Showing papers in "Journal of Internet Technology in 2015"
TL;DR: Simulation results show that the proposed LARP outperforms the existing routing protocols in terms of packet delivery ratio and normalized routing overhead, and are expected to be of greater value than other existing solutions in underwater environment.
Abstract: As the network communications technology developing, a new type of networks has appeared in the daily life which is named underwater sensor networks (UWSNs). UWSNs are a class of emerging networks that experience variable and high propagation delays and limited available bandwidth. There are comprehensive applications in this area such as oceanographic data collection, pollution monitoring, offshore exploration, assisted navigation and so on. Due to the different environment under the ocean, routing protocols in UWSNs should be re-designed to fit for the surroundings. In particular, routing protocols in UWSNs should ensure the reliability of message transmission, not just decrease the delay. In this paper, we propose a novel routing protocol named Location-Aware Routing Protocol (LARP) for UWSNs, where the location information of nodes is used to help the transmission of the message. Simulation results show that the proposed LARP outperforms the existing routing protocols in terms of packet delivery ratio and normalized routing overhead. We expect LARP to be of greater value than other existing solutions in underwater environment.
384 citations
TL;DR: This paper proposes an efficient mutual verifiable provable data possession scheme, which utilizes Diffie-Hellman shared key to construct the homomorphic authenticator and is very efficient compared with the previous PDP schemes, since the bilinear operation is not required.
Abstract: Cloud storage is now a hot research topic in information technology. In cloud storage, date security properties such as data confidentiality, integrity and availability become more and more important in many commercial applications. Recently, many provable data possession (PDP) schemes are proposed to protect data integrity. In some cases, it has to delegate the remote data possession checking task to some proxy. However, these PDP schemes are not secure since the proxy stores some state information in cloud storage servers. Hence, in this paper, we propose an efficient mutual verifiable provable data possession scheme, which utilizes Diffie-Hellman shared key to construct the homomorphic authenticator. In particular, the verifier in our scheme is stateless and independent of the cloud storage service. It is worth noting that the presented scheme is very efficient compared with the previous PDP schemes, since the bilinear operation is not required.
349 citations
TL;DR: Through a similarity search method for encrypted document based on simhash, data users can find similar encrypted documents stored in cloud by submitting a query document and build a triebased index to improve search efficiency.
Abstract: In recent years, due to the appealing features of cloud computing, more and more sensitive or private information has been outsourced onto the cloud. Although cloud computing provides convenience, privacy and security of data becomes a big concern. For protecting data privacy, it is desirable for the data owner to outsource sensitive data in encrypted form rather than in plain text. However, encrypted storage will hinder our legal access, e.g., searching function. To deal with this dilemma, a considerable number of searchable encryption schemes have been proposed in this field. However, almost all of existing schemes focus on keyword-based query rather than document-based query, which is a crucial requirement for real world application. In this paper, we propose a similarity search method for encrypted document based on simhash. Through our scheme, data users can find similar encrypted documents stored in cloud by submitting a query document. In order to scale well for large data sources, we build a triebased index to improve search efficiency in our solution. Through rigorous privacy analysis and experiment on realworld dataset, our scheme is secure and efficient.
39 citations
TL;DR: The background and state-of-the-art of Health-IoT is reviewed, including physiological information collection, data transmission, wearable computing, and health big data analysis, and the researches closely related to the Health- IoT are focused on.
Abstract: The Health Internet of Things (Health-IoT) is a milestone of the health information system development, and it will play an important role in improving people's healthy level and enhancing the quality of life and health service level, and will promote the change of health service mode. Health-IoT is a very complex system, involving computer science, microelectronics systems, wireless communications, medical and health, and many other related fields. In this paper, we review the background and state-of-the-art of Health-IoT. We first introduce the general background of Health-IoT and the difference between Health-IoT and traditional IoT are given. Then we give the definition of Health-IoT and the Health-IoT's features. We then focus on the researches closely related to the Health-IoT, including physiological information collection, data transmission, wearable computing, and health big data analysis. We introduce the four typical application scenarios of the Health-IoT, including the medical industry, health monitoring, exercise promotion and mental support. Finally, we give the outlook of Health-IoT and the summary.
