# Green Communication in Internet of Things: A Hybrid Bio-Inspired Intelligent Approach
## 2 Related Works  
As mentioned above, recent studies [13; 14; 15; 16; 17; 21; 22; 23; 24] through simulation have observed that artificial intelligence and precisely bio-inspired techniques are preferable to traditional probabilistic and deterministic approaches subject to optimizing the energy consumption in IoT networks. In [17; 21], the authors proved the proposed algorithm based on CSOobtains excellent performance over traditional approaches such as PSO, FL and GA for robust beam-forming approach, whereas authors in papers [24; 25] CSO-based clustering routing protocol plays a significant role in reducing energy consumption over integration with bio-inspired approaches. In paper [26], a Fuzzy-based routing protocol (FRP-LEACH) is proposed to enhance the traditional Low Energy Adaptive Clustering Hierarchy (LEACH) protocol that forms the clusters in an energy efficient manner. The proposed FRP-LEACH works over cross-layers, and the authors claim that cloud-based services such as the proposed algorithm protect medical staff and patients from the ongoing COVID-19 pandemic. However, the proposed algorithm is restricted to a lower dataset as the COVID-19 dataset increases, then fuzzy logic fails to adapt the changes delay in transferring the packet and node death ratio increases according to data growth rate. The authors in paper [27] enhanced the performance of LEACH using a fuzzy logic inference system and renamed LEACH-FL. However, both approaches carry forward the clustering process efficiently by considering the fuzzy logic inputs as closeness to the base station and residual energy but left out the node density parameter. This cause's optimum number of CHs generated through approaches to cover the entire network was not ensured. In [28], authors have proposed EC-PSO models based on a standard PSO approach to optimize the energy consumption of the network to overcome the hotspot problem. When some of the sensor nodes may be left out from the coverage area of CHs and live spontaneously, they are called isolated nodes. These nodes continuously search for CHs and forces to communicate base station directly to exhaust more energy. However, the authors completely ignore the node distance from the base station, which causes it to affect the fitness function tremendously and ultimately consume uneven energy in each time slot. In [29], the authors proposed an enhanced version of a cluster-based genetic algorithm referred to as CRCGA by coding the fitness function to minimize the energy consumption and load balancing of the network. This proposed algorithm mainly considers three factors: formation of clusters, finding the best route and then maintaining the clusters appropriately in each time slot. Further, adaptive round-trip time is considered over the traditional TDMA schedule to further improve network performance. However, CRCGA outperforms the traditional algorithm but fails to consider the scenarios of different sink node positions. In the paper [30], authors have reduced the localization error in WSN using the CSO technique, and further statistical analysis compared to benchmarks optimization technique PSO and GA reveals that CSO provides an upper hand to robustness, precision and performance in terms of convergence speed over the IoT network. To boost the performance of CSO, a cuckoo search is integrated called CSCSO [31] to find the optimal route for data transfer between node and base station. An enhanced version of LEACH using CSO is presented as LEACH-PSO [32] to form optimal clusters and routing paths. Whereas in the paper [33], authors have addressed the problem of balance between power supply and demand using the CSO technique in the residential area and tertiary industry. Further simulation proved that the improved CSO algorithm outperforms in interruptible load scheduling over peak demand in real time over the GA and PSO algorithms.