Gaussian functional shapes-based type-II fuzzy membership-based cluster protocol for energy harvesting IoT networks

With the advancements in Internet of Things (IoT) technologies, energy harvesting IoT devices are becoming significantly important. These tiny IoT devices can harvest bounded energy, thus need an efficient protocol to conserve the energy in more efficient manner. From the review, it is found that the development of an efficient energy efficient protocol for energy harvesting IoT is still an open area of research. It is found that fuzzy based energy harvesting IoTs has shown significant improvement over the existing protocols. However, the fuzzy logic suffers from the data uncertainty issue. Therefore, in this paper, Gaussian functional shapes-based type-II fuzzy membership function is used to elect the cluster heads among the IoT devices to reduce the energy consumption of energy harvest IoTs. Thereafter, inter-cluster data aggregation is used. Finally, the communication between the elected cluster heads and the cloud servers or sink. Extensive experiments are drawn by considering the existing and the proposed protocols for energy harvesting IoTs. Comparative analysis reveals that the proposed type-II fuzzy membership function-based protocol outperforms the existing protocols in terms of bandwidth analysis, throughput, conserve energy, network lifetime, and average consumed energy

Desarrollo e implementación de un modelo de captura de datos con frecuencia de dinámica orientado a la optimización del tráfico en plataformas IOT

En la última década, el ser humano ha incrementado el deseo de controlar todo a través de elementos tecnológicos, las 24 horas al día los 7 días a la semana, teniendo en cuenta que en los hogares se encuentran los electrodomésticos, en los talleres o lugares de trabajo se encuentra la maquinaria, en los centros comerciales elementos para nuestra diversión y otros, la definición para ello es el internet de las cosas conocido también como IoT. Este tipo de evoluciones son consideradas un gran reto, pues para llevarlo cabo se integran funcionalmente múltiples dispositivos que hacen compleja la implementación de esta nueva forma de vivir, de administrar la vida y la cotidianidad y es por ello ,que el presente trabajo de grado se centra en mostrar la implementación, captura y transmisión de datos con frecuencia dinámica de tiempo y posteriormente, una transformación de la información a intervalos constantes a través del uso de modelos matemáticos como la interpolación por medio de splines cúbicos, determinando finalmente que es posible mejorar el uso eficiente de energía y transmisión de datos desde dispositivos IoT, mediante el uso de las técnicas trabajadas en el proyecto teniendo algunas consideraciones pertinentes para su implementación.Ingeniero de Sistemaspregrad

Low-complexity three-dimensional AOA-cross geometric center localization methods via multi-UAV network

The angle of arrival (AOA) is widely used to locate a wireless signal emitter in unmanned aerial vehicle (UAV) localization. Compared with received signal strength (RSS) and time of arrival (TOA), AOA has higher accuracy and is not sensitive to the time synchronization of the distributed sensors. However, there are few works focusing on three-dimensional (3-D) scenarios. Furthermore, although the maximum likelihood estimator (MLE) has a relatively high performance, its computational complexity is ultra-high. Therefore, it is hard to employ it in practical applications. This paper proposed two center of inscribed sphere-based methods for 3-D AOA positioning via multiple UAVs. The first method could estimate the source position and angle measurement noise at the same time by seeking the center of an inscribed sphere, called the CIS. Firstly, every sensor measures two angles, the azimuth angle and the elevation angle. Based on that, two planes are constructed. Then, the estimated values of the source position and the angle noise are achieved by seeking the center and radius of the corresponding inscribed sphere. Deleting the estimation of the radius, the second algorithm, called MSD-LS, is born. It is not able to estimate angle noise but has lower computational complexity. Theoretical analysis and simulation results show that proposed methods could approach the Cramér–Rao lower bound (CRLB) and have lower complexity than the MLE