Recent survey articles (Spring, J., 2011; Spring, J., 2011; Wei, Y. and M. Blake, 2010; Zhang, Q., et al., 2010) summarize challenges for cloud computing to provide mechanisms to mitigate the issues listed above, the need for monitoring the various layers of the cloud computing environment, and the need to provide end-to-end solutions for the problems. The architectural frameworks considered in these excellent surveys are one dimensional and principally deal with the infrastructure, platform, and software as service layers of cloud computing. Federated clouds with cross-cloud connectivity provide an opportunity mentioned in (Wei, Y. and M. Blake, 2010) to support service-oriented computing, and QoS is discussed in (Calheiros, R., R. Ranjan and R. Buyya, 2011) but is limited in application to the task of virtual machine provisioning in data centers rather than to end-user satisfiability. None treats the clouds from a system-of-systems point of view as is done here. The architectural model used here to support a service-oriented architecture (SOA) is multidimensional, extending to layers of business and governance as services, while reducing complexity by combining the traditional platform and applications layers into one software layer that provides overall Software as a Service (SaaS) to end users. Furthermore, specific locations and the use of software agents for monitoring and observation in this system of systems (SoS) are presented.
Barnes and Vidgen (2000) assessed the quality of Internet Auction sites by observing the quality dimensions identified by WebQual 3.0, which they modified from WebQual 1.0 and WebQual 2.0. Web information quality, web interaction quality and site design quality are the quality dimensions in WebQual 3.0. Another study identified the design issues of university websites (Farkas, 1997). The design issues are (1) indicating the identity of individual units within the hierarchical structure of the institution; (2) maintaining visual consistency throughout the site and (3) harmonizing the messages conveyed by the university’s homepage and the homepages of the university’s colleges and departments. In yet another study, Udo and Marquis (2000) conducted a survey involving 117 e- commerce customers, which aimed at gathering opinions of commercial web users in order to determine the critical design features of an effective website. The features of good website quality used in the study are cohesion, navigation, interactivity, graphics and aesthetics; download time, advertisement, frames, use of color, use of language and basic website maintenance. Jenamani, et al. (2002) developed a design benchmark by studying the design features of 148 corporate websites. The features that were considered for studying the websites are Marketing, Accessibility, Presentation, Informative and Special features. Content quality, service quality and technical quality are considered as the dimensions of a framework for evaluating the global quality of websites by Rocha (2011).
Observation 3: The temporal variation of link quality is due to changes in the en- vironment characteristics. Several studies confirmed that the temporal variation of link quality is due to the changes in the environment characteristics, such as climate conditions (e.g., temperature, humidity), human presence, interference and obstacles [Cerpa et al. 2005; Zhao and Govindan 2003; Reijers et al. 2004; Lin et al. 2006; Tang et al. 2007; Lin et al. 2009]. Particularly, Tang et al.  found that the tempo- ral variation of LQI, RSSI, and Packet Error Rate (PER), in a “clean” environment, (i.e., indoor, with no moving obstacles and well air-conditioned) is not significant. The same observation was made by Mottola et al. . Lin et al.  distinguished three patterns for link quality temporal variation: small fluctuations, large fluctu- ations/disturbance, and continuous large fluctuations. The first is mainly caused by multi-path fading of wireless signals; the second is caused by shadowing effect of hu- mans, doors, and other objects; and the last is caused by interference (e.g., Wi-Fi). A deeper analysis of the causes of links temporal variation was presented by Lal et al. , Lee et al. , and Srinivasan et al. . Lal et al.  reported that the transitional region can be identified by the PRR/SNR curve using two thresholds (refer to Figure 6). Above the first threshold the PRR is consistently high, about 100%, and below the second threshold the PRR is often less than 25%. In between is the tran- sitional region, where a small variation in the SNR can cause a shift between good and bad quality link, which results in a bursty link. In fact, SNR is the ratio of the pure received signal strength to the noise floor. When no interference is present, the noise floor varies with temperature, and so is typically quite stable over time periods of sec- onds or even minutes [Srinivasan et al. 2010]. Therefore, what makes the SNR vary according to time leading to link burstiness is mainly the received signal strength vari- ation [Srinivasan et al. 2008]. The variation of the received signal strength may also be due to the constructive/destructive interference in the deployment environment [Mot- tola et al. 2010].
