Data aggregator

About: Data aggregator is a research topic. Over the lifetime, 2615 publications have been published within this topic receiving 40265 citations.

Fuzzy Based Secure Data Aggregation Technique in Wireless Sensor Networks

Abstract: Problem statement: Secure data aggregation is a challenging task in wireless sensor network due to the facts like more complexity, greater overhead in the case of cryptographic techniques. These issues need to be overcome using efficient technique. Approach: We propose a fuzzy based secure data aggregation technique which was having 3 phases. In its first phase, it performs clustering and cluster head election process. In the second phase, within each clusters, power consumed, distance and trust values were calculated for each member. In the third phase, based on these parameters, fuzzy logic technique was used to select the secure and non-faulty node members for data aggregation. Finally, the aggregated data from the cluster heads was transmitted to the sink. Results: By simulation results we show that our technique had improved throughput and packet delivery ratio with reduced packet drop and less energy consumption. Conclusion: The proposed technique efficiently checks for malicious nodes based on the system parameters and maintains a secure aggregation process in the network.

SAS: A Secure Data Aggregation Scheme in Vehicular Sensing Networks

Abstract: Vehicular ad hoc networks support a wide range of promising applications including vehicular sensing networks, which enable vehicles to cooperatively collect and transmit the aggregated traffic data for the purpose of traffic monitoring. The reported literatures mainly focus on how to achieve the data aggregation in dynamic vehicular environment while the security issue especially on the authenticity and integrity of aggregation results receive less attention. In this study, we introduce a secure probabilistic data aggregation scheme based on Flajolet-Martin sketch and \emph{sketch proof} technique. We also discuss the tradeoff between the bandwidth efficiency and the estimation accuracy. Extensive simulations and analysis demonstrate the efficiency and effectiveness of the proposed scheme.

Privacy-Preserving Data Aggregation for Mobile Crowdsensing With Externality: An Auction Approach

Abstract: We develop an auction framework for privacy-preserving data aggregation in mobile crowdsensing, where the platform plays the role as an auctioneer to recruit workers for sensing tasks. The workers are allowed to report noisy versions of their data for privacy protection; and the platform selects workers by taking into account their sensing capabilities to ensure the accuracy level of the aggregated result. Observe that when moving the control of data privacy from the data aggregator to the workers, the data aggregator has limited market power in the sense that it can only partially control the noise by judiciously choosing a subset of workers based on workers’ privacy preferences. This introduces externalities because the privacy of each worker depends on the total noise in the aggregated result that in turn relies on which workers are selected. Specifically, we first consider a privacy-passive scenario where workers participate if their privacy loss can be adequately compensated by the rewards. We explicitly characterize the externalities and the hidden monotonicity property of the problem, making it possible to design a truthful, individually rational and computationally efficient incentive mechanism. We then extend the results to a privacy-proactive scenario where workers have individual requirements for their perceivable data privacy levels. Our proposed mechanisms for both scenarios can select a subset of workers to (nearly) minimize the cost of purchasing their private sensing data subject to the accuracy requirement of the aggregated result. We validate the proposed scheme through theoretical analysis as well as extensive simulations.

Energy-Efficient and Privacy-Preserving Data Aggregation Algorithm for Wireless Sensor Networks

Abstract: Privacy-preserving data aggregation is a kind of fundamental and essential algorithm for wireless sensor networks. However, the existing aggregation algorithms consume a large amount of energy to assure sensory data security. In this article, we propose an energy-efficient and privacy-preserving data aggregation algorithm (EPDA). We organize a sensor network into a tree and connect the leaf nodes of the tree to form many chains. EPDA requires only the data sensed by the tail nodes of the chains to be sliced to ensure privacy. Also, EPDA significantly decreases energy consumption and prolongs the lifetime of the network. We compare our scheme with the existing schemes through theoretical analysis and simulations. The analysis and simulation results show that EPDA outperforms the existing schemes.