E-TPE: E fficient T humbnail- P reserving E ncryption for Privacy Protection in Visual Sensor Networks

Wireless visual sensor network (VSN) can be said to be a special class of wireless sensor network (WSN) with smart-cameras. Due to its visual sensing capability, it has become an effective tool for applications such as large area surveillance, environmental monitoring and objects tracking. Different from a conventional WSN, VSN typically includes relatively expensive camera sensors, enhanced flash memory and a powerful CPU. While energy consumption is dominated primarily by data transmission and reception, VSN consumes extra power onimage sensing, processing and storing operations. The well-known energy-hole problem of WSNs has a drastic impact on the lifetime of VSN, because of the additional energy consumption of a VSN. Most prior research on VSN energy issues are primarily focusedon a single device or a given specific scenario. In this paper, we propose a novel optimal two-tier deployment strategy for a large scale VSN. Our two-tier VSN architecture includes tier-1 sensing network with visual sensor nodes (VNs) and tier-2 network having only relay nodes (RNs). While sensing network mainly performs image data collection, relay network only for wards image data packets to the central sink node. We use uniform random distribution of VNs to minimize the cost of VSN and RNs are deployed following two dimensional Gaussian distribution so as to avoid energy-hole problem. Algorithms are also introduced that optimizes deployment parameters and are shown to enhance the lifetime of the VSN in a cost effective manner.

Visual Sensor Networks (VSNs), formed by large number of low-cost, small-size visual sensor nodes, represent a new trend in surveillance and monitoring practices. Sensor collaboration is essential to VSNs and normally performed among sensors having similar measurements. The directional sensing characteristics of imagers and the presence of visual occlusion present unique challenges to neighborhood formation, as geographically-close neighbors might not monitor similar scenes. In this paper, we propose the concept of forming semantic neighbors, where collaboration is only performed among geographically-close nodes that capture similar images, thus requiring image comparison as a necessary step. To avoid large amount of data transfer, we propose feature-based image comparison as features provide more compact representation of the image. The paper studies several representative feature detectors and descriptors, in order to identify a suitable feature-based image comparison system for the resource-constrained VSN. We consider two sets of metrics from both the resource consumption and accuracy perspectives to evaluate various combinations of feature detectors and descriptors. Based on experimental results obtained from the Oxford dataset and the MSP dataset, we conclude that the combination of Harris detector and moment invariants presents the best balance between resource consumption and accuracy for semantic neighbor formation in VSNs.

A prediction-based approach for features aggregation in Visual Sensor Networks

Recent developments in image and video technologies enabled easy access to a new type of sensor-based networks, Visual Sensor Networks (VSN). VSNs are gaining a lot of attention lately. They are used in several applications including surveillance and telepresence. They consist of several low-cost, low-power visual nodes with sensing, data processing, and communication capabilities. These tiny nodes are able to collect large volumes of images, process them, and send extracted data to each other and to the base station for further analysis. Unfortunately, the huge amount of data captured and processed is faced with the limited resources of such platforms. There are several challenges involved with the design and implementation of VSNs. This chapter presents an overview of visual nodes, architectures, and challenges. It also reviews available VSN platforms and compares their processing capabilities, highlighting the need for new lightweight but efficient image processing algorithms and architectures.

In this era of the information age with digitalization, the transmission of sensitive real-time image information over insecure channels is highly-likely to be accessed or even attacked by an adversary. To prevent such unauthorized access, cryptography is being used to convert sensitive information in real-time images into unintelligible data. Most of the time, schemes are proposed with a high level of security. However, the challenge always remains the slower speeds due to their high complexity which makes them unusable in the applications of real-time images. In this paper, an efficient image encryption algorithm has been developed and tested for real-time images. The proposed scheme makes use of encryption with an efficient permutation technique based on a modular logistic map to bring down the size of the chaotic value vector, required to permute real-time image. We show that an efficient permutation is obtained using only $$\sqrt{N}$$ chaotic numbers for a square image with 3N pixels (N Pixels in each color bit plane). The algorithm makes use of a 192-bit key; divided into smaller blocks and each block selected chaotically to diffuse the pixel using multiple XOR operations. The experimental analysis reveals that the proposed algorithm is immune to various statistical and differential attacks such as entropy, histogram analysis, spectral characteristic analysis, etc. A comparison of the proposed scheme with some state-of-the-art techniques show that it performs better, and as such, can be utilized for efficient real-time image encryption.