22 citations
TL;DR: A clustering algorithm based on GSO with mobile sink (CAGM) for WSNs, which tries to combine GSO algorithm, clustering technique and sink mobility strategies together to extend network lifetime and improve network performance is proposed.
Abstract: In recent years, many energy efficient algorithms and routing protocols have been proposed for wireless sensor networks (WSNs) with the object to balance the energy consumption and maximize the network lifetime. It has been proved that utilizing clustering technique and adding sink mobility into sensor networks can bring in new opportunities to improve energy efficiency for WSNs. Glowworm Swarm Optimization (GSO), which belongs to the swarm intelligence field inspired by simulated experiments of the lighting worms' behavior, is also an effective method to improve network performance. In this paper, we proposed a clustering algorithm based on GSO with mobile sink (CAGM) for WSNs, which tries to combine GSO algorithm, clustering technique and sink mobility strategies together to extend network lifetime and improve network performance. We first divide the whole network into several clusters and select cluster heads of each cluster by utilizing GSO algorithm. After then, cluster heads collect data from their member nodes and do data fusion. In order to improve network performance, a mobile sink was used to roam about the network and collect data from cluster heads through really short communication range. Extensive simulation results show that our proposed algorithm can efficiently prolong the network lifetime of WSNs.
22 citations
TL;DR: Experimental results demonstrated that the proposed non-local regularized variational model for image restoration was more capable of removing both blurring effect and mixed Gaussian-impulse noise, as well as preserving fine image details.
Abstract: The presence of mixed Gaussian-impulse noise makes image deblurring much more challenging in real-world applications. To guarantee high-quality deblurred images, a non-local regularized variational model is proposed in this paper for restoration of blurred images corrupted by mixed Gaussian-impulse noise. The proposed image restoration model is mainly composed of a mixed L^(1,2) data-fidelity term and a non-local total variation regularizer. The nonlocal regularizer is capable of suppressing staircase-like artifacts and preserving fine structures since it takes full advantage of high degree of self-similarity and redundancy within images. However, the mixed data-fidelity term and non-local regularizer make the image deblurring problem arithmetically difficult to solve. To guarantee solution stability and efficiency, an alternating minimization algorithm is developed to solve the resulting optimization problem. In particular, the original optimization problem can be decomposed into two simpler subproblems. Each subproblem is then efficiently solved using existing numerical method. Comprehensive experiments on grayscale and color test images have been carried out to compare our proposed method with several state-of the- art image restoration methods. Experimental results demonstrated that our proposed method was more capable of removing both blurring effect and mixed Gaussian-impulse noise, as well as preserving fine image details.
14 citations
TL;DR: A novel optimization algorithm based on hybrid Particle Swarm Optimization with Artificial Bee Colony optimization is proposed to solve the complex constrained optimization problems of topology control in wireless sensor networks (WSN).
Abstract: Easy convergence to a local optimum, rather than global optimum, could unexpectedly happen in both multimodal optimization problems and complex constrained optimization problems. The enhanced diversity agent in optimal algorithms is one of the solutions to this issue. In this paper, a novel optimization algorithm based on hybrid Particle Swarm Optimization (PSO) with Artificial Bee Colony optimization (ABC) is proposed to solve the complex constrained optimization problems of topology control in wireless sensor networks (WSN). A new communication strategy for hybrid PSO with ABC (HPAO) is to take advantages of the strength points of each side type of algorithms to explore and exploit the algorithm diversity. Six benchmark functions are used to verify the convergence behavior, the accuracy, and the speed of the proposed algorithm. Experimental results show that the proposal increases the accuracy more than 42.1% and 16.3% with respect to PSO and ABC.
14 citations
TL;DR: A novel Min-Max Ant System algorithm with the new utilities heuristic information to search the global approximate optimal is proposed, which is efficiency and feasibility more than the recently proposed related algorithms.
