The ship ad-hoc network (SANET) extends the coverage of the high data-rate terrestrial communications to the ships with the reduced cost in maritime communications. Cognitive radio (CR) has the ability of sensing the radio environment and dynamically reconfiguring the operating parameters, which can make SANET utilize the spectrum efficiently. However, due to the dynamic topology nature and no central entity for data fusion in SANET, the interference brought into the primary network caused by the hidden primary user requires to be carefully managed by a sort of decentralized cooperative spectrum sensing schemes. In this paper, we propose a self-weighted decentralized cooperative spectrum sensing (SWDCSS) scheme to solve such a problem. The analytical and simulation results show that the proposed SWDCSS scheme is reliable to detect the primary user in SANET. As a result, secondary network can efficiently utilize the spectrum band of primary network with little interference to primary network. Referring the complementary receiver operating characteristic (ROC) curves, we observe that with a given false alarm probability, our proposed algorithm reduces the missing probability by 27% than the traditional embedded spectrally agile radio protocol for evacuation (ESCAPE) algorithm in the best condition.

In this paper, we present an Energy-Aware Self-Stabilizing Distributed Clustering protocol based on message-passing model for Ad Hoc networks. The latter does not require any initialization. Starting from an arbitrary configuration, the network converges to a stable state in a finite time. Our contribution is twofold. We firstly give the formal proof that the stabilization is reached after at most n+2 transitions and requires at most $n{\times}log(2n+{\kappa}+3)$ memory space, where n is the number of network nodes and ${\kappa}$ represents the maximum hops number in the clusters. Furthermore, using the OMNeT++ simulator, we perform an evaluation of our approach. Secondly, we propose an adaptation of our solution in the context of Wireless Sensor Networks (WSNs) with energy constraint. We notably show that our protocol can be easily used for constructing clusters according to multiple criteria in the election of cluster-heads, such as nodes' identity, residual energy or degree. We give a comparison under the different election metrics by evaluating their communication cost and energy consumption. Simulation results show that in terms of number of exchanged messages and energy consumption, it is better to use the Highest-ID metric for electing CHs.

This paper proposes a dynamic downlink load control scheme that jointly employs dynamic load threshold management and virtual coverage management schemes to reduce the degree of performance degradation due to traffic overload in two-tier femtocell networks. With the proposed scheme, the downlink load in a serving macrocell is controlled with a load threshold which is adjusted dynamically depending on the varying downlink load conditions of neighboring macrocells. In addition, traffic overloading is alleviated by virtually adjusting the coverage of the overloaded serving macrocell, based on the adjusted load threshold of the serving macrocell. Simulation results show that the proposed scheme improves the performance of two-tier femtocell networks in terms of the outage probability and sum throughput. This improvement is significantly increased with appropriate values of load thresholds and with an intermediate-level adjustment of the virtual coverage area (i.e., handover hysteresis margin: HOM). Furthermore, the proposed scheme outperforms both a previously proposed load control scheme with a static load threshold and the LTE system without a HOM adjustment.

In this paper, we propose an iterative transceiver design in a multi-relay multi-user multiple-input multiple-output (MIMO) system. The design criterion is to minimize sum mean squared error (SMSE) under relay sum power constraint (RSPC) where only local channel state information (CSI)s are available at relays. Local CSI at a relay is defined as the CSI of the channel between BS and the relay in the $1^{st}$ hop link, and the CSI of the channel between the relay and all users in the $2^{nd}$ hop link. Exploiting BS transmitter structure which is concatenated with block diagonalization (BD) precoder, each relay's precoder can be determined using local CSI at the relay. The proposed scheme is based on sequential iteration of two stages; stage 1 determines BS transmitter and relay precoders jointly with SMSE duality, and stage 2 determines user receivers. We verify that the proposed scheme outperforms simple amplify-and-forward (SAF), minimum mean squared error (MMSE) relay, and an existing good scheme of [13] in terms of both SMSE and sum-rate performances.

