Successfully Defended my Master's Thesis with Highest Honors

Hey everyone,

I’m thrilled to share that I successfully defended my master’s thesis and received the highest possible grade: Matrícula de Honor! 🎓 This has been an incredible journey, and I’m excited to tell you a bit about my work.

In my thesis, I developed a system that uses fuzzy logic to adjust the power of wireless sensor nodes, helping them save energy while staying connected. This is especially important for devices that run on batteries, as it extends their life. The system was tested in a real-world setting and proved to be more efficient than traditional methods (Lucas-Martínez, 2016).

Key Highlights

Energy Efficiency: My thesis focused on developing a self-adaptive system using fuzzy control to optimize energy consumption in wireless sensor networks (WSNs).
Real-World Application: The system was designed to maintain network connectivity while minimizing energy use, which is crucial for the longevity of battery-powered devices.
Successful Implementation: I implemented and tested the system in a real deployment, confirming its effectiveness in saving energy compared to fixed transmission power methods.

Master Thesis Data

Title: Development of a Fuzzy Control Based Self-Adaptive System for Energy Saving in Constrained Networks for the Internet of Things

Author: Néstor Lucas Martínez

Supervisor: José-Fernán Martínez Ortega

Visual References

The area where the experiments were carried out.

On the left, total network energy discharge per experiment. Right, packet deliver rate per experiment.

References

2016

UPM
Development of a Fuzzy Control Based Self-Adaptive System for Energy Saving in Constrained Networks for the Internet of Things

Néstor Lucas-Martínez

Feb 2016

Highest Honors Abs Bib PDF Website

Néstor Lucas Martínez received the highest qualification, “Matrícula de Honor”, for his Master Thesis.

Among the design goals for future networks, including next generation networks, we can find the energy consumption and the connectivity. These two goals are of special relevance when dealing with constrained networks. That is the case of Wireless Sensors Networks (WSN). These networks consist of devices with low or very low processing capabilities. They also depend on batteries for their operation. Thus energy optimization becomes a very important issue. Several proposals have been made for optimizing the energy consumption in this kind of networks. Perhaps the best known are those based on the coordinated planning of active and sleep intervals. They are indeed one of the most effective ways to extend the lifetime of the batteries. The proposal presented in this work uses a probabilistic approach to control the connectivity of a network. The underlying idea is that it is highly probable that the network will have a good connectivity if all the nodes have a minimum number of neighbors. By using some mechanism to reach that number, we hope that we can preserve the connectivity with a lower energy consumption compared to the required one if a fixed transmission power is used to achieve a similar connectivity. The mechanism must have the smallest footprint possible on the devices being used in order to be efficient. Therefore a fuzzy control based self-adaptive system is proposed. This work includes the design and implementation of the described system. It also has been validated in a real scenario deployment. We have obtained results supporting that there exist configurations where it is possible to get a good connectivity saving energy when compared to the use of a fixed transmission power for a similar connectivity.
@phdthesis{LucasMartinezMScThesis2016, type = {{{MSc Thesis}}}, title = {Development of a {{Fuzzy Control Based Self-Adaptive System}} for {{Energy Saving}} in {{Constrained Networks}} for the {{Internet}} of {{Things}}}, author = {{Lucas-Mart{\'i}nez}, N{\'e}stor}, year = {2016}, month = feb, date = {2016-02-22}, address = {Madrid, Spain}, copyright = {CC BY-NC-ND 4.0}, langid = {english}, school = {Universidad Polit{\'e}cnica de Madrid}, keywords = {No DOI found}, google_scholar_id = {IjCSPb-OGe4C}, }