An Authenticated Key Management Scheme for Hierarchical Wireless Sensor Networks

  • Firdous Kausar
  • Ashraf Masood
  • Sajid Hussain
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 4)

Due to recent advances in electronic industry, wireless sensors can be used in various ubiqutous and pervasive applications such as military, security, healthcare [1,2], industry automation, and environmental and habitat monitoring [3,4].Wireless sensor networks (WSNs) consist of a large number of low-power nodes, with limited processing, communication, and storage resources [5]. Due to limited resources of WSNs, it is challenging to incorporate basic security functions such as authentication, access control, data integrity, privacy, and key distribution. For instance, asymmetric cryptography such as RSA or elliptic curve cryptography (ECC) is unsuitable for most sensor architectures due to high energy consumption and increased code storage requirements. To avoid the use of asymmetric cryptography, several alternative approaches have been developed to perform key management on resourceconstrained sensor networks, such as random key predistribution schemes, plain text key exchange schemes, and transitory master key schemes

In WSNs, hierarchical clustering provides scalability, self-organization, and energy efficient data dissemination [6]. A number of cluster formation protocols have been proposed, but most existing protocols assume benign environments, and are vulnerable to attacks from malicious nodes. In this chapter, we use an existing ring structure, energy-efficient, clustering architecture (RECA) [7] to divide nodes into clusters. However, we modify its initial cluster formation algorithm by providing an authentication mechanism so that no malicious node can take part in the cluster formation process. The proposed authenticated key management (AKM) scheme enables only legitimate nodes to join the network.


Sensor Network Sensor Node Wireless Sensor Network Cluster Head Malicious Node 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  • Firdous Kausar
    • 1
  • Ashraf Masood
    • 1
  • Sajid Hussain
    • 2
  1. 1.College of SignalsNational University of Science and TechnologyPakistan
  2. 2.Jodrey School of Computer ScienceAcadia UniversityCanada

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