To improve the cooperative efficiency of node cooperation and multiple access performance for multihop wireless networks, a priority-differentiated cooperative medium access control protocol with contention resolution (CRP-CMAC) is proposed. In the protocol, the helper selection process is divided into the priority differentiation phase and the contention resolution phase for the helpers with the same priority. A higher priority helper can choose an earlier minislot in the priority differentiation phase to send a busy tone. As a result, the protocol promptly selects all the highest priority helpers. The contention resolution phase of the same priority helpers consists of k round contention resolution procedures. The helpers that had sent the first busy tone and are now sending the longest busy tone can continue to the next round, and then the other helpers that sense the busy tone withdraw from the contention. Therefore, it can select the unique best helper from the highest priority helpers with high probability. A packet piggyback mechanism is also adopted to make the high data rate helper with packet to send transmit its data packets to its recipient without reservation. It can significantly decrease the reservation overhead and effectively improve the cooperation efficiency and channel utilization. Simulation results show that the maximum throughput of CRP-CMAC is 74%, 36.1% and 15% higher than those of the 802.11 DCF, CoopMACA and 2rcMAC protocols in a wireless local area network (WLAN) environment, and 82.6%, 37.6% and 46.3% higher in an ad hoc network environment, respectively.

A novel neighbor selection-based fingerprinting algorithm using matrix correlation (MC) for Wi-Fi localization is presented in this paper. Compared with classic fingerprinting algorithms that usually employ a single received signal strength (RSS) sample, the presented algorithm uses multiple on-line RSS samples in the form of a matrix and measures correlations between the on-line RSS matrix and RSS matrices in the radio-map. The algorithm makes efficient use of on-line RSS information and considers RSS variations of reference points (RPs) for localization, so it offers more accurate localization results than classic neighbor selection-based algorithms. Based on the MC algorithm, an error estimation method using artificial neural network is also presented to fuse available information that includes RSS samples and localization results computed by the MC algorithm and model the nonlinear relationship between the available information and localization errors. In the on-line phase, localization errors are estimated and then used to correct the localization results to reduce negative influences caused by a static radio-map and RP distribution. Experimental results demonstrate that the MC algorithm outperforms the other neighbor selection-based algorithms and the error estimation method can reduce the mean of localization errors by nearly half.

Due to increased consumption of energy in the building environment, the building energy management systems (BEMS) solution has been developed to achieve energy saving and efficiency. However, because of the shortage of building energy management specialists and incompatibility among the energy management systems of different vendors, the BEMS solution can only be applied to limited buildings individually. To solve these problems, we propose a building cluster based remote energy monitoring and management (EMM) system and its functionalities and roles of each sub-system to simultaneously manage the energy problems of several buildings. We also introduce a novel energy demand forecasting algorithm by using past energy consumption data. Extensive performance evaluation study shows that the proposed regression based energy demand forecasting model is well fitted to the actual energy consumption model, and it also outperforms the artificial neural network (ANN) based forecasting model.

Humans are able to recognize small number of people they know well by the way they walk. This ability represents basic motivation for using human gait as the means for biometric identification. Such biometric can be captured at public places from a distance without subject's collaboration, awareness or even consent. Although current approaches give encouraging results, we are still far from effective use in practical applications. In general, methods set various constraints to circumvent the influence factors like changes of view, walking speed, capture environment, clothing, footwear, object carrying, that have negative impact on recognition results. In this paper we investigate the influence of walking speed variation to different visual based gait recognition approaches and propose normalization based on geometric transformations, which mitigates its influence on recognition results. With the evaluation on MoBo gait dataset we demonstrate the benefits of using such normalization in combination with different types of gait recognition approaches.

A new optimal scheme based on curvelet transform is proposed for retinal image enhancement (RIE) using real-coded quantum genetic algorithm. Curvelet transform has better performance in representing edges than classical wavelet transform for its anisotropy and directional decomposition capabilities. For more precise reconstruction and better visualization, curvelet coefficients in corresponding subbands are modified by using a nonlinear enhancement mapping function. An automatic method is presented for selecting optimal parameter settings of the nonlinear mapping function via quantum genetic search strategy. The performance measures used in this paper provide some quantitative comparison among different RIE methods. The proposed method is tested on the DRIVE and STARE retinal databases and compared with some popular image enhancement methods. The experimental results demonstrate that proposed method can provide superior enhanced retinal image in terms of several image quantitative evaluation indexes.

