Context-aware resource management in multi-inhabitant smart homes: A framework based on Nash -learning

A smart home aims at building intelligent automation with a goal to provide its inhabitants with maximum possible comfort, minimum resource consumption and thus reduced cost of home maintenance. ‘Context Awareness’ is perhaps the most salient feature of such an intelligent environment. An inhabitant’s mobility and activities play a significant role in defining his/her contexts in and around the home. Although there exists an optimal algorithm for location and activity tracking of a single inhabitant, the correlation and dependence between multiple inhabitants’ contexts within the same environment make the location and activity tracking more challenging. In this paper, we first prove that the optimal location prediction across multiple inhabitants in smart homes is an NP-hard problem. Next, to capture the correlation and interactions between different inhabitants’ movements (and hence activities), we develop a novel framework based on a game theoretic, Nash H-learning approach that attempts to minimize the joint location uncertainty of inhabitants. Our framework achieves a Nash equilibrium such that no inhabitant is given preference over others. This results in more accurate prediction of contexts and more adaptive control of automated devices, thus leading to a mobility-aware resource (say, energy) management scheme in multi-inhabitant smart homes. Experimental results demonstrate that the proposed framework is capable of adaptively controlling a smart environment, significantly reduces energy consumption and enhances the comfort of the inhabitants.     

An open framework for translating portable applications into operating system‐specific wireless sensor networks applications

Wireless sensor networks (WSNs) are distributed systems integrated by tiny devices, called sensor nodes, with capabilities to monitor the environment and forward their measurements to a special node, the sink, where the results can be collected and further processed. The trend in WSN is moving towards heterogeneous networks that will contain different sensor nodes running different instances of custom operating systems. Given the growing demand of new hardware platforms and operating systems specifically designed for sensor nodes, the applications programming for sensor nodes is becoming a challenging process that needs to be alleviated. Currently, application programming for sensor nodes is a complex, ad hoc, and error‐prone process where the portability among different platforms has been sacrificed. In this paper, we propose an open framework aimed to achieve application portability in heterogeneous sensor networks. Our approach provides the programming abstractions needed to support the application development process for sensor nodes. We have implemented an open framework that provides a set of tools on top of the most popular WSN operating systems to translate portable applications to the native operating system in an automatic, simple, and transparent way for developers. We have also evaluated the applications thus generated in terms of productivity and overhead, by comparing their footprint to those originally developed in each specific operating system. The results show that the overhead is minimal—4% in the worst case—and in some cases, it was even possible to reduce the footprint by using code optimizations. Copyright © 2012 John Wiley & Sons, Ltd.     

北斗卫星在智慧城市建设中的应用评述

在“数字城市”的概念后提出了“智慧城市”,用来解决或减轻城市化和经济转型所造成的社会问题。随着我国北斗卫星组网日益完善,以及物联网、云计算等技术的快速发展,使城市运行管理更加智慧化提供了可能性。通过智慧交通、智慧减灾、智慧社区等北斗项目积极探索“智慧城市”的建设,使北斗真正融入百姓生活,逐渐建立我国北斗在社会中的置信度、认可度。对北斗卫星顺利投入我国全方位、多维度应用领域具有重要意义,以便更好服务于城市建设和管理,进一步推进城市和谐发展。     

A contribution-based framework for the creation of semantically-enabled web applications

We present Fortunata, a wiki-based framework designed to simplify the creation of semantically-enabled web applications. This framework facilitates the management and publication of semantic data in web-based applications, to the extent that application developers do not need to be skilled in client-side technologies, and promotes application reuse by fostering collaboration among developers by means of wiki plugins. We illustrate the use of this framework with two Fortunata-based applications named OMEMO and VPOET, and we evaluate it with two experiments performed with usability evaluators and application developers respectively. These experiments show a good balance between the usability of the applications created with this framework and the effort and skills required by developers.     

A framework for efficient regression tests on database applications

Regression testing is an important software maintenance activity to ensure the integrity of a software after modification. However, most methods and tools developed for software testing today do not work well for database applications; these tools only work well if applications are stateless or tests can be designed in such a way that they do not alter the state. To execute tests for database applications efficiently, the challenge is to control the state of the database during testing and to order the test runs such that expensive database reset operations that bring the database into the right state need to be executed as seldom as possible. This work devises a regression testing framework for database applications so that test runs can be executed in parallel. The goal is to achieve linear speed-up and/or exploit the available resources as well as possible. This problem is challenging because parallel testing needs to consider both load balancing and controlling the state of the database. Experimental results show that test run execution can achieve linear speed-up by using the proposed framework.     

