Small, compact and embedded sensors are a pervasive technology in everyday life for a wide number of applications (e.g., wearable devices, domotics, e-health systems, etc.). In this context, wireless transmission plays a key role, and among available solutions, Bluetooth Low Energy (BLE) is gaining more and more popularity. BLE merges together good performance, low-energy consumption and widespread diffusion. The aim of this work is to review the main methodologies adopted to investigate BLE performance. The first part of this review is an in-depth description of the protocol, highlighting the main characteristics and implementation details. The second part reviews the state of the art on BLE characteristics and performance. In particular, we analyze throughput, maximum number of connectable sensors, power consumption, latency and maximum reachable range, with the aim to identify what are the current limits of BLE technology. The main results can be resumed as follows: throughput may theoretically reach the limit of ~230 kbps, but actual applications analyzed in this review show throughputs limited to ~100 kbps; the maximum reachable range is strictly dependent on the radio power, and it goes up to a few tens of meters; the maximum number of nodes in the network depends on connection parameters, on the network architecture and specific device characteristics, but it is usually lower than 10; power consumption and latency are largely modeled and analyzed and are strictly dependent on a huge number of parameters. Most of these characteristics are based on analytical models, but there is a need for rigorous experimental evaluations to understand the actual limits.

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Disabled people, especially the ones with motor skill impairments, have difficulties in interacting with personal computers and smartphones. Indeed Automatic Speech Recognition (ASR) could be helpful for those people, but it's limited in scenarios not affected by environmental noise that can decrease performance of the recognition, limiting user experience. We propose a speech enhancement system based on MEMS microphone array and a digital signal processor in order to increase signal-to-noise ratio (SNR) of the user's voice. The audio delay between microphones is exploited by the array using the Differential Microphone Array (DMA) and an Adaptive Noise Reduction techniques. In such way the system can obtain an increment in SNR about 16.5 dB, when noise and voice come from opposite directions. A voice activity detection (VAD) block recognizes when the user speaks and sends the data to a cloud-based ASR system. Due to the small array size, the embedded system can be integrated in a wearable device. Theoretical analysis and in-system measurements prove the effectiveness of the proposed solution