The technology of cloud computing is growing very quickly, thus it is required to manage the process of resource allocation. In this paper, load balancing algorithm based on honey bee behavior (LBA_HB) is proposed. Its main goal is distribute workload of multiple network links in the way that avoid underutilization and over utilization of the resources. This can be achieved by allocating the incoming task to a virtual machine (VM) which meets two conditions; number of tasks currently processing by this VM is less than number of tasks currently processing by other VMs and the deviation of this VM processing time from average processing time of all VMs is less than a threshold value. The proposed algorithm is compared with different scheduling algorithms; honey bee, ant colony, modified throttled and round robin algorithms. The results of experiments show the efficiency of the proposed algorithm in terms of execution time, response time, makespan, standard deviation of load, and degree of imbalance.

Recently, there are many studies, that considering green wireless cellular networks, have taken the energy consumption of the base station (BS) into consideration. In this work, we first introduce an energy consumption model of multi-mode sharing BS powered by multiple energy sources including renewable energy, local storage and power grid. Then communication load requests of the BS are transformed to energy demand queues, and battery energy level and worst-case delay constraints are considered into the virtual queue to ensure the network QoS when our objective is to minimize the long term electricity cost of BSs. Lyapunov optimization method is applied to work out the optimization objective without knowing the future information of the communication load, real-time electricity market price and renewable energy availability. Finally, linear programming is used, and the corresponding energy efficient scheduling policy is obtained. The performance analysis of our proposed online algorithm based on real-world traces demonstrates that it can greatly reduce one day's electricity cost of individual BS.

Vehicular Ad Hoc Networks (VANET) in the large-scale road section usually have typical characteristics of large number of vehicles and unevenly distribution over geographic spaces. These two inherent characteristics lead to the unsatisfactory performance of VANETs. This poor performance is mainly due to fragile communication link and low dissemination efficiency. We propose a novel routing mechanism to address the issue in the paper, which includes a competition-based routing discovery with priority metrics and a local routing repair strategy. In the routing discovery stage, the algorithm uses adaptive scheme to select a stable route by the priorities of routing metrics, which are the length of each hop, as well as the residual lifetime of each link. Comparisons of different ratios over link length and link stability further show outstanding improvements. In the routing repair process, upstream and downstream nodes also compete for the right to establish repair process and to remain as a member of the active route after repair. Our simulation results confirm the improved performance of the proposed algorithm.

In this paper, we develop a simple but very effective 4 by 4 Multiple-Input Multiple-Output (MIMO) antenna system for mobile phones consisting of different types of antennas to achieve low correlation property at the frequency ranges of 1710 to 2170 MHz, which covers wide LTE service bands, from band 1 to band 4. The proposed antenna system consists of two pair of antennas. Each pair consists of a planar inverted-F antenna (PIFA) and a coupling antenna which has the property of the loop. The use of two different antenna types of IFA and a coupling achieves high isolation. Proposed antenna system occupies relatively small area and positions at the four corners of a printed circuit board. The gap between the two antennas is 4 mm, in order to realize the good isolation performance. To evaluate the performance of our proposed antenna system, we perform various experiments. The proposed antenna shows a wide operating bandwidth greater than 460 MHz with isolation between the feeding ports higher than 17.5-dB. It also shows that the proposed antenna has low Envelop Correlation Coefficient (ECC) values smaller than 0.08 over the all desired frequency tuning ranges.

As the main means to cope with the stubborn problem of drunk driving, the inspection of drunk driving has already been paid more attention and thus reinforced. In this paper, we model this scenario as a Stackelberg game, where the police department (called defender) allocates resources dynamically in terms of the traffic situation on the traffic network to arrest drink drivers and drivers who drink (called attacker), whether choosing drunk driving or designated driving service, expect to minimize their cost for given travel routes. However, with the number of resources are limited, our goal is to calculate the optimal resource allocation strategy for the defender. Therefore, first, we provide an effective approach (named OISDD) to fulfill our goal, i.e., generate the optimal strategy to inspect drunk driving. Second, we apply OISDD to directed graphs (which are abstracted from Dalian traffic network) to analyze and test its correctness and rationality. The experimental results show that OISDD is feasible and efficient.

