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Secure and Autonomic Framework for SCHC Context Management in LoRaWAN-IPv6 Networks

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Secure and Autonomic Framework for SCHC Context Management in LoRaWAN-IPv6 Networks

Hatami, Maryam (2024) Secure and Autonomic Framework for SCHC Context Management in LoRaWAN-IPv6 Networks. Masters thesis, Concordia University.

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Abstract

The rapid growth of IoT technologies demands seamless integration of communication protocols, es- pecially for devices in constrained environments. This thesis focuses on integrating LoRaWAN with IPv6 networks by developing a secure and autonomous framework for managing SCHC (Static Context Header Compression) contexts. The research aims to create a secure, human-free solution that maintains reasonable data transmission times between LoRaWAN and IPv6, essential for widespread IoT adoption. For instance, in smart agriculture, dynamic SCHC updates support equipment mobility; in industrial automation, SCHC ensures consistent data transmission amid changing topologies; in smart cities, it adapts to evolving infras- tructure; and in healthcare, it manages reliable health data transmission.
The thesis introduces the concept of SCHC Zero Context, enabling immediate device communication by embedding pre-configured SCHC rules during manufacturing. This innovation facilitates an efficient bootstrapping process. Additionally, a comparative analysis of BRSKI (Bootstrapping Remote Secure Key Infrastructure) and cBRSKI (constrained BRSKI) demonstrates the feasibility of the solution for energy- constrained IoT devices.
The research underscores the importance of integrating security across IoT architecture layers. By com- bining LoRaWAN’s data link layer security with IPv6’s global internet, the proposed framework offers robust protection against common threats, ensuring secure onboarding and communication throughout a de- vice’s life-cycle.
This work provides a scalable, secure solution for integrating LoRaWAN with IPv6, laying the ground- work for future research in secure and efficient dynamic SCHC context management.

Divisions:Concordia University > Gina Cody School of Engineering and Computer Science > Computer Science and Software Engineering
Item Type:Thesis (Masters)
Authors:Hatami, Maryam
Institution:Concordia University
Degree Name:M. Comp. Sc.
Program:Computer Science
Date:August 2024
Thesis Supervisor(s):Cespedes, Sandra and Atwood, John
ID Code:994500
Deposited By: Maryam Hatami
Deposited On:24 Oct 2024 16:18
Last Modified:24 Oct 2024 16:18
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