Abstract: In this paper, we study the dynamic service composition becomes a decision problem on which component services should be selected under the E2E-to- End Quality of Service constraints. This problem is modeled is a nonlinear optimization problem under several constraints. We have two approaches to solve this problem. The first approach is local selection which is an efficient strategy with polynomial time complexity but can not handle global constraints. The second approach is the traditional global selection approach based on integer linear programming can handle global constraints, but its poor performance renders it inappropriate for practical applications with dynamic requirements. To overcome this disadvantage, in this study we proposed a novel Min-Max Ant System algorithm with the new utilities heuristic information to search the global approximate optimal. Our goal focus on its efficiency in terms of execution time and the quality in terms of the obtained best solution fitness value. The experiment results show that our algorithm is efficiency and feasibility more than the recently proposed related algorithms.
12 citations
TL;DR: This paper shows how leveraging an underlying platform-as-a-service (PaaS) cloud model can provide integration with web service patient monitoring systems while providing high availability, scalability, and security.
Abstract: Recent progression in health informatics data analysis has been impeded due to lack of hospital resources and computation power. To remedy this, some researchers have proposed a cloud-based web service patient monitoring system capable of providing offsite collection, analysis, and dissemination of remote patient physiological data. Unfortunately, some of these cloud services are not effective without utilizing next-generation hardware management techniques. In order to make cloud based patient monitoring a reality, this paper shows how leveraging an underlying platform-as-a-service (PaaS) cloud model can provide integration with web service patient monitoring systems while providing high availability, scalability, and security. We also present an analytical model of the proposed platform and obtain performance measures such as delay in servicing as well as reject probability.
10 citations
TL;DR: An Unequal Cluster-based Routing Protocol (UCRP) is proposed to alleviate the energy-hole problem in wireless heterogeneous sensor networks and improves network throughput.
Abstract: Hierarchical clustering provides an effective way to prolong the lifetime of wireless sensor networks. However, when cluster heads use multi-hop forwarding model for inter-cluster communication, the area around the sink is burdened with heavy traffic load, which introduces uneven energy consumption and causes energy-hole problem. In this paper, an Unequal Cluster-based Routing Protocol (UCRP) is proposed to alleviate the energy-hole problem in wireless heterogeneous sensor networks. In UCRP, by evenly dividing the network into multi-layer rings, three kinds of nodes with different initial energy namely normal nodes, advanced nodes and super nodes, are grouped into clusters with unequal sizes. In order to obtain the optimal number of cluster heads and the balanced average energy consumption, a mathematical method is developed to calculate the optimal cluster radius for each cluster. Based on the optimal cluster radius, an unequal clustering algorithm and a cluster-based routing protocol are proposed to balance the energy consumption in each cluster. Both theoretical analysis and simulation results indicate that UCRP effectively balances energy consumption and improves network throughput.
8 citations
TL;DR: A novel joint frequency and 2D-DOA estimation algorithm for a cylindrical conformal array that can be extended to other array structure with little modification is proposed.
Abstract: Owe to the varying curvature of the conformal carrier, the conventional direction-of-arrival (DOA) estimation algorithms could not be applied to the conformal array. In this study, a novel joint frequency and 2D-DOA estimation algorithm for a cylindrical conformal array is proposed. Firstly, the snapshot data model is established with polarization diversity (PD) of element patterns. Subsequently, the decoupling between the polarization parameter and DOA can be realized with well array structure design. The parallel factor (PARAFAC) analysis is utilized for the joint frequency and 2D-DOA estimation without parameter pairing. The proposed algorithm can be extended to other array structure with little modification. To illustrate the versatility of the proposed algorithm, the simulation results with the cylindrical conformal array are shown and discussed.
Journal Article•
TL;DR: In this article, a hierarchical gradient diffusion algorithm is proposed to solve the transmission problem and the sensor node's loading problem by adding several relay nodes and arranging the sensor nodes routing path.
Abstract: In this paper, a hierarchical gradient diffusion algorithm is proposed to solve the transmission problem and the sensor node’s loading problem by adding several relay nodes and arranging the sensor node’s routing path. The proposed hierarchical gradient diffusion aims to balance sensor node’s transmission loading, enhance sensor node’s lifetime, and reduce the data package transmission loss rate. According to the experimental results, the proposed algorithm not only reduces power consumption about 12% but also decreases data loss rate by 85.5% and increases active nodes by about 51.7%.