Cloud computing system consists of distributed resources in a dynamic and decentralized environment. Therefore, using cloud computing resources efficiently and getting the maximum profits are still challenging problems to the cloud service providers and cloud service users. It is important to provide the efficient scheduling. To schedule cloud resources, numerous heuristic algorithms such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Ant Colony Optimization (ACO), Cuckoo Search (CS) algorithms have been adopted. The paper proposes a Modified Particle Swarm Optimization (MPSO) algorithm to solve the above mentioned issues. We first formulate an optimization problem and propose a Modified PSO optimization technique. The performance of MPSO was evaluated against PSO, and GA. Our experimental results show that the proposed MPSO minimizes the task execution time, and maximizes the resource utilization rate.

In this paper, we proposed a contract theory based cooperative spectrum sharing scheme with joint power and bandwidth optimization under asymmetric information, where the primary user (PU) does not know the secondary users' (SUs) private information. To improve performance, PU needs to provide incentives to stimulate nearby SUs to help forward its signal. By using contract theory, PU and SUs' negotiations are modeled as a labor market. PU and SUs act as the employer and employees, respectively. Specifically, SUs provide labor (i.e. the relay power, which can be used for forwarding PU's signal) in exchange for the reward (i.e. the spectrum access bandwidth which can be used for transmitting their own signals). PU needs to overcome a challenge how to balance the relationship between contributions and incentives for the SUs. We study the optimal contract design which consists of relay power and spectrum access bandwidth allocation. We show that the most efficient SUs will be hired by the PU to attend the cooperative communication. PU can achieve the same maximum utility as in the symmetric information scenario. Simulation results confirm that the utility of PU is significantly enhanced with our proposed cooperative spectrum sharing scheme.

This paper investigates the channel selection problem with dynamic users and the asymmetric interference relation in distributed opportunistic spectrum access systems. Since users transmitting data are based on their traffic demands, they dynamically compete for the channel occupation. Moreover, the heterogeneous interference range leads to asymmetric interference relation. The dynamic users and asymmetric interference relation bring about new challenges such as dynamic random systems and poor fairness. In this article, we will focus on maximizing the tradeoff between the achievable utility and access cost of each user, formulate the channel selection problem as a directional graphical game and prove it as an exact potential game presenting at least one pure Nash equilibrium point. We show that the best NE point maximizes both the personal and system utility, and employ the stochastic learning approach algorithm for achieving the best NE point. Simulation results show that the algorithm converges, presents near-optimal performance and good fairness, and the directional graphical model improves the systems throughput performance in different asymmetric level systems.

The implementation of IEEE 802.15.6 in Wireless Body Area Network (WBAN) is contention based. Meanwhile, IEEE 802.15.4 MAC provides limited 16 channels in the Superframe structure, making it unfit for N heterogeneous nature of patient's data. Also, the Beacon-enabled Carrier-Sense Multiple Access/Collision-Avoidance (CSMA/CA) scheduling access scheme in WBAN, allocates Contention-free Period (CAP) channels to emergency and non-emergency Biomedical Sensors (BMSs) using contention mechanism, increasing repetition in rounds. This reduces performance of the MAC protocol causing higher data collisions and delay, low data reliability, BMSs packet retransmissions and increased energy consumption. Moreover, it has no traffic differentiation method. This paper proposes a Low-delay Traffic-Aware Medium Access Control (LTA-MAC) protocol to provide sufficient channels with a higher bandwidth, and allocates them individually to non-emergency and emergency data. Also, a Contention Differentiated Adaptive Slot Allocation CSMA-CA (CDASA-CSMA/CA) for scheduling access scheme is proposed to reduce repetition in rounds, and assists in channels allocation to BMSs. Furthermore, an On-demand (OD) slot in the LTA-MAC to resolve the patient's data drops in the CSMA/CA scheme due to exceeding of threshold values in contentions is introduced. Simulation results demonstrate advantages of the proposed schemes over the IEEE 802.15.4 MAC and CSMA/CA scheme in terms of success rate, packet delivery delay, and energy consumption.

