This paper considers the optimal cell association and resource allocation for load balancing in a heterogeneous cellular network subject to user's quality-of-service (QoS) constraints. We adopt the proportional fairness (PF) utility maximization formulation which also accommodates the QoS constraints in terms of minimum rate requirements. With equal resource allocation this joint optimization problem is either infeasible or requires relaxation that yields a solution which is difficult to implement. Nevertheless, we show that this joint optimization problem can be effectively solved without any priori assumption on resource allocation and yields a cell association scheme which enforces single BS association for each user. We re-formulated the joint optimization problem as a network-wide resource allocation problem with cardinality constraints. A reweighted heuristic l₁-norm regularization method is used to obtain a sparse solution to the re-formulated problem. The cell association scheme is then derived from the sparsity pattern of the solution, which guarantees a single BS association for each user. Compared with the previously proposed method based on equal resource allocation, the proposed framework results in a feasible cell association scheme and yields a robust solution on resource allocation that satisfies the QoS constraints. Our simulations illustrate the impact of user's minimum rate requirements on cell association and demonstrate that the proposed approach achieves load balancing and enforces single BS association for users.

Mobile cloud computing (MCC) combines mobile Internet and cloud computing to improve the performance of applications. In MCC, the data processing and storage for mobile devices (MDs) is provided on the remote cloud. However, MCC faces the problem of energy efficiency caused by randomly varying channels. In this paper, by introducing the Lyapunov optimization method, we propose a combined service subscription and delivery (CSSD) algorithm that can guide the users to subscribe to services reasonably. This algorithm can also determine whether to deliver the data and to whom data is sent in the current time unit based on the queue backlog and the channel state. Numerical results validate the correctness and effectiveness of our proposed CSSD algorithm.

Optical switching will be applied in datacenter networks because electronic switching is costly and power-consuming. In this paper, considering the ring-based interconnection using optical switching in the core of a datacenter, we study the home circuit (HC) construction for the labeled optical burst switching with home circuit (LOBS-HC), a new paradigm trying to share wavelengths among the HCs from the same source. In particular, aiming to construct HCs dynamically and properly, a scheme named optimal path matching and symmetric HC matching (OPM-SHM) is proposed. The main idea of OPM-SHM is to dynamically construct HCs by sharing wavelength(s) not only among the same-source HCs but also with symmetric HCs which have different sources other than the original LOBS-HC features. The simulation results demonstrate that OPM-SHM achieves better performance than some other methods in terms of burst loss rate and wavelength utilization of physical links. More specially, it maintains good load balancing for the datacenter network using an LOBS-HC ring. In addition, due to the symmetric feature of SHM, the proposed scheme can decrease the upper bound of the average hop count of the routing paths to half of the ring size.

Instantaneous relay (relay-without-delay) using interference alignment is a promising approach to neutralizing interference and improving system capacity. In Wang Chenwei's work, a 2-user scenario required both source and relay to access the global channel state information (CSI). This paper shows a new method of interference alignment improves the degrees of freedom (DoF) prominently for the 3-user MIMO interference channel with instantaneous relay. This new method is focused on the relay node that completes the alignment interference neutralization so the global CSI is obtained only once and the pressure on the base station can be mitigated. In addition, the 3-user MIMO interference channels with instantaneous relay can achieve 2M DoF when source and destination have M antennas, respectively. This method shows 33% improvement over the conventional method using interference alignment which obtains 3M/2 DoF.

Wireless mesh networks are a special type of broadcast networks which cover the qualifications of both ad-hoc as well as infrastructure mode networks. These networks offer connectivity to the last mile through hop to hop communication and by comparatively reducing the cost of infrastructure in terms of wire and hardware. Channel assignment has always been the focused area for such networks specifically when using non-overlapping channels and sharing radio frequency spectrum while using multiple radios. It has always been a challenge for mesh network on impartial utilization of the resources (channels), with the increase in users. The rational utilization of multiple channels and multiple radios, not only increases the overall throughput, capacity and scalability, but also creates significant complexities for channel assignment methods. For a better understanding of research challenges, this paper discusses heuristic methods, measurements and channel utilization applications and also examines various researches that yield to overcome this problem. Finally, we highlight prospective directions of research.

The efficient send and receive of information in VANET improves the efficiency of the safety and traffic services advertisment and discovery. However, if the V2V is the only communication system used, the restrictions of the urban environment and network drop the performance of VANET. In order to improve the performance of the network, it is necessary to use V2I communication as well as V2V communication. Therefore, RSUs must be placed in the environment. However due to the high costs of placement, the full coverage of the environment would not be possible. Therefore, it is necessary to optimally install a limited number of RSUs in the environment.

