Matrix-based pairwise key establishment in wireless mesh networks using deployment knowledge

Due to the nature of wireless transmission, communication in wireless mesh networks (WMNs) is vulnerable to many adversarial activities including eavesdropping. Pairwise key establishment is one of the fundamental issues in securing WMNs. This paper presents a new matrix-based pairwise key establishment scheme for mesh clients. Our design is motivated by the fact that in WMNs, mesh routers are more powerful than mesh clients, both in computation and communication. By exploiting this heterogeneity, expensive operations can be delegated to mesh routers, which help alleviate the overhead of mesh clients during key establishment. The new scheme possesses two desirable features: (1) Neighbor mesh clients can directly establish pairwise keys; and (2) Communication and storage costs at mesh clients are significantly reduced.

Matrix-based pairwise key establishment with pre and post deployment knowledge for wireless mesh networks

The nature of wireless transmission leads to vulnerabilities to many malicious activities, and communication in wireless mesh networks (WMNs) must be protected by proper security measures. This paper focuses on symmetric pair wise key establishment and presents a new matrix-based pair wise key establishment scheme for mesh clients. In WMNs, mesh routers are much more powerful than mesh clients, both in communication and computation. By taking advantage of this heterogeneity, our new scheme delegates energy-consuming operations to mesh routers when establishing pair wise keys for mesh clients. Additionally, neighbor mesh clients in our scheme can directly establish pair wise keys with significantly reduced communication and storage costs, due to the use of both pre and post deployment knowledge.

Polynomial based key predistribution scheme in wireless mesh networks

Wireless mesh networks (WMNs) have the ability to integrate with other networks while providing a fast and cost-saving deployment. The network security is one of important challenge problems in this kind of networks. This paper is focused on key management between mesh and sensor networks. We propose an efficient key pre-distribution scheme based on two polynomials in wireless mesh networks by employing the nature of heterogeneity. Our scheme realizes the property of bloom filters, i.e., neighbor nodes can discover their shared keys but have no knowledge on the different keys possessed by the other node, without the probability of false positive. The analysis presented in this paper shows that our scheme has the ability to establish three different security level keys and achieves the property of self adaptive security for sensor networks with acceptable computation and communication consumption.

Sustainability analysis and resource management for wireless mesh networks with renewable energy supplies

 There is a growing interest in the use of renewable energy sources to power wireless networks in order to mitigate the detrimental effects of conventional energy production or to enable deployment in off-grid locations. However, renewable energy sources, such as solar and wind, are by nature unstable in their availability and capacity. The dynamics of energy supply hence impose new challenges for network planning and resource management. In this paper, the sustainable performance of a wireless mesh network powered by renewable energy sources is studied. To address the intermittently available capacity of the energy supply, adaptive resource management and admission control schemes are proposed. Specifically, the goal is to maximize the energy sustainability of the network, or equivalently, to minimize the failure probability that the mesh access points (APs) deplete their energy and go out of service due to the unreliable energy supply. To this end, the energy buffer of a mesh AP is modeled as a G/G/1(/N) queue with arbitrary patterns of energy charging and discharging. Diffusion approximation is applied to analyze the transient evolution of the queue length and the energy depletion duration. Based on the analysis, an adaptive resource management scheme is proposed to balance traffic loads across the mesh network according to the energy adequacy at different mesh APs. A distributed admission control strategy to guarantee high resource utilization and to improve energy sustainability is presented. By considering the first and second order statistics of the energy charging and discharging processes at each mesh AP, it is demonstrated that the proposed schemes outperform some existing state-of-the-art solutions.

Development of a whole agency approach to market segmentation in parks

Park agencies must plan to accommodate a diversity of visitors in order to satisfy visitor expectations and encourage future visitation. This study applies a market segmentation approach to develop a visitor typology that is effective across a broad spectrum of parks and applicable to a range of priorities, both strategic and operational, within park management agencies. Over a four-year period, data was sourced from over 11,000 interviews conducted at 33 diverse Australian national and metropolitan parks managed by the agency Parks Victoria. Factor analysis and cluster analysis was used to identify seven distinct visitor segments on the basis of numerous variables including, crucially, benefits sought. The applied and theoretical contributions of this study to the parks literature are discussed.

