SPECTRA: Secure Power Efficient Clustered Topology Routing Algorithm

Wireless Sensor Networks (WSNs) have emerged as one of the hottest fields today due to their low-cost, self-organizing behavior, sensing ability in harsh environments, and their large application scope. One of the most challenging topics in WSNs is security. In some applications it is critical to provide confidentiality and authentication in order to prevent information from being compromised. However, providing key management for confidentiality and authentication is difficult due to the ad hoc nature, intermittent connectivity, and resource limitations of the network. Though traditional public keybased security protocols do exist, they need large memory bandwidths and complex algorithms, and are thus unsuitable for WSNs. Current solutions to the security issue in WSNs were created with only authentication and confidentiality in mind. This is far from optimal, because routing and security are closely correlated. Routing and security are alike because similar steps are taken in order to achieve these functions within a given network. Therefore, security and routing can be combined together in a cross-layer design, reducing the consumption of resources. The focus of this work is on the integration of routing and key management to provide an energy efficient security and routing solution. Towards this goal, this work proposes a security protocol that encompasses the following features: integration of security and routing, dynamic security, robust re-keying, low-complexity, and dual levels of encryption. This work combines all the robust features of current security implementations while adding additional features like dual layer encryption, resulting in an extremely efficient security protocol

Combinatorial design-based key pre-distribution schemes for wireless sensor networks

Identification of interface residues involved in protein-protein and protein-DNA interactions is critical for understanding the functions of biological systems. Because identifying interface residues using experimental methods cannot catch up with the pace at which protein sequences are determined, computational methods that can identify interface residues are urgently needed. In this study, we apply machine-learning methods to identify interface residues with the focus on the methods using amino acid sequence information alone. We have developed classifiers for identification of the residues involved in protein-protein and protein-DNA interactions using a window of primary sequence as input. The classifiers were evaluated using both representative datasets and specific cases of interest based on multiple measurements. The results have shown the feasibility of identifying interface residues from sequence. We have also explored information besides primary sequence to improve the performance of sequence-based classifiers. The results show that the performance of sequence-based classifiers can be improved by using solvent accessibility and sequence entropy of the target residue as additional inputs. We have developed a database of protein-protein interfaces that consists of all the protein-protein interfaces derived from the Protein Data Bank. This database, for the fist time, makes possible the quick and flexible retrieval of interface sets and various interface features. We have systematically analyzed the characteristics of interfaces using the largest dataset available. In particular, we compared interfaces with the samples that had the same solvent accessibility as the interfaces. This strategy excludes the effect of solvent accessibility on the distributions of residues, secondary structure, and sequence entropy

Security wireless sensor networks: prospects, challenges, and future

With the advancements of networking technologies and miniaturization of electronic devices, wireless sensor network (WSN) has become an emerging area of research in academic, industrial, and defense sectors. Different types of sensing technologies combined with processing power and wireless communication capability make sensor networks very lucrative for their abundant use in near future. However, many issues are yet to be solved before their full-scale practical implementations. Among all the research issues in WSN, security is one of the most challenging topics to deal with. The major hurdle of securing a WSN is imposed by the limited resources of the sensors participating in the network. Again, the reliance on wireless communication technology opens the door for various types of security threats and attacks. Considering the special features of this type of network, in this chapter we address the critical security issues in wireless sensor networks. We talk about cryptography, steganography, and other basics of network security and their applicability in WSN. We explore various types of threats and attacks against wireless sensor networks, possible countermeasures, mentionable works done so far, other research issues, etc. We also introduce the view of holistic security and future trends towards research in wireless sensor network security

Security in Distributed, Grid, Mobile, and Pervasive Computing

This book addresses the increasing demand to guarantee privacy, integrity, and availability of resources in networks and distributed systems. It first reviews security issues and challenges in content distribution networks, describes key agreement protocols based on the Diffie-Hellman key exchange and key management protocols for complex distributed systems like the Internet, and discusses securing design patterns for distributed systems. The next section focuses on security in mobile computing and wireless networks. After a section on grid computing security, the book presents an overview of security solutions for pervasive healthcare systems and surveys wireless sensor network security

