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https://ptsldigital.ukm.my/jspui/handle/123456789/476463Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.advisor | Rosilah Hassan, Assoc. Prof. Dr. | |
| dc.contributor.author | Ahmed Mahdi Jubair (P82850) | |
| dc.date.accessioned | 2023-10-06T09:18:56Z | - |
| dc.date.available | 2023-10-06T09:18:56Z | - |
| dc.date.issued | 2017-04-28 | |
| dc.identifier.other | ukmvital:97961 | |
| dc.identifier.uri | https://ptsldigital.ukm.my/jspui/handle/123456789/476463 | - |
| dc.description | The Internet of Things (IoT) has emerged as a leading technology for smart object communication. IoT is defined as physical objects networks that involves embedded technology which allows these objects to sense, communicate and interact with each other or with external devices to provide data for different purposes. IoT applications' wide range results in an increasing demand for different types of data communication to meet application requirements. Application requirements can include both categories of communication: reliable and unreliable. Congestion control is considered as a critical issue for reliable data communication so it is crucial to have a flexible and efficient congestion control to provide reliable IoT communication. Constrained Application Protocol (CoAP) provides a basic congestion control to enhance the performance of the network and minimize the number of packet loss. The previously proposed congestion control mechanism suffers from different shortages including exponential Back-Off Timer and increased retransmitting timeout (RTO). CoAP does not consider or collect any end-to-end (e2e) communication details to optimize the value of RTO. Therefore, it behaves in the same manner with any network regardless of network state, number of connecting or transmitting devices and congestion level. CoAP protocol requires a robust, adaptive and efficient congestion control mechanism to fit the changing environment of IoT wireless network and meet the requirement of the increasing congestion and data loss rates. In this thesis, an adaptive congestion mechanism is proposed. It mainly depends on the traffic priority class and the loss rate. Data is classified into two classes: High Priority (HP) and Low Priority (LP). This classification is important to implement quality of service (QoS) for CoAP. The proposed solution has three main stages: traffic priority assignment, adaptive RTO, and adaptive Back-Off Timer. RTO and Back-Off Timer are adaptively calculated based on priority classes. Adaptive CoAP congestion control allows HP data to overcome the congestion state faster than low LP data. Adaptive mechanism results in an enhanced data transfer for critical types of traffic such as medical or surveillance traffic. Adaptive Congestion mechanism is compared against basic congestion mechanism and CoAP Simple Congestion Control/Advanced (CoCoA). Proposed adaptive mechanism has enhanced the network throughput up to 32.6% compared to the basic congestion mechanism and 13% better than CoCoA. In addition, Packet Delivery Ratio (PDR) has been enhanced up to 27.3% compared to the basic congestion mechanism and 10% better than CoCoA.,Certification of Master's/Doctoral Thesis" is not available | |
| dc.language.iso | eng | |
| dc.publisher | UKM, Bangi | |
| dc.relation | Faculty of Information Science and Technology / Fakulti Teknologi dan Sains Maklumat | |
| dc.rights | UKM | |
| dc.subject | Internet of Things | |
| dc.subject | Network | |
| dc.subject | Protocol | |
| dc.subject | Data communication | |
| dc.subject | Universiti Kebangsaan Malaysia -- Dissertations | |
| dc.title | Enhancing CoAP performance using adaptive congestion control mechanism for IoT | |
| dc.type | theses | |
| dc.format.pages | 122 | |
| dc.identifier.barcode | 003045(2017) | |
| Appears in Collections: | Faculty of Information Science and Technology / Fakulti Teknologi dan Sains Maklumat | |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| ukmvital_97961+SOURCE1+SOURCE1.0.PDF Restricted Access | 589.99 kB | Adobe PDF |  View/Open |
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