In view of personal health and disease management based on cost effective healthcare services, there is a growing need for real-time monitoring services. The electrocardiogram (ECG) signal is one of the most important of health information and real-time monitoring of the ECG can provide an efficient way to cope with emergency situations, as well as assist in everyday health care. In this system, it is essential to continuously collect and transmit large amount of ECG data within a given time and provide maximum user convenience at the same time. When considering limited wireless capacity and unstable channel conditions, appropriate signal processing and transmission techniques such as compression are required. However, ISO/IEEE 11073 standards for interoperability between personal health devices cannot properly support compressed data transmission. Therefore, in the present study, the problems for handling compressed data are specified and new extended agent and manager are proposed to address the problems while maintaining compatibility with existing devices. Extended devices have two PM-stores enabling compression and a novel transmission scheme. A variety of compression techniques can be applied; in this paper, discrete cosine transformation (DCT) is used. And the priority of information after DCT compression enables new transmission techniques for performance improvement. The performance of the compressed signal and the original uncompressed signal transmitted over the noisy channel are compared in terms of percent root mean square difference (PRD) using our simulation results. Our transmission scheme shows a better performance and complies with 11073 standards.

This paper proposes a novel intelligent wheelchair vehicle system that enables upper limb exercises, lower limb standing exercises and rehabilitation training in a daily life. The proposed system, which can be used to prevent at least the degeneration of body movements and further atrophy of musculoskeletal system functions, considers the characteristics and mobility of the old and the disabled. Its main purpose is to help the old and the disabled perform their daily activities as much as they can, minimizing the extent of secondary disabilities. In other words, the system will provide the old and the disabled with regular and quantitative rehabilitation exercises and diagnosis using the wheelchair-based upper/lower limb rehabilitation vehicle system and then verify their effectiveness. The system comprises an electric wheelchair, a biometric module to identify individual characteristics, and an upper/lower limb rehabilitation vehicle. In this paper the design and configuration of the developed vehicle is described, and its operation method is presented. Moreover, to verify the tracking performance of the proposed system, dangerous situations according to biosignal changes occurring during the rehabilitation exercise of a non-disabled examinee are analyzed and the performance of the upper/lower limb rehabilitation exercise function depending on muscle strength is evaluated through a neural network algorithm.

Sentiments can profoundly affect individual behavior as well as decision-making. Confronted with the ever-increasing amount of review information available online, it is desirable to provide an effective sentiment model to both detect and organize the available information to improve understanding, and to present the information in a more constructive way for consumers. This study developed a unified phrase-based topic and sentiment detection model, combined with a tracking model using incremental hierarchical dirichlet allocation (PTSM_IHDP). This model was proposed to discover the evolutionary trend of topic-based sentiments from online reviews. PTSM_IHDP model firstly assumed that each review document has been composed by a series of independent phrases, which can be represented as both topic information and sentiment information. PTSM_IHDP model secondly depended on an improved time-dependency non-parametric Bayesian model, integrating incremental hierarchical dirichlet allocation, to estimate the optimal number of topics by incrementally building an up-to-date model. To evaluate the effectiveness of our model, we tested our model on a collected dataset, and compared the result with the predictions of traditional models. The results demonstrate the effectiveness and advantages of our model compared to several state-of-the-art methods.

Many real networks exhibit overlapping community structures. Recent studies have been performed that analyze the impact of overlapping community structure on information propagation, but few of them concerned with individual behaviors. From this point of view, we propose a Markov process model to evaluate the performance of information propagation in social networks with overlapping community structures. In addition, many individual social behaviors are combined in the model. For example, individuals may exhibit selfish behaviors, such as individual and social selfishness, and people may discard the information after they have used it. The accuracy of the model is verified by simulation. Furthermore, the numerical results show that both overlapping community structure of the network and individual behaviors have a significant impact on the outbreak size and propagation speed of the information. Additionally, the overlapping community structure of the social network can reduce the impact of selfishness on information propagation.

A resource allocation algorithm based on simultaneous wireless information and power transfer (SWIPT) to maximize the system throughput is proposed in orthogonal frequency division multiplexing (OFDM) relay networks. The algorithm formulates the problem under the peak power constraints of the source and each subcarrier (SC), and the energy causality constraint of the relay. With the given SC allocation of the source, we give and prove the optimal propositions of the formulated problem. Then, the formulated problem could be decomposed into two separate throughput maximization sub-problems by setting the total power to transfer energy. Finally, several SC allocation schemes are proposed, which are energy priority scheme, information priority scheme, balanced allocation scheme and exhaustive scheme. The simulation results reveal that the energy priority scheme can significantly reduce computational complexity and achieve approximate performance with the exhaustive scheme.

