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—Wireless Sensor Networks (WSNs) have enjoyed dramatic developments over the last decade. The availability of CMOS cameras and microphones enlarged the scope of WSNs paving the way to the development of Wireless Multimedia Sensor Networks (WMSN). Among the envisaged WMSN applications, Real-time Multimedia Monitoring constitutes one of the most promising.(More)
— A critical aspect of applications with wireless sensor networks is network lifetime. Sensing and communications consume energy particularly in wireless multimedia sensor networks (WMSN) due to huge amount of data generated by the multimedia sensors. Therefore, judicious power management and sensor scheduling can effectively extend network lifetime. In(More)
Clustering techniques are proposed for developing performance of wireless sensor networks from several points of view. Multimedia nodes have different sensing region than scalar sensors that usually sense a circle or polygon regions centred by the node, and thus traditional clustering methods do not satisfy a Wireless Multimedia Sensor Network (WMSN). In(More)
Network lifetime is always an important topic in wireless sensor networks. The case of Wireless Multimedia Sensor Networks present additional features such as the fact of transmitting multimedia rich data or the fact of having directional sensing areas. This paper presents a scheduling scheme for clustered nodes according to overlapped Field of View (FoV)(More)
Prolongation of the lifetime has become a key challenge in design and implementation of Wireless Multimedia Sensor Networks (WMSNs). The energy consumed in multimedia sensor nodes is much more than in the scalar sensors; a multimedia sensor captures images or acoustic signals containing a huge amount of data while in the scalar sensors a scalar value is(More)
For current high performance computing systems, exploiting concurrency is a serious and important challenge. Recently, several dynamic software task management mechanisms have been proposed. In particular, task-based dataflow programming models which benefit from dataflow principles to improve task-level parallelism and overcome the limitations of static(More)