The availability of high resolution images allows for using steganography to hide the presence of secret information. An issue of importance is to guarantee that the hidden data neither affects the quality of the images nor is it visually perceived. Another factor to consider is the capacity of embedding in the cover images without detection. In this paper we employ the Discrete Cosine Transform compression with quantization for lossy compression. We use the Salp Swarm Algorithm in conjunction with chaotic maps to find optimal locations in the cover images for data embedding in the LSBs. When chaotic maps are inserted into the SSA algorithm environment, the proposed method has achieved satisfactory results with minimal impact on the PSNR.

Visual crowdsensing applications using built-in cameras in smartphones have recently attracted researchers' interest. Making the most out of the limited resources to acquire the most helpful images from the public is a challenge in disaster recovery applications. Proposed solutions should adequately address several constraints, including limited bandwidth, limited energy resources, and interrupted communication links with the command center or server. Furthermore, data redundancy is considered one of the main challenges in visual crowdsensing. In distributed visual crowdsensing systems, photo sharing duplicates and expands the amount of data stored on each sensor node. As a result, if any node can communicate with the server, then more photos of the target region would be available to the server. Methods for recognizing and removing redundant data provide a range of benefits, including decreased transmission costs and energy consumption overall. To handle the interrupted communication with the server and the restricted resources of the sensor nodes, this paper proposes a distributed visual crowdsensing system for full-view area coverage. The target area is divided into virtual sub-regions, each of which is represented by a set of boundary points of interest. Then, based on the criteria for full-view area coverage, a specific data structure theme is developed to represent each photo with a set of features. The geometric context parameters of each photo are utilized to extract the features of each photo based on the full-view area coverage criteria. Finally, data redundancy removal algorithms are implemented based on the proposed clustering scheme to eliminate duplicate photos. As a result, each sensor node may filter redundant photographs in dispersed contexts without requiring high computational complexity, resources, or global awareness of all photos from all sensor nodes inside the target area. Compared to the most recent state-of-the-art, the improvement ratio of the added values of the photos provided by the proposed method is more than 38%. In terms of traffic transfer, the proposed method requires fewer data to be transferred between sensor nodes and between sensor nodes and the command center. The overall reduction in traffic exceeds 20% and the overall savings in energy consumption is more than 25%. It was evident that in the proposed system, sending photos between sensor nodes, as well as between sensor nodes and the command center, consumes less energy than existing approaches due to the considerable amount of photo exchange required. Thus, the proposed technique effectively transfers only the most valuable photos needed.

Image steganography has been widely adopted to protect confidential data. Researchers have been seeking to improve the steganographic techniques in order to increase the embedding capacity while preserving the stego-image quality. In this paper, we propose a steganography method using particle swarm optimization and chaos theory aiming at finding the best pixel locations in the cover image to hide the secret data while maintaining the quality of the resultant stego-image. To enhance the embedding capacity, the host and secret images are divided into blocks and each block stores an appropriate amount of secret bits. Experimental results show that the proposed scheme outperforms existing methods in terms of the PSNR and SSIM image quality metrics.

With the expansive demand for video streaming over mobile networks, it is necessary to adopt schemes that balance the need for high video quality with the available network resources when streaming or downloading the video. Several approaches were proposed in the literature, including Dynamic Adaptive Streaming over HTTP (DASH). In this work, we consider an approach in which we place sufficient emphasis on the constrained battery resources in mobile devices when making decisions on the quality (or bitrate) of the video to be requested. This is done by using a fuzzy logic controller that enhances the performance of the Fuzzy-based DASH (FDASH) scheme. Simulation results show that our proposed approach conserves more energy than its predecessor while maintaining similar video quality and avoiding playback interruptions.

