Date of this Version
S. El Alaoui, (2020). "Routing Optimization in Heterogeneous Wireless Networks for Space and Mission-Driven Internet of Things (IoT) Environments." Doctoral dissertation, University of Nebraska-Lincoln, Lincoln, U.S.
As technological advances have made it possible to build cheap devices with more processing power and storage, and that are capable of continuously generating large amounts of data, the network has to undergo significant changes as well. The rising number of vendors and variety in platforms and wireless communication technologies have introduced heterogeneity to networks compromising the efficiency of existing routing algorithms. Furthermore, most of the existing solutions assume and require connection to the backbone network and involve changes to the infrastructures, which are not always possible -- a 2018 report by the Federal Communications Commission shows that over 31% of the population living in rural areas has little to no broadband coverage.
In this dissertation, we study routing optimization in heterogeneous wireless networks in order to fill this gap in research and properly address the challenges they pose. We first propose a novel mathematical classification based on their contacts (i.e. communication windows between two network entities) in order to aid routing. We define four types of contacts: predicted, scheduled, discovered and continuous. Next, we investigate single-attribute and multi-attribute message scheduling and routing in scheduled contacts using Space Networks as a case study and proposed N-Look Ahead Routing and scheduling Algorithm (N-LARS) and the Multi-Attribute Routing Algorithm (MARS). We then study all four contact types and develop a statistical analysis framework (STAN), and predictive routing algorithm, PETRA. We evaluated our work on a disaster recovery Mission-Driven IoT (MD-IoT) network. Finally, we consider predicted and continuous contacts by designing and formulating the low-latency routing problem as QIP and ILP models and by developing an edge computing framework, ERGO, which we applied on Agricultural-IoT networks.
Through this dissertation on routing in heterogeneous wireless networks for space and Mission-Driven IoT, we show that precise modeling of network heterogeneity properties enables us to enhance network performance in terms of various metrics. By using different tools including machine learning, edge computing, statistical analysis, MADM and age of information, we demonstrate that heterogeneity and the lack of network infrastructure can be overcome paving the way for heterogeneous wireless networks that are highly efficient and dynamic.
Adviser: Byrav Ramamurthy