In this paper, we propose an intergraded unified imperfect CSI model and investigate the joined effects of feedback delay and channel estimation errors (CEE) for two-hop relaying systems with transmit beamforming and relay selection. We derived closed-form expressions for important performance measures including the exact analysis and lower bounds of outage probability as well as error performance. The ergodic capacity is also included with closed-form results. Furthermore, diversity and coding gains based on the asymptotic analysis at high SNRs are also presented, which are simple and concise and provide new analytical insights into the corresponding power allocation scheme. The analysis indicates that delay effect results in the coding gain loss and the diversity order loss, while CEE will merely cause the coding gain loss. Numerical results verify the theoretical analysis and illustrate the system is more sensitive to transmit beamforming delay compared with relay selection delay and also verify the superiority of optimum power allocation. We further investigate the outage loss due to the CEE and feedback delays, which indicates that the effect of the CEE is more influential at low-to-medium SNR, and then it will hand over the dominate role to the feedback delay.

Customer product reviews have become great influencers of purchase decision making. To assist potential customers, online stores provide various ways to sort customer reviews. Different methods have been developed to identify and recommend useful reviews to customers, primarily using feedback provided by customers about the helpfulness of reviews. Most of the methods consider the preferences of all users to determine whether reviews are helpful, and all users receive the same recommendations.

The widespread use of location-based services (LBSs), which allows untrusted service provider to collect large number of user request records, leads to serious privacy concerns. In response to these issues, a number of LBS privacy protection mechanisms (LPPMs) have been recently proposed. However, the evaluation of these LPPMs usually disregards the background knowledge that the adversary may possess about users' contextual information, which runs the risk of wrongly evaluating users' query privacy. In this paper, we address these issues by proposing a generic formal quantification framework,which comprehensively contemplate the various elements that influence the query privacy of users and explicitly states the knowledge that an adversary might have in the context of query privacy. Moreover, a way to model the adversary's attack on query privacy is proposed, which allows us to show the insufficiency of the existing query privacy metrics, e.g., k-anonymity. Thus we propose two new metrics: entropy anonymity and mutual information anonymity. Lastly, we run a set of experiments on datasets generated by network based generator of moving objects proposed by Thomas Brinkhoff. The results show the effectiveness and efficient of our framework to measure the LPPM.

The IEEE 802.15.4 standard not only provides a maximum of seven guaranteed time slots (GTSs) for allocation within a superframe to support time-critical traffic, but also achieves ultralow complexity, cost, and power in low-rate and short-distance wireless personal area networks (WPANs). Real-time wireless body area sensor networks (WBASNs), as a special purpose WPAN, can perfectly use the IEEE 802. 15. 4 standard for its wireless connection. In this paper, we propose an adaptive GTS allocation scheme for real-time WBASN data transmissions with different priorities in consideration of low latency, fairness, and bandwidth utilization. The proposed GTS allocation scheme combines a weight-based priority assignment algorithm with an innovative starvation avoidance scheme. Simulation results show that the proposed method significantly outperforms the existing GTS implementation for the traditional IEEE 802.15.4 in terms of average delay, contention free period bandwidth utilization, and fairness.

As a special type of mobile ad-hoc network, Vehicular Ad-hoc Network (VANET) is considered as an attractive topic by many researchers. In VANETs, vehicles act as routers and clients, which are connected with each other through unreliable wireless links. Due to the dynamic nature of vehicles, developing communication protocols for VANETs is a challenging task. In this paper, we tackle the problem of real-world VANET operations and propose a new dual-level communication scheme through the combination of power and rate control algorithms. Based on the game theoretic approach, the proposed scheme effectively formulates the interactive situation among several vehicles. With a simulation study, it is confirmed that the proposed scheme can achieve better performance than other existing schemes under widely diverse VANET environments.

The maximal signal-to-interference-plus-noise ratio (Max-SINR) algorithm for interference alignment (IA) has received considerable attention for its high sum rate achievement in the multiple-input multiple-output (MIMO) interference channel. However, its complexity may increase dramatically when the number of users approaches the IA feasibility bound, and the number of iterations and computational time may become unacceptable. In this paper, we study the properties of the Max-SINR algorithm thoroughly by presenting theoretical insight into the algorithm and by providing the potential of reducing the overall computational cost. Furthermore, a novel IA algorithm based on the principle direction search is proposed, which can converge more rapidly than the conventional Max-SINR method. In the proposed algorithm, it searches along the principle direction, which is found to approximately point to the convergence values, and can approach the convergence solutions rapidly. In addition, the closed-form solution of the optimal step size can be formulated in the sense of minimal interference leakage. Simulation results demonstrate that the proposed algorithm outperforms the conventional minimal interference leakage and Max-SINR algorithms in terms of the convergence rate while guaranteeing the high throughput of IA networks.

