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Currently, there is an emerging requirement for applications related to the Internet of Things (IoT). Though the capability of IoT applications is huge, there are frequent limitations namely energy optimization, heterogeneity of devices, memory, security, privacy, and load balancing (LB) that should be solved. Such constraints must be optimised to enhance the network’s efficiency. Hence, the core objective of this study was to formulate the intelligent-related cluster head (CH) selection method to establish green communication in IoT. Therefore, this study develops a chaotic equilibrium optimizer-based green communication with deep learning-enabled load prediction (CEOGC-DLLP) in the IoT environment. The study recognizes the emerging need for IoT applications and acknowledges the critical challenges, such as energy optimization, device heterogeneity, memory constraints, security, privacy, and load balancing, which are essential to enhancing the efficiency of IoT networks. The presented CEOGC-DLLP technique mainly accomplishes green communication via clustering and future load prediction processes. To do so, the presented CEOGC-DLLP model derives the CEOGC technique with a fitness function encompassing multiple parameters. In addition, the presented CEOGC-DLLP technique follows the deep belief network (DBN) model for the load prediction process, which helps to balance the load among the IoT devices for effective green communication. The experimental assessment of the CEOGC-DLLP technique is performed and the outcomes are investigated under different aspects. The comparison study represents the supremacy of the CEOGC-DLLP method compared to existing techniques with a maximum throughput of 64662 packets and minimum MSE of 0.2956.

Continuous communication and information technology advancements facilitate the modernization of the conventional energy grid into an integrated platform. Internet-of-Things (IoT) incorporates power systems, particularly smart grid features and the delivery of new services from the utility side to the end user over a two-way communication channel. However, severe security vulnerabilities have been created due to over-dependency on IoT based communication systems. In addition, critical information exchange between any two entities or devices is always an appealing target for cyber-attackers, especially with financial interest motive by damaging integrity, confidentiality and authenticity in a communication channel. Maintaining data security and preserving privacy in between two entities during the transmission or any data distribution are essential. The potential attacks and impacts of those attacks need to be investigated to develop an effective cyber security infrastructure. Thus, considerable researchers focused on detection and mitigation of these vulnerable cyber-attacks using advanced computation tools. This review article thoroughly investigated possible ways to address cyber security challenges such as smart meter security, end-users privacy, electricity theft cyber-attacks using blockchain and cryptography against communication attacks in smart grid. The operational impacts of cyber-attacks on power system security, as well as the economic impact on deregulated energy markets, have been extensively explored. In addition, the robustness of security features and cryptographic methods against various cyber-attacks is investigated to suggest unexplored cyber-attacks for future scope. Specially, the study of real-world cyber security events, case studies, new findings and new scopes in diverse power industries are carried out. More than 135 research articles has been examined for this review article. This paper mainly concentrates on distribution-side cyber-attacks with impact analysis, detection and protection techniques.

The Internet of Things (IoT) infrastructure enables smart devices to learn, think, speak and perform. The facilities of the IoT devices can be enhanced to support an intelligent application through technologies like fog computing, smart networks, federated learning or explainable artificial intelligence infrastructures. In all these cases networking of IoT devices becomes inevitable. Whereever there exists a network, a threat to the network infrastructure is also possible. The proposed work classifies various attacks on the hosts with the support of proven machine learning (ML) algorithms. This work performs the comparative analysis of all these classification parameters of the machine learning algorithms with the use of fuzzy-based recommendation systems. This work also lists out various incidents of intrusions on the IoT hosts in appropriate layers of the interface and proposes an efficient algorithm and framework to overcome the occurrences of intrusions on the host side. In particular, we propose an effective security framework to deal with the intrusions that can deteriorate the host-based systems. The ranking of the algorithms is evaluated using fuzzy-based recommendation systems such as TOPSIS, VIKOR, MORA, WASPAS. The ensemble of machine learning algorithms such as Decision Tree, Lite Gradient Boost, Xtra Gradient Boost and Random Forest provide better values of accuracy (around 99%) with higher precision, re-call and F1-scores, thus proving their efficacy for intrusion detection in IoT networks.

The easy and accurate identification of varieties is fundamental in agriculture, especially in the olive sector, where more than 1200 olive varieties are currently known worldwide. Varietal misidentification leads to many potential problems for all the actors in the sector: farmers and nursery workers may establish the wrong variety, leading to its maladaptation in the field; olive oil and table olive producers may label and sell a non-authentic product; consumers may be misled; and breeders may commit errors during targeted crossings between different varieties. To date, the standard for varietal identification and certification consists of two methods: morphological classification and genetic analysis. The morphological classification consists of the visual pairwise comparison of different organs of the olive tree, where the most important organ is considered to be the endocarp. In contrast, different methods for genetic classification exist (RAPDs, SSR, and SNP). Both classification methods present advantages and disadvantages. Visual morphological classification requires highly specialized personnel and is prone to human error. Genetic identification methods are more accurate but incur a high cost and are difficult to implement. This paper introduces OliVaR, a novel approach to olive varietal identification. OliVaR uses a teacher-student deep learning architecture to learn the defining characteristics of the endocarp of each specific olive variety and perform classification. We construct what is, to the best of our knowledge, the largest olive variety dataset to date, comprising image data for 131 varieties from the Mediterranean basin. We thoroughly test OliVaR on this dataset and show that it correctly predicts olive varieties with over 86% accuracy. Comment: 10 pages, 9 figures

