# Rendezvous Based Adaptive Path Construction for Mobile Sink in WSNs Using Fuzzy Logic
## 2 Related Work  
Several studies on random or controlled-based schemes for MSs have been conducted in WSNs ([7; 8] are examples on random movement and [9; 10] are examples of controlled-based movements). However, in this section, we address RP-based solutions that present a tradeoff  
Figure 1: An example of a WSN where RP-based model is used for data collection by MS.  
between the random and controlled-based schemes in terms of the buffer flow problem in random movement schemes and the long delay problem in controlled-based schemes.  
[PERSON] et al. [11] presented an approach in which the mobile sink moves along a fixed path and stops at a number of locations for collecting the data. The number of stop points over the path is selected using the Tabu search algorithm where the objective is minimizing the number of hop count from the sensor nodes to the mobile sink. Two algorithms called reduced k-means (RkM) and delay bound RkM (DBRkM) were proposed by [PERSON] et al. [12] for generating a set of RPs that will be visited by the MS. The MS will then move over a fixed path that connects the selected RPs to gather the data from the sensor nodes. [PERSON] et al. [13] proposed an algorithm to generate a fixed path for the MS using principal component analysis (PCA), where the data can be gathered using either direct or multi-hop data transmission modes. A rendezvous-based routing protocol (RRP) was proposed by [PERSON] et al. [14] to address the need for energy efficiency and lower end-to-end latency. In the RRP, a rendezvous region is created in the middle of the network where the nodes, called backbone nodes, in this region form a tree, and where the other nodes communicate with the rendezvous region. [PERSON] et al. [6] proposed a routing method in WSN that depends on RPs and multiple MSs. At the beginning, the sensor nodes are distributed into a set of clusters using mean shift clustering (MSC). A cluster head (CH) for each cluster is then selected using the Bald Eagle Search (BES) algorithm. After that, the authors used the hybrid seagull optimization and salp swarm (SOSS) algorithm in order to find the RPs and the travelling route of each mobile sink in the network.  
[PERSON] et al. [15] proposed an approach that chooses a group of RPs outside the pre-determined trajectory such that the defined path can accomplish the goals of minimizing sensor node energy consumption and decreasing network average data delivery time. [PERSON] et al. [16] proposed an approach that builds a reliable and smart route for the mobile sink utilizing game theory and improves ACO-based MS route selection and the Data Gathering (GTAC-DG) algorithm. A set of rendezvous points (RPs) is selected to construct the path for the MS using an ACO-based algorithm. The GTAC-DG algorithm is used to create a path for the MS. [PERSON] et al. [17] proposed an approach called the ant colony optimization (ACO)-based mechanism (ACO-RMS) for selecting the RPs and scheduling the mobile sink in the event-driven WSNs. The load of each sensor in the ACO-RMS approach is initially identified using a spanning tree. Different factors, such as distance, remaining energy, and total packets generated by the sensor nodes at a particular time, are used for the RPs' selection and path use. [PERSON] et al. [18] proposed an extended ant colony optimization (ACO)-based MS path construction for event-driven WSNs, where the ACO algorithm selects the best set of RPs and the path that the MS will travel between these RPs.  
[PERSON] et al. [19] proposed an approach where RPs are used to collect the data using data compression techniques from nearby sources and then send the data to the mobile sink when it travels over the path connecting these RPs. Furthermore, the authors proposed an algorithm called a minimal constrained rendezvous point (MCRP), which ensures that the collected data are relayed to the RPs taking into consideration three constraints: RPs' locations, bounded relay hop, and number of nearby sources.