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Sensors 2015, 15(8), 19597-19617; doi:10.3390/s150819597

On the Optimization of a Probabilistic Data Aggregation Framework for Energy Efficiency in Wireless Sensor Networks

School of Electrical and Computer Engineering, National Technical University of Athens (NTUA), 9 Iroon Polytechniou str., Zografou, 15780 Athens, Greece
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Academic Editor: Leonhard M. Reindl
Received: 28 January 2015 / Revised: 15 May 2015 / Accepted: 5 August 2015 / Published: 11 August 2015
(This article belongs to the Special Issue Sensors and Smart Cities)
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Abstract

Among the key aspects of the Internet of Things (IoT) is the integration of heterogeneous sensors in a distributed system that performs actions on the physical world based on environmental information gathered by sensors and application-related constraints and requirements. Numerous applications of Wireless Sensor Networks (WSNs) have appeared in various fields, from environmental monitoring, to tactical fields, and healthcare at home, promising to change our quality of life and facilitating the vision of sensor network enabled smart cities. Given the enormous requirements that emerge in such a setting—both in terms of data and energy—data aggregation appears as a key element in reducing the amount of traffic in wireless sensor networks and achieving energy conservation. Probabilistic frameworks have been introduced as operational efficient and performance effective solutions for data aggregation in distributed sensor networks. In this work, we introduce an overall optimization approach that improves and complements such frameworks towards identifying the optimal probability for a node to aggregate packets as well as the optimal aggregation period that a node should wait for performing aggregation, so as to minimize the overall energy consumption, while satisfying certain imposed delay constraints. Primal dual decomposition is employed to solve the corresponding optimization problem while simulation results demonstrate the operational efficiency of the proposed approach under different traffic and topology scenarios. View Full-Text
Keywords: wireless sensor networks; smart cities; data gathering and aggregation; network optimization wireless sensor networks; smart cities; data gathering and aggregation; network optimization
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Kafetzoglou, S.; Aristomenopoulos, G.; Papavassiliou, S. On the Optimization of a Probabilistic Data Aggregation Framework for Energy Efficiency in Wireless Sensor Networks. Sensors 2015, 15, 19597-19617.

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