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Multi-factor optimized mobile sink data collection framework for hybrid WSN-LTE assisted IoT network.

Saranga Mohan1, Sunita Panda1

  • 1Department of Electrical, Electronics, and Communication Engineering, GITAM School of Technology, GITAM (Deemed to Be University), Bengaluru, India.

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Summary

This study enhances wireless sensor network (WSN) and Internet of Things (IoT) systems by optimizing energy usage for longer device lifespans. The new hybrid approach improves network performance and data delivery efficiency.

Keywords:
Clustering optimizationGraph theoryInternet of thingsMobile sinkMulti-criteria quality of serviceWireless sensor networks

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Network Engineering

Background:

  • Wireless Sensor Networks (WSNs) are crucial for Internet of Things (IoT) applications.
  • Battery-powered sensors necessitate energy conservation for extended operational duration.
  • Mobile sinks are employed to prolong network lifespan, but scalability and multi-criteria optimization remain challenging.

Purpose of the Study:

  • To address the challenge of maximizing network lifetime and Quality of Service (QoS) in mobile sink-assisted WSNs.
  • To propose a hybrid architecture integrating WSNs with Long-Term Evolution (LTE).
  • To develop a two-stage optimization strategy for clustering and data collection path scheduling.

Main Methods:

  • A hybrid meta-heuristics approach is utilized for the multi-criteria optimal clustering problem.
  • Minimal Steiner tree-based graph theory is applied for efficient data collection path scheduling.
  • Network adaptive traffic class-based data scheduling is integrated for enhanced QoS.

Main Results:

  • The proposed hybrid approach achieves lifetime maximization without compromising QoS.
  • Simulations show a noteworthy increase in network lifetime (at least 6%).
  • Packet delivery delay is reduced by 26% compared to existing methods.

Conclusions:

  • The hybridized strategy effectively extends WSN lifetime and enhances QoS.
  • The integration of multi-criteria clustering, optimal path scheduling, and adaptive data scheduling is key.
  • The proposed architecture offers a scalable and effective solution for mobile sink-assisted WSNs.