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Foreman, J. C., Pacheco, F. E..  2017.  Aggregation architecture for data reduction and privacy in advanced metering infrastructure. 2017 IEEE PES Innovative Smart Grid Technologies Conference - Latin America (ISGT Latin America). :1–5.

Advanced Metering Infrastructure (AMI) have rapidly become a topic of international interest as governments have sponsored their deployment for the purposes of utility service reliability and efficiency, e.g., water and electricity conservation. Two problems plague such deployments. First is the protection of consumer privacy. Second is the problem of huge amounts of data from such deployments. A new architecture is proposed to address these problems through the use of Aggregators, which incorporate temporary data buffering and the modularization of utility grid analysis. These Aggregators are used to deliver anonymized summary data to the central utility while preserving billing and automated connection services.

Naureen, Ayesha, Zhang, Ning.  2016.  A Comparative Study of Data Aggregation Approaches for Wireless Sensor Networks. Proceedings of the 12th ACM Symposium on QoS and Security for Wireless and Mobile Networks. :125–128.

In Wireless Sensor Networks (WSNs), data aggregation has been used to reduce bandwidth and energy costs during a data collection process. However, data aggregation, while bringing us the benefit of improving bandwidth usage and energy efficiency, also introduces opportunities for security attacks, thus reducing data delivery reliability. There is a trade-off between bandwidth and energy efficiency and achieving data delivery reliability. In this paper, we present a comparative study on the reliability and efficiency characteristics of different data aggregation approaches using both simulation studies and test bed evaluations. We also analyse the factors that contribute to network congestion and affect data delivery reliability. Finally, we investigate an optimal trade-off between reliability and efficiency properties of the different approaches by using an intermediate approach, called Multi-Aggregator based Multi-Cast (MAMC) data aggregation approach. Our evaluation results for MAMC show that it is possible to achieve reliability and efficiency at the same time.