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Wireless image sensor networks (WISNs) collect surveillance images, resulting in copious quantities of data requiring processing and transmission within the network. To reduce and balance energy expenditure during in-network image data processing and transmission, this study introduces an energy-saving strategy based on image super-resolution for WISNs assisted by cloud. The strategy constructs an image data processing and transmission system for WISNs, leveraging cloud infrastructure. By utilizing image quality feedback, the adaptive adjustment of image node sensing resolution is facilitated, minimizing image data transmission volume while ensuring high-quality super-resolution reconstructed image. The establishment of low-overhead, multipath transmission routes enables image nodes to transmit data in blocks, contingent upon neighboring transmission node statuses, thereby equalizing energy consumption for image data transmission. Cloud servers are employed to reconstruct high-resolution images and provide users with surveillance images that satisfy quality requisites. Empirical findings demonstrate that the proposed approach extends the lifetime of WISNs, furnishes users with superior-quality surveillance images, and markedly enhances network monitoring efficacy. [ABSTRACT FROM AUTHOR]

When camera-enabled sensors are deployed for visual monitoring, a new set of innovative applications is allowed, enriching the use of wireless sensor network technologies. In these networks, energy-efficiency is a highly desired optimization issue, mainly because transmission of images and video streams over resource-constrained sensor networks is more stringent than transmission of conventional scalar data. Due to the nature of visual monitoring, that follows a directional sensing model, camera-enabled sensors may have different relevancies for the application, according to the desired monitoring tasks and the current sensors’ poses and fields of view. Exploiting this concept, each data packet may be associated with a priority level related to the packet’s origins, which may be in turn mapped to an energy threshold level. In such way, we propose an energy-efficient relaying mechanism where data packets are only forwarded to the next hop if the associated energy threshold level is below the current energy level of the relaying node. Thus, packets from low-relevant source nodes will be silently dropped when the current energy level of intermediate nodes run below the pre-defined thresholds. Doing so, energy is saved potentially prolonging the network lifetime. Besides the sensing relevancies of source nodes, the relevance of DWT subbands for reconstruction of original images is also considered. This allows the creation of a second level of packet prioritization, assuring a minimal level of image quality even for the least relevant source nodes. We performed simulations for the proposed relaying mechanism, assessing the expected performance over a traditional relaying paradigm.

Source nodes in wireless image sensor networks transmit much more information than traditional scalar sensor networks, thereby demanding more energy of intermediate relaying nodes and putting energy efficiency as a key design issue. Intermediate nodes are usually interconnected by error-prone links where bit-errors are common, potentially degrading the application monitoring quality. When reliability is assured by retransmission mechanisms, higher packet error rates do not affect the application quality but result in additional energy consumption due to packet retransmission, even though many monitoring applications can tolerate some loss in the quality of the received image. DWT coding can decompose an image in data subbands, each one with different relevancies for the reconstruction of the original image at the receiver side. We propose an energy-efficient selective hop-by-hop retransmission mechanism where the reliability level of each packet is a function of the relevance of the payload data, according to the resulting subbands and the number of times a 2D DWT is applied over the images captured by the sensors’ cameras. In so doing, some lost packets are not retransmitted, saving energy of intermediate nodes with low impact to the quality of the reconstructed images. In order to estimate the benefits of this tradeoff between energy consumption and image quality, we designed a comprehensive energy consumption model and applied it in extensive mathematic simulations, providing substantial information about the mean performance of the proposed approach when compared with a fully-reliable transmission mechanism.

When camera-enabled sensors are deployed for visual monitoring, a new set of innovative applications is allowed, enriching the use of wireless sensor network technologies. In these networks, energy-efficiency is a highly desired optimization issue, mainly because transmission of images and video streams over resource-constrained sensor networks is more stringent than transmission of conventional scalar data. Due to the nature of visual monitoring, that follows a directional sensing model, camera-enabled sensors may have different relevancies for the application, according to the desired monitoring tasks and the current sensors' poses and fields of view. Exploiting this concept, each data packet may be associated with a priority level related to the packet's origins, which may be in turn mapped to an energy threshold level. In such way, we propose an energy-efficient relaying mechanism where data packets are only forwarded to the next hop if the associated energy threshold level is below the current energy level of the relaying node. Thus, packets from low-relevant source nodes will be silently dropped when the current energy level of intermediate nodes run below the pre-defined thresholds. Doing so, energy is saved potentially prolonging the network lifetime. Besides the sensing relevancies of source nodes, the relevance of DWT subbands for reconstruction of original images is also considered. This allows the creation of a second level of packet prioritization, assuring a minimal level of image quality even for the least relevant source nodes. We performed simulations for the proposed relaying mechanism, assessing the expected performance over a traditional relaying paradigm. [ABSTRACT FROM AUTHOR]