller groups. In each group, every chicken moves toward the optimal one simultaneously, which motivates the idea of rotating CH in each time slot. The major contributions of the proposed model are as follows:  
* The system model includes cluster-based IoT architecture with aid the feature of cloud network, where energy consumption of the cluster network is evaluated. * An energy optimization problem is formulated in terms of a fitness function that minimizes the intra- and inter-cluster distance in the IoT network.