Database as a service is one of the important services provided by Cloud Computing. Recently, a Two Layered Protection Scheme for securing the database has been proposed. The scheme employs two symmetric key encryption algorithms, the Order Preserving Encryption and Format Preserving Encryption. Each of the two encryption algorithms uses a different encryption key that is derived from Key Splitting module. Key Splitting module generates two keys from a Main Key by using a randomized algorithm. Randomized algorithm does not guarantee that the generated keys are always different because the resulting keys depend on the generated random numbers. Hence, in order to increase the security of the Two Layered Protection Scheme, a Genetic based Key Splitting algorithm is proposed. The purpose of the proposed algorithm is to generate the best random two keys that have maximum difference so that the two keys cannot be derived from each other. Simulation results have shown that the proposed algorithm generates random keys with maximum difference.

Data hiding in digital images provides a secure communication medium by embedding the payload in a cover image without compromising the image quality. In this work, a secure and high-capacity data hiding scheme is proposed using efficient compression, encryption, and steganography techniques. Dictionary-based coding along with Huffman coding is used to compress the secret data and hence increase the embedding capacity. Two encrypted keys are generated to control the data decompression and hence improve the level of security. In addition, a block-based histogram modification technique is applied to embed the compressed data into the chrominance components of the cover image to minimize the image distortion. The results indicate that the proposed scheme achieves high embedding capacity without degrading the quality of the cover image.

In this paper, a data hiding method to transfer secret data over unsecure networks is proposed. The method is based on a matrix pattern approach to hide the secret data into color images. In the proposed method, the image is first transformed into the YCbCr color space, and then each image component is divided into blocks. The Y component is used to generate matrix patterns using the texture of each Y block and assign these patterns to ASCII characters. The Cb and Cr components are then used to hide the secret data using the generated matrix patterns. The results show that the proposed scheme has achieved higher embedding capacity compared to other methods while maintaining the stego-image quality.

The evolution of wireless multimedia sensor networks (WMSN) has opened the door to a wide range of applications such as telemedicine, surveillance, and intrusion detection. However, the delivery of multimedia content over wireless sensor networks requires maintaining the quality of service demands of the applications as well as the resource constraints of the network. This has motivated the design of cross-layer architectures as a promising solution for efficiently meeting the quality of service requirements and the resource constraints in WMSN. In this paper, a cross-layer design for image transmission, using fuzzy logic, is proposed to provide quality of service guarantees and efficient resource utilization in WMSN. The proposed cross-layer design takes into consideration the trade-off between the network performance and the quality of received images by exploiting the information exchange between different layers in the protocol stack. The simulation results demonstrate that the proposed approach can efficiently improve the network performance in terms of packet loss, delay, and energy consumption, while maintaining an acceptable level of quality of the received images.

Wireless multimedia sensor networks (WMSN) are used in various applications such as surveillance, health care, and environment monitoring. There is a need to design energy-efficient routing protocol capable of supporting the QoS requirements of multimedia traffic. In this work, we study the Adaptive Greedy-compass Energy-aware Multipath routing protocol and consider its shortcomings when applied for multimedia data. We then present three enhancements to the original protocol. First, the routing mechanism is changed to extend a wider angle scope for path generation. Second, we add the angle of view and distance towards the sink as metrics in the cost function of the original protocol. Finally, the protocol is modified to support different traffic types with different QoS requirements. Simulation results show that the enhanced protocol achieves a higher number of generated paths and an improvement in the overall network lifetime. These two improvements are necessary for dependable delivery of multimedia traffic.

Data compression plays an important role in data storage and transmission by reducing the amount of data stored or transmitted, thus achieving efficient use of storage and network resources. In this paper, an adaptive word-based pattern matching and text substitution, compression scheme is proposed. First, a dynamic pattern-matching dictionary is created, and then the word patterns of the text are replaced by permutation symbols in the dictionary. The experimental results show that the proposed method achieves an average compression ratio of 0.18 and an average saving percentage of 81.8%.