TL;DR: This work is the first attempt to provide a secure user anonymity protocol without using smart cards and symmetric keyen/decryptions in remote login environments and designs an improved protocol for password authenticated key agreement with user anonymity.
Abstract: Password authenticated key agreement protocol allowsusers to use an easy-to-remember password and establish asecure session key with the help of a trusted server.Recently, Farash and Attari proposed an improved keyagreement protocol based on chaotic maps and theypointed out that Gong et al.'s protocol is vulnerable tostolen-verifier attack and password change pitfalls.However, in this paper, we analyze the security ofFarash-Attari's protocol and show that it fails to resistknown-key attack if the previous session key sharedbetween two parties is compromised. In addition, theirprotocol is insecure against many logged-in users' attackand the server is not aware of having caused problem. Tofill the security gaps, we further design an improvedprotocol for password authenticated key agreement withuser anonymity. To the best of our knowledge, none of therecently proposed password authenticated key agreementprotocols can ensure anonymous interactions between thelogin user and the remote server and this work is the firstattempt to provide a secure user anonymity protocolwithout using smart cards and symmetric keyen/decryptions in remote login environments.
TL;DR: This paper culminates previous studies as well as current research to form a method of evaluation and ranking of cloud based services based on their ability to meet the user requirements.
Abstract: Cloud computing has revolutionized the world’s approach to providing computing services. Cloud computing has provided numerous advantages like quick deployment, cost efficiencies, pay as you go, flexibility, and the ability to work from anywhere and at any time. The number of providers of cloud computing has increased rapidly around the world. It, therefore, has become difficult for customers to decide which provider has the ability to fulfill the customer’s requirements as well as determining what to base their selection on. Currently, there are studies for the evaluation and ranking of cloud based services based on their ability to meet the user requirements. This paper culminates previous studies as well as current research to form a method of evaluation and ranking. This paper will begin by presenting the necessary background by introducing cloud computing and multi-criteria decision making MCDM. Then evaluate a subset of papers [45] which represent the majority of researches proposed in the field of service evaluation conducted over the last decade (2005 ~ 2014) based on our own taxonomy. The paper will conclude with highlights from the lessons from the past and directions for future research. Our goal is not only to analyze but also to compare and consolidate past research.
Journal Article•
TL;DR: A Micro-Blog public opinion trends forecasting system using BP neural network is developed that can collect and processes data automatically and forecast the tendency of Micro-blog public opinion.
Abstract: The development and application of Micro-Blog has made it simpler and easier for people to express their feelings. However, with the characteristics of openness and fast-spreading, Micro-Blog can quickly turn a point of view into public opinion. Therefore, Micro-blog opinion evolution and trends prediction has become the focus of people's attentions. This paper develops a Micro-Blog public opinion trends forecasting system using BP neural network. This system can collect and processes data automatically and forecast the tendency of Micro-Blog public opinion.
TL;DR: This study first integrates entropy-based embedding technique and SNR into an optimization problem and the results verify the better SNR and the strong robustness against most signal processing or attacks.
Abstract: This study aims to present an optimization-based audio watermarking using entropy-based watermarking technique in the wavelet domain. In general, the performance of a watermarking system is measured in terms of signal-tonoise ratio (SNR) and bit error rate (BER). However, there is a tradeoff between them which issues a challenge in the field of the watermarking. To overcome this challenge, this study first integrates entropy-based embedding technique and SNR into an optimization problem. Because of uncertain service environment for the audio media, an optimization algorithm with less hardware requirement is required to solve this problem. Since both performance index and constraint are nonlinear, the compact particle swarm optimization (cPSO) which suits for embedded system can do this well. In addition, the hidden information can be extracted without knowledge of the original audio. In the experiments, the performance of the proposed method is tested and the results verify the better SNR and the strong robustness against most signal processing or attacks.
