Cloud Connectivity Research Articles

CardinalKit: open-source standards-based, interoperable mobile development platform to help translate the promise of digital health.

Smartphone devices capable of monitoring users' health, physiology, activity, and environment revolutionize care delivery, medical research, and remote patient monitoring. Such devices, laden with clinical-grade sensors and cloud connectivity, allow clinicians, researchers, and patients to monitor health longitudinally, passively, and persistently, shifting the paradigm of care and research from low-resolution, intermittent, and discrete to one of persistent, continuous, and high resolution. The collection, transmission, and storage of sensitive health data using mobile devices presents unique challenges that serve as significant barriers to entry for care providers and researchers alike. Compliance with standards like HIPAA and GDPR requires unique skills and practices. These requirements make off-the-shelf technologies insufficient for use in the digital health space. As a result, budget, timeline, talent, and resource constraints are the largest barriers to new digital technologies. The CardinalKit platform is an open-source project addressing these challenges by focusing on reducing these barriers and accelerating the innovation, adoption, and use of digital health technologies. CardinalKit provides a mobile template application and web dashboard to enable an interoperable foundation for developing digital health applications. We demonstrate the applicability of CardinalKit to a wide variety of digital health applications across 18 innovative digital health prototypes.

Toward Immersive Underwater Cloud-Enabled Networks: Prospects and Challenges

Underwater engineering significantly increases the liveability and sustainability of our planet's ecosystems. Enhancing the connectivity of ocean-based engineering systems and ensuring its digital resilience is, therefore, indispensable for reaping the benefits of underwater engineering systems. To this end, this work proposes augmenting underwater engineering systems with cloud connectivity platforms as a paradigm shift for upscaling underwater communication networks and applications. The envisioned underwater cloud-assisted network (UCAN) provides joint underwater computing, processing, communication, and acquisition while benefitting from the cooling features of the aquatic environment. We first survey the literature on conventional underwater wireless communication networks. We then describe the prospects for UCANs and advocate the deployment of UCANs for sustainable connectivity. Next, we explore the opportunities stemming from the deployment of UCANs—e.g., resource management, natural cooling, and underwater intelligence. Finally, we highlight the challenges and open issues arising from such a futuristic cloud-enabled underwater communication network—e.g., channel modeling, characterizing achievable-rate, quality-of-service retrofitting, and preserving marine biodiversity.

Respiration frequency rate monitoring using smartphone-integrated polymer optical fibers sensors with cloud connectivity

This paper presents a respiratory frequency system composed by a polymer optical fiber sensor using a smartphone as the interrogator and a remote access to information through a Web server. The system developed a locally-based signal processing method for a dedicated designed application while ensuring remote access using the Thingspeak Web server and application. To check the sensor accuracy, tests with known respiratory rates of 30, 35 and 40 Breaths per Minute (BPM), respectively, are performed using a metronome. The sensor is manually stretched on the metronome beats frequency and the results achieved by the system were of 30.3599, 35.88 and 41.4 BPM. Furthermore, tests are performed in volunteers to check system performance in a real environment, where the volunteers are asked to breathe normally so their respiratory rate could be counted manually and compared to the values delivered by the experimental system in a given period. The error rate verified in the validation experiments are less than 2%. To verify the remote access to information, it is checked whether the respiratory rate data measured by the acquisition system is accordingly sent and visualized by Thingview: the application used in this prototype. The frequency read on the app is the same as measured by the sensor, which shows the suitability of the proposed approach for remote sensing applications with cloud integration.

Electric Vehicle Lithium-ion Battery Ageing Analysis under Dynamic Condition: A Machine Learning Approach

