RDSC: Range-Based Device Spatial Clustering for IoT Networks.

Area of Science:

• Computer Science
• Electrical Engineering
• Network Engineering

Background:

• The proliferation of Internet of Things (IoT) devices presents significant challenges in data management, privacy, and network stability.
• Limited device capacity and power consumption are critical constraints affecting IoT network performance and scalability.
• Existing research often focuses on high-power devices, overlooking efficient management of resource-constrained IoT nodes.

Purpose of the Study:

• To propose a novel device spatial clustering technique for efficient Internet of Things (IoT) device management.
• To address key challenges including device capacity, power consumption, and network topology complexity.
• To improve IoT network lifetime and data processing through intelligent device grouping.

Main Methods:

• Developed a new device spatial clustering technique that groups dispersed IoT devices into clusters.
• Introduced a novel clustering protocol and algorithm considering device coverage, storage, and power.
• Implemented a technique for non-sensed area extraction to optimize network resource utilization.

Main Results:

• The proposed spatial clustering technique effectively addresses critical IoT management challenges.
• Extensive experiments demonstrated notable improvements in network performance and device management.
• Comparative analysis showed the proposed technique's advantages over existing clustering algorithms.

Conclusions:

• The spatial clustering technique offers a viable solution for managing resource-constrained IoT devices.
• The approach enhances network efficiency, stability, and extends the operational lifetime of IoT networks.
• This work provides a foundation for more robust and scalable IoT systems.