A Dynamic Multi-Mobile Agent Itinerary Planning Approach in Wireless Sensor Networks via Intuitionistic Fuzzy Set

Area of Science:

• Computer Science
• Network Engineering
• Artificial Intelligence

Background:

• Mobile agents (MAs) offer energy conservation and bandwidth savings in wireless sensor networks (WSNs).
• Existing dynamic itinerary planning algorithms efficiently handle node failures but create inefficient MA groupings, causing data transmission delays and not accounting for MA size expansion.
• These limitations hinder overall network performance and data retrieval efficiency.

Purpose of the Study:

• To propose a novel Graph-based Dynamic Multi-Mobile Agent Itinerary Planning (GDMIP) approach for WSNs.
• To address the inefficiencies in MA grouping and data transmission delays caused by existing algorithms.
• To improve energy efficiency and reduce task completion time in WSNs.

Main Methods:

• The GDMIP approach utilizes Directed Acyclic Graph (DAG) techniques.
• It employs intuitionistic fuzzy sets to distribute sensor nodes into efficient, group-based shortest routes.
• Mobile agents are assigned to specific groups for data collection, operating within predefined routes.

Main Results:

• Experimental results demonstrate the effectiveness and expediency of GDMIP compared to existing approaches.
• The proposed GDMIP algorithm shows significant improvements in energy efficiency.
• GDMIP effectively reduces task delay (time) for data collection in WSNs.

Conclusions:

• GDMIP offers a more energy-efficient and time-effective solution for mobile agent-based data collection in WSNs.
• The approach successfully optimizes MA itineraries and mitigates issues related to node failure and MA size.
• GDMIP represents a significant advancement in dynamic itinerary planning for wireless sensor networks.