IMEC- Holst center is currently rolling out its 3rd generation Wireless Sensor Network (WSN) within the Internet of Things (IoT) framework. Each node of this network consists of numerous sensors, capable of measuring air-quality of the environment and communicating this information to a consumer accessible cloud. For efficient processing and communication, each data point measured by the sensor must be paired with an accurate time-stamp and the node location. Thus to ensure coherence, all the nodes in the network must be tightly synchronized and localized. The problem at hand is to develop and implement optimal solutions for joint localization and synchronization in constrained devices with limited resources.
In the burgeoning era of IoT and Big data, the location information and accurate time-stamping of the collected data are vital for efficient processing, communication and in general to ensure coherence functioning of any wireless sensor network. Localization is broadly classified under various categories, such anchored vs anchorless, indoor vs outdoor localization and also depending on the radios employed for measurements and also based on the measurement techniques. There are numerous measurement techniques to capture the location information e.g., AoA, ToA, TDoA, RToF. However to decouple the position information and time discrepancies of on-board clocks, ToA based trilateration and synchronization is traditionally used. There are numerous challenges to improve the accuracy of the position estimate and synchronization. These include, but are not limited to, channel estimation in NLoS conditions, optimal anchor placement for anchored scenarios, distributed clock drift estimation, and implementing hybrid localization strategies e.g., ToA + AoA. Finally, the estimation algorithms for cooperative localization and synchronization e.g., Maximum likelihood, Least squares, ANN and Bayesian frameworks, must be implementable on constrained embedded systems with limited memory, processing and communication capabilities.
- Literature survey on the current state of the art for joint localization and synchronization;
- Developing algorithm(s) for joint localization and synchronization;
- Simulations to validate the algorithm(s)Implementing the solution(s) on the IMEC IoT sensor network;
- Testing and analyzing the performance of the implemented algorithm;
- Thesis writing and documentation at IMEC-Holst Centre;
- (Option) submit the work to a top-ranking publication.
- Experience with signal processing/machine learning tools;
- Proven experience with MATLAB/Python/R/C/C++;
- Previous experience with localization and/or synchronization is an added plus;
- Motivated student eager to work independently and expand knowledge in the field;
- Good written and verbal English skills.
For all inquiries, please contact:
Ms Najat Loiazizi, HR specialist.
Telephone number: +31 (0)40 40 20 675