Image scrambling is an important technology in information hiding, where the Arnold transformation is widely used. Several researchers have proposed the application of $H{\acute{e}}non$ map in square image scrambling, and certain improved technologies require scrambling many times to achieve a good effect without resisting chosen-plaintext attack although it can be directly applied to non-square images. This paper presents a non-square image scrambling algorithm, which can resist chosen-plaintext attack based on a chaotic two-dimensional Sine Logistic modulation map and $H{\acute{e}}non$ map (2D-SLHM). Theoretical analysis and experimental results show that the proposed algorithm has advantages in terms of key space, efficiency, scrambling degree, ability of anti-attack and robustness to noise interference.

Advanced Television Systems Committee (ATSC) produced ATSC 2.0 standard, which has been used for a HD/SD digital broadcasting service with MPEG-2 Transport Stream (TS) as a delivery protocol. Recently, users might want to consume multimedia services without being constrained by transport network. However, MPEG-2 TS is not suitable protocol for IP network. Thus, ATSC has enacted ATSC 3.0 standard, which is designed for a hybrid service through a conversion of both broadcasting and broadband channels. The ATSC 3.0 specifies delivery protocols as MPEG Media Transport (MMT) for a broadcasting environment and Dynamic Adaptive Streaming over HTTP (DASH) for a broadband. However, it seems difficult to combine broadcast and broadband for hybrid broadcasting, and it is difficult to synchronize media data between MMT and DASH. This paper suggests various new service scenarios to be brought up by a hybrid broadcasting, ATSC 3.0, and also proposes a stable mechanism for switching contents between different delivery protocols such as MMT and DASH.

In thin-film transistor liquid-crystal displays (TFT-LCDs), which are most commonly used in mobile devices, the backlight accounts for about 70% of the power consumption. Therefore, most low-power-related studies focus on realizing power savings through backlight dimming. Image compensation is performed to mitigate the visual distortion caused by the backlight dimming. Therefore, popular techniques include pixel compensation for brightness recovery and contrast enhancement, such as histogram equalization. However, existing pixel compensation techniques often have limitations with respect to blur owing to the pixel saturation phenomenon, or because contrast enhancement cannot adequately satisfy the human visual system (HVS). To overcome these, in this study, we propose a novel dimming technique to achieve both power saving and HVS-awareness by combining the pixel compensation and histogram specifications, which convert the original cumulative density function (CDF) by designing and using the desired CDF of an image. Because the process of obtaining the desired CDF is customized to consider image characteristics, histogram specification is found to achieve better HVS-awareness than histogram equalization. For the experiments, we employ the LIVE image database, and we use the structural similarity (SSIM) index to measure the degree of visual satisfaction. The experimental results show that the proposed technique achieves up to 15.9% increase in the SSIM index compared with existing dimming techniques that use pixel compensation and histogram equalization in the case of the same low-power ratio. Further, the results indicate that it achieves improved HVS-awareness and increased power saving concurrently compared with previous techniques.

In a multi-resolution image encoding system, the image is encoded into a single file as a layer of bit streams, and then it is transmitted layer by layer progressively to reduce the transmission time across a low bandwidth connection. This encoding scheme is also suitable for multiple decoders, each with different capabilities ranging from a handheld device to a PC. In our previous work, we proposed an edge adaptive hierarchical interpolation algorithm for multi-resolution image coding system. In this paper, we enhanced its compression efficiency by adding three major components. First, its prediction accuracy is improved using context adaptive error modeling as a feedback. Second, the conditional probability of prediction errors is sharpened by removing the sign redundancy among local prediction errors by applying sign flipping. Third, the conditional probability is sharpened further by reducing the number of distinct error symbols using error remapping function. Experimental results on benchmark data sets reveal that the enhanced algorithm achieves a better compression bit rate than our previous algorithm and other algorithms. It is shown that compression bit rate is much better for images that are rich in directional edges and textures. The enhanced algorithm also shows better rate-distortion performance and visual quality at the intermediate stages of progressive image transmission.

