Data aggregator

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

PRDA: polynomial regression-based privacy-preserving data aggregation for wireless sensor networks

Abstract: In wireless sensor networks, data aggregation protocols are used to prolong the network lifetime. However, the problem of how to perform data aggregation while preserving data privacy is challenging. This paper presents a polynomial regression-based data aggregation protocol that preserves the privacy of sensor data. In the proposed protocol, sensor nodes represent their data as polynomial functions to reduce the amount of data transmission. In order to protect data privacy, sensor nodes secretly send coefficients of the polynomial functions to data aggregators instead of their original data. Data aggregation is performed on the basis of the concealed polynomial coefficients, and the base station is able to extract a good approximation of the network data from the aggregation result. The security analysis and simulation results show that the proposed scheme is able to reduce the amount of data transmission in the network while preserving data privacy. Copyright © 2013 John Wiley & Sons, Ltd.

Secure data aggregation with multiple encryption

Abstract: Data aggregation has been put forward as an essential technique to achieve power efficiency in sensor networks. Data aggregation consists of processing data collected by source nodes at each intermediate node enroute to the sink in order to reduce redundancy and minimize bandwidth usage. The deployment of sensor networks in hostile environments call for security measures such as data encryption and authentication to prevent data tampering by intruders or disclosure by compromised nodes. Aggregation of encrypted and/or integrity-protected data by intermediate nodes that are not necessarily trusted due to potential node compromise is a challenging problem. We propose a secure data aggregation scheme that ensures that sensors participating to the aggregation mechanism do not have access to the content of the data while adding their sensed values thanks to the use of an efficient homomorphic encryption scheme. We provide a layered secure aggregation mechanism and the related key attribution algorithm that limits the impact of security threats such as node compromises. We also evaluate the robustness of the scheme against node failures and show that such failures are efficiently recovered by a small subset of nodes that are at most m hops away from the failure.

Real-time Event Detection on Social Data Stream

Abstract: Social Network Services (SNS) are becoming more popular in our daily life, the process is boosted by various kinds of smart devices integrating utility modules such as 3G/WIFI connector, GPS tracker, Camera, Heartbeat sensor and so on. It makes the information flow (or Social Data Stream) on SNS have a real-time nature characteristic, where each SNS user is an information sensor and also a data connector for diffusing interesting news to his/her communication networks. Hiding inside the information flow are pieces of real social events. The events draw attention from users evidencing by the number of relevant announces and communication interactions toward that topic. However, traditional topic detection approaches are not designed to detect the kind of the event efficiently in real-time, particularly if the data sources are influenced by noise data and containing diverse topics. To overcome the issue, in this paper we proposed a model for extracting and tracking real social events on Social Data Stream, which can work well in real-time by using distributing computation and data aggregation technique on the discrete signals as a new representation of the original data.

Improved throughput for Power Line Communication (PLC) for smart meters using fog computing based data aggregation approach

Abstract: The smart grid is now being deployed in many countries to provide cleaner and greener energy to the consumers. To implement this efficiently and effectively appropriate data collecting, processing and transmitting infrastructures are needed to be in place. Smart meters are used to measure the energy consumption details and patterns and transmit to meter data management system (MDMS) with the help of data aggregation units (DAU). The sheer amount of data need to be collected from the consumers are very huge and they fall under the category of big data. Currently, all the data collected from smart meters are stored in a centralized place for processing to forecast the energy demand. This approach is becoming a bottleneck for efficient data collection due to limited bandwidth capacities of Power Line Communication (PLC). In this paper, we propose a framework for distributed data aggregation approach with the help of fog computing architecture. With this approach the amount of data sent to the centralized storage space is limited and, therefore, the capacity of PLC is virtually improved without compromising the functionality.

Grouping of clusters for efficient data aggregation (GCEDA) in wireless sensor network

Abstract: In the application based WSN environment, energy and bandwidth of the sensor are valuable resources and need to utilize efficiently. Data aggregation at the sink by individual node causes flooding of the data which results in maximum energy consumption. To minimize this problem we propose and evaluate the group based data aggregation method, where grouping of nodes based on available data and correlation in the intra-cluster and grouping of cluster heads at the network level help to reduce the energy consumption. In addition, proposed method uses additive and divisible data aggregation function at cluster head (CH) as in-network processing to reduce energy consumption. Cluster head transmits aggregated information to remote sink and cluster head nodes transmit data to CH. Simulation result shows, proposed algorithm provides an improvement of 14.94% in energy consumption as compared with primary cluster based protocol LEACH which uses only one CH, it also improves the network stability.