In this work, we propose the cubature Kalman filter (CKF) based distributed object tracking algorithm in a visual sensor network (VSN). A VSN consists of several distributed smart cameras having the ability to process and analyze the retrieved data locally. The first objective is to optimize the tracking process within the VSN through the CKF. Under the conditions of non-linear motion and observation model, the CKF based method features a considerably better tracking accuracy than the extended Kalman filter (EKF) based method in terms of the mean square error (MSE). Although, the particle filter (PF) based method shows better performance than the CKF, it is computationally very complex. The second objective is to optimize the object tracking by aggregating the tracking results from multiple cameras. Assuming the VSN is a multi-camera network with overlapping field of views (FOVs), cameras having the same object in their FOV exchange their local estimates of the object's position and velocity. During the estimation process, each of the participating cameras aggregates the received states via a consensus algorithm. Thus, the object's real state is more accurately predicted by the resulting joint state than it would be by processing only a single camera's observation.

Nowadays visual sensor networks are used in emergency or time of surveillance of remote area where human reach is not possible. There situations arise when mobility of nodes is required in visual sensor network. In this paper we considers mobility of visual sensor nodes and tries to maximize the lifetime of network using priority if nodes. We use hierarchical and heterogeneous architecture for deploying nodes. In this we use two types of nodes. On this architecture we use prioritized scheduling technique to maximize the lifetime of network. Result shows lifetime is increased compare to the stationary nodes as after same time we get more live sensors than stationary system.

Recently much research has been conducted in visual sensor networks. Compared to traditional sensor networks, vision networks differ in various aspects such as the amount of data to be processed and transmitted, the requirements on quality-of-service, and the level of collaboration among the sensor nodes. This paper deals with sensor fusion on visual sensor networks. We focus here on methods for fusing data from various distributed sensors and present a generic framework for fusion on embedded sensor nodes. This paper extends our previous work on distributed smart cameras and presents our approach toward the transformation of smart cameras into a distributed, embedded multisensor network. Our generic fusion model has been completely implemented on a distributed embedded system. It provides a middleware which supports automatic mapping of our fusion model to the target hardware. This middleware features dynamic reconfiguration to support modification of the fusion application at runtime without loss of sensor data. The feasibility and reusability of the I-SENSE concept is demonstrated with experimental results of two case studies: vehicle classification and bulk good separation. Qualitative and quantitative benefits of multilevel information fusion are outlined in this article.

During the last decade, visual monitoring has gained an increasing interest especially in the field of smart cities and intelligent transportation systems. This paper describes a feature-based vehicle tracking system in visual sensor networks at roundabouts. In the proposed system, the tasks for vehicle tracking are divided among smart camera nodes and the receiver. Smart camera nodes are embedded devices equipped with camera sensor, multiprocessors for video processing and computer vision tasks, and an RF transceiver for wireless communication. They are responsible for the vehicle detection and classification as well as features extraction. In order to satisfy the constraints of the visual sensor network in terms of power and communication resources, we assume that only the features are transmitted to the sink node. The received features will then be used to retrieve the vehicle trajectories. In order to train the vehicle classifier used at the camera nodes and to test the proposed tracking approach, a training database was built by placing cameras around a realworld roundabout. The system is shown to be capable of tracking vehicles even in partial occlusion cases.

