A scalable framework for healthcare monitoring application using the Internet of Medical Things

Internet of Things (IoT) is finding application in many areas, particularly in health care where an IoT can be effectively used in the form of an Internet of Medical Things (IoMT) to monitor the patients remotely. The quality of life of the patients and health care outcomes can be improved with the deployment of an IoMT because health care professionals can monitor conditions; access the electronic medical records and communicates with each other. This remote monitoring and consultations might reduce the traditional stressful and costly exercise of frequent hospitalization. Also, the rising costs of health care in many developed countries have influenced the introduction of the Healthcare Monitoring Application (HMA) to their existing health care practices. To materialize the HMA concepts for successful deployment for civilian and commercial use with ease, application developers can benefit from a generic, scalable framework that provides significant components for building an HMA. In this chapter, a generic maintainable HMA is advanced by amalgamating the advantages of event-driven and the layered architecture. The proposed framework is used to establish an HMA with an end-to-end Assistive Care Loop Framework (ACLF) to provide a real-time alarm and assistance to monitor pregnant women. © 2020 John Wiley & Sons, Ltd

Characterisation of advanced high strength strip steels using electromagnetic sensor system

The mechanical properties of steel are strongly influenced by its microstructural features such as phase balance, grain size, dislocations and precipitates. In order to obtain accurate quality control of steel products, it is desirable to be able to monitor the mechanical properties non-destructively. It is known that the low frequency inductance (where the effect of eddy currents are negligible) measured using an EM sensor depends on the relative permeability of the sample and that the permeability is affected by microstructural parameters (i.e. phase fraction / distribution and, to a lesser extent, grain size are the important features in dual phase, DP, steel). A variety of electromagnetic sensors have been reported for non-destructively assessing the state of steel microstructures including; monitoring the recovery and recrystallisation processes in-situ during heat treatment, phase transformation and detecting decarburisation in steel rod both on-line and off-line, etc. Recently it has been shown that electromagnetic sensors can measure the phase fraction in DP steel but the effect of strip thickness was not assessed. This research work discusses the development of an EM sensor system that can be used to assess the microstructure (and hence mechanical properties) of commercially produced DP steels (in particular phase balance and grain size) with a range of thicknesses in a steel works test house environment, specifically, it focuses on employing an EM sensor system in the prediction of ultimate tensile strength for DP steels of any sheet thickness. In this project, a set of heat treated DP600 grade of 1.4mm thickness and commercial DP steel samples, including DP600, DP800 and DP1000 with a range of strength levels and thicknesses, and produced in different strip mills, have been assessed. The sensor outputs have been correlated to microstructural phase fraction and mechanical properties. Firstly, the magnetic properties of commercial DP steel samples were investigated through the major hysteresis loop and minor hysteresis loops. Measured coercivity from the major loop showed that the coercivity was affected by phase fraction (ferrite/martensite percentage) and ferrite grain size where the coercivity decreased with increased ferrite fraction. Three types of minor loop configurations were used to derive incremental permeability values; the minor loop deviations from the initial magnetisation curve (μIc); the minor loop deviations from the main B-H loop (μBH) and the minor loop deviations from amplitude sweep (μi). It was found that although the incremental permeability values are not precisely the same for the three sets of measurements, similar trends for the DP samples can be observed where the incremental permeability values are affected by the phase fraction and ferrite grain size. The effect of magnetic field on permeability for the DP steels was studied. It was shown that the incremental permeability increases with the applied field amplitude until reaching a maximum value at a certain applied field amplitude (i.e. very close to the coercivity values) and then drop at higher applied field amplitude and converge to a similar permeability value. The initial gradient and the peak position for the samples are different and would allow them to be distinguished from each other. It was observed in the commercial DP steels with a range of ferrite fraction (72 to 79%) and a range of average ferrite grain size (from 6 to 10μm), that the effect of ferrite grain boundaries on permeability is more significant than the effect of ferrite fraction within the range studied. Finally, the measured magnetic properties were used to develop a link between microstructure and mechanical properties for DP steels, using a readily deployable EM sensor that can be used with large strip steel samples. The deployable sensor geometry and operation rely on a relatively low magnetic field being generated in the sample and therefore low field incremental permeability being the relevant material parameter being assessed. Initially, the effect of ferrite fraction for the laboratory heat-treated DP600 samples, with the same thickness (1.4mm), on EM sensor output signal (i.e. mutual real inductance) was investigated. It was found that the real inductance value at a low frequency (below approx.100 Hz) is dominated by differences in the relative permeability of the samples, showing an approximately linear trend of increasing low frequency inductance value with increasing ferrite content. The increasing amount of ferrite, which possesses a much higher relative permeability than martensite, showed higher real inductance value (in the range of 35 -70% ferrite fraction in these DP steels). The measured real inductance at a frequency of 10Hz was compared with the mechanical property (hardness). An approximately linear decrease in real inductance at 10 Hz with the hardness value was found for these samples. EM sensor measurements were then carried out for the commercial DP600, DP800 and DP1000 samples with different thicknesses (1 to 4 mm). The EM sensor system showed a significant effect of thickness on the signal with thicker strip showing a much higher mutual inductance value for the same microstructure. This is due to the skin depth (for this sensor, operation frequency and material characteristics) being larger than the sample thickness, therefore a thicker sample gives a large sensor response. To deal with this problem, a calibration curve (a plot of real inductance versus permeability for different thickness of material) was constructed using a FE model for the sensor and sample geometry. Therefore, an electromagnetic sensor – sample FE model, developed in COMSOL multi-physics software, has been developed to determine the relationship between the low magnetic field relative permeability and microstructure (phase balance and grain size). The model has been validated using commercial DP steel sheets of 1 to 4 mm. It was found that the ferrite grain size affects the magnetic properties in DP steels as the grain boundaries act as effective pinning points to magnetic domain movement. Therefore, the magnetic permeability in DP steels is affected by ferrite grain size and ferrite fraction, both of which affect the tensile strength, therefore a single relationship between permeability and tensile strength results. The low field relative permeability, which is the permeability value derived from the EM sensor (e.g. U-shaped sensor), can therefore be used to predict the tensile strength in commercial DP steels. The relationship between permeability and field was employed to develop the technique. Therefore, U-shaped sensor modification was carried out to increase the accuracy of tensile strength determination, this was done as part of a case study for Tata Steel Jamshedpur to evaluate DP steels

