Subventions et des contributions :

Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier. (2017-2018 à 2022-2023)

Localization in wireless sensor networks (WSN) is defined as the collection of techniques to measure the spatial relationship between nodes and physical objects. Localization is the problem of determining the position of an object with respect to another object. Routing information, a crucial component in WSN, is enhanced by localization. Many real-world applications now require tracking or monitoring of physical objects in closed environments, such as hospitals, mines, airports and subways. While global positioning systems (GPS) would appear to be an obvious tracking solution, GPS is still relatively expensive, power hungry and has degraded performance when used in indoor locations. WSNs are a solution to the problem of performing wide-area monitoring and surveillance. Incorporating radio frequency identification (RFID) is essential in the monitoring and tracking of large systems. Entities, ranging from objects to users, are equipped with WSN and RFID tags to gather their context information. Using agents’ unique identifiers in WSN improves network manageability and scalability.
My research goal is to develop a new, intelligent, efficient, autonomous system for effective indoor location services, which integrates agents with WSN and RFID. This multifaceted approach has never been achieved before. My research objectives are to: 1) simplify on-demand access to RFID stored information and the integration of WSN; 2) evaluate and develop WSN efficient localization algorithms with/out RFID; 3) facilitate the interaction between WSN and RFID using intelligent agents; and 4) perform intensive simulations and develop working prototypes.
To achieve our objectives, firstly we will investigate and develop efficient and scalable power-aware localization protocols. Secondly, we will reduce the error between the estimated and the actual locations of moving targets and we will scrutinize with different ranges/range-free protocols to inspect the performance of localization protocols. Thirdly, we will test different network topologies and make recommendations based on the required real-world application. Lastly, we will continue to evaluate and enhance the performance of all of our proposed algorithms as we progress with our research program.
The amalgamation of artificial intelligence with wireless communications will lead to a novel way to deal with localization and monitoring environments. Our outcomes will be beneficial to many application domains in Canada, such as manufacturing, geo-tracking and healthcare. For example, in healthcare, the proposed methods will provide the basis for developing integrated systems for better health monitoring and management. This research will also advance techniques of using agents with WSNs and RFIDs for indoor localization systems such as warehouses, shopping malls, airports, trains and subway stations, and in the mining industry.