3D shape matching and registration : a probabilistic perspective

Dense correspondence is a key area in computer vision and medical image analysis. It has applications in registration and shape analysis. In this thesis, we develop a technique to recover dense correspondences between the surfaces of neuroanatomical objects over heterogeneous populations of individuals. We recover dense correspondences based on 3D shape matching. In this thesis, the 3D shape matching problem is formulated under the framework of Markov Random Fields (MRFs). We represent the surfaces of neuroanatomical objects as genus zero voxel-based meshes. The surface meshes are projected into a Markov random field space. The projection carries both geometric and topological information in terms of Gaussian curvature and mesh neighbourhood from the original space to the random field space. Gaussian curvature is projected to the nodes of the MRF, and the mesh neighbourhood structure is projected to the edges. 3D shape matching between two surface meshes is then performed by solving an energy function minimisation problem formulated with MRFs. The outcome of the 3D shape matching is dense point-to-point correspondences. However, the minimisation of the energy function is NP hard. In this thesis, we use belief propagation to perform the probabilistic inference for 3D shape matching. A sparse update loopy belief propagation algorithm adapted to the 3D shape matching is proposed to obtain an approximate global solution for the 3D shape matching problem. The sparse update loopy belief propagation algorithm demonstrates significant efficiency gain compared to standard belief propagation. The computational complexity and convergence property analysis for the sparse update loopy belief propagation algorithm are also conducted in the thesis. We also investigate randomised algorithms to minimise the energy function. In order to enhance the shape matching rate and increase the inlier support set, we propose a novel clamping technique. The clamping technique is realized by combining the loopy belief propagation message updating rule with the feedback from 3D rigid body registration. By using this clamping technique, the correct shape matching rate is increased significantly. Finally, we investigate 3D shape registration techniques based on the 3D shape matching result. Based on the point-to-point dense correspondences obtained from the 3D shape matching, a three-point based transformation estimation technique is combined with the RANdom SAmple Consensus (RANSAC) algorithm to obtain the inlier support set. The global registration approach is purely dependent on point-wise correspondences between two meshed surfaces. It has the advantage that the need for orientation initialisation is eliminated and that all shapes of spherical topology. The comparison of our MRF based 3D registration approach with a state-of-the-art registration algorithm, the first order ellipsoid template, is conducted in the experiments. These show dense correspondence for pairs of hippocampi from two different data sets, each of around 20 60+ year old healthy individuals

Sensor networks for social networks

Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2006.Includes bibliographical references (p. 51-55).This thesis outlines the development of software that makes use of Bayesian belief networks and signal processing techniques to make meaningful inferences about real-world phenomena using data obtained from sensor networks. The effectiveness of the software is validated by applying it to the problem of detecting face-to-face social interactions between groups of people, given data readings from sensors that record light, temperature, acceleration, sound, and proximity. This application represents a novel method for social network construction which is potentially more accurate and less intrusive than traditional methods, but also more meaningful than newer methods that analyze digitally mediated communication.by Michael P. Farry.M.Eng

Dynamic keyring update mechanism for mobile wireless sensor networks/

Wireless Sensor Networks (WSNs) are composed of small, battery-powered devices called sensor nodes. Sensor nodes have sensing, processing and communication capabilities to monitor the environment and gather data. WSNs have various application areas ranging from military surveillance to forest fire detection. Security is an important issue for Wireless Sensor Networks because sensor nodes are deployed in hostile and unattended areas. Nodes are vulnerable to physical capture attacks and the attackers can easily eavesdrop on network communications. To provide security to WSNs, many key predistribution schemes have been proposed. However, most of these schemes consider the static WSNs and they perform poorly when they are applied to Mobile Wireless Sensor Networks (MWSNs). In this thesis, we propose Dynamic Keyring Update (DKRU) mechanism for MWSNs. The aim of DKRU mechanism is to enable sensor nodes to update their keyrings periodically during movement, by observing the frequent keys in their neighbors. Our mechanism can be used together with different key predistribution schemes and it helps to increase the performance of them. For performance evaluation reasons, we used our mechanism together with an existing random key predistribution scheme and a location-based key predistribution scheme. For each of these key predistribution schemes, we analyzed our mechanism using two different mobility models. Our results show that DKRU mechanism increases the local and global connectivity when it is applied to MWSNs. Moreover, our mechanism is scalable and it does not cause significant degradation in network resiliency and communication overhead