Security of Big Data over IoT Environment by Integration of Deep Learning and Optimization

This is especially true given the spread of IoT, which makes it possible for two-way communication between various electronic devices and is therefore essential to contemporary living. However, it has been shown that IoT may be readily exploited. There is a need to develop new technology or combine existing ones to address these security issues. DL, a kind of ML, has been used in earlier studies to discover security breaches with good results. IoT device data is abundant, diverse, and trustworthy. Thus, improved performance and data management are attainable with help of big data technology. The current state of IoT security, big data, and deep learning led to an all-encompassing study of the topic. This study examines the interrelationships of big data, IoT security, and DL technologies, and draws parallels between these three areas. Technical works in all three fields have been compared, allowing for the development of a thematic taxonomy. Finally, we have laid the groundwork for further investigation into IoT security concerns by identifying and assessing the obstacles inherent in using DL for security utilizing big data. The security of large data has been taken into consideration in this article by categorizing various dangers using a deep learning method. The purpose of optimization is to raise both accuracy and performance

Autonomous Underwater Vehicle: 5G Network Design and Simulation Based on Mimetic Technique Control System

The Internet of Underwater Things (IoUT) exhibits promising advancement with underwater acoustic wireless network communication (UWSN). Conventionally, IoUT has been utilized for the offshore monitoring and exploration of the environment within the underwater region. The data exchange between the IoUT has been performed with the 5G enabled-communication to establish the connection with the futuristic underwater monitoring. However, the acoustic waves in underwater communication are subjected to longer propagation delay and higher transmission energy. To overcome those issues autonomous underwater vehicle (AUV) is implemented for the data collection and routing based on cluster formation. This paper developed a memetic algorithm-based AUV monitoring system for the underwater environment. The proposed Autonomous 5G Memetic (A5GMEMETIC) model performs the data collection and transmission to increase the USAN performance. The A5GMEMETIC model data collection through the dynamic unaware clustering model minimizes energy consumption. The A5GMemetic optimizes the location of the nodes in the underwater environment for the optimal data path estimation for the data transmission in the network. Simulation analysis is performed comparatively with the proposed A5Gmemetic with the conventional AEDG, DGS, and HAMA models. The comparative analysis expressed that the proposed A5GMeMEMETIC model exhibits the ~12% increased packet delivery ratio (PDR), ~9% reduced delay and ~8% improved network lifetime

5G with Fog Computing based Privacy System in Data Analytics for Healthcare System by AI Techniques

Fog computing architecture is an extended version of the cloud computing architecture to reduce the load of the data transmission and storage in the cloud platform. The architecture of the fog increases the performance with improved efficiency compared with the cloud environment. The fog computing architecture uses the 5G based Artificial Intelligence (AI) technology for performance enhancement. However, due to vast range of data availability privacy is challenging in the fog environment. This paper proposed a Medical Fog Computing Load Scheduling (MFCLS) model for data privacy enhancement. The developed architecture model of optimization-based delay scheduling for task assignment in the fog architecture. The healthcare data were collected and processed with the 5G technology. The developed MFCLS model uses the entropy-based feature selection for the healthcare data. The proposed MFCLS considers the total attributes of 13 for the evaluation of features. With the provision of service level violation, the fog computing network architecture will be provided with reduced energy consumption. The developed load balancing reduced the service violation count with the provision of desired data privacy in the fog model. The estimation of the time frame is minimal for the proposed MFCLS model compared with the existing DAG model. The performance analysis expressed that SLRVM and ECRVM achieved by the proposed MFCLS are 28 and 43 respectively. The comparative examination of the proposed MFCLS model with the existing DAG model expressed that the proposed model exhibits ~6% performance enhancement in the data privacy for the healthcare data

Experiencias suramericanas en telemedicina de enfermedades desatendidas

Las enfermedades tropicales desatendidas (ETD) son aquellas que comúnmente se encuentran en varios países de bajos ingresos en África, Asia y América Latina, provocadas básicamente, por el escaso acceso la higiene, agua limpia o sistemas de alcantarillado. Las ETD comprenden una diversidad de enfermedades de alta prevalencia en los países tropicales causadas por una variedad de patógenos, incluyendo bacterias, virus, parásitos y hongos. La epidemiología de las ETD es bastante compleja y se relacionan con las condiciones ambientales del entorno. Muchas son transmitidas por vectores, tienen un origen zoonótico con reservorios animales bien caracterizados y están asociadas con ciclos de vida complejos. Todos estos factores hacen que su control en salud pública sea un desafío; desafío que se caracteriza por la falta de financiamiento en investigación y control. En ese sentido, la telemedicina, o el uso de las telecomunicaciones para brindar servicios de salud, es una tecnología que ha venido ganando cuerpo durante los últimos veinte años, ya que ayudan, con una relativa baja inversión, el acceso a la atención médica por parte de los más necesitados o que viven en lugares remotos. Esta investigación se centra en el estudio y conocimiento de las las enfermedades olvidadas presentes en suramérica y cómo la telemedicina ha ayudado en su prevención, diagnóstico y tratamiento.Campus Lima Centr