# Rendezvous Based Adaptive Path Construction for Mobile Sink in WSNs Using Fuzzy Logic
## 1 Introduction  
With the continuous advances in information technology accompanied by the introduction of Internet of Things (IoT) applications, Wireless Sensor Networks (WSNs), which are the core of the IoT-based systems, are now a main player in our daily lives [1]. In WSNs, the data that is generated by the sensor nodes is usually collected by a central node called the sink or the base station (BS). Traditional WSNs with a fixed BS pose a primary limitation, which is called an energy hole problem, and which results as a consequence of depleting the energy of the nodes close to the sink rapidly because of the overwhelming traffic transmitted by other sensor nodes far from the sink. This could result in affecting the WSN efficiency, such as partitioning the network into unconnected areas. Indeed, partitioning the network is considered a crucial issue, especially in large-scale networks. Recently, the widespread use of mobile robots has opened the door to leverage mobile sinks in WSNs for performing many tasks, such as employing them as data collectors in WSNs [2]. In fact, using mobile sinks (MS) can significantly increase the balance between the nodes and thus extend the network lifetime. Furthermore, it can increase the coverage of the network by reaching isolated uncovered areas. However, in any mobile sink-based approach, the balance between power consumption and the delay of data collection is still a real challenge and an active research area [3]. Different schemes of moving sinks have been proposed by the research community. These schemes can be divided into three categories: random, controlled, and predefined movement strategies [4]. In random movement methods, the MS follows a random path in its movement to collect the data. The mainlimitation of using random movement methods is the buffer overflow problem. Moreover, uncontrolled movements extend the overhead for each node until finding the location of the new position of the mobile sink and, thus, increase the ratio of dropped packets. In controlled schemes, the speed and direction of the next MS destination are determined according to the network situation. For example, the areas where the sensor nodes have urgent information will be given priority over the other locations. In a predefined movement, the MS moves according to a known fixed or dynamic path, which is generated dynamically as a function of network parameters such as the energy of the sensor nodes and their locations. To reduce the energy consumption in predefined movement methods, MS visits each location near sensor nodes and therefore yields more energy balance. However, visiting each sensor node will pose delay limitations, such as a long path length as well as increasing the delay of data delivery. To address this issue, Rendezvous Points (RPs) schemes have been proposed [5; 6]. In RP-based schemes, specific locations are chosen in order to reduce data gathering delays and balance energy consumption amongst nodes. The challenging task in such schemes is how to select the suitable RPs that satisfy both the energy consumption and the delay requirements. Indeed, RPs influence the path that an MS will follow to gather the data from the sensor nodes. Typically, the path of the MS is established by applying the traveling salesman problem (TSP) on the chosen RPs. Hence, the position and the number of RPs play a major role in constructing a path, which balances energy consumption and delay requirements. Figure 1 shows an example of a WSN where the RP-based model is used for data collection by the MS. However, selecting fixed RPs during the whole network operation results in an imbalance of energy consumption among the nodes that are associated with the same RP and thus reduce the network lifetime. Therefore, updating the locations of the RPs dynamically during the network operation will improve the performance of the network efficiently. In this paper, an adaptive path construction approach is proposed, where the locations of the RPs are dynamically updated using a Fuzzy Inference System (FIS).