TL;DR: The positioning performance in coal mine tunnel environment demonstrates that the proposed improved fingerprinting algorithm can improve the positioning accuracy and the location of the miner can be computed in less time compared with traditional approach.
Abstract: Precise localization and target tracking in coal mine tunnel is crucial for miners' safety protection. Due to the special environment of coal mine tunnel, the conventional positioning systems can not determine the specific location of underground personnel in real-time and with high positioning accuracy. In this paper, an improved fingerprinting algorithm based on underground Wi-Fi network is proposed to increase positioning accuracy. In our localization scheme, Received Signal Strength Indication (RSSI) from wireless Access Point (AP) and Support Vector Machine (SVM) based classifier are employed for position analysis. Specifically, the outliers were excluded by data preprocessing using k nearest neighbor (kNN) rule in the training phase, and results correction was utilized in the positioning stage. The positioning performance in coal mine tunnel environment demonstrates that the proposed improved fingerprinting algorithm can improve the positioning accuracy and the location of the miner can be computed in less time compared with traditional approach.
TL;DR: This paper proposes a method of service station deployment, and an energy efficient charging scheme developed based on the fact that energy consumption of sensor nodes in a WRSN is extremely imbalanced.
Abstract: ＂Wireless Rechargeable Sensor Networks＂ (WRSNs) have received wide attention in recent years due to their potential to solve the energy problem. With a Mobile Charger (MC), the lifetime of a WRSN can be prolonged significantly, or theoretically speaking, to infinity. An important issue in deploying WRSNs is to minimize the total energy consumption of the MC. To address the problem, this paper proposes a method of service station deployment, and an energy efficient charging scheme. These methods are developed based on the fact that energy consumption of sensor nodes in a WRSN is extremely imbalanced. Simulation results validate the energy efficiency of the two methods.
TL;DR: This study proposed a design for an Appliance-oriented Home Energy Management (HEM) Platform based on the IoT architecture, where the service behaviors of electronic appliances are obtained from appliance detection, while energy management provides the service of integrating heterogeneous devices.
Abstract: In recent years, Internet of Things (IoT) has received increased attention, and government and research institutions have actively cooperated in constructing IoT infrastructures. The discussions on IoT and home energy management systems have facilitated the development of appliance recognition technology, which assists users to effectively know electronic appliance usage, thus, further improving their decisions on power utilization. In consideration of the common power utilization habit of multiple electronic appliances, this study discussed multi-appliance recognition in the parallel state, i.e., appliance recognition for simultaneous use of multiple electronic appliances. This study proposed a design for an Appliance-oriented Home Energy Management (HEM) Platform based on the IoT architecture, where the service behaviors of electronic appliances are obtained from appliance detection, while energy management provides the service of integrating heterogeneous devices. The problem of the large data volume of current appliance recognition systems is solved by creating a database mechanism, appliance feature clustering, and a waveform recognition method. Different from the experimental environment of other recognition systems, parallel multi-appliance recognition and general user habits of power utilization were considered. Experiments in the routine habits of power utilization were conducted, with an average system precision rate of 92.07%, and an average single appliance recognition rate of 93.46%. The results proved that this study is highly feasible.
TL;DR: An efficient fuzzy algorithm using in similarity measurement for selecting suitable content and exams for students, which has been applied to the e-Learning system called Fuzzy Adaptive Learning Diagnosis System (FALDS), indicates that by using FALDS to diagnose and assist learning, students in the experimental group outperform those not in the group.
Abstract: Most e-Learning systems are required to establish a flexible content structure and provide suitable learning path, content, or interface by extracting meaningful learning behavior of students for adaptive learning. However, recognizing the students' learning behavior, teaching material, and personal degree are still a challenge that needs to be resolved. In this paper, we propose an efficient fuzzy algorithm using in similarity measurement for selecting suitable content and exams for students, which has been applied to the e-Learning system called Fuzzy Adaptive Learning Diagnosis System (FALDS). The proposed system computes the relationship between exam items and teaching materials depending on the results of practice and then exams to automatically select and marks important paragraphs for the learners. In addition, an efficient system for classifying students into groups so that information for selecting appropriate items for the learners can be provided is proposed. To evaluate the performance, a total of 200 fourth-grade students from six classes participate in the experiment for a school term. The results indicate that by using FALDS to diagnose and assist learning, students in the experimental group outperform those not in the group.
