Performance evaluation of bluetooth low energy for high data rate body area networks

Bluetooth Low Energy (BLE) is a promising wireless network technology, in the context of body area network (BAN) applications, to provide the required quality of service (QoS) support concerning the communication between sensor nodes placed on a user’s body and a personal device, such as a smartphone. Most previous BLE performance studies in the literature have focused primarily in networks with a single slave (point-to-point link) or traffic scenarios with relatively low data rate. However, many BAN sensors generate high data rate traffic, and several sensor nodes (slaves) may be actively sending data in the same BAN. Therefore, this work focuses on the evaluation of the suitability of BLE mainly under these conditions. Results show that, for the same traffic, the BLE protocol presents lower energy consumption and supports more sensor nodes than an alternative IEEE 802.15.4-based protocol. This study also identifies and characterizes some implementation constraints on the tested platforms that impose limits on the achievable performance.This work has been supported by FCT (Fundação para a Ciência e Tecnologia) in the scope of the projects UID/EEA/04436/2013 and UID/CTM/50025/2013, and by FEDER funds through the COMPETE 2020 Programme

Pulse Signal System: Sensing, Data Acquisition and Body Area Network

Heart rate variability (HRV) is an important physiological signal of the human body, which can serve as a useful biomarker for the cardiovascular health status of an individual. There are many methods to measure the HRV using electrical devices, such as ECG and PPG etc. This work presents a novel HRV detection method which is based on pressure detection on the human wrist. This method has been compared with existing HRV detection methods. In this work, the proposed system for HRV detection is based on polyvinylidene difluoride (PVDF) sensor, which can measure tiny pressure on its surface. Three PVDF sensors are mounted on the wrist, and a three-channel conditioning circuit is used to amplify signals generated by the sensors. An analog-to-digital converter and Arduino microcontroller are used to sample and process the signal. Based on the obtained signals, the HRV can be processed and detected by the proposed PVDF-sensor-based system. Another contribution of this work is in designing a wireless body area network (WBAN) to transmit data acquired on the human body. This WBAN combines two different wireless network protocols, for both efficient power consumption and data rate. Bluetooth Low Energy protocol is used for transmitting data from the microcontroller to a personal device, and Wi-Fi is used to send data to other terminals. This provides the potential for remote HRV signal monitoring. A dataset consisting of two subjects was used to experimentally validate the proposed system design and signal processing method. ECG signals are acquired from subjects with wrist pulse signals for comparison as standard signal. The waveforms of ECG signals and wrist pulse signals are compared and HRV values are calculated from these two signals separately. The result shows that HRV calculated by wrist pulse has low error rate. A test of movement effect shows the sensor can resist mild motions of wrist. Some future improvements of system design and further signal processing methods are also discussed in the last chapter

Wireless Sensor Technology Selection for I4.0 Manufacturing Systems

The term smart manufacturing has surfaced as an industrial revolution in Germany known as Industry 4.0 (I4.0); this revolution aims to help the manufacturers adapt to turbulent market trends. Its main scope is implementing machine communication, both vertically and horizontally across the manufacturing hierarchy through Internet of things (IoT), technologies and servitization concepts. The main objective of this research is to help manufacturers manage the high levels of variety and the extreme turbulence of market trends through developing a selection tool that utilizes Analytic Hierarchy Process (AHP) techniques to recommend a suitable industrial wireless sensor network (IWSN) technology that fits their manufacturing requirements.In this thesis, IWSN technologies and their properties were identified, analyzed and compared to identify their potential suitability for different industrial manufacturing system application areas. The study included the identification and analysis of different industrial system types, their application areas, scenarios and respective communication requirements. The developed tool’s sensitivity is also tested to recommend different IWSN technology options with changing influential factors. Also, a prioritizing protocol is introduced in the case where more than one IWSN technology options are recommended by the AHP tool.A real industrial case study with the collaboration of SPM Automation Inc. is presented, where the industrial systems’ class, communication traffic types, and communication requirements were analyzed to recommend a suitable IWSN technology that fits their requirements and assists their shift towards I4.0 through utilizing AHP techniques. The results of this research will serve as a step forward, in the transformation process of manufacturing towards a more digitalized and better connected cyber-physical systems; thus, enhancing manufacturing attributes such as flexibility, reconfigurability, scalability and easing the shift towards implementing I4.0

