Data aggregator

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

RSSI/DoA Based Positioning Systems for Wireless Sensor Network

Abstract: A Wireless Sensor Network is an infrastructure comprised of a set independent nodes able to sense (measure), process and communicate among themselves and toward a remote sink node which operates as data aggregator and forwards the information to the final user. WSN are already actively employed in unattended and non-invasive activities like prevention of art deterioration [1, 23], agricultural monitoring [18], environment monitoring [17, 22], surveillance application [2, 10]. In most cases, if not in all cases, it is necessary to report the measured data to the position of the observed phenomenon, otherwise the measurement would be meaningless. This means that the sensor nodes have to be aware of their position, and if this information is not known, a localization service must be implemented. The position awareness that comes from this service can boost specific routing operations (adaptability, latency, throughput) with the nodes able to independently determine the best modality to cooperate and communicate the data to the end user by means of a continuous exchange of messages.

Location Privacy-Preserving Truth Discovery in Mobile Crowd Sensing

Abstract: Truth discovery techniques are commonly used in mobile crowd sensing (MCS) applications to infer accurate aggregated results based on quality-aware data aggregation. However, the location information of participants may be exposed when they upload their sensitive geo-tagged sensory data to relative platforms. While there are considerable existing privacy preserving truth discovery schemes for MCS, they mainly focus on protecting the privacy of sensory data, neglecting the tagged location information which is of equal if not higher importance for the privacy of participants. In this paper, we propose a novel and efficient location privacy preserving truth discovery (LoPPTD) mechanism, which can achieve data aggregation with high accuracy, while protecting both location privacy and data privacy of users. By structuring multi-dimensional sensory data obtained at different locations and exploiting homomorphic Paillier encryption, our approach can prevent leakage of both sensory data and tagged locations effectively. Also, super-increasing sequence techniques are employed in Lo-PPTD to ensure efficiency and feasibility. Theoretical analysis and thorough experiments performed on real-world datasets demonstrate that the proposed scheme can achieve high aggregation accuracy while providing complete privacy protection for users.

An Effective Data Aggregation Mechanism for Wireless Sensor Networks

Abstract: Wireless sensor networks are tiny devices with limited computation and energy capacities. For such devices, data transmission is a very energy- consuming operation. It thus becomes essential to increase the lifetime of a WSN by minimizing the number of bits sent by each device. One well known approach is to aggregate sensor data. Data aggregation is used to eliminate redundancy and minimize the number of transmissions in order to save energy. Most energy efficient aggregation protocols have focused on cluster-based structure approaches. In this paper, we investigate the efficiency of data aggregation by focusing on two aspects of the problem: first how to improve cluster-based routing algorithms to achieve the least of energy consumption for aggregating data, and second analyzing and utilizing some aggregation techniques which are used in different applications. We start by making comparison over three well known cluster based algorithms of LEACH, HEED, and DWEHC based on clustering features, then we will simulate two aggregation techniques of Differential and Integrated Data compression which show significant improvements in prolonging network lifetime. We then would like to extend the LEACH protocol to adaptive intra and inter-cluster topologies which are deployed to improve the energy efficiency of the system, and utilize Differential Data compression as an aggregation technique. Simulation results would be used to investigate the performance of the algorithm.

A novel efficient approach for protecting integrity of data aggregation in wireless sensor networks

Abstract: Due to the characteristics of resource-constrained in wireless sensor networks (WSNs), energy consumption is a major concern Data aggregation is an important method to reduce the energy consumption and communication overhead ; however, it suffers from the security problems of data integrity In fact, designing integrity schemes for data aggregation is an imperative problem for securing WSNs In this paper, we propose a secure data aggregation based on homomorphic MAC scheme that provide efficient secure data integrity with different trade-offs between security assumptions, computation cost, and communication payload The scheme uses two types of homomorphic MAC (ie, MACs and EMACs) Each MAC provides integrity of the transmitted message and each EMAC provides integrity of the MACs Our experiments show that our scheme requires less computation overheads and energy consumption than previously methods and can effectively preserve data integrity

Early access to user-specific data for behavior prediction

Abstract: A device-indexed data server associates persistent device identifiers of client computing devices with user behavior data devoid of personally identifiable information (ΡII). User behavior data and PII are aggregated by an off-line data aggregator and associated with non-persistent non-PII user identifiers. Users visiting third party web sites are authenticated by their device identifiers, and identified to the device-indexed data server by the device identifier or by the non-PII user identifier. The device-indexed data server retrieves from the aggregator user behavior data associated with the non-PII user identifier, returns the data to the third party server, and maintains records of user behavior associated with persistent device identifiers without maintaining ΡII. Subsequent user visits to any third party server can thereby be customized according to known user behavior without first requiring the user to identify herself.