Design of a Machine Learning-Based Intelligent Middleware Platform for a Heterogeneous Private Edge Cloud System

Recent advances in mobile technologies have facilitated the development of a new class of smart city and fifth-generation (5G) network applications. These applications have diverse requirements, such as low latencies, high data rates, significant amounts of computing and storage resources, and access to sensors and actuators. A heterogeneous private edge cloud system was proposed to address the requirements of these applications. The proposed heterogeneous private edge cloud system is characterized by a complex and dynamic multilayer network and computing infrastructure. Efficient management and utilization of this infrastructure may increase data rates and reduce data latency, data privacy risks, and traffic to the core Internet network. A novel intelligent middleware platform is proposed in the current study to manage and utilize heterogeneous private edge cloud infrastructure efficiently. The proposed platform aims to provide computing, data collection, and data storage services to support emerging resource-intensive and non-resource-intensive smart city and 5G network applications. It aims to leverage regression analysis and reinforcement learning methods to solve the problem of efficiently allocating heterogeneous resources to application tasks. This platform adopts parallel transmission techniques, dynamic interface allocation techniques, and machine learning-based algorithms in a dynamic multilayer network infrastructure to improve network and application performance. Moreover, it uses container and device virtualization technologies to address problems related to heterogeneous hardware and execution environments

Smart Health: A Novel Paradigm to Control the Chickungunya Virus

Chikungunya is a mosquito instinctive disease which spreads hurriedly in various parts of the country. For the awareness and prevention measure of this disease a new paradigm in smart health required to be devised. The auspicious prospective of evolving Internet of Things (IoT) technologies for interconnected heterogeneous devices and objects has played vital role in the next generation health care systems for eminent patient care to protect the citizens from these types of diseases. Still there is need for real time health monitoring to analyze the patients for early preventive measures and precautions for healthy life. Smart Health care IoT has substantial impending for the cognizance of analogues monitoring. It includes the interconnected apps, objects (devices & People), communication technologies, tracking system and patients’ knowledge base. This article presents an IoT enabled model where data collected from the sensors, objects and people will be gathered at the cloud to take the preventive actions by healthcare professionals. Precautionary measures will be taken by collecting the information about causes of growth of mosquitoes. The suitability of the approach is validated at the base layer of IoT and data is transmitted to the cloud with the help of edge nodes. From simulations, it is endorsed that proposed approach is better over ME-CBCCP protocol

Security and Privacy Issues in Vehicular Named Data Networks: An Overview

A tremendous amount of content and information are exchanging in a vehicular environment between vehicles, roadside units, and the Internet. This information aims to improve the driving experience and human safety. Due to the VANET’s properties and application characteristics, the security becomes an essential aspect and a more challenging task. On the contrary, named data networking has been proposed as a future Internet architecture that may improve the network performance, enhance content access and dissemination, and decrease the communication delay. NDN uses a clean design based on content names and Interest-Data exchange model. In this paper, we focus on the vehicular named data networking environment, targeting the security attacks and privacy issues. We present a state of the art of existing VANET attacks and how NDN can deal with them. We classified these attacks based on the NDN perspective. Furthermore, we define various challenges and issues faced by NDN-based VANET and highlight future research directions that should be addressed by the research community

Time-slotted scheduling schemes for multi-hop concurrent transmission in WPANs with directional antenna

To achieve high-speed (giga-bit) connectivity for shortrange wireless multimedia applications, the millimeterwave (mmWave) wireless personal area networks with directional antennas are gaining increased interest. Due to the use of directional antennas and mmWave communications, the probability of non-interfering transmissions increases in a localized region. Network throughput can be increased immensely by the concurrent time allocation of non-interfering transmissions. The problem of finding optimum time allocation for concurrent transmissions is an NP-hard problem. In this paper, we propose two enhanced versions of previously proposed multi-hop concurrent transmission (MHCT) schemes. To increase network capacity, the proposed schemes efficiently make use of the free holes in the time-Allocation map of the MHCT scheme; thus, making it more compact