Fast modified global k-means algorithm for incremental cluster construction

The k-means algorithm and its variations are known to be fast clustering algorithms. However, they are sensitive to the choice of starting points and are inefficient for solving clustering problems in large datasets. Recently, incremental approaches have been developed to resolve difficulties with the choice of starting points. The global k-means and the modified global k-means algorithms are based on such an approach. They iteratively add one cluster center at a time. Numerical experiments show that these algorithms considerably improve the k-means algorithm. However, they require storing the whole affinity matrix or computing this matrix at each iteration. This makes both algorithms time consuming and memory demanding for clustering even moderately large datasets. In this paper, a new version of the modified global k-means algorithm is proposed. We introduce an auxiliary cluster function to generate a set of starting points lying in different parts of the dataset. We exploit information gathered in previous iterations of the incremental algorithm to eliminate the need of computing or storing the whole affinity matrix and thereby to reduce computational effort and memory usage. Results of numerical experiments on six standard datasets demonstrate that the new algorithm is more efficient than the global and the modified global k-means algorithms.     

A middleware framework for application-aware and user-specific energy optimization in smart mobile devices

Mobile battery-operated devices are becoming an essential instrument for business, communication, and social interaction. In addition to the demand for an acceptable level of performance and a comprehensive set of features, users often desire extended battery lifetime. In fact, limited battery lifetime is one of the biggest obstacles facing the current utility and future growth of increasingly sophisticated “smart” mobile devices. This paper proposes a novel application-aware and user-interaction aware energy optimization middleware framework (AURA) for pervasive mobile devices. AURA optimizes CPU and screen backlight energy consumption while maintaining a minimum acceptable level of performance. The proposed framework employs a novel Bayesian application classifier and management strategies based on Markov Decision Processes and Q-Learning to achieve energy savings. Real-world user evaluation studies on Google Android based HTC Dream and Google Nexus One smartphones running the AURA framework demonstrate promising results, with up to 29% energy savings compared to the baseline device manager, and up to 5×savings over prior work on CPU and backlight energy co-optimization.     

XpressSpace: a programming framework for coupling partitioned global address space simulation codes

Complex coupled multiphysics simulations are playing increasingly important roles in scientific and engineering applications such as fusion, combustion, and climate modeling. At the same time, extreme scales, increased levels of concurrency, and the advent of multicores are making programming of high‐end parallel computing systems on which these simulations run challenging. Although partitioned global address space (PGAS) languages attempt to address the problem by providing a shared memory abstraction for parallel processes within a single program, the PGAS model does not easily support data coupling across multiple heterogeneous programs, which is necessary for coupled multiphysics simulations. This paper explores how multiphysics‐coupled simulations can be supported by the PGAS programming model. Specifically, in this paper, we present the design and implementation of the XpressSpace programming system, which extends existing PGAS data sharing and data access models with a semantically specialized shared data space abstraction to enable data coupling across multiple independent PGAS executables. XpressSpace supports a global‐view style programming interface that is consistent with the PGAS memory model, and provides an efficient runtime system that can dynamically capture the data decomposition of global‐view data‐structures such as arrays, and enable fast exchange of these distributed data‐structures between coupled applications. In this paper, we also evaluate the performance and scalability of a prototype implementation of XpressSpace by using different coupling patterns extracted from real world multiphysics simulation scenarios, on the Jaguar Cray XT5 system at Oak Ridge National Laboratory. Copyright © 2013 John Wiley & Sons, Ltd.     

A hybrid biometric identification framework for high security applications

Research on biometrics for high security applications has not attracted as much attention as civilian or forensic applications. Limited research and deficient analysis so far has led to a lack of general solutions and leaves this as a challenging issue. This work provides a systematic analysis and identification of the problems to be solved in order to meet the performance requirements for high security applications, a double low problem. A hybrid ensemble framework is proposed to solve this problem. Setting an adequately high threshold for each matcher can guarantee a zero false acceptance rate (FAR) and then use the hybrid ensemble framework makes the false reject rate (FRR) as low as possible. Three experiments are performed to verify the effectiveness and generalization of the framework. First, two fingerprint verification algorithms are fused. In this test only 10.55% of fingerprints are falsely rejected with zero false acceptance rate, this is significantly lower than other state of the art methods. Second, in face verification, the framework also results in a large reduction in incorrect classification. Finally, assessing the performance of the framework on a combination of face and gait verification using a heterogeneous database show this framework can achieve both 0% false rejection and 0% false acceptance simultaneously.     