Energy efficiency in short and wide-area IoT technologies—A survey

In the last years, the Internet of Things (IoT) has emerged as a key application context in the design and evolution of technologies in the transition toward a 5G ecosystem. More and more IoT technologies have entered the market and represent important enablers in the deployment of networks of interconnected devices. As network and spatial device densities grow, energy efficiency and consumption are becoming an important aspect in analyzing the performance and suitability of different technologies. In this framework, this survey presents an extensive review of IoT technologies, including both Low-Power Short-Area Networks (LPSANs) and Low-Power Wide-Area Networks (LPWANs), from the perspective of energy efficiency and power consumption. Existing consumption models and energy efficiency mechanisms are categorized, analyzed and discussed, in order to highlight the main trends proposed in literature and standards toward achieving energy-efficient IoT networks. Current limitations and open challenges are also discussed, aiming at highlighting new possible research directions

New Waves of IoT Technologies Research – Transcending Intelligence and Senses at the Edge to Create Multi Experience Environments

The next wave of Internet of Things (IoT) and Industrial Internet of Things (IIoT) brings new technological developments that incorporate radical advances in Artificial Intelligence (AI), edge computing processing, new sensing capabilities, more security protection and autonomous functions accelerating progress towards the ability for IoT systems to self-develop, self-maintain and self-optimise. The emergence of hyper autonomous IoT applications with enhanced sensing, distributed intelligence, edge processing and connectivity, combined with human augmentation, has the potential to power the transformation and optimisation of industrial sectors and to change the innovation landscape. This chapter is reviewing the most recent advances in the next wave of the IoT by looking not only at the technology enabling the IoT but also at the platforms and smart data aspects that will bring intelligence, sustainability, dependability, autonomy, and will support human-centric solutions.acceptedVersio

Smart Sensor Technologies for IoT

The recent development in wireless networks and devices has led to novel services that will utilize wireless communication on a new level. Much effort and resources have been dedicated to establishing new communication networks that will support machine-to-machine communication and the Internet of Things (IoT). In these systems, various smart and sensory devices are deployed and connected, enabling large amounts of data to be streamed. Smart services represent new trends in mobile services, i.e., a completely new spectrum of context-aware, personalized, and intelligent services and applications. A variety of existing services utilize information about the position of the user or mobile device. The position of mobile devices is often achieved using the Global Navigation Satellite System (GNSS) chips that are integrated into all modern mobile devices (smartphones). However, GNSS is not always a reliable source of position estimates due to multipath propagation and signal blockage. Moreover, integrating GNSS chips into all devices might have a negative impact on the battery life of future IoT applications. Therefore, alternative solutions to position estimation should be investigated and implemented in IoT applications. This Special Issue, “Smart Sensor Technologies for IoT” aims to report on some of the recent research efforts on this increasingly important topic. The twelve accepted papers in this issue cover various aspects of Smart Sensor Technologies for IoT

Sensing and Signal Processing in Smart Healthcare

In the last decade, we have witnessed the rapid development of electronic technologies that are transforming our daily lives. Such technologies are often integrated with various sensors that facilitate the collection of human motion and physiological data and are equipped with wireless communication modules such as Bluetooth, radio frequency identification, and near-field communication. In smart healthcare applications, designing ergonomic and intuitive human–computer interfaces is crucial because a system that is not easy to use will create a huge obstacle to adoption and may significantly reduce the efficacy of the solution. Signal and data processing is another important consideration in smart healthcare applications because it must ensure high accuracy with a high level of confidence in order for the applications to be useful for clinicians in making diagnosis and treatment decisions. This Special Issue is a collection of 10 articles selected from a total of 26 contributions. These contributions span the areas of signal processing and smart healthcare systems mostly contributed by authors from Europe, including Italy, Spain, France, Portugal, Romania, Sweden, and Netherlands. Authors from China, Korea, Taiwan, Indonesia, and Ecuador are also included