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    Design of an IoT prototype for the prevention of robberies in the young areas of Lima
    (Seventh Sense Research Group, 2022-09-01)
    Latin America is affected by citizen insecurity; street robberies are frequent. In Peru, this scenario is alarming; according to studies and reports presented by the authorities, it is estimated that a monthly average of 1,5348 robberies are reported every month. Lima, being the main capital, is exposed to a greater number of assaults, especially in young areas; because of this, in this research work, an IoT design was presented, which consists of a system of alarms implemented in specific areas where the complaint rate is higher. This alarm will be activated through a mobile application installed on the user's smartphone, This way; the user can turn on the alarm in case they witnesses or is the victim of a robbery; the application will send an alert message to the nearest authorities to take action on the scare. The Scrum Methodology was used as a methodology because its framework is adapted to the needs of this research.
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    Longitudinal Scientific Mapping of Emerging and Converging Trends Between the Internet of Things and Digital Transformation
    (Emerald Publishing Limited, 2023-08-03)
    Purpose The purpose of this study is to analyze the technological change under development linked to the convergence of the Internet of Things (IoT) and digital transformation (DT) from the perspective of a scientific mapping in a context marked by the occurrence of an unexpected event that accelerated this process such as the SARS-CoV-2 pandemic and its variants. Design/methodology/approach The study was developed under the longitudinal scientific mapping approach and considered the period 1990–2021 using as a basis the descriptors DT and IoT. The steps followed were identification and selection of keywords; design and application of an algorithm to identify these selected keywords in titles, abstracts and keywords using terms in Web of Science (WoS) to contrast them; and performing a data processing based on the journals in the Journal Citation Report during 2022. The longitudinal study uses scientific mapping to analyze the evolution of the scientific literature that seeks to understand the acceleration in the integration of technology and its impact on the human factor, processes and organizational culture. Findings This study showed that the technologies converging around IoT form the basis of the main DT processes being experienced on a global scale; furthermore, it was shown that the pandemic accelerated the convergence and application of new technologies to support the major changes required for a world with new needs. Finally, China and the USA differ significantly in the production of scientific knowledge with respect to the first eight followers. Originality/value The knowledge gap addressed by this study is to identify the production of scientific knowledge related to IoT and its impact on DT processes at the scale of individuals, organizations and the new way of delivering value to society. This knowledge about researchers, institutions, countries and the derivation is multiple indicators allows improving decision-making at multiple scales on these issues.
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    Intelligent Fog Computing Surveillance System for Crime and Vulnerability Identification and Tracing
    (IGI Global, 2023-01-01)
    IoT devices generate enormous amounts of data, which deep learning algorithms can learn from more effectively than shallow learning algorithms. The approach for threat detection may ultimately benefit fog computing or fog networking (fogging). The authors present a cutting-edge distributed DL method for detecting cyberattacks and vulnerability injection (CAVID) in this paper. In terms of the evaluation metrics tested in the tests, the DL model performs better than the SL models. They demonstrated a distributed DL-driven fog computing CAVID approach using the open-source NSLKDD dataset. A pre-trained SAE was utilised for feature engineering, whereas Softmax was employed for categorization. They used parametric evaluation for system assessment to evaluate the model in comparison to SL techniques. For scalability, accuracy across several worker nodes was taken into consideration. In addition to the robustness, effectiveness, and optimization of distributed parallel learning among fog nodes for enhancing accuracy, the findings demonstrate DL models exceeding classic ML architectures.