This article discusses the simulation and evaluation of a traffic congestion detection system which combines inter-vehicular communications, fixed roadside infrastructure and infrastructure-to-infrastructure connectivity and big data. The system discussed in this article permits drivers to identify traffic congestion and change their routes accordingly, thus reducing the total emissions of CO2 and decreasing travel time. This system monitors, processes and stores large amounts of data, which can detect traffic congestion in a precise way by means of a series of algorithms that reduces localized vehicular emission by rerouting vehicles. To simulate and evaluate the proposed system, a big data cluster was developed based on Cassandra, which was used in tandem with the OMNeT++ discreet event network simulator, coupled with the SUMO (Simulation of Urban MObility) traffic simulator and the Veins vehicular network framework. The results validate the efficiency of the traffic detection system and its positive impact in detecting, reporting and rerouting traffic when traffic events occur.

This paper presents a system based on WSN technology capable of monitoring heart rate and the rate of motion of seniors within their homes. The system is capable of remotely alerting specialists, caretakers or family members via a smartphone of rapid physiological changes due to falls, tachycardia or bradycardia. This work was carried out using our workgroup's WiSe platform, which we previously developed for use in WSNs. The proposed WSN architecture is flexible, allowing for greater scalability to better allow event-based monitoring. The architecture also provides security mechanisms to assure that the monitored and/or stored data can only be accessed by authorized individuals or devices. The aforementioned characteristics provide the network versatility and solidity required for use in health applications.