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.

Wellness is a term often used to talk about optimal health as "dynamic balance of physical, emotional, social, spiritual, and intellectual health." While healthcare is a term about care offered to patients for improving their health. We use both terms, as well as the Business Model Canvas (BMC) methodology, to design a digital ecosystem model for healthcare and wellness called DE4HW; the model considers economic, technological, and legal asymmetries, which are present on e-services beyond geographical regions. BMC methodology was embedded into the global project strategy called: IBOT (Initiate, Build, Operate and Transfer); it is a methodology to establish a functional, integrated national telemedicine network and virtual education network; of which we took its phases rationale. The results in this work illustrate the design of DE4HW model, into the first phase of IBOT, enriched with the BMC, which enables us to define actors, their interactions, rules and protocols, in order to build DE4HW, while IBOT strategy manages the project goal, up to the transfer phase, where an integral service platform of healthcare and wellness is turned over to stakeholders.

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.

With the educational mobile robot Worm-Type Mobile Educational Robot (Robot Móvil Educativo tipo Oruga, or ROMEO, by its Spanish acronym), the authors offer three hierarchical levels of experimental learning, where the operator can develop as far as his/her ability or imagination permits, gaining knowledge about the basics of sensors, communications, and mechanical and robot programming. Due the lack of learning focused on robotics in Mexican educational institutions, the authors present this chapter, where an early stimulation to this topic could trigger curiosity to research that leads to technological advancement. ROMEO is a mobile wireless communication platform with different types of sensors: moisture, brightness, temperature, etc., as well as a compass and accelerometers with similar characteristics to industrial and commercial applications that allow us to experiment with communication algorithms, sampling, and autonomous and semiautonomous navigation.