Data aggregator

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

Energy Efficient In-Network Aggregation Algorithms in Wireless Sensor Networks: A Survey

Abstract: Advancement in ubiquitous networking has led to the production of wireless sensor networks, consisting of many autonomous small devices called sensor nodes, able to observe and report various real world physical phenomena with no wired infrastructure. Onetheless, this feature precisely makes these nodes energy constrained, since most of the energy is consumed in data communication. In-network processing may be regarded as an efficient technique that reduces the amount of data to be transmitted in the network. We focus on data aggregation algorithms, whose fundamental idea is to gather, combine and compress data from different sources by applying simple functions in order to reduce the traffic load, thus enhancing the network’s lifetime. However, it is difficult for developers to identify data aggregation algorithms strengths and weaknesses, nor to pinpoint current open research issues to be investigated. In this paper, we propose a survey of the most energy efficient data aggregation algorithms. After reviewing over 900 papers from which we selected 15 algorithms based on the energy efficiency criteria, we classify these protocols according to the network topology, then we describe each one in order to compare them. We conclude the paper with possible future research directions for aspiring researchers and algorithm developers.

Scalable and Reliable Multi-dimensional Sensor Data Aggregation in Data Streaming Architectures

Abstract: Ever-increasing amounts of data and requirements to process them in real time lead to more and more analytics platforms and software systems designed according to the concept of stream processing. A common area of application is processing continuous data streams from sensors, for example, IoT devices or performance monitoring tools. In addition to analyzing pure sensor data, analyses of data for entire groups of sensors often need to be performed. Therefore, data streams of the individual sensors have to be continuously aggregated to a data stream for a group. Motivated by a real-world application scenario of analyzing power consumption in Industry 4.0 environments, we propose that such a stream aggregation approach has to allow for aggregating sensors in hierarchical groups, support multiple such hierarchies in parallel, provide reconfiguration at runtime, and preserve the scalability and reliability qualities of stream processing techniques. We propose a stream processing architecture fulfilling these requirements, which can be integrated into existing big data architectures. As all state-of-the-art stream processing frameworks have to handle a trade-off between latency, resource-efficiency, and correctness, our proposed architecture can be configured for low latency and resource-efficient computation or for always ensuring correct results. To assist adopters in choosing appropriate configuration options, we provide an experimental comparison. We present a pilot implementation of our proposed architecture and show how it is used in industry. Furthermore, in experimental evaluations we show that our solution scales linearly with the amount of sensors and provides adequate reliability in the presence of faults.

Energy-Efficient Data-Aggregation Technique for Correlated Spatial and Temporal Data in Cluster-Based Sensor Networks

Abstract: Continuous-monitoring applications in sensor network applications require periodic data transmissions to the base-station (BS), which may lead to unnecessary energy depletion. The energy-efficient data aggregation solutions in sensor networks have evolved as one of the favorable fields for such applications. Former research works have recommended many spatial-temporal designs and prototypes for successfully minimizing the data-gathering overheads, but these are constrained to their relevance. This work has proposed a data aggregation technique for homogeneous application set-ups in sensor networks. For this, the authors have employed two ways of model generation for reducing correlated spatial-temporal data in cluster-based sensor networks: one at the Sensor nodes (SNs) and the other at the Cluster heads (CHs). Building on this idea, the authors propose two types of data filtration, first at the SNs for determining temporal redundancies (TRs) in data readings by both relative deviation (RD) and adaptive frame method (AFM) and second at the CHs for determining spatial redundancies (SRs) by both RD and AFM.

Data Aggregation with Principal Component Analysis in Big Data Wireless Sensor Networks

Abstract: In wireless sensor networks (WSNs), numerous sensors can produce a significant portion of the big data. It remains an open issue how to timely gather and transmit such large amount of data while minimizing data latency through wireless sensor networks (WSNs). On the other hand, spatially correlated sensor observations lead to considerable data redundancy in the network. To efficiently eliminate data redundancy and improve energy efficiency, in this paper, based on the fact that the more similar the measure data are, the smaller the amount of data after aggregation is, we first develop a new distributed clustering algorithm which can categorize sensor nodes with high similarity into a cluster for data aggregation, while ensuring uniform energy consumption within the cluster. Then, we propose a data aggregation algorithm based on principal component analysis (PCA) which can be executed in the cluster head (CH). Finally, our experimental results demonstrate that the amount of data transmission can be significantly reduced based on our proposed clustering and data aggregation algorithm.