An Advanced Algorithm for Higher Network Navigation in Social Internet of Things Using Small-World Networks

Area of Science:

• Computer Science
• Network Engineering
• Internet of Things

Background:

• The Internet of Things (IoT) connects numerous devices, leading to scalability issues in service discovery.
• The Social Internet of Things (SIoT) integrates social networking concepts to enhance IoT functionality.
• Exponential growth of heterogeneous devices exacerbates search difficulties within SIoT domains.

Purpose of the Study:

• To address the link selection problem in the Social Internet of Things (SIoT).
• To propose an algorithm that enhances object navigability and service discovery efficiency in SIoT.
• To improve the performance analysis of services within SIoT domains through rule imposition.

Main Methods:

• Developed a novel algorithm based on small-world network properties like clustering coefficients and path lengths.
• Implemented link selection strategies by restricting object connections and managing existing links.
• Conducted experiments using real-world network datasets from social networking platforms (Brightkite, Facebook).

Main Results:

• The proposed algorithm demonstrated efficiency in reducing path length within SIoT networks.
• A significant increase in the average clustering coefficient was observed, indicating improved network cohesion.
• The algorithm facilitates easier network navigation for both individual nodes and entire networks.

Conclusions:

• The developed algorithm effectively enhances object navigability in the Social Internet of Things.
• Optimized link selection in SIoT leads to improved network performance and scalability.
• The findings suggest a practical approach for managing complex, large-scale SIoT environments.