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Logically-Centralized SDN-Based NDN Strategies for Wireless Mesh Smart-City Networks

Author

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  • Sarantis Kalafatidis

    (Department of Applied Informatics, University of Macedonia, 54636 Thessaloniki, Greece)

  • Sotiris Skaperas

    (Department of Applied Informatics, University of Macedonia, 54636 Thessaloniki, Greece)

  • Vassilis Demiroglou

    (Department of Electrical and Computer Engineering, Democritus University of Thrace, 67100 Xanthi, Greece)

  • Lefteris Mamatas

    (Department of Applied Informatics, University of Macedonia, 54636 Thessaloniki, Greece)

  • Vassilis Tsaoussidis

    (Department of Electrical and Computer Engineering, Democritus University of Thrace, 67100 Xanthi, Greece)

Abstract

The Internet of Things (IoT) is a key technology for smart community networks, such as smart-city environments, and its evolution calls for stringent performance requirements (e.g., low delay) to support efficient communication among a wide range of objects, including people, sensors, vehicles, etc. At the same time, these ecosystems usually adopt wireless mesh technology to extend their communication range in large-scale IoT deployments. However, due to the high range of coverage, the smart-city WMNs may face different network challenges according to the network characteristic, for example, (i) areas that include a significant number of wireless nodes or (ii) areas with frequent dynamic changes such as link failures due to unstable topologies. Named-Data Networking (NDN) can enhance WMNs to meet such IoT requirements, thanks to the content naming scheme and in-network caching, but it necessitates adaptability to the challenging conditions of WMNs. In this work, we aim at efficient end-to-end NDN communication in terms of performance (i.e., delay), performing extended experimentation over a real WMN, evaluating and discussing the benefits provided by two SDN-based NDN strategies: (1) a dynamic SDN-based solution that integrates the NDN operation with the routing decisions of a WMN routing protocol; (2) a static one which based on SDN-based clustering and real WMN performance measurements. Our key contributions include (i) the implementation of two types of NDN path selection strategies; (ii) experimentation and data collection over the w-iLab.t Fed4FIRE+ testbed with real WMN conditions; (ii) real measurements released as open-data, related to the performance of the wireless links in terms of RSSI, delay, and packet loss among the wireless nodes of the corresponding testbed.

Suggested Citation

  • Sarantis Kalafatidis & Sotiris Skaperas & Vassilis Demiroglou & Lefteris Mamatas & Vassilis Tsaoussidis, 2022. "Logically-Centralized SDN-Based NDN Strategies for Wireless Mesh Smart-City Networks," Future Internet, MDPI, vol. 15(1), pages 1-21, December.
    • Handle: RePEc:gam:jftint:v:15:y:2022:i:1:p:19-:d:1018613
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    References listed on IDEAS

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    1. Taher M. Ghazal & Mohammad Kamrul Hasan & Muhammad Turki Alshurideh & Haitham M. Alzoubi & Munir Ahmad & Syed Shehryar Akbar & Barween Al Kurdi & Iman A. Akour, 2021. "IoT for Smart Cities: Machine Learning Approaches in Smart Healthcare—A Review," Future Internet, MDPI, vol. 13(8), pages 1-19, August.
    2. Zeinab Shariat & Ali Movaghar & Mehdi Hoseinzadeh, 2017. "A learning automata and clustering-based routing protocol for named data networking," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 65(1), pages 9-29, May.
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    1. Shaopei Gao & Qiang Liu & Junjie Zeng & Li Li, 2024. "SD-GPSR: A Software-Defined Greedy Perimeter Stateless Routing Method Based on Geographic Location Information," Future Internet, MDPI, vol. 16(7), pages 1-17, July.

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