Date of Award


Document Type


Degree Name

Master of Science (MS)


Computer Science

First Advisor

Joseph Near

Second Advisor

Tian Xia


The Internet of Things (IoT) connects a vast number of smart objects for various applications,such as home automation, industrial control, and healthcare. The rapid advancement in wireless technologies and miniature embedded devices has enabled IoT systems to be deployed in various environments. However, the performance of IoT devices is limited because of the imbalance of data traffic on different router nodes. Nodes that experience high data volume will have a higher energy depletion rate and, as a result, will reach the end of their life quicker than other routers that have less data traffic. Genetic Algorithms are a well-known technique used to solve routing problems, but it is essential to pay more attention to designing data routing protocols that take into account a router’s data traffic load and position in the overall network topology. The objectives of this thesis are two-fold. First, we propose a GA-based routing protocol for hierarchical multi-hop IoT networks that identifies heavily congested routers and ranks them based on their potential load. Second, we present a centralized approach to determine optimal routing paths for IoT networks by utilizing a priori knowledge of the network topology. Additionally, we conduct a comparative analysis of the existing GA-based multi-hop routing protocols using simulation data. Our research has revealed that distributing the data load evenly on nodes can noticeably enhance the network lifetime in comparison to other routing protocols. Our extensive simulations have demonstrated that the routing approach that we have proposed, based on the Genetic Algorithm (GA), can significantly reduce energy consumption and improve network reliability.



Number of Pages

36 p.