In the era of connected vehicles, fleet management is evolving beyond traditional logistics optimization, addressing challenges like insufficient real-time tracking and the perishability of goods due to poor air quality conditions. This paper introduces a pioneering smart connected-vehicle-based Internet of Things (IoT) system selecting future optimum routes essential for fleet safety and operational efficiency based on predictive air quality data. The system features an advanced modular sensing package with integrated battery management system, continuously monitoring volatile organic compounds (VOCs), carbon monoxide (CO), carbon dioxide (CO2), relative humidity, temperature, pressure along with vehicle motion and real-time GPS location data. Data are then transmitted to a centralized server, where different predictive algorithms are deployed to predict future air pollutant concentrations. These predictions enable proactive route optimization, allowing fleet managers to navigate based on lowest predicted air quality indices (AQI), ensuring cleaner and safer routes by leveraging real-time environmental data and predictive algorithms. The system has been tested across different environments, validating its sensing, IoTenabled and predictive abilities. Results demonstrate its efficacy in accurate predictions, achieving RMSE of 1.4, 8.1 and 2.5 ppm and mean absolute errors (MAE) of 1.0,5.2 and 1.6 ppm for CO, CO2and VOCs respectively. Additionally, the artificial neural network (ANN) model achieved an impressive R2score of 0.92 in predicting CO.

Contact tracing is an effective method for mitigating the infectious diseases spread and it played a crucial role in reducing COVID-19 outbreak. Since the pandemic, there has been an increased concern regarding people's health in hospital and office settings, as these limited air exchange spaces provide a conductive medium for virus spread. Various technologies were used to recognize close contacts autonomously, in addition, multiple machine learning attempts were carried out to determine proximity in contact tracing. This study, however, proposes a unique concept in contact tracing: forecasting future close contact prior to occurrence in order to regulate and control it rather than tracking past occurrences. For our research, we constructed a completely new real-life dataset that was collected during the pandemic in a hospital infectious ward (Alfred Hospital, Melbourne, Australia) utilizing a Bluetooth Low Energy (BLE) Internet of Things (IoT) system. Our prediction technique considers two types of environments: single transceiver environments and multiple transceivers settings, these transceivers record the nearby tags' BLE received signal strength indicator (RSSI) values. The system employs mathematical models and supervised machine learning (ML) algorithms to solve regression and classification problems for workers' pattern recognition within the environment. The output is compared using different metrics, such as efficiency, which reached more than 80%, root mean square errors and mean absolute errors which were as low as 2.4 and 1.2 respectively in some models.

Since 2020, the coronavirus disease (COVID-19) pandemic has had a substantial impact on all community sectors worldwide, particularly the healthcare sector. Healthcare workers (HCWs) are at risk of COVID-19 infection due to occupational exposure to infectious patients, visitors, and staff. Contact tracing of close physical interaction is an essential control measure, especially in hospitals, to prevent onward transmission during an outbreak event. In this article, we propose an Internet of Thing-based contact tracing system for subject identification, interaction tracking, and data transmission in hospital wards. The system, based on Bluetooth low-energy (BLE) devices, tracks the duration of interactions between different HCWs, and the time each HCW spends within the patient rooms using additional information from proximity sensors in the hallway or on the door frame of the patient room. The collected data are transferred via long-range (LoRa) wireless technology and further analyzed to inform infection prevention activities. The suggested system’s performance is evaluated in a COVID-19 patient ward with both standard and negative pressure isolation rooms, and the current system’s capabilities and future research prospects are briefly discussed.

In many conventional Internet-of-Things (IoT) applications, data are transferred directly from the sensor network to the cloud via a gateway for further data processing. However, this typical usage of a gateway is not suitable for every application. For short-range IoT wireless protocols [e.g., Bluetooth low energy (BLE)], multiple gateways are required to achieve broader coverage, which is inconvenient. In this article, an edge-based hybrid network system architecture is presented. The proposed system consists of hybrid routers and an IoT gateway. The router supports two wireless protocols, BLE and long range (LoRa), and is equipped with a solar energy harvester to extend the router's lifetime. It can extend the coverage of short-range BLE network by utilizing LoRa wireless technology, and support fundamental edge computing tasks such as preliminary data processing. The IoT gateway can support multiple IoT protocols, including LoRa, BLE, and XBee. It can perform more advanced edge computing tasks, such as data filtering, storage, processing, user interface, and cloud connection. Three case studies incorporating a wearable safety monitoring sensor network, a healthcare monitoring application, and a smart hospital application are studied with the proposed edge network system to demonstrate its promising capabilities to support IoT applications. Experimental evaluations indicate that by processing data at the edge, the minimal delay is only 11.5 ms. Furthermore, with the hybrid LoRa network implementation, the BLE network can be extended to 2.4 km.

