Measurement of Energy Costs of Security in Wireless Sensor Nodes

Chang, C. K.; Muftic, Sead; Nagel, D. J.

doi:10.1109/icccn.2007.4317803

Cited by 36 publications

(17 citation statements)

References 9 publications

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“…Chang et al [8], [9] use a setup similar to ours to measure the energy consumption introduced by hash functions and symmetric-key algorithms on Mica2 motes with a CC1000 radio and Ember sensors with an EM2420 radio. They use a PicoScope 3206 oscilloscope to sample the voltage drop across two registers and, based on the measured data, speculate about the energy consumption.…”

Section: Related Workmentioning

confidence: 99%

“…Most of the previous papers, except [8] and [9], have used simulation for their evaluations. We, however, believe that directly measuring the current through the motes and the latency caused by the security algorithms provides more realistic data.…”

Section: Related Workmentioning

confidence: 99%

“…However, the results they present reveal that despite the use of energy-efficient techniques, such as elliptic curve cryptography or dedicated cryptography coprocessors, asymmetric key algorithms consume much more energy than symmetric key algorithms. Hash functions, on the other hand, are typically used for verifying the integrity of the exchanged messages and may increase the transmission cost [24], [8]. Therefore, we have focused on symmetric-key algorithms, leaving the study of the other encryption algorithms for future work.…”

Section: Introductionmentioning

confidence: 99%

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The price of security in wireless sensor networks

2010

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The price of security in wireless sensor networks

2010

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“…We take CrossBow node and Ember node as the nodes for our discussion. They will, respectively, consume 154 J and 75 J to execute a SHA-1 function once [14]. If = 40 m, = 53, and NF = 241, then the nodes CrossBow and Ember will consume 37.1 mJ and 18.0 mJ to establish their pairwise keys, respectively.…”

Section: Energy Consumption Analysismentioning

confidence: 99%

A Key Distribution Scheme for WSN Based on Hash Chains and Deployment Knowledge

You

Yuan

et al. 2015

International Journal of Distributed Sensor Networks

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Based on the deployment knowledge and the irreversibility of some hash chains, a novel pairwise key distribution scheme (DKH-KD) for wireless sensor networks is proposed. In DKH-KD scheme, before the nodes in the network are deployed, the offline server constructs a number of hash chains and uses the values from a pair of reverse hash chains to establish their pairwise keys among the nodes in the same region, while, among the neighbor nodes in the different regions, some pairs of the hash chains based on the deployment knowledge are employed to establish the pairwise keys. These procedures make the attackers hard to break the network and ensure that the probability of the pairwise key establishment is close to 1. Compared with the Dai scheme and the q-composite's scheme, our analyses show that DKH-KD scheme can improve the probability of the pairwise key establishment and the invulnerability more efficiently.

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“…This data rate is to be differentiated from the MicaZ claimed data rate (of 250 kbps) and is far less (at 121 kbps), further reduced by the headers and footers appended to the data by the lower layers of the communication stack. For symmetric encryption, we use energy measurements for the hardware [45] 1.83 µJ SHA-1 Hash (64 bits) [46] 154 µJ ECDSA-160 Sign [47] 52 mJ Transmit 1 bit [47] 0.6 µJ AES implementation provided by the CC2420 radio on the MicaZ motes. This is a far cheaper and more convenient option than using a software implementation of a symmetric cipher.…”

Section: Energy Comparisonmentioning

confidence: 99%

Securing First-Hop Data Provenance for Bodyworn Devices Using Wireless Link Fingerprints

Ali

Sivaraman

Ostry

et al. 2014

IEEE Trans.Inform.Forensic Secur.

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Abstract-Wireless bodyworn sensing devices are fast becoming popular for fitness, sports training and personalized healthcare applications. Securing data generated by these devices is essential if they are to be integrated into the current health infrastructure and employed in medical applications. In this paper, we propose a mechanism to secure data provenance for these devices by exploiting spatio-temporal characteristics of the wireless channel that these devices use for communication. Our solution enables two parties to generate closely matching 'link fingerprints' which uniquely associate a data session with a wireless link such that a third party can later verify the details of the transaction, particularly the wireless link on which the data was transmitted. These fingerprints are very hard for an eavesdropper to forge, they are lightweight compared to traditional provenance mechanisms, and enable interesting security properties such as accountability, non-repudiation, and resist man-in-the-middle attacks. We validate our technique with experiments using bodyworn sensors in scenarios approximating actual device deployment, and present some extensions which reduce energy consumption. We believe this is a promising first step towards using wireless-link characteristics for data provenance in body area networks.

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Measurement of Energy Costs of Security in Wireless Sensor Nodes

Cited by 36 publications

References 9 publications

The price of security in wireless sensor networks

The price of security in wireless sensor networks

A Key Distribution Scheme for WSN Based on Hash Chains and Deployment Knowledge

Securing First-Hop Data Provenance for Bodyworn Devices Using Wireless Link Fingerprints

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