The availability of low-cost embedded devices for multimedia sensing has encouraged their integration with low-power wireless sensors to create systems that enable advanced services and applications referred to as the Internet of Multimedia Things. Image-based sensing applications are challenged by energy efficiency and resource availability. Mainly, image sensing and transmission in Internet of Multimedia Things severely deplete the sensor energy and overflow the network bandwidth with redundant data. Some solutions presented in the literature, such as image compression, do not efficiently solve this problem because of the algorithms’ computational complexities. Thus, detecting the event of interest locally before the communication using shape-based descriptors would avoid useless data transmission and would extend the network lifetime. In this article, we propose a new approach of distributed event-based sensing scheme over a set of nodes forming a processing cluster to balance the processing load. This approach is intended to reduce per-node energy consumption in one sensing cycle. The conducted experiments show that our novel method based on the general Fourier descriptor decreases the energy consumption in the camera node to only 2.4 mJ, which corresponds to 75.32% of energy-saving compared to the centralized approach, promising to prolong the network lifetime significantly. In addition, the scheme achieved more than 95% accuracy in target recognition.

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A growing number of services and applications are developed using multimedia sensing low-cost wireless devices, thus creating the Internet of Multimedia Things (IoMT). Nevertheless, energy efficiency and resource availability are two of the most challenging issues to overcome when developing image-based sensing applications. In depth, image-based sensing and transmission in IoMT significantly drain the sensor energy and overwhelm the network with redundant data. Event-based sensing schemes can be used to provide efficient data transmission and an extended network lifetime. This paper proposes a novel approach for distributed event-based sensing achieved by a cluster of processing nodes. The proposed scheme aims to balance the processing load across the nodes in the cluster. This study demonstrates the adequacy of distributed processing to extend the lifetime of the IoMT platform and compares the efficiency of Haar wavelet decomposition and general Fourier descriptors (GFDs) as a feature extraction module in a distributed features-based target recognition system. The results show that the distributed processing of the scheme based on the Haar wavelet transform of the image outperforms the scheme based on a general Fourier shape descriptor in recognition accuracy of the target as well as the energy consumption. In contrast to a GFD-based scheme, the recognition accuracy of a Haar-based scheme was increased by 26%, and the number of sensing cycles was increased from 40 to 70 cycles, which attests to the adequacy of the proposed distributed Haar-based processing scheme for deployment in IoMT devices.