The Internet of Things (IoT) is a new communication paradigm that can connect elements from various fields through the Internet. One of the most attractive IoT applications is in the modern healthcare area, as the traditional healthcare system has an increasing demand for social resources, including doctors, nurses, hospital beds, and health monitoring devices. In this article, the design of a compact wearable sensor patch is presented for measurements of different physiological signals, such as the electrocardiogram (ECG), photoplethysmography (PPG), and body temperature. As ECG and PPG sensors are integrated with the same device, the proposed sensor patch can be used to estimate blood pressure (BP) continuously based on the pulse arrival time (PAT) without extra wires and devices. The sensor patch consists of a center board for signal acquisition and processing, a power board for energy supply and charging batteries, and three sensors for vital signs monitoring. All the components are designed in a rigid-flex structure, which can be easily attached to the human body for remote health monitoring applications. The sensors can be detached from the center board for customized measurements of a certain physiological signal (e.g., ECG) to reduce power consumption. Experiments are conducted to validate the performance of the proposed sensor patch by comparison with a commercial reference device. With the integration of a miniaturized Bluetooth low-energy (BLE) module, the proposed sensor system can transmit physiological measurements wirelessly to a gateway. Data encryption is applied on both the sensor patch and gateways to protect data for privacy and security purposes during transmission. Both a mobile gateway (based on smartphones) and a fixed gateway (based on portable computers) are designed as the bridge between the wearable sensor system and the Internet cloud, where health data can be stored and further analyzed. The experimental results demonstrate the feasibility of the overall platform for IoT-connected healthcare applications.

On-body Continuous Wave Radar (CWR) sensors, along with Photoplethysmogram (PPG) and Electrocardiogram (ECG) sensors, have been introduced to estimate blood pressure(BP) using time parameters as Pulse Arrival Time (PAT), Pre-ejection Period (PEP) and Pulse Transit Time (PTT) during different postures and exercises tasks. The wavelet transform is applied to the signals to remove the noise components. The on-body CWR sensor and PPG sensor are placed on the sternum and left earlobe, respectively. The time parameters of 43 healthy subjects are collected in the different sitting, standing, and supine postures, and also for 26 healthy subjects in six different exercise tasks as handgrip, light, moderate and heavy bike exercise, and two recoveries. Two different regression methods are used to estimate BP from PTT and the results are compared with the estimated BP from PAT. The best Cumulative Error Percentage (<; 10mmHg) is 98.2% for 38 subjects, participated in posture tasks and 88.89% for 21 subjects, participated in exercise tasks. The results show the proposed method is promising for estimating BP using PTT obtained from radar and PPG signals.

Linear-feedback shift register (LFSR) counters have been shown to be well suited to applications requiring large arrays of counters and can improve the area and performance compared with conventional binary counters. However, significant logic is required to decode the count order into binary, causing system-on-chip designs to be unfeasible. This paper presents a counter design based on multiple LFSR stages that retains the advantages of a single-stage LFSR but only requires decoding logic that scales logarithmically with the number of stages rather than exponentially with the number of bits as required by other methods. A four-stage four-bit LFSR proof of concept was fabricated in 130-nm CMOS and was characterized in a time-to-digital converter application at 800 MHz.

This paper presents a wireless capsule microsystem to detect and monitor the pH, pressure, and temperature of the gastrointestinal tract in real time. This research contributes to the integration of sensors (microfabricated capacitive pH, capacitive pressure, and resistive temperature sensors), frequency modulation and pulse width modulation based interface IC circuits, microcontroller, and transceiver with meandered conformal antenna for the development of a capsule system. The challenges associated with the system miniaturization, higher sensitivity and resolution of sensors, and lower power consumption of interface circuits are addressed. The layout, PCB design, and packaging of a miniaturized wireless capsule, having diameter of 13 mm and length of 28 mm, have successfully been implemented. A data receiver and recorder system is also designed to receive physiological data from the wireless capsule and to send it to a computer for real-time display and recording. Experiments are performed in vitro using a stomach model and minced pork as tissue simulating material. The real-time measurements also validate the suitability of sensors, interface circuits, and meandered antenna for wireless capsule applications.

