## 1 Introduction
To minimize the devastation caused by forest fires, the authors suggested a system that could identify forest fires early on using a WSN. Furthermore, a machine learning algorithm was used to obtain more precise fire detection. In the study, a sensor node using an Arduino Nano board with a 2.4 GHz nrf24L01 module was used to measure the temperature, humidity, light intensity level, and CO level. Experiments in actual tropical forest locations revealed that the system was effective in alerting the authors to forest fires with a lower latency. A WSN for the real-time surveillance of the Mar Menor lagoon was installed in [9]. Sensor nodes were strategically positioned in the ocean and recorded water factors such as \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Works** & **Multi-Hop Technology** & **Applications** & **Major Study** \\ \hline
[11] & 2.4 GHz, IEEE 802.15.4 & Multi-hop network for indoor environments & \(\bullet\) & The development of a multi-hop WSN for LoS communications, the NLoS, different floor communications, and spiral staircase tower scenarios \\  & & & \(\bullet\) & The evaluation of the effects of communication directions and transmission powers \\ \hline
[12] & UAV with IEEE 802.15.4 & UAV WSNs & The evaluation of the quality of aerial links in low-power WSNs in terms of RSSI signals and the PDR \\ \hline
[12] & & & \(\bullet\) & The development of a 2.4 GHz multi-hop network for mountainous forest and watercourse environments \\ This work & 2.4 GHz, IEEE 802.15.4 & Multi-hop network for mountainous forest and watercourse environments & \(\bullet\) & A wireless communication protocol and sensor node deployment solution \\  & & & \(\bullet\) & The evaluation of communication reliability in terms of the RSSI, PDR, and ETED \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._temperature, pressure, salinity, suspended nutrients, current velocity, and so on. ZigBee sensing nodes were installed at a maritime buoy, where the radio module was a Texas Instruments CC2520-CC2591 EM with an output power ratio of 17 dBm. A sensing node was placed 1.5 m above sea level to ensure reliable communication with another Zigbee node in the LoS. The authors concluded that their system could provide useful information to oceanographic experts, allowing them to obtain an in-depth understanding of the lagoon's hydrodynamic behavior. Communication reliability in terms of the PDR in a 2.4 GHz IEEE 802.15.4 multi-hop WSN was assessed in [10]. Experiments using Tmote Sky sensing nodes with a basic communication protocol were conducted in various indoor situations, at the Prince of Songkla University's Faculty of Engineering. According to the results, the PDR could achieve nearly 100% for the LoS, but only 14.40% for outdoor-to-indoor environments with different floors and the NLoS scenario. The work in [11] developed a 2.4 GHz indoor multi-hop WSN system utilizing IEEE 802.15.4. This system was tested in various indoor environments, including LoS communications, a NLoS, different floor communications, and spiral staircase tower scenarios. The effects of communication directions and transmission powers were investigated. Finally, in [12], the evaluation of the quality of aerial links in low-power WSNs was presented. The experiments consisted of eleven IEEE 802.15.4-compliant transceivers. Nine of these were deployed on the ground in a grid topology, while two were attached to a UAV. RSSI signals and the PDR were evaluated. The experimental results indicated that radio signal interference was the most significant factor affecting the quality of aerial links, where the radio technology used by the UAV significantly impacted the link quality of the IEEE 802.15.4. According to the literature review discussed above, existing works have tried to implement multi-hop WSNs for their specific applications and focused on different points to fulfill their system efficiency. In this work, a 2.4 GHz multi-hop wireless network is developed and evaluated.