Radio frequency identification is a modern technology for item identification that has widely spread over the recent years. In order to securely identify the items and transfer their ownership from one entity to another, several authentication and ownership management protocols have been proposed. One of the recent protocols is Niu et al.'s EPC Class 1 Generation 2 RFID Standard Authentication and Ownership Management protocol. This lightweight protocol is mainly based on the use of bitwise XOR and permutation operations in addition to the lightweight 16-bit Pseudo Random Number Generator. However, Bagheri et al. launched de-synchronization and secret-disclosure attacks against Niu et al.'s protocol and proved that it does not fulfill the requirements for a secure authentication and ownership management protocol. In this paper, we propose an enhancement for Niu et al.'s protocol such that it can resist the de-synchronization and secret-disclosure attacks. The enhanced protocol is based on using the bitwise conversion and Hamming Weight-based left rotation operations instead of the 16-bit Pseudo Random Number Generator and the permutation operation in order to resist the secret-disclosure attack. Moreover, the proposed protocol employs the old-ids-denial mechanism and adds extra messages to the mutual authentication and ownership delegation phases so that it can defend against the de-synchronization attack.

In practical wireless multimedia sensor networks, efficient utilization of available resources is very critical. However, in dense networks with randomly deployed camera nodes, it is very likely that the fields of view of spatially proximal camera nodes would be highly correlated as the views among these nodes are overlapped. This causes the nodes to send redundant information, wasting network resources. This paper proposes a novel geometrical correlation model to estimate the correlation characteristics among randomly deployed heterogeneous camera nodes. The proposed model is mathematically analyzed and a closed-form analytical correlation function is provided. The results demonstrate that the proposed model is appropriate for applications with heterogeneous camera nodes being deployed in a random manner and it can also be efficiently used in applications that require a bounded distortion-based camera selection strategy. That is, selecting the minimum number of nodes that deliver the maximum amount of information with the least distortion.

Memetic algorithms (MAs) are hybrid algorithms aimed to improve traditional evolutionary algorithms such as genetic algorithms (GAs). Recently, MAs have been widely used in the image processing field. This paper proposes an image segmentation approach using MA. The proposed approach employs local search as an improvement operator added to the GA in order to speed up the searching process and generate the best solutions faster. The results of the experiments that were conducted on eight different images have shown that MA converges to the solutions faster than GA. In terms of processing time, MA has recorded significant improvement ranging from 17.5% to 79.8%. The comparison results have also shown that MA always achieves the same or better quality of the segmented images.

In wireless sensor networks, cluster-based routing was proven to be the most energy-efficient strategy to deal with the scaling problem. In addition, selecting the proper number of clusters is a critical decision that can impose a significant impact on the energy consumption and the network lifetime. This paper presents FL-LEACH, a variant of the well-known LEACH clustering protocol, which attempts to relax the stringent strategy of determining the number of clusters used by LEACH via fuzzy logic decision-making scheme. This relates the number of clusters to a number of network characteristics such as the number of sensor nodes, the area of the sensing field, and the location of the base station. The performance of FL-LEACH was evaluated via simulation and was compared against LEACH using standard metrics such as network lifetime and remaining network energy. The results depicted that the proposed approach has the potential to substantially conserve the sensor node energy and extend lifetime of the network.

Node clustering in wireless sensor networks helps in extending the network life time by reducing the nodes' communication energy and balancing their remaining energy. This paper presents a new genetic-based approach that improves the performance of the LEACH clustering protocol used in wireless sensor networks. The proposed approach utilizes the mobility feature of sensor nodes in order to reduce the communication distances between the cluster heads and the base station. In each round, new locations of the cluster heads are determined using a genetic algorithm. The simulation results demonstrate that the proposed approach outperforms LEACH in terms of network lifetime and average remaining energy.

