Authentication issue has been mostly ignored to ensure fast handoff in 802.11 Wireless Mesh Network (WMN). With the proliferation of WMNs in recent years for practical deployment, secured fast handoff has drawn much attention to enforce authenticated access while reduce the extra delay caused by enabling authentication operations. In this paper, we present an overview on the state-of-the-art advance in this field and tackle the problem from a practical perspective based on experiments and analysis on our real-world testbed HAWK. We propose a novel fast handoff scheme Network-assisted Radio Signature to eliminate probing delay by taking advantage of the characteristic of the actual dynamic topology about mesh routers in WMN. Moreover, we apply an optimistic authentication mechanism Dual Re-authentication to counteract the authentication delay while providing the secured wireless access. In this manner, we have reduced the end-to-end handoff delay of WMN back again to a level below 50ms to achieve secured handoff and support time-sensitive applications. We describe detailed mechanisms, simulation, implementation and experimental results. To our best knowledge, we are the first to achieve such an optimal performance of secured fast handoff.

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Many applications of wireless sensor networks (WSNs) require secure group communications. The WSNs are normally operated in unattended, harsh, or hostile environment. The adversaries may easily compromise some sensor nodes and abuse their shared keys to inject false sensing reports or modify the reports sent by other nodes. Once a malicious node is detected, the group key should be renewed immediately for the network security. Some strategies have been proposed to develop the group rekeying protocol, but most of existing schemes are not suitable for sensor networks due to their high overhead and poor scalability. In this paper, we propose a new group rekeying protocol for hierarchical WSNs with renewable network devices. Compared with existing schemes, our rekeying method possesses the following features that are particularly beneficial to the resource-constrained large-scale WSNs: (1) robustness to the node capture attack, (2) reactive rekeying capability to malicious nodes, and (3) low communication and storage overhead.

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Trusted Network Connect provides the functionality of the platform authentication and integrity verification which is crucial for enhancing the security of authentication protocols. However, applying this functionality directly to concrete authentications is susceptible to unknown attacks and efficiency degradation. In this paper, we propose TWMAP, a novel authentication protocol for WLAN Mesh networks in a trusted environment which completed the platform authentication and integrity verification during the user authentication. And, the Schnorr asymmetric signature scheme is utilized to reduce the overhead of the client. The security properties of the new protocol are examined using the Universally Composable Security model. The analytic comparisons and simulation results show that the new protocol is very efficient in both computing and communication costs.

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As a concept stemmed from social field, we argued that, in P2P networks, peers' recommendation behaviors and functional behaviors should be explicitly separated, thus we propose the HopRec scheme which uses hop-based recommendation ability to improve the accuracy of reputation ranking in P2P networks. Our contributions lie in the following aspects: firstly, we adopt the simple but effective idea to infer peer's recommendation ability (RA): the farer away that peer is from the initial malicious seeds, the higher RA that peer should have; Then, the computation of reputation rankings appropriately reflects peer's different RA. The simulation results show that, in comparison with Eigentrust-like algorithms, HopRec can be robust to sybils and front peers attacks, and achieve significant performance improvement. Moreover, we compare HopRec with two related schemes, Poisonedwater and CredibleRank, and found that: in hospitable P2P environment, HopRec can obtain better performance than Poisonedwater, and can achieve the comparable performance as CredibleRank, with less computation overhead then CredibleRank. Finally, we also show that, if the initial good and malicious seeds could be selected based on peers' degrees, then HopRec and CredibleRank can achieve perfect performance.

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Attacks using hill-climbing methods have been reported as a vulnerability of biometric authentication systems. In this paper, we propose a robust online signature verification algorithm against such attacks. Specifically, the attack considered in this paper is a hill-climbing forged data attack. Artificial forgeries are generated offline by using the hill-climbing method, and the forgeries are input to a target system to be attacked. In this paper, we analyze the menace of hill-climbing forged data attacks using six types of hill-climbing forged data and propose a robust algorithm by incorporating the hill-climbing method into an online signature verification algorithm. Experiments to evaluate the proposed system were performed using a public online signature database. The proposed algorithm showed improved performance against this kind of attack.