Currently, the smart cities, smart vehicles, and smart gadgets will improve the way of living standard. Cloud connectivity of IoT sensed devices will capture real-time data in the cloud which helps to improve the system performance and quick response to queries. Electric Vehicle battery health diagnosis plays an important role in the proper functioning of the battery management system, guarantees safety, and warranty claim. Society 5.0 develops with the advancement in the road, infrastructure, better connectivity, transportation, and options available to purchase. Battery health cannot be measured directly. There are internal and external factors that affect battery health such as State of Charge, model parameters, charging/discharging method, temperature, Depth of Discharge, C-rate, battery chemistry, form factor, thermal management, and load change effect. Battery degrades due to both calendar ageing and cyclic ageing. Artificial Intelligence plays a significant role in Battery management system due to the nonlinear behavior of lithium-ion battery. Prediction of battery health accurately and in due time will reduce the risk of recklessness. Timely maintenance will reduce the risk of fatal accidents. This paper presents different batteries analysis under different discharge voltage and capacity conditions. Different machine learning algorithms such as Neural Network, Modified Support Vector Machine (M-SVM) and Linear Regression are used to predict state of health. The proposed M-SVM performs well with less error for all four-battery discharge data.

A Modular In-Vehicle C-ITS Architecture for Sensor Data Collection, Vehicular Communications and Cloud Connectivity.

The growth of the automobile industry in recent decades and the overuse of personal vehicles have amplified problems directly related to road safety, such as the increase in traffic congestion and number of accidents, as well as the degradation of the quality of roads. At the same time, and with the contribution of climate change effects, dangerous weather events have become more common on road infrastructure. In this context, Cooperative Intelligent Transport Systems (C-ITS) and Internet of Things (IoT) solutions emerge to overcome the limitations of human and local sensory systems, through the collection and distribution of relevant data to Connected and Automated Vehicles (CAVs). In this paper, an intra- and inter-vehicle sensory data collection system is presented, starting with the acquisition of relevant data present on the Controller Area Network (CAN) bus, collected through the vehicle's On-Board-Diagnostics II (OBD-II) port, as well as on an on-board smartphone device and possibly other additional sensors. Short-range communication technologies, such as Bluetooth Low Energy (BLE), Wi-Fi, and ITS-G5, are employed in conjunction with long-range cellular networks for data dissemination and remote cloud monitoring. The results of the experimental tests allow the analysis of the road environment, as well as the notification in near real-time of adverse road conditions to drivers. The developed data collection system reveals itself as a potentially valuable tool for improving road safety and to iterate on the current Road Weather Models (RWMs).

Secure access microcontroller system based on fingerprint template with hyperchaotic encryption

Authentication systems based on biometric and microcontroller systems are used around the world to control the access of users in restricted sites such as in offices, banks, or hospitals. Moderns authentication systems use cloud connectivity using the Internet. Nevertheless, biometric data are considered confidential, which is susceptible to hacker attacks with the aim of fraud, extortion or identity theft. In this work, we propose a low-cost secure control access microcontroller system that uses the Secure Hash Algorithm 1 (SHA-1) and hyperchaotic encryption of fingerprint template to increase the security against cybernetic attacks. The main findings can be briefly described as (1) chaotic encryption of the fingerprint template provides strong security by using the hyperchaotic 2D Lotka–Volterra map with just one round of diffusion–confusion; (2) the fingerprint template injected to SHA-1 increases the robustness against differential attack by combining a unique 160-bit hash key with the personal key; (3) the authentication system implemented in a low-cost embedded system based on 32-bit microcontroller provides high security at low cost; (4) several security analysis of data generated in the embedded authentication system validates the effectiveness of proposed scheme. Based on the high security level achieved in proposed authentication scheme, the system provides double padlock in secure access biometric systems providing robustness against malicious attacks, avoid identity theft and the unauthorized access in highly restricted areas.

Mechanism for IPv6 adaptation in LoRa topologies

One of the main requirements of an IoT solution is the support of end-to-end IPv6 connectivity. Most Wireless Personal Area (WPAN) physical and link layers like Bluetooth Low Energy (BLE), ITU-T G.9959 and IEEE 802.15.4 support adaptation mechanisms that, based on the IPv6 over Low Power Wireless Personal Area Networks (6LoWPAN) protocol, enable IPv6. On the other hand, Low Power Wide Area Network (LPWAN) technologies like LoRa and SigFox are not typically designed to support access-side IPv6 and rely on gateways for translation and overall cloud connectivity. Lack of end-to-end IPv6 connectivity implies that many of these LPWAN technologies do not fully comply with the IoT paradigm preventing the direct interaction between devices of different physical technologies. In this paper we present an adaptation mechanism that enables end-to-end IPv6 support in an LPWAN scenario that relies on a LoRa physical layer. The scheme is analyzed, modeled and implemented by means of an experimental framework that serves to examine the efficiency of the proposed solution.

