الفهرس 
IntroductionOnly 14 pages are availabe for public view
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Abstract
The Internet of Things (IoT) is one of the trendiest technologies that has been applied in many fields such as medicine, agriculture, education, smart homes, and smart cities. This technology allows different types of objects such as devices and persons to communicate using the network to exchange data collected by these objects for analyzing the data and decision-making. Many challenges have been faced while producing IoT systems such as security, link heterogeneity, transmission media, addressing schemes, big data, massive scaling, quality of service, energy consumption, and many other challenges. Energy consumption is one of the most challenging tasks in IoT systems because it has a high impact on the performance of the IoT systems. Therefore, finding solutions to conserve the energy is a must to improve the performance of IoT networks.
The routing process is the operation of exchanging the collected data between the objects until reaching their destination. This process can be done using a suitable routing algorithm, which is selected based on the network structure, the protocol operations, the maintenance routing information, or the network condition. Due to the communication process of data transmission causing the consumption of a large amount of energy, the researchers are trying to reduce the amount of energy consumed by finding energy efficient routing algorithms to maintain the performance of the IoT network and extend its lifetime as much as possible. Finding the optimal paths using a routing algorithm is considered an NP-hard problem. Therefore, optimization algorithms, such as bio-inspired algorithms, can be exploited to propose optimized routing algorithms that transmit the data without wasting a high amount of energy.
This thesis proposes three energy efficient cluster-based routing algorithms that exploit some of bio-inspired algorithms. The first algorithm is a hybrid of Genetic Algorithm (GA) and Ant Colony Optimization (ACO) (GA-ACO) algorithms that select the optimal cluster head and construct paths from cluster heads to the base station, respectively. The proposed GA-ACO algorithm outperforms an enhanced version of the Low Energy Adaptive Clustering Hierarchy (E-LEACH) and a hybrid of GA-based clustering and Particle Swarm Optimization (PSO)-based routing algorithm (GA-PSO). This second proposed algorithm is named the first version of the proposed Tunicate
IV
Swarm-based clustering and routing Algorithm (TSA-I). It presents an acceptable performance against other algorithms such as the Grey Wolf Optimization (GWO)-based routing algorithm, the PSO-based routing algorithm, and the ACO-based routing algorithm. However, the proposed algorithm did not perform well in terms of the time of the First Dead Node (FDN), and the number of packets sent from the cluster members to the cluster head. Therefore, the third proposed algorithm is a second version of the proposed Tunicate Swarm-based clustering and routing Algorithm (TSA-II) to overcome the drawbacks of the proposed TSA-I algorithm. The performance of the proposed algorithm is evaluated against the E-LEACH algorithm, the GA-PSO algorithm, and an Improved GWO (IGWO) algorithm. The proposed TSA-II algorithm proposes an outstanding performance against the other algorithms