Efficient Deployment of Edge AI on BLE-Enabled Embedded Systems for Scalable, Secure, and Low-Power IoT Networks

Abstract

Integrating Edge AI with Bluetooth Low Energy fundamentally transforms IoT architecture by enabling distributed intelligence that overcomes the limitations of cloud-based processing. Edge AI brings computational processing closer to sensor devices, significantly mitigating cloud round-trip delays and enhancing data privacy through on-device capabilities. This combination yields synergistic architectures where advanced machine learning models operate within the stringent resource constraints of microcontrollers, converting large-volume sensor data streams into compact, actionable information. Hardware/software co-design techniques are crucial for reconciling challenging trade-offs among computational complexity, memory constraints, and energy budgets while meeting real-time performance objectives. System-on-Chip (SoC) architectures leverage two primary acceleration paradigms: dual-core architectures offering application-level parallelism and specialized AI accelerators with instruction-level optimization. Quantization techniques effectively lower neural network precision from floating-point to integer representations, realizing considerable reductions in memory footprints with negligible degradation in accuracy. Realistic deployments across industrial protection, automotive safety, smart homes, and healthcare showcase significant operational benefits, including reduced system downtime, improved security metrics, energy savings, and real-time physiological tracking capabilities. This paper develops a systematic analytical framework for identifying 'intelligence crossover points,' which are critical thresholds where local processing becomes demonstrably more efficient than raw data transmission. This framework, based on a comprehensive synthesis of performance insights and quantitative data from existing literature, provides clear deployment insights for various utility use cases within BLE-enabled Edge AI ecosystems.