Search

The startup industry is experiencing a dynamic and exciting time called Startup 5.0, where technology is quickly transforming how we conduct business, and startups are at the vanguard of this change. This study aims to model the enabling technologies influencing start-up 5.0. The enabling technologies that impact start-up 5.0 are modelled in this research using TISM and MICMAC analysis. According to the findings, key enabling technologies that are changing the technical landscape and opening up new opportunities for Startup 5.0 are Edge computing, complex adaptive systems, network slicing, and 6G and beyond. Fostering effective innovation and entrepreneurial activity within the startup ecosystem requires an understanding of the dynamic relationships among various technologies. Our findings have implications for government decision-makers, entrepreneurs, and investors who want to support Startup 5.0's expansion. This study emphasizes how crucial it is to understand how enabling technologies interact in order to enable well-informed strategies for fostering innovation and long-term entrepreneurial endeavors in the dynamic startup environment. [ABSTRACT FROM AUTHOR]

Summary: In this work, we address the problem of resource allocation in Internet of Everything (IoE)‐enabled software‐defined edge networks. In the existing literature, the researchers considered optimizing the performance of the software‐defined networking (SDN) platform using a single‐tier architecture, where the Internet of Things (IoT) devices are in the same tier. However, with the advent of edge computing, we can explore the two‐tier architecture of edge networks—local tier and edge tier—in the presence of SDN, which has not been explored. Hence, we propose an evolutionary game‐based resource allocation scheme for software‐defined edge networks. Additionally, we aim to optimize the QoS of the edge‐based IoE services while optimizing the throughput of the system. The IoT devices use the proposed scheme in the local tier to identify the optimized mapping to the SDN switches. On the other hand, in the edge tier, the proposed scheme aims to optimize the throughput while allocating the IoE service to the optimal subset of edge devices. We evaluated the performance of the proposed scheme using the Python3‐based Mininet platform in the presence of Ryu Controller and Open vSwitches. Ryu is an element‐based software defined networking framework, which offers software elements with well‐described API that enables developers to produce new network control functions. We observed that by using T‐RESIN, the network throughput increases by 27.98%–31.84% than using the existing schemes. [ABSTRACT FROM AUTHOR]

This article proposes a cloud-based smart agriculture system for crop yield prediction using hybrid deep learning techniques. The study aims to improve crop yield prediction accuracy and facilitate decision-making for farmers. The system utilizes a hybrid deep learning approach that combines convolutional neural networks (CNNs) and recurrent neural networks (RNNs) to process multi-sensor data, including weather data, soil moisture data, and crop growth data. The CNNs are used to extract spatial features from the input data, while the RNNs are used to capture temporal dependencies. The proposed model is employed on a cloud platform, allowing farmers to access the system from anywhere using a web-based interface. Experimental results show that the proposed hybrid deep learning approach outperforms traditional machine learning methods for crop yield prediction, achieving a prediction accuracy of over 90%. Its ability to predict crop yields properly was demonstrated by its decreased MAE and RMSE to 2.17% and 2.94% respectively. It also showed a better fit between the expected and actual data, with a higher R-squared value. The proposed system has the potential to improve the efficiency and profitability of farming operations and contribute to sustainable agriculture practices. [ABSTRACT FROM AUTHOR]

The convergence of the Internet of Things and Machine Learning shapes key developments in information technologies, such as the Internet of Everything environment. The Internet of Things' Machine-to-Machine connectivity advances, real-time Big Data storage, and data-driven Machine Learning synergize the scope of Internet of Everything innovations. As the Internet of Things and Machine Learning merger continues, the Internet of Everything reshapes the world sector map by providing unprecedented possibilities and efficiencies. The propelled Internet of Everything is ushering in a new era of transformative projects in various sectors such as Healthcare and Life Science, Manufacturing, Agriculture, Energy, Transportation, and Construction. The high-tech transformative projects offer many applications in process optimization, cost reduction, increased productivity, and efficiency. Our work examines the prominent joint Internet of Things and Machine Learning initiatives, projects and works to encourage new initiatives and multidisciplinary work worldwide. We also discuss the benefits of transformative projects in different sectors in detail and put forward a forecast for their future roles. Our study is supported by solid academic literature and project examples. [ABSTRACT FROM AUTHOR]

