# Network Lifetime Improvement through Energy-Efficient Hybrid Routing Protocol for IoT Applications
###### Abstract  
The application of the Internet of Things (IoT) in wireless sensor networks (WSNs) poses serious challenges in preserving network longevity since the IoT necessitates a considerable amount of energy usage for sensing, processing, and data communication. As a result, there are several conventional algorithms that aim to enhance the performance of WSN networks by incorporating various optimization strategies. These algorithms primarily focus on the network layer by developing routing protocols to perform reliable communication in an energy-efficient manner, thus leading to an enhanced network life. For increasing the network lifetime in WSNs, clustering has been widely accepted as an important method that groups sensor nodes (SNs) into clusters. Additionally, numerous researchers have been focusing on devising various methods to increase the network lifetime. The prime factor that helps to maximize the network lifetime is the minimization of energy consumption. The authors of this paper propose a multi-objective optimization approach. It selects the optimal route for transmitting packets from source to sink or the base station (BS). The proposed model employs a two-step approach. The first step employs a trust model to select the cluster heads (CHs) that manage the data communication between the BS and nodes in the cluster. Further, a novel hybrid algorithm, combining a particle swarm optimization (PSO) algorithm and a genetic algorithm (GA), is proposed to determine the routes for data transmission. To validate the efficacy of the proposed hybrid algorithm, named PSOGA, simulations were conducted and the results were compared with the existing LEACH method and PSO, with a random route selection for five different cases. The obtained results establish the efficiency of the proposed approach, as it outperforms existing methods with increased energy efficiency, increased network throughput, high packet delivery rate, and high residual energy throughout the entire iterations. energy efficiency; network lifetime; hybrid routing protocol; genetic algorithm; particle swarm optimization 2021217439. [[https://doi.org/10.3390/s21227439](https://doi.org/10.3390/s21227439)]([https://doi.org/10.3390/s21227439](https://doi.org/10.3390/s21227439))  
12 October 202  
the clustering of SNs have demonstrated proven competence to enhance the lifetime of networks. [8]. The selection of CHs further reduces energy consumption, as it collects data from the cluster members (CMs) and forwards the same to the BS through the CH [9]. In general, most modern IoT devices perform faster data collection and, hence, require faster data processing and transmission to the BS [10]. The routing protocol aims to select the optimal path for data transmission, which is a challenging task. The selection of an optimal path highly depends on the various network parameters, such as channel characteristics, network type, and performance metrics [2]. In smaller IoT networks, the BS and SNs are in closer proximity and, therefore, communication may take place directly in a single hop. In contrast, the communication in large-scale IoT networks uses multi-hops, as direct communication with the BS may not be feasible. This can be attributed to many reasons--radio power, bandwidth, energy, or memory [3]. Hierarchical routing algorithms aim to enhance the network throughput and lifetime of WSNs in various geographical deployments. However, they may achieve energy efficiency only to a limited extent, which motivated the authors to present a cluster-based optimization, centralizing energy efficiency [5], scalability [6], complexity [7], and robustness [8]. Additionally, the efficacy of hierarchical clustering approaches can be further enhanced using a particle swarm optimization-genetic algorithm, a multi-objective optimization model. The optimization of a network's lifetime is a challenging and vital issue in WSNs and, thus, has grabbed the attention of various researchers, as it is required in order to conserve energy [11; 12].