Fog based intelligent transportation big data analytics in the internet of vehicles environment: motivations, architecture, challenges, and critical issues

The intelligent transportation system (ITS) concept was introduced to increase road safety, manage traffic efficiently, and preserve our green environment. Nowadays, ITS applications are becoming more data-intensive and their data are described using the '5Vs of Big Data'. Thus, to fully utilize such data, big data analytics need to be applied. The Internet of vehicles (IoV) connects the ITS devices to cloud computing centres, where data processing is performed. However, transferring huge amount of data from geographically distributed devices creates network overhead and bottlenecks, and it consumes the network resources. In addition, following the centralized approach to process the ITS big data results in high latency which cannot be tolerated by the delay-sensitive ITS applications. Fog computing is considered a promising technology for real-time big data analytics. Basically, the fog technology complements the role of cloud computing and distributes the data processing at the edge of the network, which provides faster responses to ITS application queries and saves the network resources. However, implementing fog computing and the lambda architecture for real-time big data processing is challenging in the IoV dynamic environment. In this regard, a novel architecture for real-time ITS big data analytics in the IoV environment is proposed in this paper. The proposed architecture merges three dimensions including intelligent computing (i.e. cloud and fog computing) dimension, real-time big data analytics dimension, and IoV dimension. Moreover, this paper gives a comprehensive description of the IoV environment, the ITS big data characteristics, the lambda architecture for real-time big data analytics, several intelligent computing technologies. More importantly, this paper discusses the opportunities and challenges that face the implementation of fog computing and real-time big data analytics in the IoV environment. Finally, the critical issues and future research directions section discusses some issues that should be considered in order to efficiently implement the proposed architecture

Web2Touch 2019: Semantic Technologies for Smart Information Sharing and Web Collaboration

This foreword introduces a summary of themes and papers of the Web2Touch (W2T) 2019 Track at the 28th IEEE WETICE Conference held in Capri, June 2019. W2T 2019 includes ten full papers and one short paper. They all address relevant issues in the field of information sharing for collaboration, including, big data analytics, knowledge engineering, linked open data, applications of smart Web technologies, and smart care. The papers are a portfolio of hot issues in research and applications of semantics, smart technologies (e.g., IoT, sensors, devices for tele-monitoring, and smart contents management) with crucial topics, such as big data analysis, knowledge representation, smart enterprise management, among the others. This track shows how cooperative technologies based on knowledge representation, intelligent tools, and enhanced Web engineering can enhance collaborative work through smart service design and delivery, so it contributes to radically change the role of the semantic Web and applications

Applications of Emerging Smart Technologies in Farming Systems: A Review

The future of farming systems depends mainly on adopting innovative intelligent and smart technologies The agricultural sector s growth and progress are more critical to human survival than any other industry Extensive multidisciplinary research is happening worldwide for adopting intelligent technologies in farming systems Nevertheless when it comes to handling realistic challenges in making autonomous decisions and predictive solutions in farming applications of Information Communications Technologies ICT need to be utilized more Information derived from data worked best on year-to-year outcomes disease risk market patterns prices or customer needs and ultimately facilitated farmers in decision-making to increase crop and livestock production Innovative technologies allow the analysis and correlation of information on seed quality soil types infestation agents weather conditions etc This review analysis highlights the concept methods and applications of various futuristic cognitive innovative technologies along with their critical roles played in different aspects of farming systems like Artificial Intelligence AI IoT Neural Networks utilization of unmanned vehicles UAV Big data analytics Blok chain technology et

A Review of Big Data and Predictive Analytics Application in Supply Chain Management; New Areas, Challenges and Future Research

Big data has become a global phenomenon with companies in almost all industries trying in some way to identify and exploit this is untapped asset. The big data application in supply chain management (SCM) has also caught the management’s attention, and with the high influx of data being generated at different points in supply chain can be used to stimulate data driven decisions, build supply chain flexibility, adaptability and agility. With the inception and wide adoption of the Industry 4.0 technologies like Internet of things (IoT), cloud computing (CC), Smart manufacturing (SM), Artificial intelligence (AI), the need for integration of big data and analytics has been felt more than ever. The purpose of this survey is to investigate the applications of predictive analytics in different supply chain areas and provide a classification based on the different techniques/algorithm used at various supply chain areas, detect gaps and propose the future direction for research. The review also investigates the application of big data analytics (specifically, predictive analytics) along with these disruptive technologies in the SCM areas. The survey review indicated that manufacturing and demand forecasting are the two major areas with the most predictive analytics application, whereas the clustering, regression, and artificial neural networks are the more commonly used algorithms. The new SCM areas identified for Big data analytics applications integrated with the emerging technology are smart manufacturing and intelligent logistics management. Furthermore, the immediate need for future studies in other SCM areas like product development and inventory management are pointed out due to its immense potential benefits for the supply chain management

IEEE Access Special Section Editorial: Big Data Technology and Applications in Intelligent Transportation