In applied wireless multimedia sensor networks, heterogeneous camera nodes with different sensing capabilities are usually deployed due to their role in enhancing the overall network performance and lifetime. Exploiting the correlation characteristics of the overlapping fields of view of different camera nodes would enable very efficient collaborative in-network processing algorithms. This paper introduces a novel geometrical model to extract the spatial correlation characteristics of heterogeneous camera nodes in wireless multimedia sensor networks, taking into consideration the different sensing radii and the angles of view of the camera nodes. The novelty in the proposed model is in using virtual cameras at the two far ends of the camera's field-of-view. In order to provide better coverage of the field-of-view and hence better estimation of the correlation characteristics, key points in the observed scene are projected at the virtual cameras; in addition to the physical camera. This is shown to significantly improve the estimation of the spatial correlation characteristics to be almost identical to that extracted by well-known image processing techniques. An analytical closed-form solution of the proposed model is derived and validated and its performance is evaluated and compared against the state-of-the-art models; in terms of correlation characteristics estimating accuracy, visual information gain, and distortion ratio. The experimental and simulation results demonstrate that, compared to similar existing models, the proposed model achieves very accurate estimation of the correlation characteristics and significant improvement on the overall network resource utilization for a negligible increase in the camera node's computational cost.

As the demand for large-scale wireless multimedia sensor networks increases, so does the need for well-designed protocols that optimize the utilization of available networks resources. This requires experimental testing for realistic performance evaluation and design tuning. However, experimental testing of large-scale wireless networks using hardware testbeds is usually very hard to perform due to the need for collecting and monitoring the performance metrics data for multiple sensor nodes all at the same time, especially the node's energy consumption data. On the other hand, pure simulation testing may not accurately replicate the real-life scenarios, especially those parameters that are related to the wireless signal behavior in special environments. Therefore, this work attempts to close this gap between experimental and simulation testing. This paper presents a scalable simulation testbed that attempts to mimic our previously designed small-scale hardware testbed for wireless multimedia sensor networks by tuning the simulation parameters to match the real-life measurements obtained via experimental testing. The proposed simulation testbed embeds the JPEG and JPEG2000 image compression algorithms and potentially allows for network-controlled image compression and transmission decisions. The simulation results show very close match to the small-scale experimental testing as well as to the hypothetical large-scale extensions that were based on the experimental results.

The unique characteristics of the elliptic curve cryptography (ECC) such as the small key size, fast computations and bandwidth saving make its use attractive for multimedia encryption. In this study, the ECC is used to perform encryption along with multimedia compression, and two ECC-based encryption algorithms are introduced and applied before and during compression. The first algorithm performs selective encryption on the transform coefficients during compression, whereas the second algorithm achieves perceptual encryption based on selective bit-plane encryption before compression. The results of applying ECC to multimedia encryption are presented and analysed with respect to the requirements of multimedia encryption: the encryption efficiency, compression efficiency, codec compliance and security level. It is shown that ECC is highly efficient and does not affect the compression efficiency, and accordingly can meet the multimedia encryption requirements.

This paper proposes a novel energy-efficient clustering protocol for wireless sensor networks. It combines the benefits of using the k-means clustering algorithm with the, recently developed, LEACH with virtual forces (LEACH-VF) protocol. In this work, the k-means algorithm is employed to determine k centroids around which the clusters will be formed. After that, the virtual field force method is applied to these clusters to determine the most suitable positions for each node. The main target of such an approach is to improve the energy balance in the network and to extend the network lifetime. Simulation results show that the proposed protocol extends the time before the first node death, minimizes the variance of the average node energy, and reduces the distance that the sensor nodes travel within their respective clusters.

Visual target classification is one of the challenging tasks in resource-constrained wireless sensor networks. This article presents binary and multicast animal classification techniques, which use rule-based decision tree, for wireless multimedia sensor networks. In order to reduce the computational complexity on the sensor nodes, the expensive training phase is carried out by a high-power base station. Then, the best IF-THEN rules are extracted from the decision tree classifier and stored in the sensor nodes before being deployed. This would decrease the learning phase time and the energy consumption, while attaining high classification accuracy. Experimental results demonstrated that the proposed classification model can effectively perform visual target classification in wireless multimedia sensor networks.