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Vol. 224, Issue 8, August 2018, pp. 28-38




Design of an Energy Efficient Scheme for Target Recognition
in Wireless Acoustic Sensor Networks

Afnan Algobail, * Adel Soudani and Saad Alahmadi

Department of Computer Science, College of Computer and Information Science, King Saud University,

P. O. Box 51178, Riyadh 11543, Saudi Arabia
* Tel.: +966114676578

* E-mail: asoudani@ksu.edu.sa


Received: 30 April 2018 /Accepted: 31 July 2018 /Published: 31 August 2018

Digital Sensors and Sensor Sysstems


Abstract: The conservation of ecosystem diversity has become a major concern worldwide. To optimize the management of ecosystems, animal recognition has become an important topic of research. Animal-monitoring systems can produce valuable information that assists in the protection of animals, planets and the natural environment in general. The majority of monitoring applications require a lightweight recognition system that can effectively achieve an effective trade-off between cost and performance. In this context, designing a solution for target recognition using Wireless Acoustic Sensor Networks (WASNs) has become a viable low-cost approach for monitoring animals. However, acoustic sensors generate large volumes of data, which represents a major restriction on the network deployment. As sensors are massive energy-constrained devices, energy consumption becomes the most critical consideration for data-intensive computation and communication. In this context, this paper proposes a low-energy scheme that focuses on developing efficient processing algorithms and communication techniques that can optimize energy consumption and prolong the network’s lifetime, while guaranteeing the required level of application performance. This scheme, based on temporal feature extraction methods, aims to recognize the target object locally at the sensor node, and then report the event with small-size packets. The results showed that the proposed approach was capable of reducing the amount of consumed energy in the network, while maintaining competitive recognition accuracy.


Keywords: Bio-acoustic, Feature’s extraction, Wireless acoustic sensor networks, Recognition, Low-energy processing.


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