First-principles study on microstructure of CaO-Al2O3-B2O3 slag

The structure evolution of CaO-Al2O3-B2O3 slag at 1873 K with various CaO/Al2O3 ratio in 0.7 to 1.5 was studied by first-principles molecular dynamics. The bond stability was in the order of B-O > Al-O > Ca-O > O-O. In addition, the average coordination number of B and Al was observed as the increase in the CaO/Al2O3 ratio from 0.7 to 1.1, while decrease with further CaO/Al2O3 addition to 1.5. Combining the results of oxygen distribution and FTIR spectra, the slag polymerization degree was decreased with CaO/Al2O3 enhancement. Ca-O was ionic bond, while B-O and Al-O were charge-transfer bond, so that the structure evolutions of B-O and Al-O network were accompanied with charge transfer. Each O bonded with B formed a stable electron cloud connection, which was beneficial to the stability of B-O network structure, and there were few stable electron cloud connections between each O bonded to Al.

Home Automation and Application With Bolt IoT

Even with advancement of communications technologies, home automation and application have received major attention. Today's digital world is mostly reliant on the internet so that there is a need to design such a solution to make more efficient from unauthorized components to safeguard the data that may work in wireless mode alone. This system provides guidance towards operational operations specifically surveillance on the areas and its structure and managing assets over the internet utilizing web applications employing IoT techniques. Home automation is a vital unit which must meet the main criteria like user interface and safety. In the current situation, the complete process depends on the Bolt IoT platform, a home automation system where it can access and manage house devices remotely from anywhere. The user may use this system to operate different equipment like LEDs, fans, or DC motors as well as to keep an eye on the environment's temperature. In addition, the Bolt IoT module enables cloud connectivity to the system, and sensor data is communicated to the cloud.

Remote Management for Autarkic Field Devices

Field devices are increasingly operated with a battery and/or direct cloud connection. These autarkically operated field devices allow live data to be obtained from locations that are difficult to access, e.g., flow measurement for a water spring in the forest. The location could also be moving/mobile, e.g., fill level on a tanker truck. The resulting requirements for the device managementalong the life cycle (commissioning, configuration, update, repair) are manifold and will be discussed in this article.

Promoting Information Privacy Protection Awareness for Internet of Things (IoT)

The Internet of Things (IoT) has had a considerable influence on our daily lives by enabling enhanced connection of devices, systems, and services that extends beyond machine-to-machine interactions and encompasses a wide range of protocols, domains, and applications. However, despite privacy concerns shown by IoT users, little has been done to reduce and protect individual information exposure. It is extremely difficult to mitigate IoT devices from reidentification threats which is why it is still a major challenge for IoT users to securely protect their information. The trust controls how we regulate privacy in our IoT platforms in the same way that it governs personal relationships. As IoT devices become increasingly linked, more data is shared across individuals, businesses, governments, and ecosystems. Technologies, sensors, machines, data, and cloud connections all rely largely on trust relationships that have been formed. With the rapid growth of additional types of IoT devices that are being introduced, it, therefore, expands privacy concerns and is difficult to develop trust with an IoT system or device without the option to regulate information privacy settings. Privacy has always been a barrier for many devices as they race for the early adoption of IoT technologies. Several Internet of Things devices or systems will continue to pose privacy threats. As a result, the main objective of this study was to examine the individual understanding of privacy and to promote information privacy protection awareness not only to IoT users but also to organizations that use IoT devices or platforms to run their day-to-day business operations. Furthermore, the objective extends to compare user knowledge and concerns about IoT privacy, as well as to identify any common attitudes and variances. However, in terms of enhancing individuals’ knowledge, an artifact was developed to educate and enhance information privacy awareness among IoT users. A pre- and postquestionnaire was generated to test and validate user knowledge regarding information privacy protection in IoT. The study was conducted using a quantitative research method. Findings indicate that IoT users’ awareness of information privacy protection turned out to be average, suggesting a need for education and awareness. Several participants stated that information privacy protection awareness is required within the community to educate, raise awareness, eliminate human error, and enable individuals to be conscious of their privacy when surfing the Internet.