The Internet of Medical Things (IoMT) is the subset of the Internet of Things (IoT) that connects multiple medical devices, collect information/data from devices, and transmits and process data in real-time. IoMT is crucial for increasing electronic device accuracy, reliability, and productivity in the healthcare industry. IoMT has emerged as a next-generation bio-analytical tool that converges network-linked biomedical devices with relevant software applications for advancing human health. Adapting IoMT and associated technologies has fixed several problems using telemedicine, remote monitoring, sensors, robotics, etc. However, adopting IoMT technologies for a large population is challenging due to extensive data management, privacy, security, upgradation, scalability, etc. Although significant research has been carried out in this domain, identifying emerging trends and highlighting the technological advancement and challenges within IoMT is required for its success. Moreover, it will aid policymakers, scientists, healthcare practitioners, and researchers to measure the pertinence of IoMT in healthcare sectors more efficiently. This review discusses the evolution of IoMT, Machine Learning Integration, Security, and interoperability challenges of IoMT devices.

At present, on the one hand, domestic enterprises carry out digital transformation one after another due to the pressure of survival and development, and on the other hand, due to environmental regulations, enterprises are facing the pressure of carbon emission reduction. This paper discusses the impact of enterprises’ digital transformation on carbon emissions and finds that enterprises’ digital transformation will increase carbon emissions, mainly because technological progress will bring about more extensive use of computers in production services, and the use of fossil energy will increase carbon emissions before the energy structure is effectively improved. Even if enterprises’ digital transformation can improve of total factor productivity, it is difficult to offset the increase in carbon emissions caused by the use of more fossil energy. In addition, it is also found that for state-owned enterprises, the carbon emission pressure brought about by digital transformation is higher than that of non-state-owned enterprises; environmental regulation can significantly restrain carbon emissions. This study reveals the real impact of enterprises’ digital transformation on carbon emissions and provides a direction for enterprises to implement digital transformation and carbon emission reduction.

5G and the Internet of Things (IoT) are a potent combination that offers a vast IoT infrastructure that can support billions of connected devices while maintaining reliability, affordability, and high-speed connectivity. Nevertheless, the integration of 5G-enabled IoT has received insufficient attention from security analysts, engineers, and researchers, resulting in a lack of information and viable solutions. This study investigates the benefits and issues associated with 5G-enabled IoT, including its privacy and security concerns as well as the technology drivers that enable its various layers. By incorporating 5G technology into IoT, several enhancements have been achieved, including enhanced reliability, simplicity, practicability, analysis, efficiency, agility, flexibility, and accessibility. To achieve the full potential of 5G for IoT, however, researchers must also address many research obstacles, such as designing the 5G-IoT architecture, managing committed machine interactions, and addressing security concerns. A promising strategy for overcoming these obstacles involves ensuring compatibility of operating systems with all devices, which will lead to the development of modern open-source standardization for smooth communication across diverse devices. This innovation will improve the security and privacy of 5G-enabled IoT devices by equipping their hardware with the intelligence to recognize, verify, and authorize processes with predetermined characteristics. This will provide authorized users with full autonomy and persistent connectivity, enhancing the overall user experience. However, 5G requires a thorough examination of its implications and difficulties. A safer and more efficient ecosystem for 5G-enabled IoT may be established by addressing these concerns and encouraging industry-wide collaboration.

The Internet of Things (IoT) is an emerging technology with a high market growth rate. In the IoT, machines and users from different levels must collaborate to exchange data and share resources. The IoT opens the door for a vast improvement in all aspects of human life. However, the increasing adoption of the IoT in many sectors makes it difficult to control security risks. For this reason, there is a need for more effort in both research and industry to address the risks and find convenient solutions. This systematic literature review delves deeply into understanding the unique challenges and requirements posed by IoT environments. Through a detailed examination of 96 selected studies, this paper primarily addresses three research questions. The study concludes by summarizing key requirements, technologies, and metrics founded on our comprehensive analysis, aiming to steer further research in the domain. As IoT continues its expansion into various facets of our daily lives, there’s a paramount need to integrate with emerging technologies and ensure scalability. Prioritizing real-world implementations is crucial for the next wave of innovations in access control systems.

Construction sector spending makes a significant contribution to the global economy, with approximately ${\$}$ 10 trillion being spent on building and construction activities annually. However, the construction industry has traditionally been perceived as slow to adapt to new technologies compared to other sectors. Recently, the construction industry has experienced a substantial shift towards Digital Transformation. As new technologies have emerged, the construction industry has begun to realize the importance of Digital Transformation in the pre-construction, construction, and facility management phases. A high degree of Digital Transformation has been seen regarding site monitoring, wearables, sensors, and identifying hazards. This paper intends to sketch a global picture of digital technologies implemented in the construction industry throughout the entire project lifecycle. By fully analyzing more than 200 papers, the paper finds that various aspects of the construction industry, including technologies, policies, regulations, and infrastructures, are still in the early stages of Digital Transformation. The findings from this review will help researchers and practitioners in the construction industry understand the global picture of digital technology implementation and where the construction industry stands in the Digital Transformation process. This paper also serves as a starting point for future work on Digital Transformation in the construction industry. The research paper is limited to vertical aspects in building projects and does not include horizontal integration. Finally, this study will give a guideline with successful examples of which technologies are being used in specific phases, so future researchers can get a holistic view of the use of digital technology in the entire building environment.