Multi-view face detection has become an active area for research in the last few years. In this paper, a novel multi-view human face detection algorithm based on improved real Adaboost is presented. Real Adaboost algorithm is improved by weighted combination of weak classifiers and the approximately best combination coefficients are obtained. After that, we proved that the function of sample weight adjusting method and weak classifier training method is to guarantee the independence of weak classifiers. A coarse-to-fine hierarchical face detector combining the high efficiency of Haar feature with pose estimation phase based on our real Adaboost algorithm is proposed. This algorithm reduces training time cost greatly compared with classical real Adaboost algorithm. In addition, it speeds up strong classifier converging and reduces the number of weak classifiers. For frontal face detection, the experiments on MIT+CMU frontal face test set result a 96.4% correct rate with 528 false alarms; for multi-view face in real time test set result a 94.7 % correct rate. The experimental results verified the effectiveness of the proposed approach.

In this study, we propose a novel salient object detection strategy based on regional contrast and relative spatial compactness. Our algorithm consists of four basic steps. First, we learn color names offline using the probabilistic latent semantic analysis (PLSA) model to find the mapping between basic color names and pixel values. The color names can be used for image segmentation and region description. Second, image pixels are assigned to special color names according to their values, forming different color clusters. The saliency measure for every cluster is evaluated by its spatial compactness relative to other clusters rather than by the intra variance of the cluster alone. Third, every cluster is divided into local regions that are described with color name descriptors. The regional contrast is evaluated by computing the color distance between different regions in the entire image. Last, the final saliency map is constructed by incorporating the color cluster's spatial compactness measure and the corresponding regional contrast. Experiments show that our algorithm outperforms several existing salient object detection methods with higher precision and better recall rates when evaluated using public datasets.

Video fingerprints generated from global features are usually vulnerable against general geometric transformations. In this paper, a novel video fingerprinting algorithm is proposed, in which a new spatio-temporal gradient is designed to represent the spatial and temporal information for each frame, and a new partition scheme, based on concentric circle and rings, is developed to resist the attacks efficiently. The centroids of spatio-temporal gradient orientations (CSTGO) within the circle and rings are then calculated to generate a robust fingerprint. Our experiments with different attacks have demonstrated that the proposed approach outperforms the state-of-the-art methods in terms of robustness and discrimination.

In this paper, we propose an authenticated key agreement scheme, TinyIBAK, based on the identity-based cryptography and bilinear paring, for large scale sensor networks. We prove the security of our proposal in the random oracle model. According to the formal security validation using AVISPA, the proposed scheme is strongly secure against the passive and active attacks, such as replay, man-in-the middle and node compromise attacks, etc. We implemented our proposal for TinyOS-2.1, analyzed the memory occupation, and evaluated the time and energy performance on the MICAz motes using the Avrora toolkits. Moreover, we deployed our proposal within the TOSSIM simulation framework, and investigated the effect of node density on the performance of our scheme. Experimental results indicate that our proposal consumes an acceptable amount of resources, and is feasible for infrequent key distribution and rekeying in large scale sensor networks. Compared with other ID-based key agreement approaches, TinyIBAK is much more efficient or comparable in performance but provides rekeying. Compared with the traditional key pre-distribution schemes, TinyIBAK achieves significant improvements in terms of security strength, key connectivity, scalability, communication and storage overhead, and enables efficient secure rekeying.

In H.264, three different data partition types are used, which have unequal importance to the reconstructed video quality. To improve the performance of H.264 video streaming transmission over IEEE 802.11e Wireless Local Area Networks, a prioritization mechanism that categorizes different partition types to different priority classes according to the calculated distortion within one Group of Pictures. In the proposed scheme, video streams have been encoded based on the H.264 codec with its data partition enabled. The dynamic scheduling scheme based on Enhanced Distributed Channel Access has been configured to differentiate the data partitions according to their distortion impact and the queue utilization ratio. Simulation results show that the proposed scheme improves the received video quality by 1dB in PSNR compared with the existing Enhanced Distributed Channel Access static mapping scheme.