Finally these data and the location ID are transmitted at a predefined time interval to a web server through HTTP via GPRS module, which makes it easy to access data in real time on the internet web application. When the server receives a request for the web page, it inserts each data to the corresponding field in the database. This link is bidirectional and permits to change the threshold values through the website interface; scheduled watering or remote watering can be performed. The WIU has also a push button to perform manual irrigation for a programmed period and a LED to indicate when the
An issue complementary to the integration of heterogenous educational data is the large scale exploitation of open educational data, is addressed by the LinkedUp project, setting up to push forward the exploitation of the vast amounts of public, open data available on the Web, in particular by educational institutions and organizations. This will be achieved by identifying and supporting highly innovative large-scale Web information management applications through an open competition (the LinkedUp Challenge) and a dedicated evaluation framework. The vision of the LinkedUp Challenge is to realise personalised university degree-level education of global impact based on open Web data and information. Drawing on the diversity of Web information relevant to education, ranging from OER metadata to the vast body of knowledge offered by the LD approach, this aim requires overcoming substantial issues related to Web-scale data and information management involving Big Data, such as performance and scalability, interoperability, multilinguality and heterogeneity problems, to offer personalised and accessible education services. Therefore, the LinkedUp Challenge provides a focused scenario to derive challenging requirements, evaluation criteria, benchmarks and thresholds which are reflected in the LinkedUp evaluation framework. Information management solutions have to apply data and learning analytics methods to provide highly personalised and context- aware views on heterogeneous Web data. Building on the strong alliance of institutions with expertise in areas such as open Web data management, data integration and Web-based education, key outcomes of LinkedUp include a general- purpose evaluation framework for Web-data driven applications, a set of quality- assured educational datasets, innovative applications of large-scale Web information
We used user satisfaction to indicate the effective- ness of a Web-based customer support system, deter- mined by the information quality, system quality, and service quality of the system. Higher levels of user satisfaction are assumed to correspond to higher levels of Web-based customer support system effectiveness. The survey items used to measure satisfaction employ disconfirmation scales (i.e. scales couched in terms of the evaluation target being ‘‘better than expected’’ or ‘‘worse than expected’’, rather than in terms of the target being ‘‘poor’’ or ‘‘excellent’’ or in terms of the respondent being ‘‘very satisfied’’ or ‘‘very dissatisfied’’). Such scaling is preferred for measurement of customer satisfaction in terms of discriminant and convergent validity, as well as les- sened asymmetry of responses  . Response asym- metry has been a problem with nearly all satisfaction measures; they produce negatively skewed distribu- tions where the majority of responses indicate a satisfied customer  .
In CSN systems, the constraints on weight, size and power consumption (usually powered by small capacity batteries) of the sensor nodes are extraordinary serious. These sensor nodes are typically light weight and small size, and impossible to equip with assisting instruments like temperature and humidity controllers. Moreover, the sensor nodes are not well maintained and may be put into bags or pockets that further lower the data quality. In VSN systems, these constraints are not as critical as that in CSN systems. Adding assisting tools to the sensor nodes is possible and the sensor nodes are well maintained by the professionals. However, the high mobility of the sensor nodes becomes a major factor affecting the accuracy of the sensor readings due to the varying air flow around the sensor head [ 10 ]. In SSN systems, limitations on the weight, size and power consumption (powered by power line or renewable energy source) of the sensor nodes are relaxed. The sensor nodes are able to equip with assisting equipment to ensure the data quality. The network connectivity and the sensor node’s power supply are guaranteed and the reliability of the sensed data is improved due to stationary characteristic. The data quality of SSN systems is the highest among these three types of systems, followed by the data quality of VSN systems and CSN Systems.
Given that the 104 respondents could give 3 replies each, there could have been a potential 312 suggestions. Sometimes people's responses all fell in one category, but this was a small minority. Thus it is fair to say that nearly half of respondents listed lack of resources/investment as one of their 3 replies. This was often linked to lack of management buy in/strategy. Lack of control over web authors was another key issue, especially as siloing of information was also mentioned. Security was mentioned surprisingly frequently. In some ways it is the problems which are mentioned infrequently that are most interesting. For example, outsourcing though often talked of as a problem, was only mentioned by one person. Only 3 persons mentioned excessive marketing focus; one person excessive technical focus. Quality of
There has been a steady rise in the way context-aware distribution is done. Earlier the research focus was on small scale deployments like smart home or smaller in- frastructure deployments. Currently the changes are to adapt the wireless context-aware deployments in large scale deployments often reaching the internet scales. To support such large context-aware deployments there are many shortfalls that require to be fulﬁlled: (a) Context information distribution to route produced information to all interesting sinks in the system; (b) Support for het- erogeneous sensor nodes with varied capabilities rang- ing from computing speeds, communicating standards, diﬀerent operational scenario etc.; (c) Presenting var- ied visibility scopes for context information, taking into consideration physical locality, user reference context; so as to limit management overheads; (d) QoC-based con- straints fulﬁlments like, quality of the received informa- tion, adaptation based on the topology changes, meeting delivery guarantees, timeliness and reliability and avoid- ing redundant and conﬂicting copies in the system; (e) End-to-end Context-information life cycle management . Activities like distributed information aggregation and ﬁltering have to be handled to reduce unnecessary management overheads.