A mixed-integer programming model for global logistics transportation problems

In today's highly competitive global environment, companies are forced to compete on price and delivery speed. Global logistics transportation presents some special challenges and issues for business organizations, and these issues differ from those posed by domestic logistics transportation. This study considers road transportation problems between two countries. A mixed-integer programming model is formulated to determine the optimal fleet components, route plans, and warehouse control in two countries. A series of experiments is designed to test the effectiveness of the proposed model. To enhance the practical implications of the model, different logistics plans are evaluated according to future changes.     

基于IOS的智能家居终端控制系统

为了更好地满足用户对生活居住需求,给用户提供更好的对智能家用电器的控制体验,提升家居生活质量,基于IOS的手机平台和平板电脑,设计了基于IOS的智能家居终端控制系统。该系统建立在IOS平台上,利用IOS系统支持的各种资源建立并且使用户使用智能手机对智能家用电器的远程控制。该系统成本低,操作灵活,界面友好,经实际使用获得了很好的用户体验。     

Downscaling global information for regional benefit: coupling spatial models at varying space and time scales

Every environmental activity to a large extent is dependent on climate as natural processes are intrinsically linked with the waxing and waning of the seasons. The goal is to integrate global seasonal climate forecasts with local environmental decision support systems within an operational framework to deliver community benefits. This framework is designed to support the downscaling of coarse resolution seasonal forecasts to drive biological or hydrological applications at the regional level. Some of the challenges and complexities in coupling spatial simulations operating at varying spatial and temporal resolutions will be discussed from several viewpoints, illustrating the value of multidisciplinary collaboration in a virtual team and benefits from the globalisation of research. This project demonstrates how a state Government is evolving an existing service to enhance the use of seasonal climate forecasts for sustainable environmental and natural resource management.     

A framework based on sparse representation model for time series prediction in smart city

Smart city driven by Big Data and Internet of Things(loT)has become a most promising trend of the future.As one important function of smart city,event alert based on time series prediction is faced with the challenge of how to extract and represent discriminative features of sensing knowledge from the massive sequential data generated by IoT devices.In this paper,a framework based on sparse representa-tion model(SRM)for time series prediction is proposed as an efficient approach to tackle this challenge.After dividing the over-complete dictionary into upper and lower parts,the main idea of SRM is to obtain the sparse representation of time series based on the upper part firstly,and then realize the prediction of future values based on the lower part.The choice of different dictionaries has a significant impact on the performance of SRM.This paper focuses on the study of dictionary construction strategy and summarizes eight variants of SRM.Experimental results demonstrate that SRM can deal with different types of time series prediction flexibly and effectively.     

The implementation of generic smart pointers for advanced defensive programming

Smart pointers denote a well‐known technique for collective resource ownership, e.g. sharing dynamic object instances, while usually supporting automatic garbage collection based on reference counting. The original method has been retargeted to serve as a generic defensive programming method for ‘exhaustive tracking’ of erroneous pointer use in C++. Automatic bug tracking is supported in a unified manner both for pointers to heap memory, i.e. free storage, as well as for pointers to stack or global memory, i.e. auto or static storage. Overall, the presented technique (a) offers a simple contract for memory allocation and use; (b) supports type and indirection depth genericity; (c) implements most operators supported for built‐in pointers with embedded bug defense; (d) offers an alternative way of employing a garbage collection facility for memory leak detection; and (e) provides an appropriate collection of utility macros, through which defensive pointers should be used, with an alternative version re‐targeted to normal native pointers. Copyright © 2004 John Wiley & Sons, Ltd.     

A global energy optimization framework for 2.1D sketch extraction from monocular images

The 2.1D sketch is a layered image representation, which assigns a partial depth ordering of over-segmented regions in a monocular image. This paper presents a global optimization framework for inferring the 2.1D sketch from a monocular image. Our method only uses over-segmented image regions (i.e., superpixels) as input, without any information of objects in the image, since (1) segmenting objects in images is a difficult problem on its own and (2) the objective of our proposed method is to be generic as an initial module useful for downstream high-level vision tasks. This paper formulates the inference of the 2.1D sketch using a global energy optimization framework. The proposed energy function consists of two components: (1) one is defined based on the local partial ordering relations (i.e., figure-ground) between two adjacent over-segmented regions, which captures the marginal information of the global partial depth ordering and (2) the other is defined based on the same depth layer relations among all the over-segmented regions, which groups regions of the same object to account for the over-segmentation issues. A hybrid evolution algorithm is utilized to minimize the global energy function efficiently. In experiments, we evaluated our method on a test data set containing 100 diverse real images from Berkeley segmentation data set (BSDS500) with the annotated ground truth. Experimental results show that our method can infer the 2.1D sketch with high accuracy.     