Wireless sensor network (WSN) is a promising technology for monitoring physical phenomena at fine-grained spatial and temporal resolution. However, the typical approach of sending each sensed measurement out of the network for detailed spatial analysis of transient physical phenomena may not be an efficient or scalable solution. This paper focuses on in-network physical phenomena detection schemes, particularly the distributed computation of the boundary of physical phenomena (i.e. event), to support energy efficient spatial analysis in wireless sensor networks. In-network processing approach reduces the amount of network traffic and thus achieves network scalability and lifetime longevity. This study investigates the recent advances in distributed event detection based on in-network processing and includes a concise comparison of various existing schemes. These boundary detection schemes identify not only those sensor nodes that lie on the boundary of the physical phenomena but also the interior nodes. This constitutes an event geometry which is a basic building block of many spatial queries. In this paper, we introduce the challenges and opportunities for research in the field of in-network distributed event geometry boundary detection as well as illustrate the current status of research in this field. We also present new areas where the event geometry boundary detection can be of significant importance.

There has been a lot of attention paid recently to analyze dynamic human gestures that vary over time. Most attention to dynamic gestures concerns with spatio-temporal features, as compared to analyzing each frame of gestures separately. For accurate dynamic gesture recognition, motion feature extraction algorithms need to find representative features that uniquely identify time-varying gestures. This paper proposes a new feature-extraction algorithm using temporal self-similarity based on a hierarchical human model. Because a conventional temporal self-similarity method computes a whole movement among the continuous frames, the conventional temporal self-similarity method cannot recognize different gestures with the same amount of movement. The proposed model-based temporal self-similarity method groups body parts of a hierarchical model into several sets and calculates movements for each set. While recognition results can depend on how the sets are made, the best way to find optimal sets is to separate frequently used body parts from less-used body parts. Then, we apply a multiclass support vector machine whose optimization algorithm is based on structural support vector machines. In this paper, the effectiveness of the proposed feature extraction algorithm is demonstrated in an application for taebo gesture recognition. We show that the model-based temporal self-similarity method can overcome the shortcomings of the conventional temporal self-similarity method and the recognition results of the model-based method are superior to that of the conventional method.

Context-awareness is an essential part of ubiquitous computing, and over the past decade video based activity recognition (VAR) has emerged as an important component to identify user's context for automatic service delivery in context-aware applications. The accuracy of VAR significantly depends on the performance of the employed human body segmentation algorithm. Previous human body segmentation algorithms often engage modeling of the human body that normally requires bulky amount of training data and cannot competently handle changes over time. Recently, active contours have emerged as a successful segmentation technique in still images. In this paper, an active contour model with the integration of Chan Vese (CV) energy and Bhattacharya distance functions are adapted for automatic human body segmentation using depth cameras for VAR. The proposed technique not only outperforms existing segmentation methods in normal scenarios but it is also more robust to noise. Moreover, it is unsupervised, i.e., no prior human body model is needed. The performance of the proposed segmentation technique is compared against conventional CV Active Contour (AC) model using a depth-camera and obtained much better performance over it.

Over the last few years, one of the most common purposes of smart homes is to provide human centric services in the domain of u-healthcare by analyzing inhabitants' daily living. Currently, the major challenges in activity recognition include the reliability of prediction of each classifier as they differ according to smart homes characteristics. Smart homes indicate variation in terms of performed activities, deployed sensors, environment settings, and inhabitants' characteristics. It is not possible that one classifier always performs better than all the other classifiers for every possible situation. This observation has motivated towards combining multiple classifiers to take advantage of their complementary performance for high accuracy. Therefore, in this paper, a method for activity recognition is proposed by optimizing the output of multiple classifiers with Genetic Algorithm (GA). Our proposed method combines the measurement level output of different classifiers for each activity class to make up the ensemble. For the evaluation of the proposed method, experiments are performed on three real datasets from CASAS smart home. The results show that our method systematically outperforms single classifier and traditional multiclass models. The significant improvement is achieved from 0.82 to 0.90 in the F-measures of recognized activities as compare to existing methods.

