The Intemnets Lab conducts research on Wireless Multihop Networks (WMNs) that adapt to human activity and the systems that integrate with these networks to provide services for a variety of applications. The research consists of two main fundamental aspects that have a common goal: a set of intelligent and embedded wireless networked systems that are aware of the users' activities and their environments, and that adapt to their variations. These systems can cover personal areas and areas for team work (i.e., body-area and local-area networks).
The first aspect is the recurrent formation of WMNs as networks of networks, which considers a broad range of WMN aspects including cognitive networking, topology control, network stability, network coding, overlay networks, and quality of service. These problems are addressed while considering the users' behavior such as traffic patterns, body postures, and relative movement among them. Applications of the planned research activity include: body-movement monitoring, biological-signal monitoring, reality mining (i.e., real-time monitoring of human behavior and its automated analysis), tactical field communications resilience, and disaster recovery.
G. Newell and G. Vejarano, "Motion-Based Routing and Transmission Power Control in Wireless Body Area Networks," in IEEE Open Journal of the Communications Society, vol. 1, pp. 444-461, 2020, doi: 10.1109/OJCOMS.2020.2986396
Dhruvil Darji and Gustavo Vejarano, "Counting Static Targets using an Unmanned Aerial Vehicle On-The-Fly and Autonomously," in 2018 Computer and Robot Vision (CRV 2018), Toronto, ON, Canada, May 201
Dai Meng, Todd Shoepe, and Gustavo Vejarano, "Accuracy Improvement on the Measurement of Human Joint Angles," IEEE Journal of Biomedical and Health Informatics, vol. 20, no. 2, pp. 498-507, Mar. 2016. URL: https://doi.org/10.1109/JBHI.2015.2394467
Garrett Newell and Gustavo Vejarano, "Human-Motion Based Transmission Power Control in Wireless Body Area Networks," in 2016 IEEE 3rd World Forum on Internet of Things (WF-IoT 2016), Reston, VA, USA, Dec. 2016. URL https://doi.org/10.1109/WF-IoT.2016.7845404