Segmentation of gray scale image based on intuitionistic fuzzy sets constructed from several membership functions

Segmentation is the process of extraction of objects from an image. This paper proposes a new algorithm to construct intuitionistic fuzzy set (IFS) from multiple fuzzy sets as an application to image segmentation. Hesitation degree in IFS is formulated as the degree of ignorance (due to the lack of knowledge) to determine whether the chosen membership function is best for image segmentation. By minimizing entropy of IFS generated from various fuzzy sets, an image is thresholded. Experimental results are provided to show the effectiveness of the proposed method.

A cross-layer key establishment scheme in wireless mesh networks

Cryptographic keys are necessary to secure communications among mesh clients in wireless mesh networks. Traditional key establishment schemes are implemented at higher layers, and the security of most such designs relies on the complexity of computational problems. Extracting cryptographic keys at the physical layer is a promising approach with information-theoretical security. But due to the nature of communications at the physical layer, none of the existing designs supports key establishment if communicating parties are out of each other's radio range, and all schemes are insecure against man-in-the-middle attacks. This paper presents a cross-layer key establishment scheme where the established key is determined by two partial keys: one extracted at the physical layer and the other generated at higher layers. The analysis shows that the proposed cross-layer key establishment scheme not only eliminates the aforementioned shortcomings of key establishment at each layer but also provides a flexible solution to the key generation rate problem. © 2014 Springer International Publishing Switzerland.

Learning complementary saliency priors for foreground object segmentation in complex scenes

Object segmentation is widely recognized as one of the most challenging problems in computer vision. One major problem of existing methods is that most of them are vulnerable to the cluttered background. Moreover, human intervention is often required to specify foreground/background priors, which restricts the usage of object segmentation in real-world scenario. To address these problems, we propose a novel approach to learn complementary saliency priors for foreground object segmentation in complex scenes. Different from existing saliency-based segmentation approaches, we propose to learn two complementary saliency maps that reveal the most reliable foreground and background regions. Given such priors, foreground object segmentation is formulated as a binary pixel labelling problem that can be efficiently solved using graph cuts. As such, the confident saliency priors can be utilized to extract the most salient objects and reduce the distraction of cluttered background. Extensive experiments show that our approach outperforms 16 state-of-the-art methods remarkably on three public image benchmarks.

3D Intervertebral disc localization and segmentation from MR images by data-driven regression and classification

In this paper we propose a new fully-automatic method for localizing and segmenting 3D intervertebral discs from MR images, where the two problems are solved in a unified data-driven regression and classification framework. We estimate the output (image displacements for localization, or fg/bg labels for segmentation) of image points by exploiting both training data and geometric constraints simultaneously. The problem is formulated in a unified objective function which is then solved globally and efficiently. We validate our method on MR images of 25 patients. Taking manually labeled data as the ground truth, our method achieves a mean localization error of 1.3 mm, a mean Dice metric of 87%, and a mean surface distance of 1.3 mm. Our method can be applied to other localization and segmentation tasks.