Heuristics for optimizing 3D mapping missions over swarm-powered ad-hoc clouds

Drones have been getting more and more popular in many economy sectors. Both scientific and industrial communities aim at making the impact of drones even more disruptive by empowering collaborative autonomous behaviors—also known as swarming behaviors—within fleets of multiple drones. In swarming-powered 3D mapping missions, unmanned aerial vehicles typically collect the aerial pictures of the target area whereas the 3D reconstruction process is performed in a centralized manner. However, such approaches do not leverage computational and storage resources from the swarm members. We address the optimization of a swarm-powered distributed 3D mapping mission for a real-life humanitarian emergency response application through the exploitation of a swarm-powered ad hoc cloud. Producing the relevant 3D maps in a timely manner, even when the cloud connectivity is not available, is crucial to increase the chances of success of the operation. In this work, we present a mathematical programming heuristic based on decomposition and a variable neighborhood search heuristic to minimize the completion time of the 3D reconstruction process necessary in such missions. Our computational results reveal that the proposed heuristics either quickly reach optimality or improve the best known solutions for almost all tested realistic instances comprising up to 1000 images and fifteen drones.

A novel scalable electrode array and system for non-invasively assessing gastric function using flexible electronics.

Disorders of gastric function are highly prevalent, but diagnosis often remains symptom-based and inconclusive. Body surface gastric mapping is an emerging diagnostic solution, but current approaches lack scalability and are cumbersome and clinically impractical. We present a novel scalable system for non-invasively mapping gastric electrophysiology in high-resolution (HR) at the body surface. The system comprises a custom-designed stretchable high-resolution "peel-and-stick" sensor array (8 × 8 pre-gelled Ag/AgCl electrodes at 2cm spacing; area 225 cm2 ), wearable data logger with custom electronics incorporating bioamplifier chips, accelerometer and Bluetooth synchronized in real-time to an App with cloud connectivity. Automated algorithms filter and extract HR biomarkers including propagation (phase) mapping. The system was tested in a cohort of 24 healthy subjects to define reliability and characterize features of normal gastric activity (30 m fasting, standardized meal, and 4h postprandial). Gastric mapping was successfully achieved non-invasively in all cases (16 male; 8 female; aged 20-73 years; BMI 24.2± 3.5). In all subjects, gastric electrophysiology and meal responses were successfully captured and quantified non-invasively (mean frequency 2.9± 0.3cycles per minute; peak amplitude at mean 60 m postprandially with return to baseline in <4h). Spatiotemporal mapping showed regular and consistent wave activity of mean direction 182.7° ± 73 (74.7% antegrade, 7.8% retrograde, 17.5% indeterminate). BSGM is a new diagnostic tool for assessing gastric function that is scalable and ready for clinical applications, offering several biomarkers that are improved or new to gastroenterology practice.

Zero Defect Manufacturing in the Food Industry: Virgin Olive Oil Production

This paper provides a zero defect manufacturing (ZDM) approach designed for the virgin olive oil (VOO) industry, with the objective of producing the best possible product using sustainable methods. A deep analysis of related work for ZDM and the current state-of-the-art technology in the VOO elaboration process is presented, along with the implications of the well-known trade-off between quality and extraction yield and the importance of having the right information on the state of the fruits and the main technological variables of the process. Currently available new technologies, such as smart devices with cloud connectivity, enable having the required amount of data and information in real-time, thus making the concept of ZDM possible. Together with the proposed ZDM approach and strategies, the basic requirements and the first steps towards the implementation of ZDM in this productive sector are identified.

