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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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Socially Aware Heterogeneous Wireless Networks.

Pavlos Kosmides1, Evgenia Adamopoulou2, Konstantinos Demestichas3

  • 1School of Electrical and Computer Engineering, National Technical University of Athens, Athens 15773, Greece. pkosmidis@cn.ntua.gr.

Sensors (Basel, Switzerland)
|June 26, 2015
PubMed
Summary
This summary is machine-generated.

This study proposes a smart city architecture integrating wireless and social networks using software-defined networking (SDN). Machine learning predicts crowded areas, enabling optimized mobile network resource allocation for enhanced urban connectivity.

Keywords:
heterogeneous wireless networkslearning algorithmsmobile operator recommendationssocial networkssoftware defined networkssoftware-based controllers

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Urban Planning

Background:

  • Smart city development necessitates robust mobility and high-quality wireless communication.
  • Interoperability challenges exist among heterogeneous wireless and sensor networks in smart cities.
  • Software-defined networking (SDN) is a proposed solution for enhancing network interoperability.

Purpose of the Study:

  • To propose an architecture combining heterogeneous wireless networks with social networks using SDNs.
  • To leverage location-based social network data for predicting crowded areas in smart cities.
  • To provide mobile operators with data-driven recommendations for resource allocation.

Main Methods:

  • Integration of heterogeneous wireless networks with social networks via SDNs.
  • Utilization of location-based social network data for user location information.
  • Application of machine learning techniques for predicting crowded urban areas.

Main Results:

  • A novel architecture for smart city network management is presented.
  • Machine learning models effectively predict potential crowd hotspots.
  • The system enables informed decisions for datacell activation/deactivation.

Conclusions:

  • The proposed SDN-based architecture enhances interoperability and resource management in smart cities.
  • Predictive analytics of social network data improve wireless network efficiency.
  • This approach supports dynamic resource allocation for mobile operators, optimizing network performance.