A new design concept for knitted external vein-graft support mesh

Autologous vein-graft failure significantly limits the long-term efficacy of coronary artery bypass procedures. The major cause behind this complication is biomechanical mismatch between the vein and coronary artery. The implanted vein experiences a sudden increase (10-12 fold) in luminal pressures. The resulting vein over-distension or 'ballooning' initiates wall thickening phenomenon and ultimate occlusion. Therefore, a primary goal in improving the longevity of a coronary bypass procedure is to inhibit vein over-distension using mechanical constriction. The idea of using an external vein-graft support mesh has demonstrated sustained benefits and wide acceptance in experimental studies. Nitinol based knitted structures have offered more promising mechanical features than other mesh designs owing to their unique loosely looped construction. However, the conventional plain knit construction still exhibits limitations (radial compliance, deployment ease, flexibility, and bending stresses) which limit this design from proving its real clinical advantage. The new knitted mesh design presented in this study is based on the concept of composite knitting utilising high modulus (nitinol and polyester) and low modulus (polyurethane) material components. The experimental comparison of the new design with a plain knit design demonstrated significant improvement in biomechanical (compliance, flexibility, extensibility, viscoelasticity) and procedural (deployment limit) parameters. The results are indicative of the promising role of new mesh in restoring the lost compliance and pulsatility of vein-graft at high arterial pressures. This way it can assist in controlled vein-graft remodelling and stepwise restoration of vein mechanical homoeostasis. Also, improvement in deployment limit parameter offers more flexibility for a surgeon to use a wide range of vein diameters, which may otherwise be rendered unusable for a plain knit mesh.

Fully automatic localization and segmentation of 3D vertebral bodies from CT/MR images via a learning-based method

In this paper, we address the problems of fully automatic localization and segmentation of 3D vertebral bodies from CT/MR images. We propose a learning-based, unified random forest regression and classification framework to tackle these two problems. More specifically, in the first stage, the localization of 3D vertebral bodies is solved with random forest regression where we aggregate the votes from a set of randomly sampled image patches to get a probability map of the center of a target vertebral body in a given image. The resultant probability map is then further regularized by Hidden Markov Model (HMM) to eliminate potential ambiguity caused by the neighboring vertebral bodies. The output from the first stage allows us to define a region of interest (ROI) for the segmentation step, where we use random forest classification to estimate the likelihood of a voxel in the ROI being foreground or background. The estimated likelihood is combined with the prior probability, which is learned from a set of training data, to get the posterior probability of the voxel. The segmentation of the target vertebral body is then done by a binary thresholding of the estimated probability. We evaluated the present approach on two openly available datasets: 1) 3D T2-weighted spine MR images from 23 patients and 2) 3D spine CT images from 10 patients. Taking manual segmentation as the ground truth (each MR image contains at least 7 vertebral bodies from T11 to L5 and each CT image contains 5 vertebral bodies from L1 to L5), we evaluated the present approach with leave-one-out experiments. Specifically, for the T2-weighted MR images, we achieved for localization a mean error of 1.6 mm, and for segmentation a mean Dice metric of 88.7% and a mean surface distance of 1.5 mm, respectively. For the CT images we achieved for localization a mean error of 1.9 mm, and for segmentation a mean Dice metric of 91.0% and a mean surface distance of 0.9 mm, respectively.

A robust approach for automated lung segmentation in thoracic CT

Lung segmentation in thoracic computed tomography (CT) scans is an important preprocessing step for computer-aided diagnosis (CAD) of lung diseases. This paper focuses on the segmentation of the lung field in thoracic CT images. Traditional lung segmentation is based on Gray level thresholding techniques, which often requires setting a threshold and is sensitive to image contrasts. In this paper, we present a fully automated method for robust and accurate lung segmentation, which includes a enhanced thresholding algorithm and a refinement scheme based on a texture-aware active contour model. In our thresholding algorithm, a histogram based image stretch technique is performed in advance to uniformly increase contrasts between areas with low Hounsfield unit (HU) values and areas with high HU in all CT images. This stretch step enables the following threshold-free segmentation, which is the Otsu algorithm with contour analysis. However, as a threshold based segmentation, it has common issues such as holes, noises and inaccurate segmentation boundaries that will cause problems in future CAD for lung disease detection. To solve these problems, a refinement technique is proposed that captures vessel structures and lung boundaries and then smooths variations via texture-aware active contour model. Experiments on 2,342 diagnosis CT images demonstrate the effectiveness of the proposed method. Performance comparison with existing methods shows the advantages of our method.