Security and Privacy Techniques in IoT Environment

Experimental Evaluation of Covariates Effects on Periocular Biometrics: A Robust Security Assessment Framework

The growing integration of technology into our lives has resulted in unprecedented amounts of data that are being exchanged among devices in an Internet of Things (IoT) environment. Authentication, identification, and device heterogeneities are major security and privacy concerns in IoT. One of the most effective solutions to avoid unauthorized access to sensitive information is biometrics. Deep learning based biometric systems have been proven to outperform traditional image processing and machine learning techniques. However, the image quality covariates associated with blur, resolution, illumination, and noise predominantly affect recognition performance. Therefore, assessing the robustness of the developed solution is another important concern that still needs to be investigated. This paper proposes a periocular region based biometric system and explores the effect of image quality covariates (artifacts) on the performance of periocular recognition. To simulate the real-time scenarios and understand the consequences of blur, resolution, and bit-depth of images on the recognition accuracy of periocular biometrics, we modeled out-of-focus blur, camera shake blur, low-resolution, and low bit-depth image acquisition using Gaussian function, linear motion, interpolation, and bit plan slicing respectively. All the images of the UBIRIS.v1 database are degraded by varying strength of image quality covariates to obtain degraded versions of the database. Afterward, deep models are trained with each degraded version of the database. The performance of the model is evaluated by measuring statistical parameters calculated from a confusion matrix. Experimental results show that among all types of covariates, camera shake blur has less effect on the recognition performance, while out-of-focus blur significantly impacts it. Irrespective of image quality, the convolutional neural network produces excellent results, which proves the robustness of the developed model.

A Privacy-Preserved ID-Based Secure Communication Scheme in 5G-IoT Telemedicine Systems

5G networks have an efficient effect in providing quality of experience and massive Internet of things (IoT) communication. Applications of 5G-IoT networks have been expanded rapidly, including in smart medical healthcare. Emergency medical services (EMS) hold an assignable proportion in our lives, which has become a complex network of all types of professionals, including care in an ambulance. A 5G network with EMS can simplify the medical treatment process and improve the efficiency of patient treatment. The importance of healthcare-related privacy preservation is rising. If the work of privacy preservation fails, not only will medical institutes have economic and credibility losses but also property losses and even the lives of patients will be harmed. This paper proposes a privacy-preserved ID-based secure communication scheme in 5G-IoT telemedicine systems that can achieve the features below. (i) The proposed scheme is the first scheme that integrates the process of telemedicine systems and EMS; (ii) the proposed scheme allows emergency signals to be transmitted immediately with decreasing risk of secret key leakage; (iii) the information of the patient and their prehospital treatments can be transmitted securely while transferring the patient to the destination medical institute; (iv) the quality of healthcare services can be assured while preserving the privacy of the patient; (v) the proposed scheme supports not only normal situations but also emergencies. (vi) the proposed scheme can resist potential attacks.

Artificial Intelligence Based Body Sensor Network Framework-Narrative Review: Proposing an End-to-End Framework using Wearable Sensors, Real-Time Location Systems and Artificial Intelligence/Machine Learning Algorithms for Data Collection, Data Mining and Knowledge Discovery in Sports and Healthcare

With the rising amount of data in the sports and health sectors, a plethora of applications using big data mining have become possible. Multiple frameworks have been proposed to mine, store, preprocess, and analyze physiological vitals data using artificial intelligence and machine learning algorithms. Comparatively, less research has been done to collect potentially high volume, high-quality 'big data' in an organized, time-synchronized, and holistic manner to solve similar problems in multiple fields. Although a large number of data collection devices exist in the form of sensors. They are either highly specialized, univariate and fragmented in nature or exist in a lab setting. The current study aims to propose artificial intelligence-based body sensor network framework (AIBSNF), a framework for strategic use of body sensor networks (BSN), which combines with real-time location system (RTLS) and wearable biosensors to collect multivariate, low noise, and high-fidelity data. This facilitates gathering of time-synchronized location and physiological vitals data, which allows artificial intelligence and machine learning (AI/ML)-based time series analysis. The study gives a brief overview of wearable sensor technology, RTLS, and provides use cases of AI/ML algorithms in the field of sensor fusion. The study also elaborates sample scenarios using a specific sensor network consisting of pressure sensors (insoles), accelerometers, gyroscopes, ECG, EMG, and RTLS position detectors for particular applications in the field of health care and sports. The AIBSNF may provide a solid blueprint for conducting research and development, forming a smooth end-to-end pipeline from data collection using BSN, RTLS and final stage analytics based on AI/ML algorithms.

Reliable Industry 4.0 Based on Machine Learning and IoT for Analyzing, Monitoring, and Securing Smart Meters

The modern control infrastructure that manages and monitors the communication between the smart machines represents the most effective way to increase the efficiency of the industrial environment, such as smart grids. The cyber-physical systems utilize the embedded software and internet to connect and control the smart machines that are addressed by the internet of things (IoT). These cyber-physical systems are the basis of the fourth industrial revolution which is indexed by industry 4.0. In particular, industry 4.0 relies heavily on the IoT and smart sensors such as smart energy meters. The reliability and security represent the main challenges that face the industry 4.0 implementation. This paper introduces a new infrastructure based on machine learning to analyze and monitor the output data of the smart meters to investigate if this data is real data or fake. The fake data are due to the hacking and the inefficient meters. The industrial environment affects the efficiency of the meters by temperature, humidity, and noise signals. Furthermore, the proposed infrastructure validates the amount of data loss via communication channels and the internet connection. The decision tree is utilized as an effective machine learning algorithm to carry out both regression and classification for the meters’ data. The data monitoring is carried based on the industrial digital twins’ platform. The proposed infrastructure results provide a reliable and effective industrial decision that enhances the investments in industry 4.0.