# Green Communication in IoT for Enabling Next-Generation Wireless Systems
## 2 Related Work
### Artificial Intelligence-Based Routing Protocols (AIRP)  
The authors in [19] introduced a novel routing protocol, utilizing the Breadth-First Search (BFS) algorithm. The simulation results showed that the proposed protocol extends the lifespan of IoT networks, and outperforms established protocols in terms of transmission delay, throughput, and packet delivery ratio. To enable effective data transmission, wireless technology adheres to standards like IEEE 802.15.4 [20]. These standards ensure optimal communication between endpoints by utilizing the Energy Optimization Algorithm. The proposed algorithm in [20] boosts data packet transmission efficiency by up to 25%. Furthermore, it extends the lifespan of IoT networks, facilitating efficient data transmission for healthcare monitoring purposes. The authors introduced the ECQSR routing protocol in [21]. The main goal of the proposed scheme was achieving quality of service in sensor networks while maximizing the network's lifetime. The protocol incorporates service differentiation, utilizes the nearest neighbor algorithm for finding low-cost paths, and imposes delay constraints for real-time data. In [22], the authors have presented a genetic-algorithm-based unequal clustering and routing protocol for wireless sensor networks (GA-UCR) protocol for IoT networks that focuses on unequal clustering and routing and makes use of genetic algorithm techniques. The protocol employs the genetic algorithm for cluster head (CH) selection and inter-cluster multi-hopping, effectively addressing the challenging NP-Hard nature of the problem. By incorporating fitness functions such as remaining energy, distances, and inter-cluster separation, GA-UCR surpasses existing algorithms such as direct propagation, Low-energy Adaptive Clustering Hierarchy (LEACH), Two-Level Hierarchy for Low-Energy Adaptive Clustering Hierarchy (TL-LEACH) protocol, Gateway Classification Algorithm (GCA), Energy-aware Evolutionary Routing Protocol (EAERP), and genetic algorithm-based energy-efficient clustering hierarchy (GAECH) in terms of energy consumption, network lifetime, and scalability. In [23], the authors propose routing algorithms for 3D IoT networks to extend the lifespan of sensor nodes. These algorithms utilize GA instead of a Greedy algorithm to construct chains based on the Power-Efficient Gathering in Sensor Information System (PEGASIS) routing technique. The authors also incorporate a CH selection technique that considers energy and distance for improving load balance in IoT networks. The simulation results indicate significant improvements in node lifespan compared to PEG-ASIS, with increases of 817% and 420% for the base station inside and outside the network, respectively. The authors in [24] suggested the Multi-Channel (MC) scheme as a solution for heterogeneous IoT networks, employing super nodes such as cluster heads (CHs) with multiple radios for transmitting data. The authors used the GA to achieve efficient clustering, routing, and channel assignment. The extensive simulations validated the superiority of the proposed approach to prolong the network lifetime, enhancing throughput, and reducing energy consumption compared to previous approaches. The authors introduced antion optimization (ALO) as a method for selecting optimal sink placement locations in [25]. Inspired by the hunting behavior of antilions, ALO incorporates distance and hop count in its energy efficiency calculations for sensor networks. Experimental results demonstrate superior performance compared to the Bacteria-Forging Algorithm (BFA) and Gray Wolf Optimizer (GWO). The proposed approach effectively reduces network delay, conserves energy, and enhances the overall network lifetime. The authors in [26] present non-dominated sorting multi-objective antion optimization (NSIMOALO), an enhanced version of the multi-objective antion optimization (MOALO) algorithm designed for multi-objective optimization.