Energy efficiency on location based applications in mobile cloud computing: a survey

The constrained battery power of mobile devices poses a serious impact on user experience. As an increasingly prevalent type of applications in mobile cloud environments, location-based applications (LBAs) present some inherent limitations concerning energy. For example, the Global Positioning System based positioning mechanism is well-known for its extremely power-hungry attribute. Due to the severity of the issue, considerable researches have focused on energy-efficient locating sensing mechanism in the last a few years. In this paper, we provide a comprehensive survey of recent work on low-power design of LBAs. An overview of LBAs and different locating sensing technologies used today are introduced. Methods for energy saving with existing locating technologies are investigated. Reductions of location updating queries and simplifications of trajectory data are also mentioned. Moreover, we discuss cloud-based schemes in detail which try to develop new energy-efficient locating technologies by leveraging the cloud capabilities of storage, computation and sharing. Finally, we conclude the survey and discuss the future research directions. © 2013 Springer-Verlag Wien.

Reliability-aware distributed computing scheduling policy

One of the primary issues associated with the efficient and effective utilization of distributed computing is resource management and scheduling. As distributed computing resource failure is a common occurrence, the issue of deploying support for integrated scheduling and fault-tolerant approaches becomes paramount importance. To this end, we propose a fault-tolerant dynamic scheduling policy that loosely couples dynamic job scheduling with job replication scheme such that jobs are efficiently and reliably executed. The novelty of the proposed algorithm is that it uses passive replication approach under high system load and active replication approach under low system loads. The switch between these two replication methods is also done dynamically and transparently. Performance evaluation of the proposed fault-tolerant scheduler and a comparison with similar fault-tolerant scheduling policy is presented and shown that the proposed policy performs better than the existing approach.

Editorial : towards merging grid and pervasive computing

This special issue is in response to the increasing convergence between grids and pervasive computing, while different approaches exist, challenges and opportunities are numerous in this context (Parashar and Pierson, to appear). The research papers selected for this special issue represent recent progresses in the field, including works on mobile ad-hoc grids, service and data discovery, context-aware application building and context accuracy, and communication. All of these papers not only provide novel ideas and state-of-the-art techniques in the field, but also stimulate future research in the Pervasive Grid environment.

A locality aware cache diffusion system

Web caching is a widely deployed technique to reduce the load to web servers and to reduce the latency for web browsers. Peer-to-Peer (P2P) web caching has been a hot research topic in recent years as it can create scalable and robust designs for decentralized internet-scale applications. However, many P2P web caching systems suffer expensive overheads such as lookup and publish messages, and lack locality awareness. In this paper, we present the development of a locality aware cache diffusion system that makes use of routing table locality, aggregation, and soft state to overcome these limitations. The analysis and experiments show that our cache diffusion system reduces the amount of information processed by nodes, reduces the number of index messages sent by nodes, and improves the locality of cache pointers.

Local energy and pre-envelope

We observe that the local energy is the pre-envelope for analytic function. The maxima and phase of this function can be used to compute and classify visual features such as motion and stereo disparity, texture, etc. We examine the construction of new filters for computing Local Energy, and compare these filters with the Gabor filters and the three-point-filter of Venkatesh and Owens.

Local energy, the pre-envelope, and filter resolution

We examine the construction of new filters for computing local energy, and compare these filters with the Gabor filters and the three-point-filter of Venkatesh [l]. Further, we demonstrate that the effect of convolution with complex Gabor filters is to band-pass (with some differentiating effect) and compute the local energy of the result. The magnitude of the resulting local energy is then used to detect features [2], [3] (step features, texture etc.), and the phase is used to classify the detected features [l], [4] or provide disparity information for stereo [5] and motion work [6], [7]. Each of these types of information can be obtained at multiple resolutions, enabling the use of course to fine strategies for computing disparity, and allowing the discrimination of image textures on the basis of which parts of the Fourier domain they dominate [8], [9].

Energy deficiency, menstrual disturbances, and low bone mass : what do exercising Australian women know about the female athlete triad?

Purpose: Prevention of the female athlete triad is essential to protect female athletes’ health. The aim of this study was to investigate the knowledge, attitudes, and behaviors of regularly exercising adult women in Australia toward eating patterns, menstrual cycles, and bone health.
Methods: A total of 191 female exercisers, age 18–40 yr, engaging in ≥2 hr/wk of strenuous activity, completed a survey. After 11 surveys were excluded (due to incomplete answers), the 180 participants were categorized into lean-build sports (n = 82; running/ athletics, triathlon, swimming, cycling, dancing, rowing), non-lean-build sports (n = 94; basketball, netball, soccer, hockey, volleyball, tennis, trampoline, squash, Australian football), or gym/fitness activities (n = 4).
Results: Mean (± SD) training volume was 9.0 ± 5.5 hr/wk, with participants competing from local up to international level. Only 10% of respondents could name the 3 components of the female athlete triad. Regardless of reported history of stress fracture, 45% of the respondents did not think that amenorrhea (absence of menses for ≥3 months) could affect bone health, and 22% of those involved in lean-build sports would do nothing if experiencing amenorrhea (vs. 3.2% in non-lean-build sports, p = .005). Lean-build sports, history of amenorrhea, and history of stress fracture were all significantly associated with not taking action in the presence of amenorrhea (all p < .005). Conclusions: Few active Australian women are aware of the detrimental effects of menstrual dysfunction on bone health. Education programs are needed to prevent the female athlete triad and ensure that appropriate actions are taken by athletes when experiencing amenorrhea.