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Multiple hop based routing in homogeneous sensor networks with a single sink suffers performance degradation and severe security threats with the increase of the size of sensor networks. Large-scale sensor networks need to be deployed with multiple powerful nodes as sinks and they should be scheduled to move to different places during the lifetime of the networks. Existing routing mechanisms lack of such supports for large-scale sensor networks. In this paper, we propose a heterogeneous network model where multiple mesh nodes are deployed in a sensor network, and sensed data are collected through two tiers: firstly from a source sensor node to the closest mesh node in a multiple-hop fashion (called sensor routing), and then from the mesh node to the base station through long-distance mesh routing (called mesh routing). Based on this network model, we propose an energy-efficient and secure protocol for the sensor routing that can work well in large-scale sensor networks and resist most of attacks. Experiments demonstrate that our routing protocol significantly reduces average hops for data transmission. Our lightweight security mechanism enables the routing protocol to defend most attacks against sensor networks.

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The security of P2P networks depends on building trust management among peers. However, current trust management models focus on preventing untrustworthy resources from spreading by malicious providers, but have few effects on reducing denial-of-service attacks of malicious consumers and free riding of selfish peers. Pointing to these problems, a bi-evaluation^*** trust management model, called BiTrust, is proposed. In this model, the trustworthiness of a peer is divided into service and request trustworthiness. Service trustworthiness shows the resources reliability of providers, and request trustworthiness is used to deal with requests from consumers, which can keep away malicious consumers and encourage selfish peers to share resources. A generic method for evaluating service and request trustworthiness is described. Furthermore, the implementation strategies of the model are also depicted in this paper. The following analysis and simulation show that BiTrust is more effective on enhancing high-quality resources sharing among peers and more advanced in successful exchanges rate.

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Lawful Interception (LI) refers to a lawfully authorized process of looking into private communication under a court-issued warrant. Quite a number of countries have been drafting and enacting laws authorizing the LI procedures on packet-switched IP networks including traditional circuit-switched ones. As the IP mobility becomes more ubiquitous, propelled by wireless networks, it becomes an issue in the LI domain to keep track of a migrating target. However, with the world's focus on the current LI architectures, little consideration has been given to a seamless LI triggering, which accommodates IP mobility and vertical handover. Proposed herein are a seamless LI architecture and relevant triggering algorithms for the heterogeneous wireless networks. The simulation results demonstrate that the proposed architecture secures a seamless LI by capturing all the suspected target traffics without any time delay, which usually occurs during an LI triggering between different service providers. Furthermore, when compared with the existing LI architectures, the architecture significantly helps reduce transmission and the time consumed for analysis of the content of communication (CC) and intercept related information (IRI).

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Security and privacy of wireless sensor networks are key research issues recently. Most existing researches regarding wireless sensor networks security consider homogenous sensor networks. To achieve better security and performance, we adopt a heterogeneous wireless sensor network (HWSN) model that consists of physically different types of sensor nodes. This paper presents a secure message distribution scheme with configurable privacy for HWSNs, which takes advantage of powerful high-end sensor nodes. The scheme establishes a message distribution topology in an efficient and secure manner. The sensor node only need generate one signature for all the messages for all the users, which can greatly save the communication and computation cost of the sensor node. On the other hand, the user can only know the messages that let him know based on a pre-set policy, which can meet the requirement of the privacy. We show that the scheme has small bandwidth requirements and it is resilient against the node compromise attack.

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The safety and availability policies are very important in an access control system for ensuring security and success when performing a certain task. However, conflicts may arise between safety and availability policies due to their opposite focuses. In this paper, we address the problem of consistency checking for safety and availability policies, especially for the co-existence of static separation-of-duty (SSoD) policies with availability policies, which determines whether there exists an access control state that satisfies all of these policies. We present criteria for determining consistency with a number of special cases, and show that the general case and partial subcases of the problem are intractable (NP-hard) and in the Polynomial Hierarchy NP^NP. We design an algorithm to efficiently solve the nontrivial size instances for the intractable cases of the problem. The running example shows the validity of the proposed algorithm. The investigation will help the security officer to specify reasonable access control policies when both safety and availability policies coexist.

