# Energy-Aware 3D Path Planning by Autonomous Ground Vehicle in Wireless Sensor Networks
## 1 Introduction  
Wireless sensor networks (WSNs) are crucial for data collecting, resource exploration, and navigation due to their rapid development [1]. Contrary to conventional networks, a Wireless Sensor Network (WSN) possesses a distinctive architecture and constraints on the resources it can access. Resource limits include a limited communication range, a finite energy supply from a battery, restricted bandwidth, and a confined CPU and storage capacity that cannot handle complex control algorithms. Optimising communication in Wireless Sensor Networks (WSNs) is critical for minimising energy utilization due to the short battery life of sensor nodes and the high energy consumption of data transmission [2]. In WSNs, a key goal is to provide efficient and reliable wireless connectivity to maximize the network's longevity [3]. There exist several research studies on gathering data with a static sink to reduce total energy consumption in a WSN [4, 5, 6]. On the other hand, gathering data with a mobile sink reduces energy consumption more in WSNs; therefore, path planning of a mobile sink is an important problem in WSNs. The proposed metaheuristic-based path planning strategy for a WSN expedites sensor data collection while conserving energy, enabling a faster monitoring system reaction to accident hazards. Positioning communicating sensorscloser minimizes their energy consumption. This increases the lifespan of a WSN, which monitors the environment to detect anomalies and prevent accidents. Several studies have tackled 3D path planning problems in underwater wireless sensor networks (UWSNs) similar to the 3D path planning problem tackled in this paper. Firstly, WSNs need massive amounts of energy for data transmission. Sensory data are compressed and optimized for reducing energy consumption and transmission [7]. Second, clever node placement and routing can boost UWSN energy efficiency. Through the optimization of deployment and routing operations, it is possible to decrease the amount of energy that is consumed and to increase network lifetime. This is due to variation in the distance between data sensor nodes as well as the amount of energy that is expended between them [8]. Even with these approaches, changing the battery when it dies is required. Underwater sensors can be charged using energy transfer technologies for long-term monitoring and transmitting data without the need for battery replacement [9]. The work [10] developed a rechargeable lithium-ion battery module for underwater use, addressing high water pressure and short circuits. Due to energy transfer distance limitations, autonomous underwater vehicles need help charging and require route planning. An Autonomous Underwater Vehicle (AUV) is a self-propelled submarine capable of moderate activities without human intervention [11]. AUVs are widely used in undersea study, environmental monitoring, and marine safety because of their affordability and security in seabed inquiry, search, identification, and rescue [12]. The AUV's limited power-carrying capacity and charging area make data loss from its consecutive nodes problematic. This makes it difficult to guarantee the AUV's usefulness for broader detecting regions, especially in marine conditions. Magnetically charged cars for wireless rechargeable sensor networks (WRSNs) were proposed in [13]. In ground-based wireless rechargeable sensor networks, WSNs have a three-dimensional structure and improve transmission power with distance. In this paper, we focus on the path planning problem in both cases without an obstacle and with an obstacle, which is where the difficulty of our problem comes from. On the other hand, we assume perfect accuracy to hit the sensor nodes. The most significant contributions are summarized in the following, which is a shortened version:  
* This paper consider the energy-aware path planning problem with an automated guided vehicle (AGV) in a 3D environment different from most of the related literature.