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Evolutionary Clustering for Integrated WSN-RFID Networks

Published: 09 May 2016 Publication History

Abstract

Emerging practical deployments for the Internet of Things (IoT) trigger a need to integrate and inter-operate a variety of heterogenous networks to realize real business values. Several applications require the integration of wireless sensor networks (WSN), WiFi, and Radio Frequency Identifiers (RFID) into one single network to fulfil business requirements. As most of such deployments are characterized as being large-scale and heterogeneous, special algorithms and techniques are needed in order to deal with data collection, processing, and transmission in such networks. Results reported in the literature confirm that clustering techniques can be very efficient in dealing with routing in large-scale networks. However; due to the heterogeneity of IoT networks, the use of conventional clustering techniques may not result in an efficient clustering. Accordingly, in this paper, we attempt to address this problem by studying the use of evolutionary clustering algorithms in integrated WSN-RFID networks. In particular, the performance of two evolutionary algorithms; namely the Genetic Algorithms (GA) and the Harmony Search (HS), is analyzed and compared. It is shown that, the GA outperforms the HS significantly in the cluster formation process for integrated WSN-RFID networks.

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  • (2024)A Pragmatic Review of QoS Optimisations in IoT Driven NetworksWireless Personal Communications10.1007/s11277-024-11412-9137:1(325-366)Online publication date: 8-Jul-2024
  • (2022)A Review of Network Optimization on the Internet of ThingsInnovations in Computer Science and Engineering10.1007/978-981-16-8987-1_6(49-57)Online publication date: 26-Mar-2022
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cover image ACM Other conferences
INFOS '16: Proceedings of the 10th International Conference on Informatics and Systems
May 2016
347 pages
ISBN:9781450340625
DOI:10.1145/2908446
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 09 May 2016

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Author Tags

  1. Evolutionary Algorithm
  2. Genetic Algorithms
  3. Harmony Search Algorithm
  4. IoT
  5. RFID
  6. WSN

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Cited By

View all
  • (2024)Optimization of network topology robustness in IoTs: A systematic reviewComputer Networks10.1016/j.comnet.2024.110568250(110568)Online publication date: Aug-2024
  • (2024)A Pragmatic Review of QoS Optimisations in IoT Driven NetworksWireless Personal Communications10.1007/s11277-024-11412-9137:1(325-366)Online publication date: 8-Jul-2024
  • (2022)A Review of Network Optimization on the Internet of ThingsInnovations in Computer Science and Engineering10.1007/978-981-16-8987-1_6(49-57)Online publication date: 26-Mar-2022
  • (2021)To Study and Analyze State-of-the-Art Network Optimization Techniques in an IoT Ecosystem2021 3rd International Conference on Advances in Computing, Communication Control and Networking (ICAC3N)10.1109/ICAC3N53548.2021.9725612(1400-1406)Online publication date: 17-Dec-2021
  • (2020)Fruit Fly Optimization Algorithm for Intelligent IoT ApplicationsFog, Edge, and Pervasive Computing in Intelligent IoT Driven Applications10.1002/9781119670087.ch16(287-309)Online publication date: 7-Dec-2020
  • (2018)Network optimizations in the Internet of Things: A reviewEngineering Science and Technology, an International Journal10.1016/j.jestch.2018.09.003Online publication date: Sep-2018
  • (2017)Internet of Things: A Scientometric ReviewSymmetry10.3390/sym91203019:12(301)Online publication date: 6-Dec-2017

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