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In this paper, we propose an anonymous routing protocol, LOPP, to protect the originator's location privacy in Delay/Disruption Tolerant Network (DTN). The goals of our study are to minimize the originator's probability of being localized (P_l) and maximize the destination's probability of receiving the message (P_r). The idea of LOPP is to divide a sensitive message into k segments and send each of them to n different neighbors. Although message fragmentation could reduce the destination's probability to receive a complete message, LOPP can decrease the originator's P_l. We validate LOPP on a real-world human mobility dataset. The simulation results show that LOPP can decrease the originator's P_l by over 54% with only 5.7% decrease in destination's P_r. We address the physical localization issue of DTN, which was not studied in the literature.

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Due to the distributed nature, mobile ad-hoc networks (MANETs) are vulnerable to various attacks, resulting in distrusted communications. To achieve trusted communications, it is important to build trusted routes in routing algorithms in a self-organizing and decentralized fashion. This paper proposes a trusted routing to locate and to preserve trusted routes in MANETs. Instead of using a hard security mechanism, we employ a new dynamic trust mechanism based on multiple constraints and collaborative filtering. The dynamic trust mechanism can effectively evaluate the trust and obtain the precise trust value among nodes, and can also be integrated into existing routing protocols for MANETs, such as ad hoc on-demand distance vector routing (AODV) and dynamic source routing (DSR). As an example, we present a trusted routing protocol, based on dynamic trust mechanism, by extending DSR, in which a node makes a routing decision based on the trust values on its neighboring nodes, and finally, establish a trusted route through the trust values of the nodes along the route in MANETs. The effectiveness of our approach is validated through extensive simulations.

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In 2006 EPCglobal and the International Organization for Standards (ISO) ratified the EPC Class-1 Generation-2 (Gen-2) [1] and the ISO 18000-6C standards [2], respectively. These efforts represented major advancements in the direction of universal standardization for low-cost RFID tags. However, a cause for concern is that security issues do not seem to be properly addressed. In this paper, we propose a new lightweight RFID tag-reader mutual authentication scheme for use under the EPCglobal framework. The scheme is based on previous work by Konidala and Kim [3]. We attempt to mitigate the weaknesses observed in the original scheme and, at the same time, consider other possible adversarial threats as well as constraints on low-cost RFID tags requirements.

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A RFID group scanning protocol enables a RFID reader to produce a proof of co-existence of multiple RFID tags. This type of protocol is also referred to as yoking-proof, grouping-proof and co-existence proof. In this letter, we show that all of the previous group scanning protocols are vulnerable to relay attack.

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In wireless sensor networks, preserving location privacy under successive inference attacks is extremely critical. Although this problem is NP-complete in general cases, we propose a dynamic programming based algorithm and prove it is optimal in special cases where the correlation only exists between p immediate adjacent observations.

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The algebraic path problem (APP) is a general framework which unifies several solution procedures for a number of well-known matrix and graph problems. In this paper, we present a new 3-dimensional (3-D) orbital algebraic path algorithm and corresponding 2-D toroidal array processors which solve the n × n APP in the theoretically minimal number of 3n time-steps. The coordinated time-space scheduling of the computing and data movement in this 3-D algorithm is based on the modular function which preserves the main technological advantages of systolic processing: simplicity, regularity, locality of communications, pipelining, etc. Our design of the 2-D systolic array processors is based on a classical 3-D→2-D space transformation. We have also shown how a data manipulation (copying and alignment) can be effectively implemented in these array processors in a massively-parallel fashion by using a matrix-matrix multiply-add operation.

