Dynamic traffic engineering for high-throughput data delivery in wireless mesh networks

Abstract

Wireless Mesh Network (WMN) has recently been emerged as a promising technology for wireless Internet infrastructure development because of its low cost, ease of deployment and installation facilities. The increasing number of users and diversi ed application usages as well as the incorporation of sensors and Internet of Things (IoT) devices with the WMNs have caused exponential growth in traffic ows. This increased volume of traffic causes congestion in the network and degrades application throughput, reliability and delay performances. Therefore, providing satisfactory network performance using the limited bandwidth resources, has emerged as a challenging problem. Our endeavour in this dissertation is to address high-throughput data delivery chal- lenges in WMNs. Many state-of-the-art works address ow performance improvements in WMNs in many ways, ranging from routing, scheduling, channel allocation to rate control. However, none of these approaches merely addresses the instantaneous network conditions and sudden surge of huge data traffic from diverse user applications, that cause network to become congested. To optimize network performance, a dynamic traffic engi- neering mechanism requires to consider underlying network topology, available resources and traffic demand. Furthermore, traffic forwarding should act upon network dynamics, e.g., link error, link failure, neighborhood interference, path congestion, etc. Considering the aforementioned issues, in this thesis, we rst develop an optimization framework for Dynamic Traffic Engineering, namely O-DTE, assuming that xed channels are allocated to different links. O-DTE aims to minimize neighborhood interference and backlogged traffic, and explores the least congested next-hop nodes so that the overall throughput of the network is maximized. The O-DTE belongs to mixed integer nonlinear programming (MINLP) problem and involves both combinatorial and continuous constraints, making it an NP-hard problem. A greedy heuristic alternate solution G-DTE is then developed that produces near-optimal results. Motivated by the enhanced capacity offered by dynamic channel allocation in WMNs, the second part of our thesis focus on developing a joint link-channel selection and power allocation optimization framework (OLCP), which follows hop-by-hop traffic splitting approach and exploits single-hop information to forward traffic over least-congested and minimally-interfered link-channel pairs, which in turn improves spatial reuse and thus helps to improve overall network throughput. As nding a real-time solution of OLCP is intractable in a typical mesh router, we develop a greedy heuristic solution for the problem, GLCP, to achieve a sub-optimal solution. Recently, cognitive radio (CR) enabled mesh routers have proven to mitigate spec- trum scarcity by opportunistic licensed spectrum utilization. Thus, to boost up flow throughput in Cognitive Radio Wireless Mesh Network (CR-WMNs), we present a cen- tralized optimization framework, called COTE, in the third part of this dissertation. The COTE aims at maximizing aggregated network throughput by selecting an optimal set of link-channel pairs, power allocation over those and fair traffic splitting after considering channel idle probability, link interference and path congestion. Further, a centralized Suboptimal Traffic Engineering (SOTE) solution is proposed by employing Lagrangian dual decomposition to the COTE problem, to ensure a resolution in polynomial time. Finally, a Distributed Greedy Traffic Engineering (DGTE) method is proposed to ensure fast convergence to the dynamic changing network behavior and to improve scalability. The effectiveness of our proposed dynamic traffic engineering methods are evaluated via ns-3 simulations. The simulation results demonstrate that the proposed solutions outperform the state-of-the-art works in terms of throughput, delay, reliability, fairness and convergence cost.