## 2 Literature Survey  
Many researchers have significantly contributed to studies in the literature. We offer readers the related studies implemented from year 2000 to today in a summary form. The study [39] proposed a low energy adaptive clustering hierarchy, named LEACH, which selects CHs randomly in the network and provides load balance. However, LEACH-C [40] presents a centralized control algorithm that is improved version of LEACH. In this algorithm, BS knows all of the nodes' locations and the energy levels are determined in each round. In a study, [PERSON]. [41] recommends a SEP (Stable election protocol) algorithm, where each sensor node is in a two-level heterogeneous sensor network. The algorithm independently identifies CHs selection according to the first energy relative to the other sensor nodes of the sensor network. The study [14] proposes a distributed energy efficient clustering method, called DEEC. In the CH, selection is considered to depend on the rate of the residual energy of the sensor node and the mean energy of the sensor network. A meta-data based data aggregation scheme is presented for the clustered WSNs [10]. According to this study, CH acquires the data when the sensor nodes sense some events in the transmission range. CH processes the data and then sends it to the BS immediately. However, this operation was considered in a simple manner due to taking much time in the delivery of all data. In [42], so as to gather more flexible routes for mobile collectors SenCar, a mobile data collector that is runs like a mobile BS travels the entire network, collects the data from all the nodes, and finally submits the data to the sink via heuristic approach. The study [17] presents a line-based data dissemination protocol for WSNs using mobile sink. The algorithm is executed according to a line limit value to disperse the data that are acquired in the sense area. In one study [43], the authors proposed an adaptive algorithm for networks that accommodate the traveling node, while taking into account bacterial mobility. The proposed algorithm is a technique that collects the vector as a linear combination of the last steps of neighboring nodes. This technique allows for bacteria to provide more nutrients. However, in this study, they have only presented a series of mathematical models expressing nutrient distribution for bacteria that have not considered the network model or representing sensor nodes. In a study [20], an energy-efficient mobile-sink path selection strategy for WSNs is proposed. In the clustering model, a mobile sink travels CHs, named RPs, and then collects data from them. This algorithm provides an energy consumption balance although network delay increases due to the random mobile sink movement. In [13], authors proposed an energy efficient routing algorithm for improving the network life time. The sink node in the network navigates as hexagon model based multi-hop transmission. The authors consider that the network is divided into some areas and CHs are elected in these areas. Although the algorithm is good at energy balancing, it nevertheless produces communication delay. In one study [44], a bacterial foraging optimization based clustering algorithm was proposed. Thanks to the proposed algorithm, energy efficiency along with routing protocol is provided for long tours. However, the proposed algorithm was only compared with the LEACH and [45] PEGASIS protocols. In [11], a mobile sink with intelligence improved energy efficient model is proposed and then compared with mod-LEACH and PEGASIS protocols. In [46], authors proposed a novel routing method, called the sleep-wake energy balanced distributed (SEED) clustering method. Thanks to this algorithm, network is split into three energy fields. In this way, CHs in the clusters directly communicate with BS. In one study [39], the authors proposed the improved ant colony algorithm (I-ACO), which was developed to produce more effective and accurate solutions to the classic traveler salesman problem (TSP).