# Energy-Efficient Wireless Hopping Sensor Relocation Based on Prediction of Terrain Conditions
## 1 Introduction  
Recently, there have been active studies for big data analysis using networking to collect, process, and analyze a large amount of data [1]. In order to not only collect various data but also fast process information, the technologies of collecting and transmitting data have emerged as a very important issue. Here, there is no doubt that the transmission technology among wireless sensors is important for big data technology. Research on various wireless sensor networking technologies to transmit the collected data has been actively conducted for decades [2; 3]. A lot of wireless sensors are properly distributed through the observation area to collect the interested data [4; 5]. It is not difficult to distribute the sensors to accessible areas to human, however unmanned mobile devices (unmanned air vehicle, drone, etc.) could be used in large inaccessible areas. After deploying the sensors, let us consider that data collection occurs frequently in particular area. There is an inherent problem that a small sensor has limited energy. To remedy this power limitation, wireless charging systems for IoT sensors have been researched actively recently [6]. However, charging system for the sensors is not always available, especially in inaccessible area like rugged terrain. Thus, it is necessary that the sensor relocation protocol to recover sensing hole employs energy efficient scheme as a preemptive measure. Some sensors may be quickly drained of energy while continuously collecting and transmitting data in the particular area. This case is called a sensor node failure, the communication of the entire network may be disconnected, and the desired data could not be collected in the worst case. The particular area, where a certain number of sensors become faulted and cannot collect the interested data anymore, is called as a sensing hole [7]. In order to prevent a sensing hole occurred, various studies such as schemes of minimizing energy consumption by adjusting the active/idle states and energy efficient routing protocols have been conducted [8; 9; 10]. Although the energy limit of the sensor node can be reduced by various applications, it is impossible to solve the sensor node failure problem completely. Therefore, it is necessary to recover it by moving other sensors to the sensing hole occurred, as the most realistic solution. For this reason, researches on mobile sensors have received a lot of attention. The failure sensors depleted of energy could be replaced by mobile sensors moved from other area. The authors of [11] considered a case that a wheel-based mobile node could temporarily replace the role of a fixed node by moving if a fixed sensor node is failed in the interested area. However, there are limitations in the migration of wheel-based sensors. In other words, energy consumption has to be further considered in order to move, however wheel-based movement is essentially inadequate in very rough areas. In order to overcome the limitation of wheel-based mobility, a hopping-based moving model is introduced. A hopping-based sensor node is bionically designed to jump itself, like a frog. For example, a mobile unit implemented by DARPA (Defense Advanced Research Projects Agency, USA) is able to do up to 100 jumps with one full fuel injection and jump up to 10 m high [12; 13]. The paper [14] by MSU (Michigan State University, USA) describes the performance comparisons for the maximum jump height, jumping distance, and so on for various jumping robots, in detail. Researches on not only the implementation of jump-based mobility but hopping sensor relocation algorithm to recover the sensing holes that occurred, have been actively conducted [15; 16; 17; 18]. However, most of studies so far require that every cluster header sensor node has to know all information for the current whole network area and set up routes to supply/request hopping sensors to recover sensing holes occurred.