TL;DR: The proposed three-layer HCI model and the group formation algorithm, which is predicated on a dynamic sharing niche technology, is presented and the cooperative reinforcement learning and fusion algorithms are explored.
Abstract: This paper presents a human-computer interaction model with a three layers learning mechanism in a pervasive environment. We begin with a discussion around a number of important issues related to human-computer interaction followed by a description of the architecture for a multi-agent cooperative design system for pervasive computing environment. We present our proposed three-layer HCI model and introduce the group formation algorithm, which is predicated on a dynamic sharing niche technology. Finally, we explore the cooperative reinforcement learning and fusion algorithms; the paper closes with concluding observations and a summary of the principal work and contributions of this paper.
TL;DR: Two delay-optimized offloading control schemes in HetNet are proposed to control the number of offloading users by threshold comparison and to determine whether the users are suited to offload by estimating the execution delay.
Abstract: Offloading is an efficient method for extending the lifetime and speeding up the execution rate of mobile devices. Meanwhile, heterogeneous network (HetNet), which has multiple types of radio access nodes in the network, is widely accepted as a promising way to satisfy the increased traffic demand. In this paper, we first propose two delay-optimized offloading control schemes in HetNet. One of them is to control the number of offloading users by threshold comparison. Another is to determine whether the users are suited to offload by estimating the execution delay. Both of them take the traffic load of serving cell and neighboring cells into account. Moreover, two rate-prediction methods can be used. For comparison purpose, the delay performance of different schemes is evaluated by simulations in not only HetNet but also macro-only network.
TL;DR: To calibrate arrays, this study proposed an improved particle swarm optimization (PSO) method for calculating the ML and WSF functions, and identified the optimal solution for each function.
Abstract: Considering that the estimation problem in which the direction of arrival (DOA) of sensor signals experiences array gains and positional perturbations exists within the code-division multiple-access (CDMA) system, this study employed two types of estimation functions, the maximum likelihood (ML) and weighted subspace fitting (WSF) functions. ML and WSF functions are complex non-linear, multimodal functions that feature highdimensional problem spaces and typically use calibrated arrays to estimate DOAs. Thus, to calibrate arrays, this study proposed an improved particle swarm optimization (PSO) method for calculating the ML and WSF functions, and identified the optimal solution for each function. The proposed methods do not require calibrated source signals and can estimate the sensor perturbations and DOAs of incident signals. The simulation results showed that the proposed estimator outperforms other estimation methods.
TL;DR: A novel systematic approach for performances evaluations of DSEs by leveraging the scientific methods from information retrieval systems is introduced and results obtained indicated that the methodology can provide detailed, accurate, and useful information for performance evaluation of any DSE.
Abstract: Effective and easy access to stored information is a fundamental computer users' need. Advancements in storage technology and ubiquity of digital gadgets enabled users to generate tons of information. Ultimately, raise the demand for efficient schemes to search and retrieve the stored information. Desktop search engines (DSEs) are developed for helping users in finding information on their desktops quickly and easily. However, the availability of several DSEs troubled users in their selections for making their entire experience of using personal computers less frustrating. This paper introduces a novel systematic approach for performances evaluations of DSEs by leveraging the scientific methods from information retrieval systems. The proposed methodology is based on Cranfield approach consisting of setting several null hypotheses (H_0), developing of static documents and queries collections, and using scientific measures for relevancy measurements. Statistical analysis is performed to confirm significance of the hypotheses and measurements. Copernic and Google are selected as case tools for executing and confirm validity of the methodology. The methodology is generic and can be applied for performances evaluations of any DSE. Results obtained from the test exercises indicated that the methodology can provide detailed, accurate, and useful information for performance evaluation of any DSE.
TL;DR: This work exploits the synergy between traditional queuing-cum-scheduling and polling models, and develops an analytical framework for G/M/1 queuing system which contemplates multiple classes of self-similar and LRD traffic as input.
