Description
Date depot: 4 janvier 2022
Titre: Clustering Approach of Data Aggregation for Reliable Packet Routing in Underwater Acoustic Sensor Networks (UASNs)
Directrice de thèse:
Maria TROCAN (LISITE)
Encadrante :
Shohreh AHVAR (Nokia-Bell-Labs)
Encadrant :
Ehsan AHVAR (INRIA-Saclay)
Domaine scientifique: Sciences et technologies de l'information et de la communication
Thématique CNRS : Programmation et architecture logicielle
Resumé: Today data aggregation in underwater applications is one of the major challenging factors that affect the performance of data collection in underwater acoustic sensor networks (UASNs) as a result of temporary loss of connectivity in underwater, this grabs the attention of many scholars and researcher all over the world. To this end, it’s important to provides an efficient clustering approach for aggregating the data collected from underwater sensor nodes. This would minimize the data redundancy and energy consumption in underwater packets forwarding and maximize the network lifetime. The proposed clustering approach would be implemented using Aqusim Simulator an ns2 based simulator for simulating underwater applications and the simulation result of the proposed clustering approach would be compare with the existing schemes in terms of data redundance, packets lost and energy consumption and Network lifetime in underwater.
Doctorant.e: Muhammed Dalhatu