Delay tolerant networks (DTNs) characterized by the lack of guaranteed end-to-end paths exploit opportunistic data forwarding mechanism, which depends on the hypothesis that nodes are willing to store, carry, and forward the in-transit messages in an opportunistic way. However, this hypothesis might easily be violated by the presence of selfish nodes constrained with energy, bandwidth, and processing power. To address this problem, we propose a practical reputation-based incentive scheme, named PRI, to stimulate honest forwarding and discipline selfish behavior. A novel concept of successful forwarding credential and an observation protocol are presented to cope with the detection of nodes' behavior, and a reputation model is proposed to determine egoistic individuals. The simulation results demonstrate the effectiveness and practicality of our proposal.

In this paper, throughput of IEEE 802.11 carrier-sense multiple access (CSMA) with collision-avoidance (CA) protocols in non-saturated traffic conditions is presented taking into account the impact of imperfect channel sensing. The imperfect channel sensing includes both missed-detection and false alarm and their impact on the utilization of IEEE 802.11 analyzed and expressed as a closed form. To include the imperfect channel sensing at the physical layer, we modified the state transition probabilities of well-known two state Markov process model. Simulation results closely match the theoretical expressions confirming the effectiveness of the proposed model. Based on both theoretical and simulated results, the choice of the best probability detection while maintaining probability of false alarm is less than 0.5 is a key factor for maximizing utilization of IEEE 802.11.

Improving the quality of service in IP networks is a major challenge for real-time voice communications. In particular, packet arrival-delay variation, so-called "jitter," is one of the main factors that degrade the quality of voice in mobile devices with the voice-over Internet protocol (VoIP). To resolve this issue, a receiver-based enhanced timing recovery algorithm combined with active jitter estimation is proposed. The proposed algorithm copes with the effect of transmission jitter by expanding or compressing each packet according to the predicted network delay and variations. Additionally, the active network jitter estimation incorporates rapid detection of delay spikes and reacts to changes in network conditions. Extensive simulations have shown that the proposed algorithm delivers high voice quality by pursuing an optimal trade-off between average buffering delay and packet loss rate.

For a single relay channel, we compare the capacity of two different amplify-and-forward (AF) protocols, which are orthogonal AF (OAF) and non-orthogonal AF (NAF). The NAF protocol has been proposed to overcome a significant loss of performance of OAF in the high spectral efficiency region, and it was also theoretically proved that NAF performs better than OAF in terms of the diversity-multiplexing tradeoff. However, existing results have been evaluated at the asymptotically high signal to noise ratio (SNR), thus the power allocation problem between the source and the relay was neglected. We examine which protocol has better performance in a practical system operating at a finite SNR. We also study where a relay should be located if we consider the power allocation problem. A notable conclusion is that the capacity performance depends on both SNR and power allocation ratio, which indicates OAF may perform better than NAF in a certain environment.

In this paper, we propose a hybrid cooperative scheme to improve the secrecy rate for a cooperative network in presence of multiple relays. Each relay node transmits the mixed signal consisting of weighted source signal and intentional noise. The problem of power allocation, the joint design of beamforming and jamming weights are investigated, and an iterative scheme is proposed. It is demonstrated by the numerical results that the proposed hybrid scheme further improves secrecy rate, as compared to traditional cooperative schemes.

To locate an object accurately in the wireless sensor networks, the distance measure based on time-delay plays an important role. In this paper, we propose a maximum likelihood (ML) time-delay estimation algorithm in multi-path wireless propagation channel. We get the joint probability density function after sampling the frequency domain response of the multi-path channel, which could be obtained by the vector network analyzer. Based on the ML criterion, the time-delay values of different paths are estimated. Considering the ML function is non-linear with respect to the multi-path time-delays, we first obtain the coarse values of different paths using the subspace fitting algorithm, then take them as an initial point, and finally get the ML time-delay estimation values with the pattern searching optimization method. The simulation results show that although the ML estimation variance could not reach the Cramer-Rao lower bounds (CRLB), its performance is superior to that of subspace fitting algorithm, and could be seen as a fine algorithm.

