Research Article Power Efficient Clustering for Wireless Multimedia Sensor Network Seng-Kyoun Jo, 1 Muhammad Ikram, 2 Ilgu Jung, 1 Won Ryu, 1 and Jinsul Kim 3 1 ETRI, 218 Gajeong-ro, Yuseong-gu, Daejeon 305-700, Republic of Korea 2 Darmstadt University of Technology, Hochschulstraße 10, 64289 Darmstadt, Germany 3 Chonnam National University, 77 Yongbong-ro, Buk-gu, Gwangju 500-757, Republic of Korea Correspondence should be addressed to Jinsul Kim; jsworld@jnu.ac.kr Received 4 January 2014; Accepted 20 February 2014; Published 2 April 2014 Academic Editor: Sabah Mohammed Copyright © 2014 Seng-Kyoun Jo et al. Tis 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. Te availability of inexpensive hardware such as CMOS cameras and microphones has fostered the development of wireless multimedia sensor networks (WMSNs). In WMSNs, wirelessly interconnected devices enable ubiquitously retrieving multimedia contents such as video and audio streams, and still images along with scalar data from surroundings for wide range of applications are constrained by processing, memory, and power resources. Image compression via low-complexity and resource efcient transforms has been addressed by several researchers to prolong network lifetime where energy conservation is achieved through sharing computational load among sensor nodes and by adjusting the transmission ranges of camera nodes. However, those schemes are not adaptive to the presence and changes of energy level of computational sensor nodes and to the amount of computational load. We propose a resource and energy efcient distributed image compression algorithm that dynamically confgures according to the energy levels and the forwarding strategy that is based on the entropy of the image. Te simulation results show that our adaptive distributed image compression scheme signifcantly prolongs the network lifetime and improves the network utilization efciency, while maintaining adequate image quality. 1. Introduction A wireless multimedia sensor network (WMSN) consists of sensor nodes deployed over a geographical area for monitor- ing physical phenomena like temperature, humidity, vibra- tions, seismic events, and so forth [1]. In WMSNs, a sensor node is a tiny device that includes three basic components: a sensing subsystem for data acquisition from the physical surrounding environment, a processing subsystem for local data processing and storage, and a wireless communication subsystem for data transmission. Moreover, a power source supplies the energy needed by the device to perform the sensing and reporting tasks. Te power source ofen consists of a battery with limited energy. In an unattainable environ- ment, it could not be possible or inconvenient to recharge the battery. Terefore, the sensor network should have a lifetime long enough to fulfll the application requirements. A single node failure, due to limited energy, could afect WMSNs lifetime and/or overall utilization. To prolong network lifetime in environmental moni- toring and process control applications, the bulk amount of monitored multimedia data needs to be efciently com- pressed before transmission. But, multimedia compression algorithms, image compression, are constrained by pro- cessing and communication efciency of sensor nodes in WMSNs. In this paper we consider energy and resources efcient distributed image compression in environmental monitor- ing and process control applications. Our scheme uses distributed lapped biorthogonal transform- (LBT-) based compression scheme to compress and transmit the data to base station. Our scheme has the following key contributions. (a) We prune out low-energy nodes through energy-level driven clustering algorithm and classify the potential nodes for processing LBT-based image compression. As a result, both the network lifetime and network utilization efciency are improved signifcantly. Hindawi Publishing Corporation International Journal of Distributed Sensor Networks Volume 2014, Article ID 148595, 9 pages http://dx.doi.org/10.1155/2014/148595