In this study, we propose an automatic melody extraction algorithm using deep learning. In this algorithm, feature images, generated using the energy of frequency band, are extracted from polyphonic audio files and a deep learning technique, a convolutional neural network (CNN), is applied on the feature images. In the training data, a short frame of polyphonic music is labeled as a musical note and a classifier based on CNN is learned in order to determine a pitch value of a short frame of audio signal. We want to build a novel structure of melody extraction, thus the proposed algorithm has a simple structure and instead of using various signal processing techniques for melody extraction, we use only a CNN to find a melody from a polyphonic audio. Despite of simple structure, the promising results are obtained in the experiments. Compared with state-of-the-art algorithms, the proposed algorithm did not give the best result, but comparable results were obtained and we believe they could be improved with the appropriate training data. In this paper, melody extraction and the proposed algorithm are introduced first, and the proposed algorithm is then further explained in detail. Finally, we present our experiment and the comparison of results follows.

In this paper, we defined the relative cross-sectional area of forearm cortical bone and investigated its correlation with hip bone mineral density values of total femur, femoral neck, femoral trochanter, femoral inter-trochanter and femoral ward's triangle, respectively. Based on the correlations, we established a linear transformation between the relative cross-sectional area of forearm cortical bone and each hip bone BMD. We obtained forearm images using CBCT and hip bone BMDs using dual-energy X-ray absorptiometry (DXA) for 28 subjects. We also investigated the optimal forearm region to provide the strongest correlation coefficient. We used the optimized forearm region to establish each linear transformation to estimate BMD values for total femur, femoral neck, femoral trochanter, femoral inter-trochanter and femoral ward's triangle from the relative cross-sectional area of forearm cortical bone, respectively. We observed the strong correlations with total femur (r=0.889), femoral neck (r=0.924), femoral trochanter (r=0.821), femoral inter-trochanter (r=0.867) and femoral ward's triangle (r=0.895), respectively. The strongest correlation was observed in the forearm mid-shaft regions. Our results suggest that the hip bone BMD values can be simply estimated from forearm CBCT images in a convenient sitting position without X-ray exposure on a hip including genital organs, and may be useful for screening osteoporosis.

Augmented Reality (AR) overlays virtual information on real world data, such as displaying useful information on videos/images of a scene. This paper presents an Enhanced AR (EAR) system that displays useful statistical players' information on captured images of a sports game. We focus on the situation where the input image is degraded by strong sunlight. Proposed EAR system consists of an image enhancement technique to improve the accuracy of subsequent player and face detection. The image enhancement is followed by player and face detection, face recognition, and players' statistics display. First, an algorithm based on multi-scale retinex is proposed for image enhancement. Then, to detect players' and faces', we use adaptive boosting and Haar features for feature extraction and classification. The player face recognition algorithm uses boosted linear discriminant analysis to select features and nearest neighbor classifier for classification. The system can be adjusted to work in different types of sports where the input is an image and the desired output is display of information nearby the recognized players. Simulations are carried out on 2096 different images that contain players in diverse conditions. Proposed EAR system demonstrates the great potential of computer vision based approaches to develop AR applications.

This paper demonstrates a case for an end-to-end pure Application Security Layer for reliable and confidential communications within an Internet of Things (IoT) constrained environment. To provide a secure key exchange and to setup a secure data connection, Transport Layer Security (TLS) is used, which provides native protection against replay attacks. TLS along with digital signature can be used to achieve non-repudiation within app-to-app communications. This paper studies the use of TLS over the JavaScript Object Notation (JSON) via a The Constrained Application Protocol (CoAP) RESTful service to verify the hypothesis that in this way one can provide end-to-end communication flexibility and potentially retain identity information for repudiation. As a proof of concept, a prototype has been developed to simulate an IoT software client with the capability of hosting a CoAP RESTful service. The prototype studies data requests via a network client establishing a TLS over JSON session using a hosted CoAP RESTful service. To prove reputability and integrity of TLS JSON messages, JSON messages was intercepted and verified against simulated MITM attacks. The experimental results confirm that TLS over JSON works as hypothesised.