During the last few years, information technology and transportation industries, along with automotive manufacturers and academia, are focusing on leveraging intelligent transportation systems (ITS) to improve services related to driver experience, connected cars, Internet data plans for vehicles, traffic infrastructure, urban transportation systems, traffic collaborative management, road traffic accidents analysis, road traffic flow prediction, public transportation service plan, personal travel route plans, and the development of an effective ecosystem for vehicles, drivers, traffic controllers, city planners, and transportation applications. Moreover, the emerging technologies of the Internet of Things (IoT) and cloud computing have provided unprecedented opportunities for the development and realization of innovative intelligent transportation systems where sensors and mobile devices can gather information and cloud computing, allowing knowledge discovery, information sharing, and supported decision making. However, the development of such data-driven ITS requires the integration, processing, and analysis of plentiful information obtained from millions of vehicles, traffic infrastructures, smartphones, and other collaborative systems like weather stations and road safety and early warning systems. The huge amount of data generated by ITS devices is only of value if utilized in data analytics for decision-making such as accident prevention and detection, controlling road risks, reducing traffic carbon emissions, and other applications which bring big data analytics into the picture

Industry 4.0 technologies for the manufacturing and distribution of COVID-19 vaccines

Background: The evolutionary stages of manufacturing have led us to conceptualize the use of Industry 4.0 for COVID-19 (coronavirus disease 2019), powered by Industry 4.0 technologies. Using applications of integrated process optimizations reliant on digitized data, we propose novel intelligent networks along the vaccine value chain. Vaccine 4.0 may enable maintenance processes, streamline logistics, and enable optimal production of COVID-19 vaccines.Vaccine 4.0 framework: The challenge in applying Vaccine 4.0 includes the requirement of large-scale technologies for digitally transforming manufacturing, producing, rolling-out, and distributing vaccines. With our framework, Vaccine 4.0 analytics will target process performance, process development, process stability, compliance, quality assessment, and optimized maintenance. The benefits of digitization during and post the COVID-19 pandemic include first, the continual assurance of process control, and second, the efficacy of big-data analytics in streamlining set parameter limits. Digitization including big data-analytics may potentially improve the quality of large-scale vaccine production, profitability, and manufacturing processes. The path to Vaccine 4.0 will enhance vaccine quality, improve efficacy, and compliance with data-regulated requirements.Discussion: Fiscal and logistical barriers are prevalent across resource-limited countries worldwide. The Vaccine 4.0 framework accounts for expected barriers of manufacturing and equitably distributing COVID-19 vaccines. With amalgamating big data analytics and biometrics, we enable the identification of vulnerable populations who are at higher risk of disease transmission. Artificial intelligence powered sensors and robotics support thermostable vaccine distribution in limited capacity regions, globally. Biosensors isolate COVID-19 vaccinations with low or limited efficacy. Finally, Vaccine 4.0 blockchain systems address low- and middle-income countries with limited distribution capacities.Conclusion: Vaccine 4.0 is a viable framework to optimize manufacturing of vaccines during and post the COVID-19 pandemic

Power and Energy Optimized Approach towards Sustainable Mobile Ad-hoc Networks and IoT

Investigating how real-time applications in sectors like healthcare, agriculture, construction, and manufacturing can enhance their effectiveness and sustainability through the use of autonomous sensor technologies, green computing, and big data analytics is part of the work with sustainable approaches for optimising performance of networks. This authoritative guide exposes the drawbacks of conventional technology and provides techniques and tactics for addressing Quality of Service (QOS) issues and enhancing network performance. It brings together a broad team of subject-matter specialists. Several in-depth chapters cover topics like blockchain-assisted secure data sharing, intelligent management of ad hoc networks, smart 5G Internet of Things scenarios, and the use of artificial intelligence (AI), machine learning (ML), and learning techniques (DL) techniques in smart healthcare, smart factory, and smart agriculture

Smart Sustainable Manufacturing Systems

With the advent of disruptive digital technologies, companies are facing unprecedented challenges and opportunities. Advanced manufacturing systems are of paramount importance in making key enabling technologies and new products more competitive, affordable, and accessible, as well as for fostering their economic and social impact. The manufacturing industry also serves as an innovator for sustainability since automation coupled with advanced manufacturing technologies have helped manufacturing practices transition into the circular economy. To that end, this Special Issue of the journal Applied Sciences, devoted to the broad field of Smart Sustainable Manufacturing Systems, explores recent research into the concepts, methods, tools, and applications for smart sustainable manufacturing, in order to advance and promote the development of modern and intelligent manufacturing systems. In light of the above, this Special Issue is a collection of the latest research on relevant topics and addresses the current challenging issues associated with the introduction of smart sustainable manufacturing systems. Various topics have been addressed in this Special Issue, which focuses on the design of sustainable production systems and factories; industrial big data analytics and cyberphysical systems; intelligent maintenance approaches and technologies for increased operating life of production systems; zero-defect manufacturing strategies, tools and methods towards online production management; and connected smart factories

Big data analytics:Computational intelligence techniques and application areas

Big Data has significant impact in developing functional smart cities and supporting modern societies. In this paper, we investigate the importance of Big Data in modern life and economy, and discuss challenges arising from Big Data utilization. Different computational intelligence techniques have been considered as tools for Big Data analytics. We also explore the powerful combination of Big Data and Computational Intelligence (CI) and identify a number of areas, where novel applications in real world smart city problems can be developed by utilizing these powerful tools and techniques. We present a case study for intelligent transportation in the context of a smart city, and a novel data modelling methodology based on a biologically inspired universal generative modelling approach called Hierarchical Spatial-Temporal State Machine (HSTSM). We further discuss various implications of policy, protection, valuation and commercialization related to Big Data, its applications and deployment