Home automation networks are good environments for merging sensor networks and consumer electronics technologies. It is very important to reduce the energy consumption of each sensor node because sensor nodes operate with limited power based on a battery that cannot be easily replaced. One of the primary mechanisms for achieving low energy operation in energy-constrained wireless sensor networks is the duty-cycle operation, but this operation has several problems. For example, unnecessary energy consumption occurs during synchronization between transmission schedules and sleep schedules. In addition, a low duty-cycle usually causes more performance degradation, if the network becomes congested. Therefore, an appropriate control scheme is required to solve these problems. In this paper, we propose UDC (Unsynchronized Duty-cycle Control), which prevents energy waste caused by unnecessary preamble transmission and avoids congestion using duty-cycle adjustment. In addition, the scheme adjusts the starting point of the duty-cycle in order to reduce sleep delay. Our simulation results show that UDC improves the reliability and energy efficiency while reducing the end-to-end delay of the unsynchronized duty-cycled MAC (Media Access Control) protocol in sensor-based home automation networks.

A multi-modal sensing M2M healthcare monitoring system for the continuous monitoring of patients under their natural physiological states or elderly persons with chronic diseases is summarized. The system is designed for homecare or the monitoring of the elderly who live in country side or small rest home without enough support from caregivers or doctors, instead of patient monitoring in big hospital environment. Further insights into the natural cause and progression of diseases are afforded by context-aware sensing, which includes the use of accelerometers to monitor patient activities, or by location-aware indoor tracking based on ultrasonic and RF sensing. Moreover, indoor location tracking provides information about the location of patients in their physical environment and helps the caregiver in the provision of appropriate support.

Service oriented architecture (SOA) lends itself to model the application components to coarse-grained services in such a way that the composition of different services could be feasible. Service composition fulfills numerous service requirements by constructing composite applications with various services. As it is the case in many real-world applications, different users have diverse QoS demands issuing for composite applications. In this paper, we present a service composition framework for a typical service overlay network (SON) considering both multiple QoS constraints and load balancing factors. Moreover, a service selection algorithm based on niching technique and particle swarm optimization (PSO) is proposed for the service composition problem. It supports optimization problems with multiple constraints and objective functions, whether linear or nonlinear. Simulation results show that the proposed algorithm results in an acceptable level of efficiency regarding the service composition objective under different circumstances.

With the development of three-dimensional display and related technologies, depth video coding becomes a new topic and attracts great attention from industries and research institutes. Because (1) the depth video is not a sequence of images for final viewing by end users but an aid for rendering, and (2) depth video is simpler than the corresponding color video, fast algorithm for depth video is necessary and possible to reduce the computational burden of the encoder. This paper proposes a fast mode decision algorithm for depth video coding based on depth segmentation. Firstly, based on depth perception, the depth video is segmented into three regions: edge, foreground and background. Then, different mode candidates are searched to decide the encoding macroblock mode. Finally, encoding time, bit rate and video quality of virtual view of the proposed algorithm are tested. Experimental results show that the proposed algorithm save encoding time ranging from 82.49% to 93.21% with negligible quality degradation of rendered virtual view image and bit rate increment.

A novel and universal method of video image text detection is proposed. A coarse-to-fine text detection method is implemented. Firstly, the spectral clustering (SC) method is adopted to coarsely detect text regions based on the stationary wavelet transform (SWT). In order to make full use of the information, multi-parameters kernel function which combining the features similarity information and spatial adjacency information is employed in the SC method. Secondly, 28 dimension classifying features are proposed and support vector machine (SVM) is implemented to classify text regions with non-text regions. Experimental results on video images show the encouraging performance of the proposed algorithm and classifying features.

Multimedia computing has attracted considerable attention with the rapid growth in the development and application of multimedia technology. Current studies have attempted to support the increasing resource consumption and computational overhead caused by multimedia computing. In this paper, we propose $MediaCloud$, a new multimedia computing paradigm that integrates the concept of cloud computing in handling multimedia applications and services effectively and efficiently. $MediaCloud$ faces the following key challenges: heterogeneity, scalability, and multimedia Quality of Service (QoS) provisioning. To address the challenges above, first, a layered architecture of $MediaCloud$, which can provide scalable multimedia services, is presented. Then, $MediaCloud$ technologies by which users can access multimedia services from different terminals anytime and anywhere with QoS provisioning are introduced. Finally, $MediaCloud$ implementation and applications are presented, and media retrieval and delivery are adopted as case studies to demonstrate the feasibility of the proposed $MediaCloud$ design.