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This paper proposes a sensing system for a behavior detection system using an ultrasonic oscillosensor and an air pressure sensor. The ultrasonic oscillosensor sensor has a cylindrical tank filled with water. It detects the vibration of the target object from the signal reflected from the water surface. This sensor can detect a biological vibration by setting to the bottom bed frame. The air pressure sensor consists of a polypropylene sheet and an air pressure sensor, and detects the pressure information by setting under the bed's mattress. An increase (decrease) in the load placed on the bed is detected by the increase (decrease) in the pressure of the air held in the tube attached to the sheet. We propose a behavior detection system using both sensors, complementally. The system recognizes three states (nobody in bed, keeping quiet in bed, moving in bed) using both sensors, and we detect the behavior before getting out of bed by recognized these states. Fuzzy logic plays a primary role in the system. As the fundamental experiment, we applied the system to five healthy volunteers, the system successfully recognized three states, and detected the behavior before getting out of bed. As the clinical experiment, we applied the system to four elderly patients with dementia, the system exactly detected the behavior before getting out of the bed with enough time for medical care support.

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The usage of light-weight mobile devices is increasing rapidly, leading to demand for more telecommunication services. Consequently, mobile ad hoc networks and their applications have become feasible with the proliferation of light-weight mobile devices. Many protocols have been developed to handle service discovery and routing in ad hoc networks. However, the majority of them did not consider one critical aspect of this type of network, which is the limited of available energy in each node. Cluster Based Routing Protocol (CBRP) is a robust/scalable routing protocol for Mobile Ad hoc Networks (MANETs) and superior to existing protocols such as Ad hoc On-demand Distance Vector (AODV) in terms of throughput and overhead. Therefore, based on this strength, methods to increase the efficiency of energy usage are incorporated into CBRP in this work. In order to increase the stability (in term of life-time) of the network and to decrease the energy consumption of inter-cluster gateway nodes, an Enhanced Gateway Cluster Based Routing Protocol (EGCBRP) is proposed. Three methods have been introduced by EGCBRP as enhancements to the CBRP: improving the election of cluster Heads (CHs) in CBRP which is based on the maximum available energy level, implementing load balancing for inter-cluster traffic using multiple gateways, and implementing sleep state for gateway nodes to further save the energy. Furthermore, we propose an Energy Efficient Cluster Based Routing Protocol (EECBRP) which extends the EGCBRP sleep state concept into all idle member nodes, excluding the active nodes in all clusters. The experiment results show that the EGCBRP decreases the overall energy consumption of the gateway nodes up to 10% and the EECBRP reduces the energy consumption of the member nodes up to 60%, both of which in turn contribute to stabilizing the network.

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This paper investigates the autonomous decision-making process of the selection of alternative countermeasures against threats in electronic warfare settings. We introduce a threat model, which represents a specific threat pattern, and a methodology that decides the best countermeasure against real-time threats using the decision theory. To determine the optimal countermeasure, we model the probabilities of the effects of countermeasures, if executed, and combine the probabilities with their utilities. This methodology based upon the inductive threat model calculates the expected utilities of countermeasures which are applicable given a situation, and provide an intelligent command and control agent with the best countermeasure to threats. We present empirical results that demonstrate the agent's capabilities of choosing countermeasures to threats in simulated electronic warfare settings.

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Robot path-planning is one of the important issues in robotic navigation. This paper presents a novel robot path-planning approach based on the associative memory using Self-Organizing Incremental Neural Networks (SOINN). By the proposed method, an environment is first autonomously divided into a set of path-fragments by junctions. Each fragment is represented by a sequence of preliminarily generated common patterns (CPs). In an online manner, a robot regards the current path as the associative path-fragments, each connected by junctions. The reasoning technique is additionally proposed for decision making at each junction to speed up the exploration time. Distinct from other methods, our method does not ignore the important information about the regions between junctions (path-fragments). The resultant number of path-fragments is also less than other method. Evaluation is done via Webots physical 3D-simulated and real robot experiments, where only distance sensors are available. Results show that our method can represent the environment effectively; it enables the robot to solve the goal-oriented navigation problem in only one episode, which is actually less than that necessary for most of the Reinforcement Learning (RL) based methods. The running time is proved finite and scales well with the environment. The resultant number of path-fragments matches well to the environment.

