MAX–MIN aggregation in wireless sensor networks: mechanism and modeling

  • Authors:

  • Hanlin Deng;Baoxian Zhang_af2n3;Cheng Li;Kui Huang;Haitao Liu

  • Affiliations:

  • Key Laboratory of Wireless Sensor Networks and Communications, Shanghai Institute of Microsystem and Information Technology of Chinese Academy of Sciences, Shanghai 200050, China;af2 Research Center of Ubiquitous Sensor Network, Graduate University of Chinese Academy of Sciences, Beijing 100049, China;Electrical and Computer Engineering, Faculty of Engineering and Applied Science, Memorial University of Newfoundland, St. John's, NL A1B 3X5, Canada;af3 Research Center of IoT, Institute of Microelectronics of Chinese Academy of Sciences, Beijing 100029, China;Key Laboratory of Wireless Sensor Networks and Communications, Shanghai Institute of Microsystem and Information Technology of Chinese Academy of Sciences, Shanghai 200050, China

  • Venue:
  • Wireless Communications & Mobile Computing
  • Year:
  • 2012

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Abstract

In-network aggregation is crucial in the design of a wireless sensor network (WSN) due to the potential redundancy in the data collected by sensors. Based on the characteristics of sensor data and the requirements of WSN applications, data can be aggregated by using different functions. MAX—MIN aggregation is one such aggregation function that works to extract the maximum and minimum readings among all the sensors in the network or the sensors in a concerned region. MAX—MIN aggregation is a critical operation in many WSN applications. In this paper, we propose an effective mechanism for MAX—MIN aggregation in a WSN, which is called Sensor MAX—MIN Aggregation (SMMA). SMMA aggregates data in an energy-efficient manner and outputs the accurate aggregate result. We build an analytical model to analyze the performance of SMMA as well as to optimize its parameter settings. Simulation results are used to validate our models and also evaluate the performance of SMMA. Copyright © 2010 John Wiley & Sons, Ltd. (To suppress the redundant data transmissions in the process of MAX—MIN aggregation in wireless sensor networks (WSNs), we propose an effective data gathering mechanism, which is called SMMA. SMMA aggregates data in an energy-efficient manner and outputs the accurate result. We mainly measure the performance of different versions of SMMA in terms of average number of suppressed redundant data transmissions in a round of data gathering.)