Utility-based routing is a special type of routing approach using a composite utility metric when making routing decisions in ad hoc and sensor networks. Previous studies on the utility-based routing all use fixed retry limit and a very simple distance related energy model, which makes the utility maximization less efficient and the implementation separated from practice. In this paper, we refine the basic utility model by capturing the correlation of the transmit power, the retry limit, the link reliability and the energy cost. A routing algorithm based on the refined utility model with adaptive transmit power and retry limit allocation is proposed. With this algorithm, packets with different priorities will automatically receive utility-optimal delivery. The design of this algorithm is based on the observation that for a given benefit, there exists a utility-maximum route with optimal transmit power and retry limit allocated to intermediate forwarding nodes. Delivery along the utility-optimal route makes a good balance between the energy cost and the reliability according to the value of the packets. Both centralized algorithm and distributed implementations are discussed. Simulations prove the satisfying performance of the proposed algorithm.

As the technologies advance, mobile peer-to-peer (MP2P) networks or systems become one of the major ways to share resources and information. On such a system, the information retrieval (IR), including the development of scalable infrastructures for indexing, becomes more complicated due to a huge increase on the amount of information and rapid information change. To keep the systems on MP2P networks more reliable and consistent, the index structures need to be updated frequently. For a semantic IR system, the index structure is even more complicated than a classic IR system and generally has higher update cost. The most well-known indexing technique used in semantic IR systems is Latent Semantic Indexing (LSI), of which the index structure is generated by singular value decomposition (SVD). Although LSI performs well, updating the index structure is not easy and time consuming. In an MP2P environment, which is fully distributed and dynamic, the update becomes more challenging. In this work, we consider how to update the sematic index generated by LSI and keep the index consistent in the whole MP2P network. The proposed Concept Space Update (CSU) protocol, based on distributed 2-Phase locking strategy, can effectively achieve the objectives in terms of two measurements: coverage speed and update cost. Using the proposed effective synchronization mechanism with the efficient updates on the SVD, re-computing the whole index on the P2P overlay can be avoided and the consistency can be achieved. Simulated experiments are also performed to validate our analysis on the proposed CSU protocol. The experimental results indicate that CSU is effective on updating the concept space with LSI/SVD index structure in MP2P semantic IR systems.

The final draft of the latest video coding standard, High Efficiency Video Coding (HEVC), was approved in January 2013. The coding efficiency of HEVC surpasses its predecessor, H.264/MPEG-4 Advanced Video Coding (AVC), by using only half of the bitrate to encode the same sequence with similar quality. However, the complexity of HEVC is sharply increased compared to H.264/AVC. In this paper, a method is proposed to decrease the complexity of intra coding in HEVC. Early pruning and an early splitting strategy are applied to the quadtree structure of coding tree units (CTU) and residual quadtree (RQT). According to our experiment, when our method is applied to sequences from Class A to Class E, the coding time is decreased by 44% at the cost of a 1.08% Bjontegaard delta rate (BD-rate) increase on average.

Troubles are often met when traditional active contours extract boundaries of medical images with inhomogeneous bias and various noises. Focusing on such a circumstance, a localized mutual information active contour model is discussed in the paper. By defining neighborhood of each point on the level set, mutual information is introduced to describe the relationship between the zero level set and image field. A driving energy term is then generated by integrating all the information. In addition, an expanding energy and internal energy are designed to regularize the driving energy. Contrary to piecewise constant model, new model has a better command of driving the contours without initialization.

This paper addresses the issues of 3D human activity detection, tracking and recognition from RGB-D video sequences using a feature structured framework. During human tracking and activity recognition, initially, dense depth images are captured using depth camera. In order to track human silhouettes, we considered spatial/temporal continuity, constraints of human motion information and compute centroids of each activity based on chain coding mechanism and centroids point extraction. In body skin joints features, we estimate human body skin color to identify human body parts (i.e., head, hands, and feet) likely to extract joint points information. These joints points are further processed as feature extraction process including distance position features and centroid distance features. Lastly, self-organized maps are used to recognize different activities. Experimental results demonstrate that the proposed method is reliable and efficient in recognizing human poses at different realistic scenes. The proposed system should be applicable to different consumer application systems such as healthcare system, video surveillance system and indoor monitoring systems which track and recognize different activities of multiple users.

In this paper, we studied facial impression construction with smartphones in a series of experiments with two smartphone applications: SmartRing and SystemSens+. In the first experiment, impressions of faces associated with different music genres (trot vs. classical) were compared to impressions formed from a facial image alone along the social warmth and intelligence dimensions. In the second experiment, the effect of similarity attraction was investigated by manipulating the extroversion of facial images. Results indicated that impressions of faces cannot only be constructed along the social warmth and intelligence dimensions, but can also be made more or less attractive based on their similarity to the viewer's personality. Our experiments provide interesting insights into facial impressions formed in a smartphone environment.

