## 1 Introduction  
Wireless Sensor Networks (WSNs) are advantageous for monitoring vast and remote areas such as forests, primarily due to the lower operational costs of installation and maintenance [1]. However, these scenarios pose significant challenges for WSNs, such as energy constraints of sensor nodes, typically battery-powered, and the requirement for long-distance communication to cover extensive areas [2]. Recent studies, such as [3], highlight the importance of addressing these challenges by optimizing resource allocation strategies to enhance the performance and efficiency of WSNs, particularly in scenarios where real-time information timeliness is critical. Monitoring vast and remote areas (such as forests) using WSNs presents challenges, such as energy limitations and signal attenuation due to natural obstacles. Traditional solutions require extensive infrastructure, which is impractical in such isolated environments. Therefore, there is a need for architectures that can overcome these obstacles, especially in regions like the Amazon, where network coverage and energy efficiency are essential for the success of environmental monitoring operations. Low-Power Wide-Area Networks (LPWAN) enable long-range communication with low energy consumption. However, they face limitations in forested areas due to natural obstacles like trees and leaves, which can significantly reduce signal range. For instance, in an open environment, LPWANs can reach up to 15 km; however, in densely forested areas, this range may be reduced to only 1-2 km [4]. Additionally, LPWANs require infrastructure, including installing gateways with a continuous electricity supply and connectivity to the Internet for transmitting the collected data [5]. Delay and Disruption Tolerant Networks (DTNs) can handle network interruptions, operating effectively in environments with high latencies and disconnections. However, DTNs require superior processing capability and energy consumption from nodes, which can be impractical for battery-powered devices like sensors [6; 7; 8]. To mitigate or eliminate the need for network infrastructure and to address the challenge of long-distance communications, different studies propose the adoption of mobile communication nodes known as data mules [9; 10], relay nodes [6; 11], or sink nodes [12]. These nodes collect sensor data on the ground and forward it to a gateway, processing, control station, or another forwarding node. A promising strategy in this area involves using data mules through Unmanned Aerial Vehicles (UAVs) that can fly over remote areas, gather sensor data, and transmit it to destinations, mitigating disconnection issues and reducing ground infrastructure [13; 14]. In this context, we present the \"FloatingBlue\" architecture, which combines Bluetooth Low Energy (BLE) technology with Delay Tolerant Networks (DTN) and data mules. The \"FloatingBlue\" stands out for its innovation by uniquely integrating these components, providing a monitoring solution that does not depend on constant network connectivity. This approach enables the efficient collection of data from dispersed sensors, leveraging BLE's low energy consumption and the robustness of DTN in scenarios of frequent disconnection [15]. Furthermore, a BLE node can provide a lower energy consumption alternative through its broadcast mode, where the protocol allows communication without the need for a connection to transmit packets, further optimizing energy consumption [16; 17]. In this study, we also analyze the influence of energy consumption by embedded software in BLE devices, comparing different firmwares based on bare-metal programming [18]. In particular, we investigate the development of low-level software to control the hardware of a BLE sensor directly, compared to devices for which we developed software using Real-Time Operating System (RTOS). Thus, in this article, we propose the practical development of a new wireless sensor network architecture called \"FloatingBlue\", and the main contributions of this work are:  
1.