Asymmetric Self-Coherent Detection Based on Mach-Zehnder Interferometers

We propose an asymmetric self-coherent detection scheme (ASCD) based on Mach-Zehnder interferometers (MZI) for the field reconstruction of self-coherent (SC) complex double-sideband (DSB) signals. The MZI-ASCD scheme approaches the high electrical spectral efficiency (ESE) of homodyne coherent detection via a direct detection (DD) receiver having only two photodiodes (PD) and two analog-to-digital converters. The incoming SC-DSB signal is split into two parts at the receiver in this approach, one of which is delayed and beats with the other part at the outputs of an MZI. We show that the field reconstruction can be performed from the two tributaries of photocurrents. In addition, we present a modified MZI-ASCD scheme referred to as AUX-ASCD which introduces an auxiliary DD branch to improve the SNR of the detected signal. It is found that both the MZI-ASCD scheme and the AUX-ASCD scheme achieve higher OSNR sensitivity compared to the Kramers-Kronig scheme and in the meantime increases the ESE by a factor of 2 using a cost-effective DD receiver. These advantages make the ASCD scheme attractive for short-reach optical communications including edge cloud connections and mobile X-haul systems.

Novel Method for Safeguarding Personal Health Record in Cloud Connection Using Deep Learning Models.

It is a new online service paradigm that allows consumers to exchange their health data. Health information management software allows individuals to control and share their health data with other users and healthcare experts. Patient health records (PHR) may be intelligently examined to predict patient criticality in healthcare systems. Unauthorized access, privacy, security, key management, and increased keyword query search time all occur when personal health records (PHR) are moved to a third-party semitrusted server. This paper presents security measures for cloud-based personal health records (PHR). The cost of keeping health records on a hospital server grows. This is particularly true in healthcare. As a consequence, keeping PHRs in the cloud helps healthcare institutions save money on infrastructure. The proposed security solutions include an optimized rule-based fuzzy inference system (ORFIS) to determine the patient's criticality. Patients are classified into three groups (sometimes known as protective rings) based on their severity: very critical, less critical, and normal. In trials using the UCI machine learning archive, the new ORFIS outperformed existing fuzzy inference approaches in detecting the criticality of PHR. Using a graph-based access policy and anonymous authentication with a NoSQL database in a private cloud environment improves data storage and retrieval efficiency, granularity of data access, and response time.

A Novel Bike-Mounted Sensing Device with Cloud Connectivity for Dynamic Air-Quality Monitoring by Urban Cyclists.

We present a device based on low-cost electrochemical and optical sensors, designed to be attached to bicycle handlebars, with the aim of monitoring the air quality in urban environments. The system has three electrochemical sensors for measuring NO2 and O3 and an optical particle-matter (PM) sensor for PM2.5 and PM10 concentrations. The electronic instrumentation was home-developed for this application. To ensure a constant air flow, the input fan of the particle sensor is used as an air supply pump to the rest of the sensors. Eight identical devices were built; two were collocated in parallel with a reference urban-air-quality-monitoring station and calibrated using a neural network (R2 > 0.83). Several bicycle routes were carried out throughout the city of Badajoz (Spain) to allow the device to be tested in real field conditions. An air-quality index was calculated to facilitate the user’s understanding. The results show that this index provides data on the spatiotemporal variability of pollutants between the central and peripheral areas, including changes between weekdays and weekends and between different times of the day, thus providing valuable information for citizens through a dedicated cloud-based data platform.

Smart AMD prognosis through cellphone: an innovative localized AI-based prediction system for anti-VEGF treatment prognosis in nonagenarians and centenarians.

