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|Titre: ||Mesh routers placement in wireless mesh networks|
|Auteur(s): ||Nouri, Nabil Abdelkader|
|Date de publication: ||23-nov-2021|
|Résumé: ||Wireless mesh networks (WMNs), in the form of WiFi (802.11x) or WiMax (802.16x), or their integrations, have been proposed as an effective communication alternative for ubiquitous last mile wireless broadband access. They can be viewed as a hybrid between traditional cellular, point-to-point wireless systems, and ad-hoc networks. They offer more flexibility, mobility, coverage, and expandability compared to their traditional counterparts at the expense of complex architecture and deployment structure. Though WMNs hold great promise in abetting network ubiquity, there still remain several challenges in the design and development of WMNs to support diverse services with different quality of service (QoS) requirements and large-scale deployment. The focus of this thesis is to address some of the core issues that directly affect the mesh client’s coverage, mesh routers connectivity and guarantee some QoS level.In this thesis, we investigate the placement problem of the wireless mesh routers. The deployment issue of WMNs has a significant impact on the network’s throughput and performance, cost, and capacity to satisfy the quality of service requirements. In the context of mesh router placement, the QoS is influenced by the location of mesh routers, the number of mesh clients served by each mesh router, and the load on each wireless router.
While finding an optimal solution to simultaneously satisfy all the above constraints is known to be an NP-hard problem, near-optimal solutions can be found within the feasibility region in polynomial time using various meta-heuristic
In the initial part of this thesis, we first present a near optimal meta-heuristics algorithm called Accelerated PSO for mesh routers placement that facilitates QoS provisioning in WMNs.
We then propose a new objective function to achieve optimal client coverage as well as to fine-tune the network connectivity for optimum performance with no need for knowledge of an aggregation coefficient.|
|Collection(s) :||Thèses de doctorat|
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