A large number of interconnected smart devices or objects interact with the physical environment known as the Internet of Things (IoT). These networks efficiently perform complex tasks with a high level of intelligence without human intervention and require run-time processing and computation. Edge computing is introduced where devices are placed at the edge between the data source and the cloud. These devices can provide more powerful computational and storage capabilities to IoT users. With various benefits, one of the significant requirements is secure and trustworthy data communication. Various trust management schemes are introduced to guarantee the trustworthiness and isolation of the malicious nodes and give preference to the most trustworthy ones. The existing schemes selected trustworthy nodes again and again from the path without considering the load factor which leads them to be more vulnerable for the Denial of Services (DoS) attacks. In this paper, we propose a Link-based Penalized Trust Management scheme to provide trust management and consider load factors for the selection of most trustworthy nodes and provide a preemptive measure to secure the network from DoS attacks based on its link's association. Simulation results indicate the better performance of the proposed scheme as compared with existing schemes. [ABSTRACT FROM AUTHOR]

The main objective of the publication is to analyze the development of additive manufacturing systems based on the concepts of Industry 4.0 and Industry 5.0, with particular emphasis on the Internet of Everything ideology. In connection with that, the authors have resolved to review the existing devices, and also IT tools, which are applied in manufacturing processses based upon additive technologies. In order to facilitate the analysis in question, three variants were developed; the first, presenting 3D printing as a self-contained manufacturing work station not involving other technologies within the Industry 4.0 conception, the second, indirectly involving 3D printing in the Industry 4.0 conception, and also the third, in which 3D printing constitutes a major and indispensable element of this conception. The contemporary literature of the subject includes dissertations claiming that the fifth industrial revolution may be coming. Taking that into account, this dissertation attempts as well to discuss the evolution of 3D printing in the context of the 5.0 Industry conception, based upon the Internet of Everything, and also Virtual Reality. This part of the article is a forecast of the things to come.

This paper delves into the intricate interplay of the Internet of Everything within the dynamic landscape of evolving data science and business intelligence, scrutinized through the discerning lens of scientometrics within the broader context of education social responsibility. The author posits that the primary objective of the Internet of Everything is to not merely bridge, but transcend the gap between business, education, and science, thereby fortifying the indispensable role of corporate sustainability in shaping societal paradigms. The meticulously validated results affirm the credibility of the proposed hypothesis and indicators. Notably, information technology, particularly within the expansive realms of the Internet and Internet of Things subdivisions, emerges as the paramount dimension influencing the underdevelopment of mountainous areas. The triad nexus established in this study remarkably diminishes the pervasive disparities between science, education, and business, with a singular focus on advancing people and human sustainability. Proposing seven innovative indicators within the triad, the research not only contributes to the existing body of knowledge but also envisages a transformative impact. Furthermore, the paper advocates for the adoption of common data science governance models across diverse country groups globally, fostering a collaborative approach to technological and societal advancement. [ABSTRACT FROM AUTHOR]

Thanks to technological advancements, our lives are getting more intertwined with the connected world as more smart devices are coming to market. As such, the clear separation of devices as things (end-devices in IoT systems) and human operated devices is getting increasingly buried. This led to the creation of the term Internet of Everything (IoE) which is defined by Cisco as the “the networked connection of people, process, data, and things” [1]. The main difference between IoT and IoE is inclusion of people in the ecosystem, which greatly increases the number of connected parties. This increase in connected parties creates a strain on our existing infrastructure which is relying on cloud computing for performing most operations. Even though this resource provides heaps of computational power, the weak link in this scenario is the network, where all the connected devices can easily overload the available bandwidth, leading to slow response speeds and low general availability. The answer to this problem lies with technology that already exists and is not yet fully exploited as a distributed computing powerhouse, IoT. This paper aims to summarise the concept of computing at the edge, common architectural patterns, existing solutions, while also discussing real-world applications. [ABSTRACT FROM AUTHOR]