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Estimating the conditional mean of an input-output relation is the goal of regression. However, regression analysis is not sufficiently informative if the conditional distribution has multi-modality, is highly asymmetric, or contains heteroscedastic noise. In such scenarios, estimating the conditional distribution itself would be more useful. In this paper, we propose a novel method of conditional density estimation that is suitable for multi-dimensional continuous variables. The basic idea of the proposed method is to express the conditional density in terms of the density ratio and the ratio is directly estimated without going through density estimation. Experiments using benchmark and robot transition datasets illustrate the usefulness of the proposed approach.

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A technique for reducing the footprints of HMM-based speech synthesis systems by tying all covariance matrices of state distributions is described. HMM-based speech synthesis systems usually leave smaller footprints than unit-selection synthesis systems because they store statistics rather than speech waveforms. However, further reduction is essential to put them on embedded devices, which have limited memory. In accordance with the empirical knowledge that covariance matrices have a smaller impact on the quality of synthesized speech than mean vectors, we propose a technique for clustering mean vectors while tying all covariance matrices. Subjective listening test results showed that the proposed technique can shrink the footprints of an HMM-based speech synthesis system while retaining the quality of the synthesized speech.

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Set-partitioning in hierarchical trees (SPIHT) is one of the well-known image compression schemes. SPIHT offers an agreeable compression ratio and produces an embedded bit-stream for progressive transmission. However, the major disadvantage of SPIHT is its large memory requirement. In this paper, we propose a memory efficient SPIHT image coder and its parallel implantation. The memory requirement is reduced without sacrificing image quality. All bit-planes are concurrently encoded in order to speed up the entire coding flow. The result shows that the proposed algorithm is roughly 6 times faster than the original SPIHT. For a 512 × 512 image, the memory requirement is reduced from 5.83Mb to 491Kb. The proposed algorithm is also realized on FPGA. With pipeline design, the circuit can run at 110MHz, which can encode a 512 × 512 image in 1.438ms. Thus, the circuit achieves very high throughput, 182MPixels/sec, and can be applied to high performance image compression applications.

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This paper proposes a method for detecting ellipses from an image despite (1) multiple colors within the ellipses, (2) partially occluded ellipses' boundaries, (3) noisy, locally deformed boundaries of ellipses, (4) presence of multiple objects other than the ellipses in the image, and (5) combinations of (1) through (4). After boundary curves are obtained by edge detection, by utilizing the first-order difference curves of the edge orientation of each pixel in the boundary curves, a segment-reconnect method obtains boundary clusters. Then, a modified RANSAC detects ellipses by choosing five pixels randomly from the boundary clusters, where overlapped ellipses are merged. Experimental results using synthesized images and real images demonstrate the effectiveness of the proposed method together with comparison with the Randomized Hough Transform, a well-known conventional method.

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Given a question and a set of its candidate answers, the task of answer validation (AV) aims to return a Boolean value indicating whether a given candidate answer is the correct answer to the question. Unlike previous works, this paper presents an unsupervised model, called the U-model, for AV. This approach regards AV as a classification task and investigates how effectively using redundancy of the Web into the proposed architecture. Experimental results with TREC factoid test sets and Chinese test sets indicate that the proposed U-model with redundancy information is very effective for AV. For example, the top@1/mrr@5 scores on the TREC05, and 06 tracks are 40.1/51.5% and 35.8/47.3%, respectively. Furthermore, a cross-model comparison experiment demonstrates that the U-model is the best among the redundancy-based models considered. Even compared with a syntax-based approach, a supervised machine learning approach and a pattern-based approach, the U-model performs much better.

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Bursty and out-of-order tuple arrivals complicate the process of determining contents and boundaries of sliding windows. To process windows over such streams efficiently, we need to address two issues regarding fast tuple insertion and disorder control. In this paper, we focus on these issues to process sliding windows efficiently over disordered data streams.

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In large-scale industrial plants, the process control system has multiple system servers to provide seamless services to plant operators irrespective of any system server's failure. In this paper, we present an autonomic connection scheme between the system server and the Human-Machine Interface application (HMI) without additional configuration. The proposed scheme is based on the concept of autonomic computing, supporting the fault-tolerant and/or load-balancing features. Finally, the mathematical analysis shows that the proposed scheme can provide high availability of services to users.

