Traffic-driven ions motion optimization-based clustering routing protocol for cognitive radio sensor networks

Area of Science:

• Computer Science
• Wireless Communication Networks
• Network Protocols

Background:

• Existing clustering protocols in cognitive radio sensor networks (CRSNs) struggle to simultaneously manage time-triggered and event-driven traffic.
• Separate protocols for each traffic type lead to inefficiencies, and event-driven traffic suffers from significant delays due to clustering and route searching post-event.

Purpose of the Study:

• To propose a unified clustering routing protocol that efficiently handles both time-triggered and event-driven traffic in CRSNs.
• To address the timeliness and energy consumption issues associated with event-driven data transmission.

Main Methods:

• Introduced the traffic-driven ions motion optimization-based clustering routing protocol (TD-IMOCRP).
• Utilized an ions motion optimization algorithm to determine optimal cluster numbers and structure for periodic time-triggered traffic.
• Implemented a priority-based schedule and frame structure for event-driven information delivery.
• Leveraged existing cluster architecture for emergent events, minimizing post-event clustering and route selection.

Main Results:

• TD-IMOCRP successfully serves both time-triggered and event-driven traffic within a single protocol.
• Compared to existing event-driven protocols, TD-IMOCRP reduced the average number of covered nodes by over 66.3% and total energy consumption by 25%.
• Performance for time-triggered traffic remains comparable to the base IMOCRP, outperforming most current time-triggered protocols.

Conclusions:

• TD-IMOCRP effectively guarantees timely delivery of event-driven information in CRSNs.
• The protocol maintains high performance for time-triggered traffic while significantly improving efficiency for event-driven data.
• This unified approach offers a promising solution for optimizing CRSN performance under diverse traffic demands.