The proxy re-encryption allows an intermediate proxy to convert a ciphertext for Alice into a ciphertext for Bob without seeing the original message and leaking out relevant information. Unlike many prior identity based proxy re-encryption schemes which are based on the number theoretic assumptions such as large integer factorization and discrete logarithm problem. In this paper, we first propose a novel identity based proxy re-encryption scheme which is based on the hardness of standard Learning With Error(LWE) problem and is CPA secure in the standard model. This scheme can be reduced to the worst-case lattice hard problem that is able to resist attacks from quantum algorithm. The key step in our construction is that the challenger how to answer the private query under a known trapdoor matrix. Our scheme enjoys properties of the non-interactivity, unidirectionality, anonymous and so on. In this paper, we utilize primitives include G-trapdoor for lattice and sample algorithms to realize simple and efficient re-encryption.

The field of periocular biometrics has gained wide attention as an alternative or supplemental means to conventional biometric traits such as the iris or the face. Periocular biometrics provide intermediate resolution between the iris and the face, which enables it to support both. We have developed a periocular recognition system by using uniform Multiscale Local Binary Pattern (uMLBP) and attribute features. The proposed system has been evaluated in terms of major factors that need to be considered on a mobile platform (e.g., distance and facial pose) to assess the feasibility of the use of periocular biometrics on mobile devices. Experimental results showed 98.7% of rank-1 identification accuracy on a subset of the Face Recognition Grand Challenge (FRGC) database, which is the best performance among similar studies.

Recently, the biometric-based authentication systems such as FIDO (Fast Identity Online) are increased in mobile computing environments. The biometric-based authentication systems are performed on the mobile devices with the battery, the improving energy efficiency is important issue. In the case, the size of images (i.e., face, fingerprint, iris, and etc.) affects both recognition accuracy and energy consumption, and hence the tradeoff analysis between the both recognition accuracy and energy consumption is necessary. In this paper, we propose an energy-efficient way to authenticate based on biometric information with tradeoff analysis between the both recognition accuracy and energy consumption in multimedia IoT (Internet of Things) transmission environments. We select the facial information among biometric information, and especially consider the multicore-based mobile devices. Based on our experimental results, we prove that the proposed approach can enhance the energy efficiency of GABOR+LBP+GRAY VALUE, GABOR+LBP, GABOR, and LBP by factors of 6.8, 3.6, 3.6, and 2.4 over the baseline, respectively, while satisfying user's face recognition accuracy.

With the fast growth of mobile services, Mobile Cloud Computing(MCC) has gained a great deal of attention from researchers in the academic and industrial field. User authentication and privacy are significant issues in MCC environment. Recently, Tsai and Lo proposed a privacy-aware authentication scheme for distributed MCC services, which claimed to support mutual authentication and user anonymity. However, Irshad et.al. pointed out this scheme cannot achieve desired security goals and improved it. Unfortunately, this paper shall show that security features of Irshad et.al.'s scheme are achieved at the price of multiple time-consuming operations, such as three bilinear pairing operations, one map-to-point hash function operation, etc. Besides, it still suffers from two minor design flaws, including incapability of achieving three-factor security and no user revocation and re-registration. To address these issues, an enhanced and provably secure authentication scheme for distributed MCC services will be designed in this work. The proposed scheme can meet all desirable security requirements and is able to resist against various kinds of attacks. Moreover, compared with previously proposed schemes, the proposed scheme provides more security features while achieving lower computation and communication costs.

Over the years, more password-based authentication key agreement schemes using chaotic maps were susceptible to attack by off-line password guess attack. This work approaches this problem by a new method--new theorem of chaotic maps: $T_{a+b}(X)+T_{a-b}(X)=2T_a(X)T_b(X)$,(a>b). In fact, this method can be used to design two-party, three-party, even in N-party intelligently. For the sake of brevity and readability, only a two-party instance: a novel Two-party Password-Authenticated Key Agreement Protocol is proposed for resisting password guess attack in this work. Compared with the related literatures recently, our proposed scheme can be not only own high efficiency and unique functionality, but is also robust to various attacks and achieves perfect forward secrecy. For capturing improved ratio of security and efficiency intuitively, the paper firstly proposes a new parameter called security/efficiency ratio(S/E Ratio). The higher the value of the S/E Ratio, the better it is. Finally, we give the security proof and the efficiency analysis of our proposed scheme.