Age-related macular degeneration (AMD) is one of the most common reasons for blindness in the world today. The most common treatment for wet AMD is the intravitreal injections for inhibiting vascular-endothelial-derived growth factor (VEGF). This treatment usually involves multiple injections and thus multiple clinic visits, which not only causes increased cost on national health services but also causes exposure to the hospital environment, which is sometimes high risk considering current COVID crisis. The treatment, in spite of the above concerns, is usually effective. However, in some cases, either the medicine fails to produce the anticipated favourable outcome, resulting in waste of time, medication, efforts, and above all, psychological distress to the patients. Hence, early predictability of anatomical as well as functional effectiveness of the treatment appears to be a very desirable capability to have. A machine learning approach using adaptive neuro-fuzzy inference system (ANFIS) of two-sample prediction model has been presented that requires only the baseline measurements and changes in visual acuity (VA) as well as macular thickness (MAC) after four months of treatment to estimate the values of VA and MAC at 8 and 12months. In contrast to most of the AI techniques, ANFIS approach has shown the capability of the algorithm to work with very small dataset as well, which makes it a perfect candidate for the presented solution. The presented model has shown to have a very high accuracy (> 92%) and works in near-real-time scenarios. It has been converted into a smart phone App, OphnosisAMD, for convenient usage. With this App, the clinician can visualize the progression of the patient for a specific treatment and can decide on continuing or changing the treatment accordingly. The complete AI engine developed with the ANFIS algorithm is localized to the phone through the App, implying that there is no need for internet or cloud connectivity for this App to function. This makes it ideal for remote usage, especially under the current COVID scenarios. With a smart AI-based App on their fingertips, the presented system provides ample opportunity to the doctors to make a better decision based on the estimated progression, if the same drug is continued with (good/fair prognosis) or alternate treatment should be sought (bad prognosis). From a functional point of view, a prediction algorithm is triggered through simple entry of the relevant parameters (baseline and 4months only). No internet/cloud connectivity is needed since the algorithm and the trained network are fully embedded in the App locally. Hence, using the App in remote and/or non-connected isolated areas is possible, especially in the secluded patients during the COVID scenarios.

MOSQUITO EDGE: An Edge-Intelligent Real-Time Mosquito Threat Prediction Using an IoT-Enabled Hardware System.

Species distribution models (SDMs) that use climate variables to make binary predictions are effective tools for niche prediction in current and future climate scenarios. In this study, a Hutchinson hypervolume is defined with temperature, humidity, air pressure, precipitation, and cloud cover climate vectors collected from the National Oceanic and Atmospheric Administration (NOAA) that were matched to mosquito presence and absence points extracted from NASA’s citizen science platform called GLOBE Observer and the National Ecological Observatory Network. An 86% accurate Random Forest model that operates on binary classification was created to predict mosquito threat. Given a location and date input, the model produces a threat level based on the number of decision trees that vote for a presence label. The feature importance chart and regression show a positive, linear correlation between humidity and mosquito threat and between temperature and mosquito threat below a threshold of 28 °C. In accordance with the statistical analysis and ecological wisdom, high threat clusters in warm, humid regions and low threat clusters in cold, dry regions were found. With the model running on the cloud and within ArcGIS Dashboard, accurate and granular real-time threat level predictions can be made at any latitude and longitude. A device leveraging Global Positioning System (GPS) smartphone technology and the Internet of Things (IoT) to collect and analyze data on the edge was developed. The data from the edge device along with its respective date and location collected are automatically inputted into the aforementioned Random Forest model to provide users with a real-time threat level prediction. This inexpensive hardware can be used in developing countries that are threatened by vector-borne diseases or in remote areas without cloud connectivity. Such devices can be linked with citizen science mosquito data platforms to build training datasets for machine learning based SDMs.

Android Head Units vs. In-Vehicle ECUs: Performance Assessment for Deploying In-Vehicle Intrusion Detection Systems for the CAN Bus

Following the numerous attacks that exploited vulnerabilities of Controller Area Networks (CAN), intrusion detection systems have become a topic of prime importance for in-vehicle buses. Newer in-vehicle communication layers, such as CAN-FD, despite the larger payloads which can easily integrate cryptographic elements, need similar attention. But detecting intrusions may call for demanding algorithms that are not computationally cheap while timely detection is necessary in order to process frames in real-time and take the appropriate actions. In this work we evaluate the performance of several binary classifiers on traditional in-vehicle Electronic Control Units (ECUs) and compare them to modern Android devices which have become widespread inside cars with the adoption of Android-capable infotainment systems. Needless to say, these modern devices benefit from higher computational and memory resources while cloud connectivity may alleviate computational costs even further. Contrasting between traditional controllers and Android devices has become necessary and so far there have been little efforts in this direction. To create a realistic testbed, we use collected in-vehicle CAN bus traffic from an SUV as well as more demanding logs from Advanced Driver-assistance Systems (ADAS) implemented on CAN-FD which we augment with adversarial activity.