# Travel Route Planning with Optimal Coverage in Difficult Wireless Sensor Network Environment
## 1 Introduction  
Wireless sensor networks (WSNs) have been widely applied recently to sense the physical environment, especially difficult environments due to their ad-hoc nature with self-organization and local collaboration characteristics. WSN, as a bond to connect the real world and digital systems, have attracted much attention over the past few decades [1; 2; 3; 4]. In WSNs, sensors are generally deployed in the target areas by vehicles, such as planes, in a random manner. However, those target areas are usually in harsh environment, and once the sensors break down or exhaust their energy, they will be invalid because repair or battery replacement is impossible. After sensor deployment, they exchange the information with their neighbors and the WSN is formed rapidly. Due to the favorable characteristics of easy deployment and self-organization, as mentioned above, WSN can be seeneverywhere in our daily life, such as environment monitoring [5], target surveillance [6; 7], smart health [8; 9] and many other intelligent systems [10; 11; 12; 13]. Although WSNs are popular for some excellent characteristics, some challenges still trouble them [14; 15]. The most severe one is the limited energy problem. Sensors are commonly powered by their carry-on batteries and battery replacement by manual operation is unrealistic, especially in some harsh environments [16]. Therefore, many researchers focus on the energy problems in WSNs. One of the efficient methods is routing protocol designing, and various energy efficient routing schemas are presented to prolong the lifetime of the network [17; 18]. Traditional routing schema commonly focuses on the network with a fixed sink. In recent years, the development of an intelligent car has made it possible to gather mobile data. Intelligent cars are usually modified as mobile collectors by equipping multiple antennas so that they can collect the data from multiple objects simultaneously. Routing schemas for WSNs can be mainly classified into three categories as follows. Figure 1 describes the classic clustering-based schema with a fixed sink [19; 20; 21; 22; 23; 24]. In this schema, the network is usually partitioned into several regions according to some special rules, and each region denotes a cluster. Each cluster chooses a leader with the most energy as its cluster head (CH). The data of member nodes are firstly transmitted to their corresponding CHs, and then CHs adopt multi-hop communication to upload the data to the remote sink. This schema has been widely used in some hierarchical routing protocols, such as Low Energy Adaptive Clustering Hierarchy (LEACH), Two-tier Data Dissemination (TTDD) algorithm and Hybrid, Energy-efficient, Distributed Clustering (HEED). Figure 2 describes the data mule-based schema with a mobile collector [25; 26; 27; 28; 29; 30]. The mobile collector moves along a predefined path, and once a sensor enters its transmission range, it will stop for data gathering. Figure 3 describes the rendezvous-based schema with a mobile data collector [31; 32; 33; 34; 35; 36; 37]. The rendezvous points are chosen in advance, and the mobile collector moves along a scheduled path to traverse all the rendezvous points. Data uploading only occurs when the mobile collector stops at any nearby rendezvous point. Data mule-based schema usually plans a long travel route for the mobile collector. Therefore, it results in serious network latency. Additionally, the changeless moving path is not flexible, and it is hard to balance the energy of different sensors. Rendezvous-based schema selects the sojourn points (SPs) for the mobile collector, and the total length of the travel route is related to the number and position of the selected SPs. Mobile collector in those schemas greatly enhances the performance of the network. However, the travel route of the mobile collector needs to be elaborately scheduled. Figure 1: Clustering-based schema with a fixed sink.