RaSEC : an intelligent framework for reliable and secure multilevel edge computing in industrial environments

Industrial applications generate big data with redundant information that is transmitted over heterogeneous networks. The transmission of big data with redundant information not only increases the overall end-to-end delay but also increases the computational load on servers which affects the performance of industrial applications. To address these challenges, we propose an intelligent framework named Reliable and Secure multi-level Edge Computing (RaSEC), which operates in three phases. In the first phase, level-one edge devices apply a lightweight aggregation technique on the generated data. This technique not only reduces the size of the generated data but also helps in preserving the privacy of data sources. In the second phase, a multistep process is used to register level-two edge devices (LTEDs) with high-level edge devices (HLEDs). Due to the registration process, only legitimate LTEDs can forward data to the HLEDs, and as a result, the computational load on HLEDs decreases. In the third phase, the HLEDs use a convolutional neural network to detect the presence of moving objects in the data forwarded by LTEDs. If a movement is detected, the data is uploaded to the cloud servers for further analysis; otherwise, the data is discarded to minimize the use of computational resources on cloud computing platforms. The proposed framework reduces the response time by forwarding useful information to the cloud servers and can be utilized by various industrial applications. Our theoretical and experimental results confirm the resiliency of our framework with respect to security and privacy threats. © 1972-2012 IEEE

Why Privacy-Preserving Protocols Are Sometimes Not Enough: A Case Study of the Brisbane Toll Collection Infrastructure

The use of Electronic Toll Collection (ETC) systems is on the rise, as these systems have a significant impact on reducing operational costs. Toll service providers (TSPs) access various information, including drivers’ IDs and monthly toll fees, to bill drivers. While this is legitimate, such information could be misused for other purposes violating drivers’ privacy, most prominent, to infer drivers’ movement patterns. To this end, privacy-preserving ETC (PPETC) schemes have been designed to minimize the amount of information leaked while still allowing drivers to be charged. We demonstrate that merely applying such PPETC schemes to current ETC infrastructures may not ensure privacy. This is due to the (inevitable) minimal information leakage, such as monthly toll fees, which can potentially result in a privacy breach when combined with additional background information, such as road maps and statistical data. To show this, we provide a counterexample using the case study of Brisbane’s ETC system. We present two attacks: the first, being a variant of the presence disclosure attack, tries to disclose the toll stations visited by a driver during a billing period as well as the frequency of visits. The second, being a stronger attack, aims to discover cycles of toll stations (e.g., the ones passed during a commute from home to work and back) and their frequencies. We evaluate the success rates of our attacks using real parameters and statistics from Brisbane’s ETC system. In one scenario, the success rate of our toll station disclosure attack can be as high as 94%. This scenario affects about 61% of drivers. In the same scenario, our cycle disclosure attack can achieve a success rate of 51%. It is remarkable that these high success rates can be achieved by only using minimal information as input, which is, e.g., available to a driver’s payment service provider or bank, and by following very simple attack strategies without exploiting optimizations. As a further contribution, we nalyze how the choice of various parameters, such as the set of toll rates, the number of toll stations, and the billing period length, impact a driver’s privacy level regarding our attacks

3D Textured Model Encryption via 3D Lu Chaotic Mapping

In the coming Virtual/Augmented Reality (VR/AR) era, 3D contents will be popularized just as images and videos today. The security and privacy of these 3D contents should be taken into consideration. 3D contents contain surface models and solid models. The surface models include point clouds, meshes and textured models. Previous work mainly focus on encryption of solid models, point clouds and meshes. This work focuses on the most complicated 3D textured model. We propose a 3D Lu chaotic mapping based encryption method of 3D textured model. We encrypt the vertexes, the polygons and the textures of 3D models separately using the 3D Lu chaotic mapping. Then the encrypted vertices, edges and texture maps are composited together to form the final encrypted 3D textured model. The experimental results reveal that our method can encrypt and decrypt 3D textured models correctly. In addition, our method can resistant several attacks such as brute-force attack and statistic attack.Comment: 13 pages, 7 figures, under review of SCI