Data-aggregation techniques in sensor networks: a survey

By Ramesh Rajagopalan, Pramod K. Varshney. IEEE Communications Surveys and Tutorials, 2006

1,035 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Environmental Engineering

View Paper: https://www.semanticscholar.org/paper/e91ac05f8c5f564bec242180dc8ba87d8174b450

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In Environmental Engineering

Citations in the corpus

A survey on routing protocols in Wireless Sensor Networks (2012, background): “…node because the number of sensor nodes is very huge This leads to data-centric routing approach which is query-based protocol in which Base station sends query to a collection of particular nodes in a area and waits Kirola, Rawat, Bhatt and Bhatt 134 for response of data from the sensors placed…”
A Wireless Sensor Network Air Pollution Monitoring System (2010, background): “For this reason, during last years considerable research effort has been devoted to data fusion and aggregation algorithms [17, 18].”
Cross-layer optimization for energy-efficient wireless communications: a survey (2009, methodology): “However, this acts as a clear platform for other potential data-centric, aggregation, and negotiation protocols which are in existence [10,14] to be able to effectively and efficiently retrieve values from all nodes concurrently.”
A Survey of Multi-Objective Optimization in Wireless Sensor Networks: Metrics, Algorithms, and Open Problems (2016, methodology): “Yu et al. [137] studied the energy-versus-latency trade-offs using the so-called data aggregation tree 4 [254] in a real-time scenario with a speciﬁed latency constraint, and developed algorithms for minimizing the overall energy dissipation of the sensor nodes.”
A Survey on Clustering Routing Protocols in Wireless Sensor Networks (2012, background): “Data Aggregation/Fusion : Data aggregation/fusion, which is the process of aggregating the data from multiple nodes to eliminate redundant transmission and provide fused data to the BS, is an effectual technique for WSNs to save energy [45].”
Data Collection and Wireless Communication in Internet of Things (IoT) Using Economic Analysis and Pricing Models: A Survey (2016, background): “redundancy as well as saving the energy required for information transmission to the sink node [99], [100].”
Energy Harvesting Sensor Nodes: Survey and Implications (2011, methodology): “While data-aggregation techniques have been proposed [82] to efficiently gather data on the axes of energy, accuracy, reliability, latency etc.”
Resource Management in Fog/Edge Computing (2018, background): “in Figure 5. i. Techniques for Modeling and Implementing Aggregation: The underlying techniques implemented for supporting aggregation have formed an important part of Wireless Sensor Networks (WSNs) [8] and distributed data stream processing [9]. Dense and large-scale sensor networks cannot route a”
Robots for Environmental Monitoring: Significant Advancements and Applications (2012, background): “communication aspects [35], deployments [36], [37], data gathering and processing [38], and energy efficiency [39].”
Computational Intelligence in Wireless Sensor Networks: A Survey (2011, methodology): “A survey of data aggregation algorithms used in WSNs is presented in [22].”
Clustering in sensor networks: A literature survey (2014, neutral): “In flat architectures, all the nodes have to transmit their data to the BS by either multi-hop or direct approach, although, some data aggregation techniques are practical only for flat architectures when as they use data-centric application (Rajagopalan and Varshney, 2006).”
Particle Swarm Optimization in Wireless-Sensor Networks: A Brief Survey (2011, methodology): “PSO algorithms for WSN localization are summarized in Table I.”
Energy efficiency in wireless sensor networks: A top-down survey (2014, methodology): “Data aggregation techniques dedicated to wireless sensor networks are surveyed in detail by Rajagopalan and Varshney in [3] and by Fasolo et al.”
The rise of low-cost sensing for managing air pollution in cities. (2015, background): “, the network sink, algorithms for data fusion and aggregation (Rajagopalan and Varshney 2006) are needed to reduce congestion and system overloading (Cao et al.”