The modern Industrial Revolution has ushered in a wave of technological advancements, including the proliferation of over 20 billion digital identities associated with the Internet of Things (IoT) devices worldwide. Amid this complexity, IoT has emerged as a beacon of hope, offering multitudinous solutions from the perspectives of school management, instructors, and learners. The prime objective of this article is to review the current state-of-the-art in IoT and education specifically in areas like remote laboratories, improved learning, and school management. The implemented method is systematic literature review of IoT technology's practical applications and case studies to meet these key educational stakeholders' unique needs. The studies focus on remote labs, learning experiences, and campus administration. This comprehensive analysis gathered data from sources like Scopus and WoS and summarized insights from 122 articles. These cases encompass the foundational principles of IoT, its diverse applications in higher education, its challenges, and future avenues for research. Our findings indicate that (a) the implementation of IoT-based remote laboratories has transformed engineering education by enhancing the safety and operational efficiency of labs, improving students' comprehension of complex concepts, and facilitating a more interactive and engaging educational experience, (b) the integration of IoT systems within educational settings has profoundly enhanced both teaching and learning experiences by creating interactive, immersive environments and significantly improving student engagement and understanding through personalized and hands-on learning approaches, and (c) the integration of IoT technology within educational administration has significantly advanced the digitalization of traditional school management systems, enhancing administrative efficiency in areas such as schedule management, student records, and financial operations through automation, thereby streamlining processes and enhancing responsiveness in educational institutions. However, it must be noted that the current infrastructure, particularly in public universities, often falls short of fully harnessing IoT technologies to optimize the learning experience. Investments in infrastructure, teacher training, and curriculum design are imperative to fully leverage IoT's benefits for education. [ABSTRACT FROM AUTHOR]

The main objective of the publication is to analyze the development of additive manufacturing systems based on the concepts of Industry 4.0 and Industry 5.0, with particular emphasis on the Internet of Everything ideology. In connection with that, the authors have resolved to review the existing devices, and also IT tools, which are applied in manufacturing processses based upon additive technologies. In order to facilitate the analysis in question, three variants were developed; the first, presenting 3D printing as a self-contained manufacturing work station not involving other technologies within the Industry 4.0 conception, the second, indirectly involving 3D printing in the Industry 4.0 conception, and also the third, in which 3D printing constitutes a major and indispensable element of this conception. The contemporary literature of the subject includes dissertations claiming that the fifth industrial revolution may be coming. Taking that into account, this dissertation attempts as well to discuss the evolution of 3D printing in the context of the 5.0 Industry conception, based upon the Internet of Everything, and also Virtual Reality. This part of the article is a forecast of the things to come. [ABSTRACT FROM AUTHOR]

Thanks to technological advancements, our lives are getting more intertwined with the connected world as more smart devices are coming to market. As such, the clear separation of devices as things (end-devices in IoT systems) and human operated devices is getting increasingly buried. This led to the creation of the term Internet of Everything (IoE) which is defined by Cisco as the “the networked connection of people, process, data, and things” [1]. The main difference between IoT and IoE is inclusion of people in the ecosystem, which greatly increases the number of connected parties. This increase in connected parties creates a strain on our existing infrastructure which is relying on cloud computing for performing most operations. Even though this resource provides heaps of computational power, the weak link in this scenario is the network, where all the connected devices can easily overload the available bandwidth, leading to slow response speeds and low general availability. The answer to this problem lies with technology that already exists and is not yet fully exploited as a distributed computing powerhouse, IoT. This paper aims to summarise the concept of computing at the edge, common architectural patterns, existing solutions, while also discussing real-world applications.

