Energy-Efficient Data Route-in-Network Aggregation with Secure EEDRINA

Sujatha, B.; Jilo, Chala Tura; Rao, Chinta Someswara

doi:10.1007/978-981-10-6319-0_1

Cited by 2 publications

(2 citation statements)

References 14 publications

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“…From Figure 8, we can observe that the accuracy of QPPDA improves as the times of query increase. Two reasons contribute to this which have already been analyzed in [8]: (i) with a longer time interval, the data messages to be sent within this duration will have less chance to collide; (ii) with a longer time interval, the data messages will have a better chance of being delivered before the deadline.…”

Section: Aggregation Accuracymentioning

confidence: 99%

“…It is important that traffic and computation overhead should be kept as low as possible to extend the lifetime of WSNs. e Data Aggregation (DA) [7][8][9][10][11][12][13] technique is one of the most effective ways for the network to save energy and improve efficiency. It can reduce the quantity of information transmission through aggregating the data from different nodes, decreasing redundancy, and achieving the goal of prolonging the lifetime of the network.…”

Section: Introductionmentioning

confidence: 99%

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Query Privacy Preserving for Data Aggregation in Wireless Sensor Networks

Liu

Zhang

et al. 2020

Wireless Communications and Mobile Computing

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Wireless Sensor Networks (WSNs) are increasingly involved in many applications. However, communication overhead and energy efficiency of sensor nodes are the major concerns in WSNs. In addition, the broadcast communication mode of WSNs makes the network vulnerable to privacy disclosure when the sensor nodes are subject to malicious behaviours. Based on the abovementioned issues, we present a Queries Privacy Preserving mechanism for Data Aggregation (QPPDA) which may reduce energy consumption by allowing multiple queries to be aggregated into a single packet and preserve data privacy effectively by employing a privacy homomorphic encryption scheme. The performance evaluations obtained from the theoretical analysis and the experimental simulation show that our mechanism can reduce the communication overhead of the network and protect the private data from being compromised.

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Section: Aggregation Accuracymentioning

confidence: 99%