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

• Published in: Sensors (Basel, Switzerland) Vol. 22; no. 20; p. 8037
• Main Authors: Alsboui, Tariq, Hill, Richard, Al-Aqrabi, Hussain, Farid, Hafiz Muhammad Athar, Riaz, Muhammad, Iram, Shamaila, Shakeel, Hafiz Muhammad, Hussain, Muhammad
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.10.2022; MDPI
• Subjects: Algorithms; dynamic itinerary; Energy; Energy conservation; Fuzzy logic; Fuzzy sets; human–robot interaction; intuitionistic fuzzy set; itinerary planning; mobile agent; Planning; Radio communications; Robots; Sensors; Wireless sensor networks
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s22208037

In recent research developments, the application of mobile agents (MAs) has attracted extensive research in wireless sensor networks (WSNs) due to the unique benefits it offers, such as energy conservation, network bandwidth saving, and flexibility of open usage for various WSN applications. The majority of the proposed research ideas on dynamic itinerary planning agent-based algorithms are efficient when dealing with node failure as a result of energy depletion. However, they generate inefficient groups for MAs itineraries, which introduces a delay in broadcasting data return back to the sink node, and they do not consider the expanding size of the MAs during moving towards a sequence of related nodes. In order to rectify these research issues, we propose a new Graph-based Dynamic Multi-Mobile Agent Itinerary Planning approach (GDMIP). GDMIP works with “Directed Acyclic Graph” (DAG) techniques and distributes sensor nodes into various and efficient group-based shortest-identified routes, which cover all nodes in the network using intuitionistic fuzzy sets. MAs are restricted from moving in the predefined path and routes and are responsible for collecting data from the assigned groups. The experimental results of our proposed work show the effectiveness and expediency compared to the published approaches. Therefore, our proposed algorithm is more energy efficient and effective for task delay (time).