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Hence, for remote applications of WVSN, the placement of battery operated VSN is essential.
The energy budget and wireless communication are the major constraints of remote applications of WVSN.
Notations WVSN: Wireless Visual Sensor Network CS: Compressive Sampling TMM: Two Measurement Matrix DCT: Discrete cosine Transform binDCT: Binary DCT NUS: Non uniform sampling EOMP: Enhanced Orthogonal Matching Pursuit OMP: Orthogonal Matching Pursuit MM: Measurement Matrix NM: Number of measurements PNM: Percentage of number of measurements from total number of pixels NIC: Number of coefficients in important component SM: Sparsity level (Number of non-zero values) of unimportant component ML: Sparsity level of unimportant component of low frequency content blocks MH: Sparsity level of unimportant component of high frequency content blocks NP: Non-deterministic Polynomial-time.
WVSNs are networks of wirelessly interconnected devices equipped with camera, enabling the retrieval of video and audio streams, still images, and scalar sensor data.
Transmit image in wireless WVSN is a challenging issue because the bandwidth and frequency spectrum are limited resources in wireless WVSN.
(3) Power-efficient design: due to the fact that most of the sensor nodes are powered by the battery and it is unlikely to exchange battery at the depleted sensor node, power-efficient design is crucial to prolong the life of WSN and WVSN. In WSN, data aggregation scheme  could not only combine the data coming from different sources to eliminate the redundancy, but also minimize the total number of transmissions.
The Power Consumption Ratio of each part of the WVSN node Event Detector(PIR)  1% System Controller 5% Video Encoder  16% Communication 70% CMOS Image sensor [W] 8% Note: Table made from pie chart.
There are many research works attempting to solve the energy problems of WVSN systems.
Different solutions are proposed by researchers for WVSN problems.
Optimization in both computation and communication energy consumption of the VSN is required for the energy constrained outdoor embedded applications of Wireless Visual Sensor Networks (WVSN).
The remainder of this paper is organized as follows: In section 2, we briefly review the reference architecture and service scenario of WVSN using smartphones and a fast algorithm for key frame extraction that can be used for WVSN.
In order to minimize data transmissions for building a global inference in a WVSN, we need to convert the logical topology from a broadcasting/multicasting network to a unicasting network.
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