Aiming at the complexity of the network transmission environment and the decentralization of the monitoring sites in the middle route of the South to North Water Diversion Project, this article presents a water conservancy monitoring architecture based on the visual sensor networks. First, a network assessment method is proposed to evaluate the sensor networks condition, and the encoder is adjusted with the result of the sensor networks condition evaluation. Then, based on distributed database architecture and visualization system construction, a three-level structure of the water conservancy monitoring system is designed and built. The experiment results show that the water conservancy monitoring system proposed in this article meets the transmission requirement under the complex sensor networks condition of water conservancy project. The water conservancy monitoring system has been applied to the middle route of the South to North Water Diversion Project, which achieves the needs of engineering applications.

This paper is to study the tracking of mobile objects and associated coverage problems in visual sensor networks. When sensors equipped with cameras are randomly deployed in a monitoring environment, many cameras are involved in covering the same mobile object. Images of the object may be captured by different camera nodes in different orientations simultaneously, and the captured images are then sent back to a base station or server. However, achieving characteristic point coverage of objects invariably involves a great deal of redundant image data consuming the transmission energy of the visual sensor network. One approach is proposed to overcome this problem. The minimum number of camera nodes required for set coverage can be determined by predicting the direction and speed of the mobile object. The simulation results show that this protocol reduces transmission cost while preserving the maximum coverage range of mobile objects.

Visual sensor networks (VSNs) usually generate a low-resolution (LR) frame-sequence due to energy and processing constraints. These LR-frames are not very appropriate for use in certain surveillance applications. It is very important to enhance the resolution of the captured LR-frames using resolution enhancement schemes. In this paper, an effective framework for a super-resolution (SR) scheme is proposed that enhances the resolution of LR key-frames extracted from frame-sequences captured by visual-sensors. In a VSN, a visual processing hub (VPH) collects a huge amount of visual data from camera sensors. In the proposed framework, at the VPH, key-frames are extracted using our recent key-frame extraction technique and are streamed to the base station (BS) after compression. A novel effective SR scheme is applied at BS to produce a high-resolution (HR) output from the received key-frames. The proposed SR scheme uses optimized orthogonal matching pursuit (OOMP) for sparse-representation recovery in SR. OOMP does better in terms of detecting true sparsity than orthogonal matching pursuit (OMP). This property of the OOMP helps produce a HR image which is closer to the original image. The K-SVD dictionary learning procedure is incorporated for dictionary learning. Batch-OMP improves the dictionary learning process by removing the limitation in handling a large set of observed signals. Experimental results validate the effectiveness of the proposed scheme and show its superiority over other state-of-the-art schemes.

Aiming at the security problem of traditional image encryption, this paper propose a mimic image encryption based on mimic defense try to improve the security of image encryption. Firstly, the encryption and decryption pools were built according to the working principle of mimic defense technology. Secondly, the redundancy execution bodies were dynamically scheduling through central controller. And the result of decryption were decided by consistency decision mechanism. The experimental results show that the new method can effectively improve the security of image encryption. And compared with traditional image encryption, the combination of consistency decision mechanism and dynamical scheduling make the new method more security.

Efficient Energy Consumption DCT-Based Image Compression in Visual Sensor Network

This thesis discusses the performance of a solar-powered wireless visual sensor network and its visual applications. We examine the performance of a layered clustering model in sparing communication energy consumption and prolonging the system lifetime. The experimental result illustrates that the system can transmit the same amount of video packets with less energy consumption when video quality is at achievable minimum distortion rate. Therefore, the visual sensor network may achieve higher performance by applying rechargeable solar cell and layered clustering. After receiving all the video data, the sink may be applied with advanced post-processing techniques. We propose an innovative post-processing algorithm, Parallel Self-Organizing Tree Map (PSOTM) that can be implemented in the sink. By means of processing visual data in parallel, PSOTM may achieve faster image segmentation with insignificant impacts on the visual quality.