Data aggregator

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

A Distance-Based Data Aggregation Technique for Periodic Sensor Networks

Abstract: Monitoring phenomena and environments is an emergent and required field in our today systems and applications Hence, wireless sensor networks (WSNs) have attracted considerable attention from the research community as an efficient way to explore various kinds of environments Sensor networks applications can be useful in different domains (terrestrial, underwater, space exploration, etc) However, one of the major constraints in such networks is the energy consumption that increases when data transmission increases Consequently, optimizing data transmission is one of the most significant criteria in WSNs that can conserve energy of sensors and extend network lifetime In this article, we propose an efficient data transmission protocol that consists in two phases of data aggregation Our proposed protocol searches, in the first phase, similarities between measures collected by each sensor In the second phase, it uses distance-based functions to find similarity between sets of collected data The main goal of these phases is to reduce the data transmitted from both sensors and cluster-heads (CHs) in a clustering-based scheme network To evaluate the performance of the proposed protocol, experiments on real sensor data from both terrestrial and underwater networks have been conducted Compared to other existing techniques, simulation and real experimentations show that our protocol can be effectively used to reduce data transmission and increase network lifetime, while still keeping data integrity of the collected data

Privacy-Preserving Truth Discovery in Crowd Sensing Systems

Abstract: The recent proliferation of human-carried mobile devices has given rise to the crowd sensing systems. However, the sensory data provided by individual participants are usually not reliable. To better utilize such sensory data, the topic of truth discovery, whose goal is to estimate user quality and infer reliable aggregated results through quality-aware data aggregation, has drawn significant attention. Though able to improve aggregation accuracy, existing truth discovery approaches fail to address the privacy concerns of individual users. In this article, we propose a novel privacy-preserving truth discovery (PPTD) framework, which can protect not only users’ sensory data but also their reliability scores derived by the truth discovery approaches. The key idea of the proposed framework is to perform weighted aggregation on users’ encrypted data using a homomorphic cryptosystem, which can guarantee both high accuracy and strong privacy protection. In order to deal with large-scale data, we also propose to parallelize PPTD with MapReduce framework. Additionally, we design an incremental PPTD scheme for the scenarios where the sensory data are collected in a streaming manner. Extensive experiments based on two real-world crowd sensing systems demonstrate that the proposed framework can generate accurate aggregated results while protecting users’ private information.

Privacy-preserving data aggregation protocols for wireless sensor networks: a survey.

Abstract: Many wireless sensor network (WSN) applications require privacy-preserving aggregation of sensor data during transmission from the source nodes to the sink node. In this paper, we explore several existing privacy-preserving data aggregation (PPDA) protocols for WSNs in order to provide some insights on their current status. For this, we evaluate the PPDA protocols on the basis of such metrics as communication and computation costs in order to demonstrate their potential for supporting privacy-preserving data aggregation in WSNs. In addition, based on the existing research, we enumerate some important future research directions in the field of privacy-preserving data aggregation for WSNs.

System and method of preventing and remedying restricted area intrusions by unmanned aerial vehicles

Abstract: An intrusion prevention system includes an unmanned aerial vehicle (UAV), a UAV controller, and a restricted area data aggregator. The restricted data aggregator collects and stores restricted area data. The UAV controller is coupled to communicate with the UAV and the restricted area data aggregator, wherein the UAV controller receives positional data from the UAV and restricted area data from the restricted area aggregator. The UAV controller determines based on the received positional data and the received restricted area data whether the UAV is currently intruding within a restricted area or is predicted to intrude within a restricted area and wherein the UAV controller initiates actions to prevent unauthorized intrusions into restricted areas.

Adaptive Localized QoS-Constrained Data Aggregation and Processing in Distributed Sensor Networks

Abstract: In this paper, an efficient quality of service (QoS)-constrained data aggregation and processing approach for distributed wireless sensor networks is investigated and analyzed. One of the key features of the proposed approach is that the task QoS requirements are taken into account to determine when and where to perform the aggregation in a distributed fashion, based on the availability of local only information. Data aggregation is performed on the fly at intermediate sensor nodes, while at the same time the end-to-end latency constraints are satisfied. Furthermore, a localized adaptive data collection algorithm performed at the source nodes is developed that balances the design tradeoffs of delay, measurement accuracy, and buffer overflow, for given QoS requirements. The performance of the proposed approach is analyzed and evaluated, through modeling and simulation, under different data aggregation scenarios and traffic loads. The impact of several design parameters and tradeoffs on various critical network and application related performance metrics, such as energy efficiency, network lifetime, end-to-end latency, and data loss are also evaluated and discussed