The estimation of systolic time intervals (STIs) is done using continuous wave (CW) radar at 2.45 GHz with an on-body antenna. MOTIVATION: In the state of the art, typically bioimpedance, heart sounds and/or ultrasound are used to measure STIs. All three methods suffer from insufficient accuracy of STI estimation due to various reasons. CW radar is investigated for its ability to overcome the deficiencies in the state of the art. METHODS: Ten healthy male subjects aged 25-45 were asked to lie down at a 30 incline. Recordings of 60 s were taken without breathing and with paced breathing. Heart sounds, electrocardiogram, respiration, and impedance cardiogram were measured simultaneously as reference. The radar antennas were placed at two positions on the chest. The antennas were placed directly on the body as well as with cotton textile in between. The beat to beat STIs have been determined from the reference signals as well as CW radar signals. RESULTS: The results indicate that CW radar can be used to estimate STIs in ambulatory monitoring. SIGNIFICANCE: The results pave way to a potentially more compact method of estimating STIs, which can be integrated into a wearable device.

Cardiovascular vital signs are measured using continuous wave (CW) radar at 2.45GHz with a body-contact antenna. Using a previously collected database of thirty second recordings at paced breathing conditions, CW radar signals were measured together with heart sounds, electrocardiogram (ECG), respiration and impedance cardiogram (ICG) were as reference signals. Using arbitrarily chosen features from the radar signal, the systolic time intervals (STI)s from radar are compared with those from the ICG. The correlation coefficients between the STIs of radar and ICG were 0.72, 0.66 and 0.81 for the pre-ejection period, left ventricular ejection time and electromechanical systole respectively. The p-value was below 0.0001 for all coefficients, indicating a significant correlation. The results indicate that the radar signals capture cardiomechanical signals, which have great potential to be used for STI estimation in ambulatory conditions.

Wireless sensor network (WSN) plays an important role in monitoring applications in many areas. This paper presents a WSN called EN-Nets which is based on a newly designed small-size and low-power sensor node (EN-Node) for monitoring real-time environmental conditions. Detailed design and implementation of the sensor node for sensing and communicating environmental parameters are described. A power management method utilizing transistor switch connections to sensors to control their operations was used to reduce the overall power consumption of each sensor node. The important testing results for the proposed sensor board are discussed, such as routing method, transmission packet delay and sensors' performance. A comprehensive and interactive graphical user interface program has been developed to allow users to interact with the sensor network system. A real-time deployment has been performed in a campus area and presented in here.

This paper presents conducted emission measurements for a group of commercially available capacitive step-up dc/dc converters. A comparison of the traditional line impedance stabilization network conducted emission method with integrated circuit test methods including 1 Ω, direct and transverse electromagnetic cell methods is presented. The frequency performance and systematic offsets of each method are examined. Relative electromagnetic compatibility of these devices is compared and correlation of results of different methods explored. Improved understanding of the electromagnetic environment improves the potential reliability of electronic systems.

THIS special issue includes seven interesting articles that tackle some of the existing challenges and design issues related to the use of RF and Communication Technologies for wireless implants. An in-body medical device is a miniature device that is inserted in the human body to collect physiological signals and images or to act like a prosthetic device restoring certain body functions. In order for these devices to communicate with the external world, radio frequency (RF) wireless technologies are often used to maintain their operation for a longer period of time. This is achieved by integrating wireless communication technologies within these devices. In some situations, a body worn device is used to receive the medical information from the devices in the human body and then transmit to remote stations for remote medical monitoring.

IR-UWB communication systems have become popular for Wireless Body Area Network (WBAN) applications in recent times. This paper presents the development and testing of an IR-UWB coordinator node that includes an IR-UWB receiver front-end and a Field Programmable Gate Array (FPGA) based controller. The IR-UWB receiver front-end down converts the high frequency (3.5-4.5 GHz) IR-UWB pulses using a mixer and a Voltage Controlled Oscillator (VCO) in to easily detectable base band pulses. IR-UWB pulse synchronization is implemented using the FPGA. The performance of the receiver node is experimentally evaluated by conducting Bit Error Rate (BER) analysis for an on-body to off-body communication scenario. The BER results show that a BER of 104is achievable for a propagation distance of 1m using the IR-UWB coordinator node described in this paper.

