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A Spawn Mobile Agent Itinerary Planning Approach for Energy-Efficient Data Gathering in Wireless Sensor Networks.

Huthiafa Q Qadori1, Zuriati A Zulkarnain2, Zurina Mohd Hanapi3

  • 1Department of Wireless and Communication Technology, Faculty of Computer Science and Information Technolog, University Putra Malaysia, Serdang 43400, Malaysia. huthiafaqadori@gmail.com.

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A new Spawn Multi-mobile agent Itinerary Planning (SMIP) approach enhances energy efficiency and reduces data gathering time in Wireless Sensor Networks (WSNs). This method optimizes mobile agent (MA) task distribution, outperforming existing multi-mobile agent (MIP) strategies.

Keywords:
data gatheringitinerary planningmobile agentspawn mobile agentwireless sensor network

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Area of Science:

  • Mobile Computing
  • Wireless Sensor Networks (WSNs)
  • Distributed Systems

Background:

  • Mobile agents (MAs) are used for energy-efficient data gathering in Wireless Sensor Networks (WSNs).
  • Existing Multi-mobile agent Itinerary Planning (MIP) algorithms face challenges in optimizing the number and itineraries of distributed MAs.
  • Current MIP approaches often increase energy and time consumption due to MA migration hops and code-carrying requirements.

Purpose of the Study:

  • To propose a novel Spawn Multi-mobile agent Itinerary Planning (SMIP) approach for energy-efficient data gathering in WSNs.
  • To mitigate the high energy and time costs associated with existing mobile agent itinerary planning methods.
  • To improve the integrated energy-delay performance in WSN data collection.

Main Methods:

  • Developed a Spawn Multi-mobile agent Itinerary Planning (SMIP) approach where a main MA spawns subordinate MAs with distinct tasks.
  • Implemented a task delegation strategy allowing MAs to spawn other MAs for distributed data gathering.
  • Conducted extensive simulation experiments to compare SMIP against selected MIP algorithms.

Main Results:

  • The proposed SMIP approach demonstrated superior performance compared to existing MIP algorithms.
  • SMIP significantly reduced energy consumption during the data gathering process.
  • Task delay (time) was substantially decreased, leading to improved overall energy-delay performance.

Conclusions:

  • The SMIP approach effectively addresses the limitations of current MIP algorithms in WSNs.
  • Agent spawning is a viable strategy for optimizing energy and time efficiency in WSN data gathering.
  • SMIP offers a promising solution for enhancing the performance of mobile computing in WSN environments.