Wireless sensor networks consists of a large number of Tiny low power sensor nodes, each with sensing, computation and wireless communication capabilities. Sensor nodes are deployed in unattended environments, they are vulnerable to a... more
Wireless sensor networks consists of a large number of
Tiny low power sensor nodes, each with sensing, computation and wireless communication capabilities. Sensor nodes are deployed in unattended environments, they are vulnerable to a wide variety of attacks. Malicious nodes can generate incorrect readings and misleading reports. In this paper, we present a malicious node detection scheme for wireless sensor networks. The malicious nodes are detected by computing the average number of event cycles. In addition, each sensor node maintains the trust values of its neighbouring nodes to reflect their behaviour in decision making. Computer simulation shows that the proposed scheme achieves high malicious node detection accuracy with lesser number of event cycles to detect the malicious node.
A wireless sensor network (WSN) is comprised of a large number of sensors that collaboratively monitor various environments. The sensors all together provide global views of the environments that offer more information than those local... more
A wireless sensor network (WSN) is comprised of a large number of sensors that collaboratively monitor various environments. The sensors all together provide global views of the environments that offer more information than those local views provided by independently operating sensors. There are numerous potential applications of WSNs in various areas such as residence, industry, military and many others. While the deployment of sensor nodes in an unattended environment makes the networks vulnerable to a variety of potential attacks. This paper focuses the various attacks associated with wireless sensor network.
Night images obtained from a surveillance camera have low visibility compared to daytime images. Images captured at night have low brightness, low contrast and high noise. A modified contrast enhancement (CE) algorithm was proposed and... more
Night images obtained from a surveillance camera have low visibility compared to daytime images. Images captured at night have low brightness, low contrast and high noise. A modified contrast enhancement (CE) algorithm was proposed and developed in luminance-chrominance space. Only the luminance channel obtained by PCA transform is processed as it contains the most valuable information. Daytime images are simulated with various degrees of contrast and Poisson noise using MATLAB. CE algorithm is applied in three scales to obtain good brightness and contrast of the images. Images are denoised by using bilateral filter that smoothes the noise while preserving edges. The brightness and contrast of the night images have been enhanced significantly and the noise is reduced effectively, preserving the details of the images. Finally, the performance of the proposed algorithm is illustrated by processing images under various lighting conditions without introducing halo and ghosting artifacts. Structural similarity and visual contrast measures demonstrate that the proposed method is more effective over other existing methods.
Under extreme low-lighting conditions, images have low contrast, low brightness, and high noise. In this paper, we propose a principal component analysis framework to enhance low-light-level images with decomposed luminance–chrominance... more
Under extreme low-lighting conditions, images have low contrast, low brightness, and high noise. In this paper, we propose a principal component analysis framework to enhance low-light-level images with decomposed luminance–chrominance components. A multi-scale retinex-based adaptive filter is developed for the luminance component to enhance contrast and brightness significantly. Noise is attenuated by a proposed collaborative filtering employed to both the luminance and chrominance components that reveal every finest detail by preserving the unique features in the image. To evaluate the effectiveness of the proposed algorithm, a simulation model is proposed to generate nighttime images for various levels of contrast and noise. The proposed algorithm can process a wide range of images without introducing ghosting and halo artifacts. The quantitative performance of the algorithm is measured in terms of both full-reference and blind performance metrics. It shows that the proposed method delivers state-of-the-art performance both in terms of objective criteria and visual quality compared to the existing methods.
Neural-network-based image denoising is one of the promising approaches to deal with problems in image processing. In this work, a deep fully symmetric convolutional–deconvolutional neural network (FSCN) is proposed for image denoising.... more
Neural-network-based image denoising is one of the promising approaches to deal with problems in image processing. In this work, a deep fully symmetric convolutional–deconvolutional neural network (FSCN) is proposed for image denoising. The proposed model comprises a novel architecture with a chain of successive symmetric convolutional–deconvolutional layers. This framework learns convolutional–deconvolutional mappings from corrupted images to the clean ones in an end-to-end fashion without using image priors. The convolutional layers act as feature extractor to encode primary components of the image contents while eliminating corruptions, and the deconvolutional layers then decode the image abstractions to recover the image content details. An adaptive moment optimizer is used to minimize the reconstruction loss as it is appropriate for large data and noisy images. Extensive experiments were conducted for image denoising to evaluate the FSCN model against the existing state-of-the-...
Wireless sensor networks consists of a large number of Tiny low power sensor nodes, each with sensing, computation and wireless communication capabilities. Sensor nodes are deployed in unattended environments, they are vulnerable to a... more
Wireless sensor networks consists of a large number of Tiny low power sensor nodes, each with sensing, computation and wireless communication capabilities. Sensor nodes are deployed in unattended environments, they are vulnerable to a wide variety of attacks. Malicious nodes can generate incorrect readings and misleading reports. In this paper, we present a malicious node detection scheme for wireless sensor networks. The malicious nodes are detected by computing the average number of event cycles. In addition, each sensor node maintains the trust values of its neighbouring nodes to reflect their behaviour in decision-making. Computer simulation shows that the proposed scheme achieves high malicious node detection accuracy with lesser number of event cycles to detect the malicious node.
Due to the low accuracy of object detection and recognition in many intelligent surveillance systems at nighttime, the quality of night images is crucial. Compared with the corresponding daytime image, nighttime image is characterized as... more
Due to the low accuracy of object detection and recognition in many intelligent surveillance systems at nighttime, the quality of night images is crucial. Compared with the corresponding daytime image, nighttime image is characterized as low brightness, low contrast and high noise. In this paper, a bio-inspired image enhancement algorithm is proposed to convert a low illuminance image to a brighter and clear one. Different from existing bio-inspired algorithm, the proposed method doesn’t use any training sequences, we depend on a novel chain of contrast enhancement and denoising algorithms without using any forms of recursive functions. Our method can largely improve the brightness and contrast of night images, besides, suppress noise. Then we implement on real experiment, and simulation experiment to test our algorithms. Both results show the advantages of proposed algorithm over contrast pair, Meylan and Retinex.