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Proposed in this paper is a low power BIST architecture using the pattern mapping method based on the transition freezing method. The transition freezing method generates frozen patterns dynamically according to the transition tendency of an LFSR. This leads to an average power reduction of 60%. However, the patterns have limitations of 100% fault coverage due to random resistant faults. Therefore, in this paper, those faults are detected by mapping useless patterns among frozen patterns to the patterns generated by an ATPG. Throughout the scheme, 100% fault coverage is achieved. Moreover, we have reduced the amount of applied patterns, the test time, and the power dissipation.

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This paper suggests utterance verification system using state-level log-likelihood ratio with frame and state selection. We use hidden Markov models for speech recognition and utterance verification as acoustic models and anti-phone models. The hidden Markov models have three states and each state represents different characteristics of a phone. Thus we propose an algorithm to compute state-level log-likelihood ratio and give weights on states for obtaining more reliable confidence measure of recognized phones. Additionally, we propose a frame selection algorithm to compute confidence measure on frames including proper speech in the input speech. In general, phone segmentation information obtained from speaker-independent speech recognition system is not accurate because triphone-based acoustic models are difficult to effectively train for covering diverse pronunciation and coarticulation effect. So, it is more difficult to find the right matched states when obtaining state segmentation information. A state selection algorithm is suggested for finding valid states. The proposed method using state-level log-likelihood ratio with frame and state selection shows that the relative reduction in equal error rate is 18.1% compared to the baseline system using simple phone-level log-likelihood ratios.

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Acquisition of accurate colors is important in the modern era of widespread exchange of electronic multimedia. The variety of device-dependent color spaces causes troubles with accurate color reproduction. In this paper we present the outlines of accomplished digital camera system with device-independent output formed from tristimulus XYZ values. The outstanding accuracy and fidelity of acquired color is achieved in our system by employing an embedded color calibration system based on emissive device generating reference calibration colors with user-defined spectral distribution and chromaticity coordinates. The system was tested by calibrating the camera using 24 reference colors spectrally reproduced from 24 color patches of the Macbeth Chart. The average color difference (CIEDE2000) has been found to be ΔE =0.83, which is an outstanding result compared to commercially available digital cameras.

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This letter proposes a method to retrieve the original image X out of multiple sets of SI (Side Information) in distributed video coding (DVC). Using Turbo decoding methods, the most reliable segments from the decoded Y_i's were selected for the composition of Y_∞, whose conditional entropy H(X|Y_∞) became much lower than any individual conditional entropy H(X|Y_i). This proposal has improved the peak signal-to-noise ratio (PSNR) by 1.1 to 1.8dB, compared to the conventional single SI-based approach.

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Human detection has witnessed significant development in recent years. The introduction of cascade structure and integral histogram has greatly improved detection speed. But real-time detection is still only possible for sparse scan of 320 × 240 sized images. In this work, we propose a matrix-based structure to reorganize the computation structure of window-scanning detection algorithms, as well as a new pre-processing method called Hierarchical HOG Matrices (HHM) in place of integral histogram. Our speed-up scheme can process 320 × 240 sized images by dense scan (≈ 12000 windows per image) at the speed of about 30fps, while maintaining accuracy comparable to the original HOG + cascade method.

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Various observation models have been introduced into the object tracking community, and combining them has become a promising direction. This paper proposes a novel approach for estimating the confidences of different observation models, and then effectively combining them in the particle filter framework. In our approach, spatial Likelihood distribution is represented by three simple but efficient parameters, reflecting the overall similarity, distribution sharpness and degree of multi peak. The balance of these three aspects leads to good estimation of confidences, which helps maintain the advantages of each observation model and further increases robustness to partial occlusion. Experiments on challenging video sequences demonstrate the effectiveness of our approach.

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Moment invariants of a discrete image are not strictly invariant to image displacements due to quantization errors. This letter introduces a weighting function such that the pixel value is smoothly reduced to zero at the boundary of the image. Image moments of the weighted image are robust against quantization errors, and the moment invariants of the weighted image are more invariant than those of the unweighted image.

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