Abstract: Queuing delay have significant impact on the performance of network applications. To meet distinct delay requirements of multi-classend-user traffic,various queuing and scheduling schemeshave been proposed. These schemes are analogous to a polling mechanism in which multiple traffic queues are concurrently handled by a single scheduler. However, researchers were unable to analyze this synergy between the conventional queuingcum- scheduling and polling models. Moreover, research on analyzing polling models assumed traditional Poisson traffic distribution which is unable to capture self-similar and long-range dependent (LRD) characteristics and hence yield misleading results. Furthermore, published work related to self-similar traffic modeling is mainly based on conventional queuing-cum-scheduling which are simple approximations.The objective of this work is to analyze different combinations of conventional queuing and polling models. In this paper, we exploit the synergy between traditional queuing-cum-scheduling and polling models. We analyze different combinations of queuing and polling mechanisms with realistic traffic distributions i.e., selfsimilar and LRD. An analytical framework for G/M/1 queuing system is developedwhich contemplatesmultiple classes of self-similar and LRDtraffic as input.We formulate the Markov chain for G/M/1 queuing system and extract closed-form expressions of queuing delay for corresponding traffic classes. We analyzea combination of limited service polling model with non-pre-emptive priority queuing. Different combinations of polling models (i.e., exhaustive, gated and limited service) are also analyzed. We validate the performance of the proposed analytical framework through simulations. Simulation results suggest that synergy of polling and schedulingdangle promising results.
TL;DR: A novel approach to devise an architectural framework for trusted cloud computing to help organizations and companies in curtailing data processing costs by outsourcing computations on-demand by ensuring confidentiality and integrity of the data sources and computation processes.
Abstract: This paper presents a novel approach to devise an architectural framework for trusted cloud computing to help organizations and companies in curtailing data processing costs by outsourcing computations on-demand by ensuring confidentiality and integrity of the data sources and computation processes. The proposed framework comprises a number of modules-Identity Matrix (IMx) being one of them. The architecture is designed in a way that it offers processes a virtual view of a network interface to support user level access to high-speed communication devices like biometric devices to supportuser level identification system. As the target audience are scrutinized and their corresponding interest groups are better understood, the framework could be further refined such that it would make a systematic data warehouse depicting meaningful information for various public and private sector organizations and businesses etc. The right information would be directed towards right people at the right time; thus helping organizations to take right actions based on global network of trusted peers, as well as eliminating risks posed by identified malicious users. The main focus of this study will be software architecture. The study also demonstrates the efficacy of software architecture and high level design for the proposed trusted cloud computing framework.
TL;DR: Around autonomic assessment problem of security risk, considering the high hybrid and heterogeneity of Internet of Things, the potential influence and occurrence possibility of each main threat is described, evaluated and measured in accordance with fuzziness and randomicity of IOT security index.
Abstract: Around autonomic assessment problem of security risk, considering the high hybrid and heterogeneity of Internet of Things (IOT, for short), the potential influence and occurrence possibility of each main threat is described, evaluated and measured in accordance with fuzziness and randomicity of IOT security index. By which, the security risk grade and system tolerance degree of hybrid IOT security scene with incremental deployment characteristics is qualitatively analyzed. Multidimensional Normal Cloud is adopted to make synthesis of risk indicators, and the autonomic assessment strategy of security risk is constructed. On the basis of above work, transmission multi-rules mapping between qualitative input of security risk and quantitative inference of assessment rules is researched, autonomic inference rules and the optimization strategy are proposed. At last, the effectiveness, autonomy and accuracy of the proposed approach are verified by simulations.
TL;DR: The playful cloud-learning indeed has good influence on junior high school students' learning outcomes, and the result of the goodness-of-fit indexes indicates that the hypothesized model of playfulcloud-learning with cooperative learning model adequately describes the observations.
