# Collision-Aware Routing Using Multi-Objective Seagull Optimization Algorithm for WSN-Based IoT
## 1 Introduction  
The WSN is a self-organizing communication system that comprises terminal structures, sensor nodes, and base stations (BSs). The IoT represents innovative thinking and progresses numerous communication structures, which depend upon WSNs for collecting the data, and it can be improved through cloud/edge/fog computing [1, 2, 3, 4]. In this way, several services and applications of the IoT have been presented to transfer the data packets. The IoT plans to create widespread Internet, which could influence different portions of consumers' requirements [5, 6]. The exploitation of energy for the duration of the routing procedure might cause a problem because of inadequate sensor strategies. Numerous nodes are transferred in the land section over a few wireless types of machinery to analyze the extensive possibility [7]. In IoT, WSN is a fundamental constituent that has increased into more than a few dissimilar practical presentations [8]. Nowadays, WSNs and IoT havebeen used in numerous risky and non-critical applications, which motivated to increase their life expectancy [9]. WSN nodes are insignificant in battery-driven technologies [10], so, the energy-efficient method raises the network lifespan, which is exceedingly important. Till now, several methodologies and procedures have been used for energy-efficient concepts in WSN-based IoT structures [11]. Moreover, the packet loss and higher delay times occur because of congestion during the communication. Since, the congestion occurs because of the restricted channel capacities [12; 13]. In the present situation, not only have WSNs developed as important equipment, but they are also recognized as playing a vital role in numerous applications, such as the IoT [14]. However, the WSN resources are inadequate, therefore the routing scheme turns out to be complex. For that reason, there is a need to create an enhanced routing protocol that deliberates the energy-related issues [15; 16]. Many types of research focus on the clustering and routing design and additional schemes for energy conservation [17]. In the current situation, quality of service (QoS) must be enhanced because the system should continue over an extended time [18]. After this process, additional information about a structure is given through the optimization method [19]. The inadequacy of WSN resources generate the designing challenge of an energy-efficient transmitting route and network configuration [20]. Generally, these ideas have not appeared for any security and these properties should be utilized sensitively [21]. This present research concentrates on clustering/routing protocols established for WSN-dependent IoT applications. Here, the routing procedure plays an essential part while transmitting the collected data from sensors to the endpoint for additional control. The processed data could be exploited in several applications for assisting individuals to advance their way of life [22; 23]. The paper presents CAR-MOSOA for enhancing the lifetime of a network using optimization-based clustering and routing in WSN-based IoT. The MOSOA is one of the metaheuristic algorithms that imitate the natural behavior of the seagulls [24; 25]. The major contribution of this research is as follows:  
* A CAR-MOSOA based cluster head (CH) with efficient fitness function is developed. This seagull optimization algorithm (SOA) is selected because it has the collision avoidance property in nature, so it is mainly helpful to generate collision-aware routing in the network. * A CAR-MOSOA-based routing algorithm is developed with a trade-off between transmission distance and a number of data that travel with an efficient particle encoding scheme for the routing process. * A simulation of the CAR-MOSOA is analyzed to demonstrate the superiority over other existing algorithms. The organization of this research paper is given as follows: The survey of the recent techniques related to clustering and routing in WSN-IoT is described in Section 2. The problem statement of this research is given in Section 3.