# A Framework for Bluetooth-Based Real-Time Audio Data Acquisition in Mobile Robotics
## 2 Literature Review  
The integration of audio sensing and mobile robotics has evolved significantly over the last 10 years with a multifaceted involvement in this multifaceted problem. The following is a non-exhaustive list of the literature that constitutes the foundation of this work. [PERSON] et al. pioneered early work in mobile acoustic sensing [1] and created a humanoid robot that can localize and separate sound sources. Although their studies highlighted the significance of auditory information in robotics, they depended on intensive computational algorithms that were impractical for embedded platforms where resources are scarce. Taking a step further, [PERSON] et al. [11] provided more robust methods for sound processing applied to mobile robots, through Rao-Blackwellized particle filtering for real-time localization of multiple acoustical sources. [PERSON] et al. [3] performed exhaustive analyses of MEMS microphones for audio applications, thus reporting meaningful performance benchmarks for noise floor, frequency response, and power consumption. While their results showed promise for mobile applications with these compact sensors, they did not consider the complexities of the coupling with movement systems. Similarly, [PERSON] et al. [12] investigated optimizations for audio capture in embedded platforms with long-lasting and long-range Bluetooth wireless sensor nodes aimed at pressure and acoustic monitoring, yet restricted only within stationary applications. The MEMS-embedded system design was the subject of research led by [PERSON] [13], contributing to the understanding of MEMS integration with digital systems. [PERSON] et al. conducted a comprehensive analysis of the shortcomings of Bluetooth-based data transmission in resource-constrained environments [5] and reported fundamental restrictions in their metric (bandwidth and reliability) when streaming sensor data into a Bluetooth Low Energy mesh network. Building on this work, [PERSON] et al. [6] performed experimental evaluations for close detection in search and rescue missions using robotic platforms, but this work does not consider the actual needs for streaming audio in terms of throughput requirements. [PERSON] et al. [14] also made advancements in this area by proposing a lighter-weight, wireless Bluetooth-low-energy neuronal recording system with a focus on power management and data transmission. [PERSON] et al. made noteworthy advances in this area in relation to resource constraints in mobile robotic systems [7]. Whereas [PERSON] and [PERSON] [8] also researched IoT multimedia sensors from an energy efficiency and security perspective by defining design principles, bringing really valuable insights on how to sustain mean sensor integrity throughout its operation. Additionally, extensive research into deep-learning-based wireless indoor localization has been conducted by [PERSON] [15], while [PERSON] [16] performed a systematic review of emerging technologies used in WSN-based localization. [PERSON] et al. advanced the theoretical basis of speech recognition and audio processing in IoT settings [17], whose model for the secure transfer of speech recognition data in the Internet of Things guides us in audio acquisition. [PERSON], [PERSON] et al. [18] investigated the impact of end-to-end automatic speech recognition on workflow documentation and thus offered some valuable insights concerning audio processing technologies adopted in practice. Systems developed by [PERSON] et al. [19] for audio-visual sound source localization and tracking based on mobile robots have shown that acoustic data can be instrumental in solving the cocktail party problem; however, they were implemented on more powerful hardware and not dedicated microcontroller-based platforms. Similarly, [PERSON] et al. [20] focus on sound-based positioning systems for mobile robots in greenhouses, achieving good accuracy but pointing out the challenges of noise filtering and feature extraction in dynamic environments. [PERSON] et al.