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Autor(en): 
  • Muhammad Ashraf
  • Muhammad Akram
  • Taeho Cho
  • GA Optimized Fuzzy Adaptive Data Routing in Wireless Sensor Networks: Energy Efficient Selection of Verification Nodes and Routes for Data Forwarding 
     

    (Buch)
    Dieser Artikel gilt, aufgrund seiner Grösse, beim Versand als 2 Artikel!


    Übersicht

    Auf mobile öffnen
     
    Lieferstatus:   i.d.R. innert 7-14 Tagen versandfertig
    Veröffentlichung:  Mai 2018  
    Genre:  Ratgeber 
     
    Digitale Musik# Anwenderinformation / Digitales Video# Anwenderinformation / E-Book-Reader, Tablets, Handheld-Geräte# Anwenderinformation / Mobiltelefone, Smartphones# Anwenderinformation
    ISBN:  9786139840038 
    EAN-Code: 
    9786139840038 
    Verlag:  LAP Lambert Academic Publishing 
    Einband:  Kartoniert  
    Sprache:  English  
    Dimensionen:  H 220 mm / B 150 mm / D 7 mm 
    Gewicht:  191 gr 
    Seiten:  116 
    Bewertung: Titel bewerten / Meinung schreiben
    Inhalt:
    Wireless sensor Networks (WSNs) are highly vulnerable to security attacks due to their reliance on wireless communication. Sensor nodes in WSNs are tiny in size and are supplied with limited energy resources and processing capabilities. These energy and hardware¿resource-constrained nodes, without suitable safeguards, may be compromised by adversaries. Several en-route filtering schemes have been proposed to filter out false data en-route. However, almost all such schemes presuppose a conventional underlying routing protocol without regard to the attack status and energy dissipation behavior along the path. This book presents fuzzy-based adaptive selection of the intermediate verification nodes and data forwarding paths with dynamic authentication key dissemination to achieve better energy saving. Fuzzy rule-based scheme potentially balances the load between data forwarding paths, chooses the most suitable path for data forwarding, and generates fresh authentication keys and shares with the intermediate verification nodes. Genetic algorithm-based optimization technique automatically optimizes the membership functions in the fuzzy system.

      



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