A task-level adaptive MapReduce framework for real-time streaming data in healthcare applications

Healthcare scientific applications, such as body area network, require of deploying hundreds of interconnected sensors to monitor the health status of a host. One of the biggest challenges is the streaming data collected by all those sensors, which needs to be processed in real time. Follow-up data analysis would normally involve moving the collected big data to a cloud data center for status reporting and record tracking purpose. Therefore, an efficient cloud platform with very elastic scaling capacity is needed to support such kind of real time streaming data applications. The current cloud platform either lacks of such a module to process streaming data, or scales in regard to coarse-grained compute nodes.In this paper, we propose a task-level adaptive MapReduce framework. This framework extends the generic MapReduce architecture by designing each Map and Reduce task as a consistent running loop daemon. The beauty of this new framework is the scaling capability being designed at the Map and Task level, rather than being scaled from the compute-node level. This strategy is capable of not only scaling up and down in real time, but also leading to effective use of compute resources in cloud data center. As a first step towards implementing this framework in real cloud, we developed a simulator that captures workload strength, and provisions the amount of Map and Reduce tasks just in need and in real time.To further enhance the framework, we applied two streaming data workload prediction methods, smoothing and Kalman filter, to estimate the unknown workload characteristics. We see 63.1% performance improvement by using the Kalman filter method to predict the workload. We also use real streaming data workload trace to test the framework. Experimental results show that this framework schedules the Map and Reduce tasks very efficiently, as the streaming data changes its arrival rate.     

A framework for automated construction of resource space based on background knowledge

Resource Space Model is a kind of data model which can effectively and flexibly manage the digital resources in cyber-physical system from multidimensional and hierarchical perspectives. This paper focuses on constructing resource space automatically. We propose a framework that organizes a set of digital resources according to different semantic dimensions combining human background knowledge in WordNet and Wikipedia. The construction process includes four steps: extracting candidate keywords, building semantic graphs, detecting semantic communities and generating resource space. An unsupervised statistical language topic model (i.e., Latent Dirichlet Allocation) is applied to extract candidate keywords of the facets. To better interpret meanings of the facets found by LDA, we map the keywords to Wikipedia concepts, calculate word relatedness using WordNet’s noun synsets and construct corresponding semantic graphs. Moreover, semantic communities are identified by GN algorithm. After extracting candidate axes based on Wikipedia concept hierarchy, the final axes of resource space are sorted and picked out through three different ranking strategies. The experimental results demonstrate that the proposed framework can organize resources automatically and effectively.     

Seine: a dynamic geometry‐based shared‐space interaction framework for parallel scientific applications

While large‐scale parallel/distributed simulations are rapidly becoming critical research modalities in academia and industry, their efficient and scalable implementations continue to present many challenges. A key challenge is that the dynamic and complex communication/coordination required by these applications (dependent on the state of the phenomenon being modeled) are determined by the specific numerical formulation, the domain decomposition and/or sub‐domain refinement algorithms used, etc. and are known only at runtime. This paper presents Seine, a dynamic geometry‐based shared‐space interaction framework for scientific applications. The framework provides the flexibility of shared‐space‐based models and supports extremely dynamic communication/coordination patterns, while still enabling scalable implementations. The design and prototype implementation of Seine are presented. Seine complements and can be used in conjunction with existing parallel programming systems such as MPI and OpenMP. An experimental evaluation using an adaptive multi‐block oil‐reservoir simulation is used to demonstrate the performance and scalability of applications using Seine. Copyright © 2006 John Wiley & Sons, Ltd.     

Perses: A framework for the continuous evaluation of the QoS of distributed mobile applications

The increasing capabilities of mobile devices have led to the emergence of new paradigms exploiting them. These paradigms foster the onload and distribution of functionalities on mobile devices, allowing the development of distributed mobile applications. This distribution reduces the latency and the data traffic overhead and improves privacy. As in any other mobile application, their success largely depends on the quality of service (QoS) they offer. Nevertheless, the evaluation of distributed mobile applications is particularly complex due to the number, heterogeneity, and interactions between the devices involved. Current techniques allow developers to assess the quality of a single device, but they are not designed for highly heterogeneous, distributed, and collaborative environments. This paper presents a framework called Perses, which allows the creation of virtual scenarios with multiple heterogeneous mobile devices to launch end-to-end tests to evaluate not only each device but also the interactions among them. The framework was evaluated against a real deployment, showing that the behavior and the quality attributes measured are similar to those of the real deployment, allowing developers to evaluate these applications before launching them. Finally, Perses was integrated into a DevOps methodology to automate its execution and further facilitate its adoption by software companies.