The Internet of Things (IoT) is rapidly becoming an integral part of our life and also multiple industries. We expect to see the number of IoT connected devices explosively grows and will reach hundreds of billions during the next few years. To support such a massive connectivity, various wireless technologies are investigated. In this survey, we provide a broad view of the existing wireless IoT connectivity technologies and discuss several new emerging technologies and solutions that can be effectively used to enable massive connectivity for IoT. In particular, we categorize the existing wireless IoT connectivity technologies based on coverage range and review diverse types of connectivity technologies with different specifications. We also point out key technical challenges of the existing connectivity technologies for enabling massive IoT connectivity. To address the challenges, we further review and discuss some examples of promising technologies such as compressive sensing (CS) random access, non-orthogonal multiple access (NOMA), and massive multiple input multiple output (mMIMO) based random access that could be employed in future standards for supporting IoT connectivity. Finally, a classification of IoT applications is considered in terms of various service requirements. For each group of classified applications, we outline its suitable IoT connectivity options.

IoT Connectivity Technologies and Applications: A Survey

Backscatter communication promises significant power and complexity advantages for Internet of Things devices such as radio frequency identification (RFID) tags and wireless sensor nodes. One perceived disadvantage of backscatter communication has been the requirement for specialized hardware such as RFID readers to receive backscatter signals. In this paper, we show how backscatter signals can be designed for compatibility with the Bluetooth 4.0 low energy (BLE) chipsets already present in billions of smart phones and tablets. We present a prototype microcontroller-based “BLE-Backscatter” tag that produces bandpass frequency-shift keying modulation at 1 Mb/s, enabling compatibility with conventional BLE advertising channels. Using a +23-dBm equivalent isotropically radiated power continuous wave (CW) carrier source, we demonstrate a range of up to 13 m between the tag and an unmodified Apple iPad Mini as well as a PC with the Nordic Semiconductor nRF51822 chipset. With the tag 1 m from the receiver, we demonstrate a range of up to 30 m between the CW carrier source and the tag. In both cases, the existing Bluetooth stack was used, with no modifications whatsoever to hardware, firmware, or software. The backscatter tag consumes only 1.56 nJ/b, over $6\times $ less than the lowest power commercial Bluetooth transmitters.

BLE-Backscatter: Ultralow-Power IoT Nodes Compatible With Bluetooth 4.0 Low Energy (BLE) Smartphones and Tablets

Heart rate (HR) estimation using photoplethysmography (PPG) has drawn increasing attention in the field of wearable technology due to its advantages with higher degree of usability and lower cost than Electrocardiograph. It has been widely used in wearable devices, such as smart-watches for fitness tracking and vital sign monitoring. However, motion artifact is a strong interference, preventing accurate estimation of HR. Signal decomposition and adaptive filtering are two popular approaches for motion artifact removal, but each of them has inherent drawbacks. In this paper, a hybrid motion artifact removal method is proposed, which combines nonlinear adaptive filtering and signal decomposition, getting the best of both approaches. The method was evaluated on the PPG database used in the 2015 IEEE Signal Processing Cup. The experimental results showed that the method achieved the average absolute error of 1.16 beat per minutes (BPM) on the 12 training data sets, and 2.98 BPM on the ten testing data sets.

Combining Nonlinear Adaptive Filtering and Signal Decomposition for Motion Artifact Removal in Wearable Photoplethysmography

Mobile and long-term recording of biomedical signals, such as electrocardiogram (ECG), electromyogram (EMG), and EEG, can improve diagnosis and monitor the evolution of several widespread diseases. However, it requires specific solutions, such as wearable devices, that should be particularly comfortable for patients, while at the same time ensuring medical-grade signal acquisition quality, including power line interference (PLI) removal. This paper focuses on the on-board real-time PLI filtering on a low-power biopotential acquisition wearable system. This paper analyzes in depth basic and advanced PLI filtering techniques and evaluates them in a wearable real-time processing scenario, assessing performance on EMG and ECG signals. Our experiments prove that most PLI removal algorithms are not usable in this challenging context, because they lack robustness or they require offline processing and large amounts of available data. On the other hand, adaptive filtering techniques are robust and well suited for lightweight online processing. We substantiate this finding with offline analysis and comparison, as well as with a complete embedded implementation on our low-power low-cost wearable device.

/pdf/power-line-interference-removal-for-high-quality-continuous-1f1ewrs4qg.pdf

Power Line Interference Removal for High-Quality Continuous Biosignal Monitoring With Low-Power Wearable Devices

https://hal.archives-ouvertes.fr/hal-01960231/document

A sensor-centric survey on the development of smartphone measurement and sensing systems

In this paper, we successfully developed a Bluetooth Low Energy (BLE) based blood pressure monitoring system, which integrated a homemade blood pressure measuring device and a smartphone. The device uses single BLE System-on-Chip to process the algorithm of blood pressure measurement and data transmission. Through the newest Bluetooth Low Energy technology, device can send measured results to the smartphone. Therefore, users can get the information of systolic blood pressure, diastolic blood pressure and pulse rate on the smartphone. Besides, the format of data transmission packet is based on the standard Blood Pressure Profile specification from Bluetooth SIG. The homemade blood pressure monitor has high compatibility to communication with other devices using the same Bluetooth profile. The accuracy of pulse rate and blood pressures are verified through the CuffLink simulator. The accuracy of pulse rate is −1.13 ± 0.52 beats, the accuracy of systolic blood pressure is 0.69 ± 1.63 mmHg, and the accuracy of diastolic blood pressure is 0.20 ± 1.02 mmHg. In clinical trials, the systolic blood pressure average difference between our method and auscultatory method is 2.66 ± 2.71 mmHg. The diastolic blood pressure average difference between our method and auscultatory method is 3.42 ± 4.42 mmHg. As a result, this device has high accuracy for measuring blood pressure and pulse rate.

Bluetooth Low Energy (BLE) based blood pressure monitoring system

Exercise is important for health, however inappropriate exercise would harm our body or get nothing affects. Therefore, a wearable exercise intensity monitoring device can assist user to manage their exercise intensity. Heart rate (HR) is an index to indicate the exercise intensity.

https://ieeexplore.ieee.org/iel7/6815920/6820888/06820931.pdf

Cheng-Hung Chang

Papers

Bluetooth Low Energy (BLE) based blood pressure monitoring system

A heart rate detection method for low power exercise intensity monitoring device