Since security plays an important role in several ZigBee applications, such as Smart Energy and medical sensor applications, ZigBee Specification includes various security mechanisms to protect ZigBee frames and infrastructure. Among them, the Join and Leave operations of ZigBee are investigated in this paper. The current Join-Leave operation is protected by the network key (a kind of group key). We claim it is not adequate to employ the network key for such purpose, and propose a new Join-Leave operation protected by the application link key (a kind of pairwise key), which is based on a more efficient key management scheme than that of ZigBee. Hence, the original Join operation consists of a total of 12 command frames, while the new Join operation consists of only 6 command frames. In particular, the security of the proposed Join-Leave operation is equivalent to or better than that of the original Join-Leave operation. The new Join-Leave operation is extensively analyzed in terms of security and efficiency, and compared with the original Join-Leave operation of ZigBee.

The design of geometrically invariant watermarking is one of the most challenging work in digital image watermarking research area. To achieve the robustness to geometrical attacks, the inherent characteristic of an image is usually used. In this paper, a geometrically invariant image watermarking scheme using connected objects and gravity center is proposed. First, the gray-scale image is converted into the binary one, and the connected objects according to the connectedness of binary image are obtained, then the coordinates of these connected objects are mapped to the gray-scale image, and the gravity centers of those bigger objects are chosen as the feature points for watermark embedding. After that, the line between each gravity center and the center of the whole image is rotated an angle to form a sector, and finally the same version of watermark is embedded into these sectors. Because the image connectedness is topologically invariant to geometrical attacks such as scaling and rotation, and the gravity center of the connected object as feature points is very stable, the watermark synchronization is realized successfully under the geometrical distortion. The proposed scheme can extract the watermark information without using the original image or template. The simulation results show the proposed scheme has a good invisibility for watermarking application, and stronger robustness than previous feature-based watermarking schemes against geometrical attacks such as rotation, scaling and cropping, and can also resist common image processing operations including JPEG compression, adding noise, median filtering, and histogram equalization, etc.

A data hiding scheme in HTML files is presented in this paper. Web pages are a very popular medium for broadcasting information and knowledge nowadays, and web pages are a good way to achieve the goal of secret message delivery because the different HTML coding codes will render the same screen in any of the popular browsers. The proposed method utilizes the HTML special space codes and sentence segmentation to conceal secret messages into a HTML file. The experimental results show that the stego HTML file generated by the proposed method is imperceptible. Also, the proposed method can conceal one more secret bit in every between-word location.

Key exposure is a major threat to secure cryptosystems. To mitigate the impact caused by key-compromise attacks, a key-insulated cryptographic mechanism is a better alternative. For securing the large message communication in peer-to-peer networks, in this paper, we propose the first novel identity-based key-insulated encryption (IB-KIE) scheme with message linkages. Our scheme has the properties of unbounded time periods and random-access key-updates. In the proposed scheme, each client can periodically update his private key while the corresponding public one remains unchanged. The essential security assumption of our proposed scheme is based on the well-known bilinear Diffie-Hellman problem (BDHP). To ensure the practical feasibility, we also formally prove that the proposed scheme achieves the security requirement of confidentiality against indistinguishability under adaptive chosen-ciphertext attacks (IND-CCA2) in the random oracle model.

In this paper, we propose an effective fragile image watermarking scheme for tampering detection and content recovery. Cover image is divided into a series of non-overlapping blocks and a block mapping relationship is constructed by the secret key. Several DCT coefficients with direct current and lower frequencies of the MSBs for each block are used to generate the reference bits, and different coefficients are assigned with different bit numbers for representation according to their importance. To enhance recovery performance, authentication bits are generated by the MSBs and the reference bits, respectively. After LSB substitution hiding, the embedded watermark bits in each block consist of the information of itself and its mapping blocks. On the receiver side, all blocks with tampered MSBs can be detected and recovered using the valid extracted reference bits. Experimental results demonstrate the effectiveness of the proposed scheme.