Fog computing (FC) is considered one of the smart and effective solutions for service provisioning to the Internet of Everything (IoE) layer. IoE layer means the platforms including homes, vehicles, infrastructures, and alike others. FC supports the IoE layer in a smart and charismatic manner by providing services near the smart devices, from vehicles to other mobilities, and fitting the response and delay time requirements. In the proposed paper, the authors focused on discussing vehicular fog computing (VFC) and how FC supports smart vehicles operating on the IoT layer. In the previous decade, scheduling algorithms are proposed by different scholars such as RTFRS, FCFS, ILP, and others to improve the working and efficacy of VFC. In the proposed paper, an analysis is done on the VFC scheduling algorithms published in the last three years in accordance with areas such as traditional, meta-heuristic, deep reinforcement learning, fuzzy logic, heuristic, and integer linear programming. The analysis is done to examine the areas of scheduling such as task scheduling, resource allocation, and others on which the existing solutions are working. This helps in inspecting the gap across which in future further work can be done. [ABSTRACT FROM AUTHOR]

The Internet of Everything (IoE) represents a paradigm shift in the world of connectivity. While the Internet of Things (IoT) initiated the era of interconnected devices, the IoE takes this concept to new heights by interlinking objects, individuals, data, and processes. Symmetry in IoE innovation and technology is essential for creating a harmonious and efficient ecosystem to ensure that the benefits are accessible to a broad spectrum of society while minimizing potential drawbacks. This comprehensive review paper explores the multifaceted landscape of the IoE, delving into its core concepts, enabling technologies, real-world applications, and the intricate web of challenges it presents. A focal point of this review is the diverse array of real-world applications spanning healthcare, smart cities, industry 4.0, agriculture, and sustainability. Previous works and examples illustrate how the IoE reshapes these domains, leading to greater efficiency, sustainability, and improved decision making. However, the transformative power of the IoE is accompanied by a host of challenges, including security and privacy concerns, interoperability issues, and the ethical implications of ubiquitous connectivity. These challenges are dissected in order to comprehensively understand the obstacles and potential solutions in the IoE landscape. As we stand on the cusp of an IoE-driven future, this review paper serves as a valuable resource for researchers, policy makers, and industry professionals seeking to navigate the complexities of this emerging paradigm. By illuminating the intricacies of the IoE, this review fosters a deeper appreciation for the transformative potential and the multifaceted challenges that lie ahead in the Internet of Everything era. [ABSTRACT FROM AUTHOR]

Rapid development of the Internet of Everything (IoE) and cloud services offer a vital role in the growth of smart applications. It provides scalability with the collaboration of cloud servers and copes with a big amount of collected data for network systems. Although, edge computing supports efficient utilization of communication bandwidth, and latency requirements to facilitate smart embedded systems. However, it faces significant research issues regarding data aggregation among heterogeneous network services and objects. Moreover, distributed systems are more precise for data access and storage, thus machine-to-machine is needed to be secured from unpredictable events. As a result, this research proposed secured data management with distributed load balancing protocol using particle swarm optimization, which aims to decrease the response time for cloud users and effectively maintain the integrity of network communication. It combines distributed computing and shift high cost computations closer to the requesting node to reduce latency and transmission overhead. Moreover, the proposed work also protects the communicating machines from malicious devices by evaluating the trust in a controlled manner. Simulation results revealed a significant performance of the proposed protocol in comparison to other solutions in terms of energy consumption by 20%, success rate by 17%, end-to-end delay by 14%, and network cost by 19% as average in the light of various performance metrics. [ABSTRACT FROM AUTHOR]

Smart services based on the Internet of Everything (IoE) are gaining considerable popularity due to the ever-increasing demands of wireless networks. This demands the appraisal of the wireless networks with enhanced properties as next-generation communication systems. Although 5G networks show great potential to support numerous IoE based services, it is not adequate to meet the complete requirements of the new smart applications. Therefore, there is an increased demand for envisioning the 6G wireless communication systems to overcome the major limitations in the existing 5G networks. Moreover, incorporating artificial intelligence in 6G will provide solutions for very complex problems relevant to network optimization. Furthermore, to add further value to the future 6G networks, researchers are investigating new technologies, such as THz and quantum communications. The requirements of future 6G wireless communications demand to support massive data-driven applications and the increasing number of users. Unlike existing works, this paper highlights the recent activities and trends toward 6G technology, network requirement, essential enabling technologies for 6G networks, and a detailed use case analysis between 5G and 6G networks. Moreover, this paper surveys emerging 6G connectivity solutions, such as holographic beamforming, artificial intelligence-enabled IoT networks, edge computing, and backscatter communications to serve smart communities. Furthermore, several future research directions to accomplish 6G-based IoT networks are also highlighted.