## 2 Related Works  
Object tracking or monitoring is a vital task in various WSN applications, including military intrusion detection and habitat monitoring. Most studies related to object tracking or monitoring focus on either accurately estimating the location of an object [5] or tracking an object through in-network data processing and data aggregation [6; 7]. Most of the previous studies in the field of object tracking have focused on collecting all data from a WSN and sending it to the sink node for object location prediction. The sink node then transmits the predicted locations to the corresponding sensing node for the precise prediction of object movement. Therefore, some studies on object tracking [8; 9; 10] have assumed that all sensor nodes in a WSN are in an active state to ensure that an object can always be located and tracked. However, the environment of a WSN is strictly energy-constrained; this indicates that continuously active nodes are quickly depleted of power, explaining the short lifespan of the WSN. Some scheduling algorithms [8; 11] have been proposed to reduce the energy consumption of WSNs. These algorithms use a state transition model to switch a sensor node from an active state into a sleep state and maintain that sleep state for as long as possible to minimize energy consumption. Other algorithms [12; 13] use the location and movement information of an object to predict its future location, waking predicted nodes for further tracking, whereas switching other nodes into a sleep state. To reduce energy consumption and extend the network lifetime, these algorithms switch most sensor nodes in a WSN into a sleep state, leaving only a few in an active state. A recent study [14] presented a sensor node movement strategy for target tracking in multimodal WSN. They employed a two-layer fuzzy tree system to select the moving nodes and control the moving paths. Subsequently, a GA was used to optimize the fuzzy tree system such that the network could achieve favorable performance in target tracking. In modern society, sensors for various types of events are required for numerous applications. Mobility is a vital characteristic of a sensor designed to fulfill such needs. Because of the revolution of the Internet of Things, interest in developing mobile sensor networks has grown rapidly. Sensors can be mounted on mobile platforms, such as mobile robots for desired areas [15; 16; 17]. Such mobile sensor networks are extremely valuable in situations where traditional deployment mechanisms fail or are unsuitable. Mobile sensor networks can also play vital roles, such as routing, area coverage, target coverage, and target tracking, when applied to traditional WSNs. [PERSON] et al. [15] reported that the continuous movement of sensors enables the coverage of initially uncovered locations and that mobile intruders can be detected by an optimal sensor mobility strategy. [PERSON] et al. [16] presented a leader-based approach to routing in mobile sensor networks. [PERSON] and [PERSON] [17] discussed the problem of scheduling mobile sensors to cover all targets and maintain network connectivity such that the total movement distance is minimized, called the mobile sensor deployment (MSD) problem, and proved to be NP-hard. Moreover, the use of WSNs has increased in applications involving mobile agents or unmanned aerial vehicles (UAVs) that can travel from hundreds to thousands of kilometers. The use of UAVs has substantially increased the lifetime and improved the connectivity of WSNs. [PERSON] et al. [18] improved the connectivity of the WSN by using UAV as a relay. [PERSON] et al. [19] developed an energy-efficient protocol to transmit the data collected by the sensor nodes to a UAV receiver located far away from the site. [PERSON] et al. [20] and [PERSON] et al. [21] discussed the design of communication protocols between a WSN and a UAV in high-speed motion. [PERSON] et al.