The basic requirement of wireless healthcare monitoring systems is to send physiological signals acquired from implantable or on-body sensor nodes to a remote location. Low-power consumption is required for wireless healthcare monitoring systems since most medical sensor nodes are battery powered. The emergence of new technologies in measuring physiological signals has increased the demand for high data rate transmission systems. Ultra-wide band (UWB) is a suitable wireless technology to achieve high data rates while keeping power consumption and form factors small. Although UWB transmitters are designed based on simple techniques, UWB receivers require complex hardware and consume comparatively higher power. In order to achieve reliable low power two-way communication, a sensor node can be constructed using a UWB transmitter and a narrow band receiver. This paper proposes a new medium access control (MAC) protocol based on a dual-band physical layer technology. Co-simulation models based on MATLAB and OPNET have been developed to analyze the performance of the proposed MAC protocol. We analyzed the performance of the MAC protocol for a realistic scenario where both implantable and wearable sensor nodes are involved in the data transmission. Priority-based packet transmission techniques have been used in the MAC protocol to serve different sensors according to their QoS requirements. Analysis is done with regard to important network parameters, such as packet loss ratio, packet delay, percentage throughput, and power consumption.

Wireless Body Area Network (WBAN) is well known for accessing data from in body and on body devices. Extensive research has been done on Medium Access Control (MAC) protocol for WBAN for different unlicensed ISM bands, such as 868MHz, 915MHz, 2.4MHz and 433 MHz. However, due to vast use of those frequencies for other applications, there is an unavoidable risk for healthcare application. Inductive link method is another conventional way to communicate with an implanted device. But some limitations are also there, such as low data rate and communication range is very low (few centimeters). To emphasize a patient's health safety and to evade the limitation of inductive link method, a new and different frequency band, Medical Implant Communication System (MICS) band (402–405 MHz), has been accepted worldwide for a small range (3 meters) communication between an external device and implanted devices only. But, different international communication authorities have put some rules and restrictions to use the MICS band. Therefore, it is necessary to design a MAC protocol that will comply with the rules and suitable for the traffics of implanted devices communication network. In this paper, we propose a MAC protocol for MICS band considering the proposed rules and probable traffics in the network. A categorization of all the possible traffics is done. The MAC protocol is verified based on our proposed traffic categorization and it is observed that eight patients, each having eight implants, can be monitored simultaneously.

This paper presents a MICS (medical implant communication service) based body sensor network design and implementation for patient physiological data collection for health monitoring purposes. The MICS band offers the advantage of miniaturized electronic devices that can either be used as an implanted node or as an external node. In this work, a prototype sensor network is implemented by incorporating temperature and pulse rate sensors on nodes. Each developed sensor node has the capability of physiological data acquisition and local processing. The sensor node can also transmit data over the air to a remote central control unit (CCU) for further processing and storage. The developed system offers medical staff to obtain patient's physiological data on demand basis via the Internet. Moreover, the system has been optimized for power consumption by using a polling architecture. Some preliminary performance data is presented in the paper.

An all-digital symbol timing recovery technique that uses a 1-bit ADC, delay-XOR unit and a digital prefilter before the all-digital loop is analyzed. This approach provides a low-power, all-digital implementation in CMOS integrated circuit and exhibits very low jitter. The prefilter is arranged to eliminate frequency offsets on the input signal. The system is robust against fast and large Doppler shift and is therefore well suited for receivers in satellite applications. A symbol timing circuit based on this technique has been implemented for a wide range of bit rates (0.1-100 Kbps). It is synchronized within 3 or 4 bits in the presence of high carrier frequency offsets. A detailed performance study is carried out by both analytical simulation and hardware implementation to provide guidelines when the proposed scheme is applied to other transmission systems.

In this paper we present extremely flexible and low power digital binary ASK, PSK, and FSK demodulator architectures for short-range applications that use a limiter amplifier (i.e. high gain comparator) at the front-end. The limiter amplifier can be used at the front-end when the SNR of the received signal is high. This technique converts the analog signal to digital via one bit processing and eliminates mixers, PLL and AGC devices or replaces them with very low power digital counterparts. The rest of circuit is then implemented using digital signal processing (DSP) technique. Detailed system analysis of such demodulators are carried out in this paper. In addition, some new low-size and low-power modulators/demodulators are also described and analyzed for low-power short-range applications