Intelligent energy management for plug-in hybrid electric vehicles : the role of ITS infrastructure in vehicle electrification

The desire to reduce carbon emissions due to transportation sources has led over the past decade to the development of new propulsion technologies, focused on vehicle electrification (including hybrid, plug-in hybrid and battery electric vehicles). These propulsion technologies, along with advances in telecommunication and computing power, have the potential of making passenger and commercial vehicles more energy efficient and environment friendly. In particular, energy management algorithms are an integral part of plug-in vehicles and are very important for achieving the performance benefits. The optimal performance of energy management algorithms depends strongly on the ability to forecast energy demand from the vehicle. Information available about environment (temperature, humidity, wind, road grade, etc.) and traffic (traffic density, traffic lights, etc.), is very important in operating a vehicle at optimal efficiency. This article outlines some current technologies that can help achieving this optimum efficiency goal. In addition to information available from telematic and geographical information systems, knowledge of projected vehicle charging demand on the power grid is necessary to build an intelligent energy management controller for future plug-in hybrid and electric vehicles. The impact of charging millions of vehicles from the power grid could be significant, in the form of increased loading of power plants, transmission and distribution lines, emissions and economics (information are given and discussed for the US case). Therefore, this effect should be considered in an intelligent way by controlling/scheduling the charging through a communication based distributed control.

Adaptive intelligent energy management system of plug-in hybrid electric vehicle

Efficient energy management in hybrid vehicles is the key for reducing fuel consumption and emissions. To capitalize on the benefits of using PHEVs (Plug-in Hybrid Electric Vehicles), an intelligent energy management system is developed and evaluated in this paper. Models of vehicle engine, air conditioning, powertrain, and hybrid electric drive system are first developed. The effect of road parameters such as bend direction and road slope angle as well as environmental factors such as wind (direction and speed) and thermal conditions are also modeled. Due to the nonlinear and complex nature of the interactions between PHEV-Environment-Driver components, a soft computing based intelligent management system is developed using three fuzzy logic controllers. The crucial fuzzy engine controller within the intelligent energy management system is made adaptive by using a hybrid multi-layer adaptive neuro-fuzzy inference system with genetic algorithm optimization. For adaptive learning, a number of datasets were created for different road conditions and a hybrid learning algorithm based on the least squared error estimate using the gradient descent method was proposed. The proposed adaptive intelligent energy management system can learn while it is running and makes proper adjustments during its operation. It is shown that the proposed intelligent energy management system is improving the performance of other existing systems. © 2014 Elsevier Ltd.

Modeling dual role preferences for trust-aware recommendation

Unlike in general recommendation scenarios where a user has only a single role, users in trust rating network, e.g. Epinions, are associated with two different roles simultaneously: as a truster and as a trustee. With different roles, users can show distinct preferences for rating items, which the previous approaches do not involve. Moreover, based on explicit single links between two users, existing methods can not capture the implicit correlation between two users who are similar but not socially connected. In this paper, we propose to learn dual role preferences (truster/trustee-specific preferences) for trust-aware recommendation by modeling explicit interactions (e.g., rating and trust) and implicit interactions. In particular, local links structure of trust network are exploited as two regularization terms to capture the implicit user correlation, in terms of truster/trustee-specific preferences. Using a real-world and open dataset, we conduct a comprehensive experimental study to investigate the performance of the proposed model, RoRec. The results show that RoRec outperforms other trust-aware recommendation approaches, in terms of prediction accuracy. Copyright 2014 ACM.

A secure cloud computing based framework for big data information management of smart grid

Smart grid is a technological innovation that improves efficiency, reliability, economics, and sustainability of electricity services. It plays a crucial role in modern energy infrastructure. The main challenges of smart grids, however, are how to manage different types of front-end intelligent devices such as power assets and smart meters efficiently; and how to process a huge amount of data received from these devices. Cloud computing, a technology that provides computational resources on demands, is a good candidate to address these challenges since it has several good properties such as energy saving, cost saving, agility, scalability, and flexibility. In this paper, we propose a secure cloud computing based framework for big data information management in smart grids, which we call 'Smart-Frame.' The main idea of our framework is to build a hierarchical structure of cloud computing centers to provide different types of computing services for information management and big data analysis. In addition to this structural framework, we present a security solution based on identity-based encryption, signature and proxy re-encryption to address critical security issues of the proposed framework.