Nodes in a mobile Ad Hoc network can change their location dynamically. The manner in which nodes can be moved can be represented by a mobility model. A mobility model defines the speed, pauses and direction of the nodes in a network. The way in which nodes move in an emergency scenario and rescue differs from a normal scenario, because the normal paths can be interrupted. AVM mobility models and UPM have been defined for this type of scenario. This paper presents a comparison of mobility models AVM, UPM, Waypoint, Brownian, Pursue on emergency and rescue scenario, with different routing protocols and different node density. The results show that Pursue, AVM and UPM models are more efficient than other models.

The current trend is to connect everything to network to be monitored and controlled remotely through own concept IoT technologies. One of the problems is to connect the devices to a network, using technologies with sufficient coverage and capacity, so this article shows the development of a mobile gateway for IoT applications under the Android platform evaluated in 4G mobile networks. The IoT mobile gateway sends the data collected from sensors by selecting the MQTT and HTTP protocols, to IoT platforms on the Internet. The architecture is composed of temperature, luminosity and pressure sensors that are integrated into a sensor node which function is for data collection, processing and then via Bluetooth is sent to an Android smartphone where an application that performs the mobile gateway function is running, here data network are sent through the 4G LTE network to the IoT platforms of Ubidots and Watson IoT. Subsequently, tests were carried out with various metrics such as latency, energy consumption and connection costs. It concludes that IoT mobile gateway has better performance under MQTT protocol compared to HTTP.

La COVID-19 ha provocado graves daños a la salud: centenas de millones de personas infectadas y varios millones de fallecidos en el mundo. Los programas de vacunación de cada Gobierno han influido en el decaimiento de estos índices, pero con la aparición de nuevas mutaciones del coronavirus más contagiosas, la preocupación sobre la efectividad de las vacunas se hace presente. Frente a esta situación el uso de mascarillas sigue siendo eficaz para prevenir la transmisión y contagio de la COVID-19. Lo que ha generado una creciente demanda de servicios de detección automática de mascarillas, que permita recordar a las personas la importancia del empleo de estas. En este trabajo se plantea un análisis del rendimiento de un sistema AIoT para la detección del uso correcto, incorrecto y sin mascarilla basado en dos modelos computacionales de Cloud y Edge, con la finalidad de determinar qué modelo se adecua mejor en un entorno real (interior y exterior) sobre la base de la confiabilidad del algoritmo, uso de recursos computacionales y tiempo de respuesta. Los resultados experimentales demuestran que el modelo computacional Edge presentó un mejor desempeño en comparación con el Cloud.