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![Figure 1. BLE protocol stack [14].](publication/354817172/figure/fig1/AS:1071637541707776@1632509708167/BLE-protocol-stack-14_Q320.jpg)
As the growth rate of the internet-of-things (IoT) sensor market is expected to exceed 30%, a technology that can easily collect and processing a large number of various types of sensor data is gradually required. However, conventional multilink IoT sensor communication based on Bluetooth low energy (BLE) enables only the processing of up to 19 per...
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... STM32F030 MCU rebooted the device by resetting the nRF52840 module for data processing. After initialization, it stood by until the group setting command was received from the STM32F030 MCU (see Figure 4). To this end, the attributes of the received data were identified through flags. ...
Citations
... With the increasing demand for the Internet-of-Things (IoT), Bluetooth Low-Energy (BLE) has become a popular solution for short-range communication of wireless applications, such as wearable devices, sensor monitoring platforms, and smart cities [1][2][3][4][5]. However, due to the limitations of application scenarios, BLE devices often have the characteristics of small volume and are easy to carry. ...
This paper proposes a low-power all-digital phase-locked loop (ADPLL) with calibration-free ring oscillator (RO)-based injection-locking time to digital converter (TDC) for BLE applications. The RO is reused as the delay cell of TDC, and the quantization step of TDC is always tracked with the RO period; hence no calibration is needed in this architecture. We adopt RO tuning to lower the injection-locking bandwidth so as to decrease the power consumption of the injection current. Moreover, the fractional part of phase error detection is turned down in the coarse tuning of ADPLL to save power. An LC-based digital-controlled oscillator (LCDCO) with a 6.4 nH inductor and a resistive bias is used to have a low power and better phase noise performance. The ADPLL is fabricated in 40 nm CMOS with a 1 V supply and consumes 1.4 mW when it is locked. The measured phase noise is −114 dBc/Hz at 1 MHz offset. The test results show significant power saving. Thus, it can be a promising candidate for BLE applications.
... However, the huge (and increasing) number of connected devices, along with the requirement for fast, reliable, and cost-effective data transmission, pose a challenge for these kinds of systems [5]. Several Radio Frequency (RF)-based wireless technologies have been used to provide Internet of Things (IoT) services: WiFi [6], Bluetooth [7], Near-Field Communication (NFC) [8], or Zigbee [9], and also Low Power Wide Area Network (LPWAN) technologies such as SigFox [10], NB-IoT [11], or LoRa/LoRaWAN [12]. Nevertheless, these technologies present some drawbacks, such as Electromagnetic Interference (EMI), a limited frequency spectrum, and, in some cases, a lower data rate [5]. ...
The evolution of ambient intelligence has introduced a range of new opportunities to improve people’s well-being. One of these opportunities is the use of these technologies to enhance workplaces and improve employees’ comfort and productivity. However, these technologies often entail two major challenges: the requirement for fast and reliable data transmission between the vast number of devices connected simultaneously, and the interoperability between these devices. Conventional communication technologies present some drawbacks in these kinds of systems, such as lower data rates and electromagnetic interference, which have prompted research into new wireless communication technologies. One of these technologies is visible light communication (VLC), which uses existing light in an environment to transmit data. Its characteristics make it an up-and-coming technology for IoT services but also aggravate the interoperability challenge. To facilitate the continuous communication of the enormous amount of heterogeneous data generated, highly agile data models are required. The semantic approach tackles this problem by switching from ad hoc application-centric representation models and formats to a formal definition of concepts and relationships. This paper aims to advance the state of the art by proposing a semantic vocabulary for an intelligent automation platform with VLC enabled, which benefits from the advantages of VLC while ensuring the scalability and interoperability of all system components. Thus, the main contributions of this work are threefold: (i) the design and definition of a semantic model for an automation platform; (ii) the development of a prototype automation platform based on a VLC-based communication system; and (iii) the integration and validation of the proposed semantic model in the VLC-based automation platform.