A depth adjustment method for visual fatigue reduction for depth-image-based rendering (DIBR) system is proposed. One important aspect of the method is that no calibration parameters are needed for adjustment. By analyzing 3D image warping, the perceived depth is expressed as a function of three adjustable parameters: virtual view number, scale factor and depth value of zero parallax setting (ZPS) plane. Adjusting these three parameters according to the proposed parameter modification algorithm when performing 3D image warping can effectively change the perceived depth of stereo pairs generated in DIBR system. As the depth adjustment is performed in simple 3D image warping equations, the proposed method is facilitative for hardware implementation. Experimental results show that the proposed depth adjustment method provides an improvement in visual comfort of stereo pairs as well as generating comfortable stereoscopic images with different perceived depths that people desire.

In this paper, we propose a new multi-scale connected coherence tree algorithm (MCCTA) by improving the connected coherence tree algorithm (CCTA). In contrast to many multi-scale image processing algorithms, MCCTA works on multiple scales space of an image and can adaptively change the parameters to capture the coarse and fine level details. Furthermore, we design a Multi-scale Connected Coherence Tree algorithm plus Spectral graph partitioning (MCCTSGP) by combining MCCTA and Spectral graph partitioning in to a new framework. Specifically, the graph nodes are the regions produced by CCTA and the image pixels, and the weights are the affinities between nodes. Then we run a spectral graph partitioning algorithm to partition on the graph which can consider the information both from pixels and regions to improve the quality of segments for providing image segmentation. The experimental results on Berkeley image database demonstrate the accuracy of our algorithm as compared to existing popular methods.

To provide active notification and enhance drivers'awareness of their surroundings, a vision-based collision warning system that detects and monitors surrounding vehicles is proposed in this paper. The main objective is to prevent possible vehicle collisions by monitoring the status of surrounding vehicles, including the distance to the other vehicles in front, behind, to the left and to the right sides. In addition, the proposed system collects and integrates this information to provide advisory warnings to drivers. To offer the correct notification, an algorithm based on features of edge and morphology to detect vehicles is also presented. The proposed system has been implemented in embedded systems and evaluated on real roads in various lighting and weather conditions. The experimental results indicate that the vehicle detection ratios were higher than 97% in the daytime, and appropriate for real road applications.

The need for securing Wireless Sensor Networks (WSNs) is essential especially in mission critical fields such as military and medical applications. Security techniques that are used to secure any network depend on the security requirements that should be achieved to protect the network from different types of attacks. Furthermore, the characteristics of wireless networks should be taken into consideration when applying security techniques to these networks. In this paper, energy efficient Security Model for Tree-based Routing protocols (SMTR) is proposed. In SMTR, different attacks that could face any tree-based routing protocol in WSNs are studied to design a security reference model that achieves authentication and data integrity using either Message Authentication Code (MAC) or Digital Signature (DS) techniques. The SMTR communication and processing costs are mathematically analyzed. Moreover, SMTR evaluation is performed by firstly, evaluating several MAC and DS techniques by applying them to tree-based routing protocol and assess their efficiency in terms of their power requirements. Secondly, the results of this assessment are utilized to evaluate SMTR phases in terms of energy saving, packet delivery success ratio and network life time.

This paper presents a multilevel reversible data hiding method based on histogram shifting which can recover the original image losslessly after the hidden data has been extracted from the stego-image. The method of prediction is adopted in our proposed scheme and prediction errors are produced to explore the similarity of neighboring pixels. In this article, we propose two different predictors to generate the prediction errors, where the prediction is carried out using the center prediction method and the JPEG-LS median edge predictor (MED) to exploit the correlation among the neighboring pixels. Instead of the original image, these prediction errors are used to hide the secret information. Moreover, we also present an improved method to search for peak and zero pairs and also talk about the analogy of the same to improve the histogram shifting method for huge embedding capacity and high peak signal-to-noise ratio (PSNR). In the one-level hiding, our method keeps image qualities larger than 53 dB and the ratio of embedding capacity has 0.43 bpp (bit per pixel). Besides, the concept with multiple layer embedding procedure is applied for obtaining high capacity, and the performance is demonstrated in the experimental results. From our experimental results and analytical reasoning, it shows that the proposed scheme has higher PSNR and high data embedding capacity than that of other reversible data hiding methods presented in the literature.