With the fast-developing of mobile terminals, positioning techniques based on fingerprinting method draws attention from many researchers even world famous companies. To conquer some shortcomings of the existing fingerprinting systems and further improve its performance, we propose a radio map building and updating technique, which is able to customize the spatial and temporal dependency of radio maps. The method includes indoor propagation and penetration modeling and the analysis of human traffic. Based on the combination of Ray-Tracing Algorithm, Finite-Different Time-Domain and Rough Set Theory, the approach of indoor propagation modeling accurately represents the spatial dependency of the radio map. In terms of temporal dependency, we specifically study the factor of moving people in the interest area. With measurement and statistics, the factor of human traffic is introduced as the temporal updating component. We improve our existing indoor positioning system with the proposed building and updating method, and compare the localization accuracy. The results show that the enhanced system can conquer the influence caused by moving people, and maintain the confidence probability stable during week, which enhance the actual availability and robustness of fingerprinting-based indoor positioning system.

Deniable authentication protocol allows a sender to deny his/her involvement after the protocol run and a receiver can identify the true source of a given message. Meanwhile, the receiver has no ability to convince any third party of the fact that the message was sent by the specific sender. However, most of the proposed protocols didn't achieve confidentiality of the transmitted message. But, in some special application scenarios such as e-mail system, electronic voting and Internet negotiations, not only the property of deniable authentication but also message confidentiality are needed. To settle this problem, in this paper, we present a non-interactive identity-based deniable authenticated encryption (IBDAE) scheme using pairings. We give the security model and formal proof of the presented IBDAE scheme in the random oracle model under bilinear Diffie-Hellman (BDH) assumption.

Network environment has been under constant threat from both malicious attackers and inherent vulnerabilities of network infrastructure. Existence of such threats calls for exhaustive vulnerability analyzing to guarantee a secure system. However, due to the diversity of security hazards, analysts have to select from massive alternative hardening strategies, which is laborious and time-consuming. In this paper, we develop an approach to seek for possible hardening strategies and prioritize them to help security analysts to handle the optimal ones. In particular, we apply a Risk Flow Attack Graph (RFAG) to represent network situation and attack scenarios, and analyze them to measure network risk. We also employ a multi-objective genetic algorithm to infer the priority of hardening strategies automatically. Finally, we present some numerical results to show the performance of prioritizing strategies by network risk and hardening cost and illustrate the application of optimal hardening strategy set in typical cases. Our novel approach provides a promising new direction for network and vulnerability analysis to take proper precautions to reduce network risk.

Security of information during transmission is a major issue in this modern era. All of the communicating bodies want confidentiality, integrity, and authenticity of their secret information. Researchers have presented various schemes to cope with these Internet security issues. In this context, both steganography and cryptography can be used effectively. However, major limitation in the existing steganographic methods is the low-quality output stego images, which consequently results in the lack of security. To cope with these issues, we present an efficient method for RGB images based on gray level modification (GLM) and multi-level encryption (MLE). The secret key and secret data is encrypted using MLE algorithm before mapping it to the grey-levels of the cover image. Then, a transposition function is applied on cover image prior to data hiding. The usage of transpose, secret key, MLE, and GLM adds four different levels of security to the proposed algorithm, making it very difficult for a malicious user to extract the original secret information. The proposed method is evaluated both quantitatively and qualitatively. The experimental results, compared with several state-of-the-art algorithms, show that the proposed algorithm not only enhances the quality of stego images but also provides multiple levels of security, which can significantly misguide image steganalysis and makes the attack on this algorithm more challenging.

With the advancement of mobile web environments, identification and analysis of the user behavior play a significant role and remains a challenging task to implement with variations observed in the model. This paper presents an efficient method for mining optimized user behavior prediction model using genetic algorithm on mobile web structure. The framework of optimized user behavior prediction model integrates the temporary and permanent register information and is stored immediately in the form of integrated logs which have higher precision and minimize the time for determining user behavior. Then by applying the temporal characteristics, suitable time interval table is obtained by segmenting the logs. The suitable time interval table that split the huge data logs is obtained using genetic algorithm. Existing cluster based temporal mobile sequential arrangement provide efficiency without bringing down the accuracy but compromise precision during the prediction of user behavior. To efficiently discover the mobile users' behavior, prediction model is associated with region and requested services, a method called optimized user behavior Prediction Model using Genetic Algorithm (PM-GA) on mobile web structure is introduced. This paper also provides a technique called MAA during the increase in the number of models related to the region and requested services are observed. Based on our analysis, we content that PM-GA provides improved performance in terms of precision, number of mobile models generated, execution time and increasing the prediction accuracy. Experiments are conducted with different parameter on real dataset in mobile web environment. Analytical and empirical result offers an efficient and effective mining and prediction of user behavior prediction model on mobile web structure.