Abstract: The major purpose of this study is to investigate the effects of Playful cloud-learning with cooperative learning on e-books outcomes. Besides, the correlation among playful cloud-learning, cooperative learning, self-determination theory, technology acceptance model, and student's learning attitude, continuance learning intention, and the learning outcomes are also investigated. This study also examines the appropriateness of the hypothesized model of the researching model. We employed the path analysis with Structural equation modeling to analyze the observations. The results of the study are summarized below: (1) Playful cloud-learning has positive influence on perceived usefulness, perceived ease of use, and perceived playfulness. (2) Cooperative learning doesn't have positive influence on perceived usefulness, perceived ease of use, and perceived playfulness. (3) Self-determination theory has positive influence on perceived usefulness, perceived ease of use, and perceived playfulness. (4) Perceived usefulness, perceived ease of use, and perceived playfulness of Technology Acceptance Model have positive influence on learning attitude. (5) Students' learning attitude has positive influence on students' continuance learning intention. (6) Students' continuance learning intention has positive influence on their learning outcomes. (7) For the appropriateness of the hypothesized model, the result of the goodness-of-fit indexes indicates that the hypothesized model of playful cloud-learning with cooperative learning model adequately describes the observations. The major contribution of this study is that reveal the playful cloud-learning indeed has good influence on junior high school students' learning outcomes. Without using cooperative learning, playful cloud-learning still has good influence on their learning outcomes.
TL;DR: A new model in which agents spontaneously participate in the discussions regarding a topic or may be persuaded by neighbors is put forward, which can help to understand and make effective measures of promoting or prohibiting information diffusion in social networks.
Abstract: Information diffusion in online social networks has attracted public attention, and epidemic models have been applied to describe the diffusion process. However, online social networks exhibit a different propagation mechanism. To better characterize online information diffusion, we put forward a new model in which agents spontaneously participate in the discussions regarding a topic or may be persuaded by neighbors. Agents may ignore the information once they contact it. The attraction of the topic decays with time, and spreaders may also lose their interest in diffusing information. A real social network and virtual scale-free network are used as interaction topology. Results show unlike traditional epidemic models, the threshold of the interpersonal spreading rate with spontaneous diffusion drops to zero. The spontaneous diffusion mechanism lowers the threshold of diffusion process, but cannot cause a large extent of infection. The effect of the average network degree is quite different from that of the network diameter. The average degree of underlying network greatly improves the density of spreaders for any value of the interpersonal spreading rate, but the network diameter has a clear effect on the dynamics only when the spreading rate is large. Our work can help to understand and make effective measures of promoting or prohibiting information diffusion in social networks.
TL;DR: This work designed a prototype system that includes a smart socket, a non-invasive data acquisition module, and an Internet of Things back-end which provides scalable communication and computation capacity and uses an embedded system that has a low energy overhead and allows high scalability.
Abstract: The production, distribution, and consumption of energy have been receiving a lot of attention in the past few years. We have witnessed the emergence of an increasing number of energy-saving technologies and standards aimed at saving energy consumption of home appliances. To reap higher energy savings, home energy management systems were attempted to monitor and coordinate the individual energy saving activities of home appliances cost-effectively. To achieve this, the working status of each power load has to be recognized and then synchronized at run-time. In this context, low-cost and stand-alone electronic home appliance recognition technologies have been widely explored to identify different types of appliances being used and analyze the power consumption of appliances' operations. It is common that many appliances are used at the same time. However, these recognition technologies cannot accurately identify electronic home appliances operating in parallel at run-time. Diverse, multiple electronic home appliance recognition technologies encounter a range of new challenges. To address these challenges, we have proposed an algorithm that recognizes multiple diverse electronic home appliances concurrently via wave-form recognitions at run-time. To address deployment and response delay issues, we designed a prototype system that includes a smart socket, a non-invasive data acquisition module, and an Internet of Things (IoT) cloud-enabled back-end which provides scalable communication and computation capacity. In contrast to existing systems, the proposed system uses an embedded system that has a low energy overhead and allows high scalability. To evaluate our system, we conducted our experimental tests in an environment consisting of daily home appliances. The experimental results show that the total recognition rate of appliances operating in parallel can reach 86.14% compared to the recognition rate of single appliance which can reach 96.14%.