NETWORK OPTIMIZATION OF INTERNET OF THINGS USING PARTICLE SWARM OPTIMIZATION ALGORITHM / SAHID MUSTAPHA KAMARA; SUPERVISOR: ASST. PROF. DR. FATMA TANSU HOCANIN

Yazar: Katkıda bulunan(lar):Dil: İngilizce 2023Tanım: viii, 65 sheets: charts; 30 cm. 1 CD ROMİçerik türü:
  • text
Ortam türü:
  • unmediated
Taşıyıcı türü:
  • volume
Konu(lar): Tez notu: Thesis (MSc) - Cyprus International University. Institute of Graduate Studies and Research Computer Engineering Department Özet: ABSTRACT The Internet of Things (IoT) is a recently popular technology and network optimization is essential to the success of its network. IoT refers to a network of interconnected physical (and maybe human) elements that can communicate with one another via mobile devices and embedded sensors. IoT is widely used in industries like healthcare, robotics, artificial intelligence, smart buildings, smart automobiles, and many more. As a result, several intriguing, interconnected research topics have lately been put forth, primarily in the areas of security, big data analysis, wireless sensor networks, and sensor technologies. Particle Swarm Optimization (PSO) is an optimization algorithm that may be used to improve the functionality of IoT networks. This thesis explores the use of PSO in network optimization for IoT and evaluates its effectiveness in improving overall network performance. In this research, IoT-related optimization challenges are solved using the particle swarm optimization technique, a strategy that may efficiently optimize IoT networks' routing, energy use, and Quality of Services (QoS). Using the PSO-based IoT network optimization algorithm, the end-to-end latency is decreased, the packet delivery ratio is increased, and the energy consumption of the nodes is decreased by 40%, 25%, and 35%, respectively, compared to the Routing Protocol for Low-Power and Lossy network routing protocol (RPL) baseline approach according to the performance comparison results. In comparison to the baseline method, the PSO-based energy optimization algorithm extends the network lifetime and decreases node energy consumption by 40% and 50%, respectively. The PSO-based QoS optimization algorithm improves the throughput, reduces the delay and reduces the packet loss rate by 30%, 40%, and 20% respectively compared to the RPL baseline approach. The performance comparison findings show that, when compared to the RPL baseline technique, the PSO-based IoT network optimization algorithm may greatly raise and improve the performance of the IoT network. Keyword: IoT, Network Optimization, Performance Metric, PSO, RPL, Quality of Services.
Materyal türü: Thesis
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Materyal türü Geçerli Kütüphane Koleksiyon Yer Numarası Durum Notlar İade tarihi Barkod Materyal Ayırtmaları
Thesis Thesis CIU LIBRARY Depo Tez Koleksiyonu YL 3264 K36 2023 (Rafa gözat(Aşağıda açılır)) Kullanılabilir Computer Engineering Department T3658
Suppl. CD Suppl. CD CIU LIBRARY Görsel İşitsel Tez Koleksiyonu YL 3264 K36 2023 (Rafa gözat(Aşağıda açılır)) Kullanılabilir Computer Engineering Department CDT3658
Toplam ayırtılanlar: 0

Thesis (MSc) - Cyprus International University. Institute of Graduate Studies and Research Computer Engineering Department

Includes References (sheets 57-60)

ABSTRACT
The Internet of Things (IoT) is a recently popular technology and network optimization is essential to the success of its network. IoT refers to a network of interconnected physical (and maybe human) elements that can communicate with one
another via mobile devices and embedded sensors. IoT is widely used in industries like healthcare, robotics, artificial intelligence, smart buildings, smart automobiles, and many more. As a result, several intriguing, interconnected research topics have lately been put forth, primarily in the areas of security, big data analysis, wireless sensor networks, and sensor technologies. Particle Swarm Optimization (PSO) is an optimization algorithm that may be used to improve the functionality of IoT networks. This thesis explores the use of PSO in network optimization for IoT and evaluates its effectiveness in improving overall network performance. In this research, IoT-related optimization challenges are solved using the particle swarm optimization technique, a strategy that may efficiently optimize IoT networks' routing, energy use, and Quality of Services (QoS). Using the PSO-based IoT network optimization algorithm, the end-to-end latency is decreased, the packet delivery ratio is increased, and the energy consumption of the nodes is decreased by 40%, 25%, and 35%, respectively, compared to the Routing Protocol for Low-Power and Lossy network routing protocol (RPL) baseline approach according to the performance comparison results. In comparison to the baseline method, the PSO-based energy optimization algorithm extends the network lifetime and decreases node energy consumption by 40% and 50%, respectively. The PSO-based QoS optimization algorithm improves the throughput, reduces the delay and reduces the packet loss rate by 30%, 40%, and 20% respectively compared to the RPL baseline approach. The performance comparison findings show that, when compared to the RPL baseline technique, the PSO-based IoT network optimization algorithm may greatly raise and improve the performance of the IoT network.
Keyword: IoT, Network Optimization, Performance Metric, PSO, RPL, Quality of Services.

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