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Energy-Efficient Implementation of ECDH Key Exchange for Wireless Sensor Networks

  • Christian Lederer
  • Roland Mader
  • Manuel Koschuch
  • Johann Großschädl
  • Alexander Szekely
  • Stefan Tillich
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5746)

Abstract

Wireless Sensor Networks (WSNs) are playing a vital role in an ever-growing number of applications ranging from environmental surveillance over medical monitoring to home automation. Since WSNs are often deployed in unattended or even hostile environments, they can be subject to various malicious attacks, including the manipulation and capture of nodes. The establishment of a shared secret key between two or more individual nodes is one of the most important security services needed to guarantee the proper functioning of a sensor network. Despite some recent advances in this field, the efficient implementation of cryptographic key establishment for WSNs remains a challenge due to the resource constraints of small sensor nodes such as the MICAz mote. In this paper we present a lightweight implementation of the elliptic curve Diffie-Hellman (ECDH) key exchange for ZigBee-compliant sensor nodes equipped with an ATmega128 processor running the TinyOS operating system. Our implementation uses a 192-bit prime field specified by the NIST as underlying algebraic structure and requires only 5.20 ·106 clock cycles to compute a scalar multiplication if the base point is fixed and known a priori. A scalar multiplication using a random base point takes about 12.33 ·106 cycles. Our results show that a full ECDH key exchange between two MICAz motes consumes an energy of 57.33 mJ (including radio communication), which is significantly better than most previously reported ECDH implementations on comparable platforms.

Keywords

Sensor Node Wireless Sensor Network Elliptic Curve Elliptic Curve Cryptography Elliptic Curve Discrete Logarithm Problem 
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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Copyright information

© IFIP International Federation for Information Processing 2009

Authors and Affiliations

  • Christian Lederer
    • 1
  • Roland Mader
    • 2
    • 3
  • Manuel Koschuch
    • 4
  • Johann Großschädl
    • 5
  • Alexander Szekely
    • 6
  • Stefan Tillich
    • 6
  1. 1.University of KlagenfurtAustria
  2. 2.ITIGraz University of TechnologyAustria
  3. 3.AVL List GmbHAustria
  4. 4.FH Campus Wien – University of Applied SciencesAustria
  5. 5.University of BristolUnited Kingdom
  6. 6.IAIKGraz University of TechnologyAustria

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