This document discusses the Japanese context of Society 5.0. Based on a society-centered approach, Society 5.0 seeks to take advantage of technological advances to finally solve the problems that currently threaten Japan, such as aging, birth rates and lack of competitiveness, among others. Additionally, another objective is to contribute to the progress of the country and develop the foundations for a better world, in which no individual can be excluded from the technological advances of our current society, to achieve this goal, the Sustainable Development Goals (SDG) have been developed. SDGs seek to assess the methods of use of modern technology and thus find the best strategies and tools to use it in a way that guarantees sustainability within the framework of a new society that demands constant renovations.

Industry 4.0 is a set of technologies that companies require to promote innovation strategies and obtain a rapid response in dynamic markets. It focuses mainly on interconnectivity, digital technology, predictive analytics and machine learning to revolutionize the way companies operate and develop. Therefore, this article proposes and motivates the implementation of Industry 4.0 in organizations. Studying the state of the art and reviewing the current situation of business intelligence (BI) technology, the way it has positively impacted organizations at the economic and business level in terms of decision-making and some success stories implemented in different business, academic, social and governmental environments. Moreover, it addresses the future expected for Industry 4.0 primarily in BI and how companies should face this revolution. This article provides knowledge contribution about the current state and positive consequences of Industry 4.0, and high development in technology when implemented in the organization and the harmonization between production and intelligent digital technology.

Recent research has shown that there is a correlation between the circular economy (CE) and Industry 4.0 (I4.0). In addition, other research papers have analyzed the way that CE uses the different I4.0 technologies to transfer from the existing linear economy to CE; however, there are still gaps in the literature regarding the challenges and impacts that society and individuals must face to be ready for the transition from a linear to a circular economy. These challenges seek to guarantee the sustainability and sustainable development of the different business models that mobilize products and services through supply chains. Here, we conducted a review and compilation of the latest bibliography of circular economy and Industry 4.0 theory. The objective of this work is to present the evolutionary relationship between CE and I4.0, as well as its multi-step model of analysis. This research is relevant because its topics are timely and pertinent, especially for academics. Further, at the time that this research was performed, none of the countries were concerned about the impact that technological changes have on the human being and on society, and up to now we do not currently have studies that show how people are being trained to face the transition from the linear economy, which is common in most societies, toward a CE.

The Internet of things (IoT) is an important technology that is highly beneficial in establishing smart items, connections and cities. However, there are worries regarding security and privacy vulnerabilities in IoT in which some emerge from numerous sources, including cyberattacks, unsecured networks, data, connections or communication. This paper provides an ensemble intrusion strategy based on Cyborg Intelligence (machine learning and biological intelligence) framework to boost security of IoT enabled networks utilized for network traffic of smart cities. To do this, multiple algorithms such Random Forest, Bayesian network (BN), C5.0, CART and Artificial Neural Network were investigated to determine their usefulness in identifying threats and attacks-botnets in IoT networks based on cyborg intelligence using the KDDcup99 dataset. The results reveal that the AdaBoost ensemble learning based on Cyborg Intelligence Intrusion Detection framework facilitates dissimilar network characteristics with the capacity to swiftly identify different botnet assaults efficiently. The suggested framework has obtained good accuracy, detection rate and a decreased false positive rate in comparison to other standard methodologies. The conclusion of this study would be a valuable complement to the efforts toward protecting IoT-powered networks and the accomplishment of safer smart cities.

Wireless Sensor Networks (WSNs) consist of a spatially distributed set of autonomous connected sensor nodes. The deployed sensor nodes are extensively used for sensing and monitoring for environmental surveillance, military operations, transportation monitoring, and healthcare monitoring. The sensor nodes in these networks have limited resources in terms of battery, storage, and processing. In some scenarios, the sensor nodes are deployed closer to the base station and responsible to forward their own and neighbor nodes’ data towards the base station and depleted energy. This issue is called a hotspot in the network. Hotspot issues mainly appear in those locations where traffic load is more on the sensor nodes. The dynamic and unequal clustering techniques have been used and mitigate the hotspot issues. However, with few benefits, these solutions have suffered from coverage overhead, network connection issues, unbalanced energy utilization among the sink nodes, and network stability issues. In this paper, a comprehensive review of various equal clustering, unequal clustering, and hybrid clustering approaches with their clustering attributes is presented to mitigate hotspot issues in heterogeneous WSNs by using various parameters such as cluster head selection, number of clusters, zone formation, transmission, and routing parameters. This review provides a detailed platform for new researchers to explore the new and novel solutions to solve the hotspot issues in these networks.

The pandemic has accelerated the use and development of video conferenc-ing platforms due to COVID-19, and it is necessary to find an application that best suits our requirements. This article evaluates nine features of nine video conferencing applications to find the best choice. A detailed analysis has been conducted in this article using surveys to collect data from two samples at national and international level. The methodology followed is the CDIO methodology, a tool to address complex problems in four stages: conception, design, implementation, and operation. The primary aim of this paper is to examine which is the best livestreaming option according to the selected criteria, using, as references, well-known applications such as Jitsi, Zoom, Microsoft Teams, Google Meet, Big Blue Button, among others. An application of multicriteria analysis is also shown to support the evalua-tion of these platforms compared to the established criteria.