BioSec: A Biometric Authentication Framework for Secure and Private Communication among Edge Devices in IoT and Industry 4.0

With the rapid increase in the usage areas of Internet of Things (IoT) devices, it brings challenges such as security and privacy. One way to ensure these in IoT-based systems is user authentication. Until today, user authentication is provided by traditional methods such as pin and token based. But traditional methods have challenges such as forgotten, stolen, and shared with another user who is unauthorized. To address these challenges, we proposed a biometric method called BioSec to provide authentication in IoT integrated with edge consumer electronics using fingerprint authentication. Further, we ensured the security of biometric data both in the transmission channel and database with the standard encryption method. BioSec ensures secure and private communication among edge devices in IoT and Industry 4.0. Finally, we have compared three encryption methods used to protect biometric templates in terms of processing times and identified that AES-128-bit key encryption method outperforms others

Next Generation Technologies for Smart Healthcare: Challenges, Vision, Model, Trends and Future Directions

Modern industry employs technologies for automation that may include Internet of Things (IoT), Cloud and/or Fog Computing, 5G as well as Artificial Intelligence (AI), Machine Learning (ML), or Blockchain. Currently, a part of research for the new industrial era is in the direction of improving healthcare services. This work throws light on some of the major challenges in providing affordable, efficient, secure and reliable healthcare from the viewpoint of computer and medical sciences. We describe a vision of how a holistic model can fulfill the growing demands of healthcare industry, and explain a conceptual model that can provide a complete solution for these increasing demands. In our model, we elucidate the components and their interaction at different levels, leveraging state‐of‐the art technologies in IoT, Fog computing, AI, ML and Blockchain. We finally describe current trends in this field and propose future directions to explore emerging paradigms and technologies on evolution of healthcare leveraging next generation computing systems

The rare presentations of a large polyp and an esophageal carcinoma in heterotropic gastric mucosa: a case series

<p>Abstract</p> <p>Background</p> <p>Heterotopic gastric mucosa (HGM) is commonly seen in the upper esophagus during endoscopyand is generally considered a benign disease. A hyperplastic polyp and an adenocarcinoma arising in heterotopic gastric mucosa are quite rare occurences.</p> <p>Case presentations</p> <p>We present two cases: The first is a patient who suffered from dysphagia because of a large hyperplastic polyp that arose from HGM; the polyp was excised endoscopically. Secondly, we report a rare case of adenocarcinoma arising in HGM of the cervical esophagus.</p> <p>Conclusion</p> <p>Morphologic changes or malignant transformation can develop in the inlet patch. Therefore, gastroenterologists should be aware of the possibility of HGM just distal to the upper esophageal sphincter.</p

Modern computing: Vision and challenges

Over the past six decades, the computing systems field has experienced significant transformations, profoundly impacting society with transformational developments, such as the Internet and the commodification of computing. Underpinned by technological advancements, computer systems, far from being static, have been continuously evolving and adapting to cover multifaceted societal niches. This has led to new paradigms such as cloud, fog, edge computing, and the Internet of Things (IoT), which offer fresh economic and creative opportunities. Nevertheless, this rapid change poses complex research challenges, especially in maximizing potential and enhancing functionality. As such, to maintain an economical level of performance that meets ever-tighter requirements, one must understand the drivers of new model emergence and expansion, and how contemporary challenges differ from past ones. To that end, this article investigates and assesses the factors influencing the evolution of computing systems, covering established systems and architectures as well as newer developments, such as serverless computing, quantum computing, and on-device AI on edge devices. Trends emerge when one traces technological trajectory, which includes the rapid obsolescence of frameworks due to business and technical constraints, a move towards specialized systems and models, and varying approaches to centralized and decentralized control. This comprehensive review of modern computing systems looks ahead to the future of research in the field, highlighting key challenges and emerging trends, and underscoring their importance in cost-effectively driving technological progress

ThermoSim: Deep Learning based Framework for Modeling and Simulation of Thermal-aware Resource Management for Cloud Computing Environments

Current cloud computing frameworks host millions of physical servers that utilize cloud computing resources in the form of different virtual machines. Cloud Data Center (CDC) infrastructures require significant amounts of energy to deliver large scale computational services. Moreover, computing nodes generate large volumes of heat, requiring cooling units in turn to eliminate the effect of this heat. Thus, overall energy consumption of the CDC increases tremendously for servers as well as for cooling units. However, current workload allocation policies do not take into account effect on temperature and it is challenging to simulate the thermal behavior of CDCs. There is a need for a thermal-aware framework to simulate and model the behavior of nodes and measure the important performance parameters which can be affected by its temperature. In this paper, we propose a lightweight framework, ThermoSim, for modeling and simulation of thermal-aware resource management for cloud computing environments. This work presents a Recurrent Neural Network based deep learning temperature predictor for CDCs which is utilized by ThermoSim for lightweight resource management in constrained cloud environments. ThermoSim extends the CloudSim toolkit helping to analyze the performance of various key parameters such as energy consumption, service level agreement violation rate, number of virtual machine migrations and temperature during the management of cloud resources for execution of workloads. Further, different energy-aware and thermal-aware resource management techniques are tested using the proposed ThermoSim framework in order to validate it against the existing framework (Thas). The experimental results demonstrate the proposed framework is capable of modeling and simulating the thermal behavior of a CDC and ThermoSim framework is better than Thas in terms of energy consumption, cost, time, memory usage and prediction accuracy

Vegetable Oil-Based Hyperbranched Thermosetting Polyurethane/Clay Nanocomposites

The highly branched polyurethanes and vegetable oil-based polymer nanocomposites have been showing fruitful advantages across a spectrum of potential field of applications.Mesua ferreaL. seed oil-based hyperbranched polyurethane (HBPU)/clay nanocomposites were prepared at different dose levels by in situ polymerization technique. The performances of epoxy-cured thermosetting nanocomposites are reported for the first time. The partially exfoliated structure of clay layers was confirmed by XRD and TEM. FTIR spectra indicate the presence of H bonding between nanoclay and the polymer matrix. The present investigation outlines the significant improvement of tensile strength, scratch hardness, thermostability, water vapor permeability, and adhesive strength without much influencing impact resistance, bending, and elongation at break of the nanocomposites compared to pristine HBPU thermoset. An increment of two times the tensile strength, 6 °C of melting point, and 111 °C of thermo-stability were achieved by the formation of nanocomposites. An excellent shape recovery of about 96–99% was observed for the nanocomposites. Thus, the formation of partially exfoliated clay/vegetable oil-based hyperbranched polyurethane nanocomposites significantly improved the performance