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Redevelopment means reengineering. Reengineering is considered the modification and analysis of an application to be presented in some different and new view. Reverse engineering, redesigning, restructuring, and re-implementing are considered some activities of the reengineering process. There are three main issues in service-oriented reengineering: service identification, service packaging, and service deployment. Identification of services from a legacy system is a complex mission. It is one of the main activities in the modeling of a service-oriented solution, and therefore errors made during identification can flow down through detailed design and implementation activities that may require multiple iterations, especially in building composite applications. Service packaging is a detailed description of a service that is available to be delivered to customers. And service deployment refers to service selection and service composition to satisfy functional and quality of service (Qos) requirements. 
Previously, some efforts have been done to standardize either sensor-based platforms or IoT-based architectures in general. For example, OGC SWE standards  are intended for SensorWebsystems while the IoT-A project  is destined to the IoT. However, the use of such frameworks remains very limited since they require an important learning phase. As a consequence, we witness the development of a great number of custom-made IoT platforms. Regarding im- plementation considerations, a lot of studies have highlighted the benefits of using Cloud-based architectures to support the IoT . Some of them have even proposed the term “Cloud of Things”  to denote this new paradigm. However, most of these studies only focus on technical considerations (by proposing protocols or integration frameworks for instance) and do not take into account observation streams and their inherent challenges. Some Cloud-based commercial IoT so- lutions like AWS IoT  or IBM Watson IoT  are also available to developers. Nevertheless, these proprietary solutions are application-oriented and do not provide any QoO insights. Besides, since these platforms are not open-source, developers can only customize them with available proprietary components.
In today’s growing and competitive scenario Service–oriented architectures are having a crucial role in the way in which systems are developed and designed. Basically, they represent an architecture in which small, loosely coupled pieces of functionality are published, used and combined over a network. The W3C consortium  describes web services as “a software application identiﬁed by a URI, whose interfaces and bindings are capable of being deﬁned, described, and discovered as XML artifacts. A web service supports direct interactions with other software agents using XML based message exchange via Internet protocols”. Web services have become popular and the need of today business because they offer several advantages: 
Distributed, coordinate-free algorithm for attaining maximum lifetime in sensor networks, subject to ensure the k-coverage of the target field during the network lifetime. The lifetime attained by algorithm approximates the highest possible lifetime with a logarithmic approximation factor. This is simple distributed mechanism for detecting the termination of the network lifetime. By definition the network lifetime terminates when there no longer exists a sensor cover such that every sensor in the cover has non-zero energy. Each node executes the activation phase at the initial of each successive time slot, and decides whether to activate itself in slot based only on state information in its neighborhood. Centralized approximation algorithm similar to that is  has been proposed by Zhao et al. in  for target coverage problem, i.e., the problem of increasing lifetime while ensuring coverage of a given set of target points and connectivity of the network. Thai et al  have proposed a distributed algorithm to maximize the network lifetime up to an O(log n) factor, while ensuring coverage of a given set of targets. Also, the coverage and lifetime guarantees in  are probabilistic, whereas we provide deterministic guarantees on both coverage and lifetime .
Jiménez-Hidalgo et al.  presented several key points to be considered before choosing any of the designed journal management and publishing systems, as well as a brief description of the most popular options, both commercial and open source systems. Also, Shapiro  discussed the concept of the online peer review tools which he considered to facilitate efficient and centralized control and/or supervision by journal staff of the submission, assignment, tracking, and publication of articles though the web; as well as enabling a central archive of various tasks performed. A general list of functions for online peer review tools were listed and discusses; such as: issues automated submission, article assignment/tracking, event logging, reviewing/copyediting, time reminders, etc. Also, some features related to the general user where identified; such as: flexibility, confidentiality, tracking changes, etc. The author highlighted the ability of the users, in different levels, to customize these processes and interfaces vary significantly among different tools. At the end, a comparative list of ten features was illustrated for six main online peer review tools. The ten features were: automated submission and notifications, article assignment/tracking, event logging, reviewing/copyediting, quality/category tags, blind/double blind option, time reminders or
Yet many researchers are trying to solve tasks related to scientific foundation for points and programs of monitoring over air quality. Since automated programs for calculating emis- sion dispersion were introduced, spatial analysis of concentration fields has become one of the most significant tools for improving a system for selecting monitoring points and admixtures to be controlled [15, 16]. Dispersion results have be- come even more relevant since geoinformation systems were implemented into practice [17, 18]. However in some authors’ opinion, dispersion calculations require validation of their results with data obtained from automated systems that pro- vide uninterrupted control over emissions and/or instrumental research ; they can’t be consid- ered a single foundation for creating monitoring programs . Health risk assessment methodol- ogy for assessing risks caused by exposure to chemicals that pollute the environment now cov- ers such safety criteria as reference levels under short-term and chronic exposure, and it has re- sulted in understanding that monitoring programs can and should be developed taking into account potential threats for people [20–22]. And a mechanism aimed at selecting priorities in this case should be more complicated than a simple calculation of air consumption.
Let’s take an example: Simulated service oriented consumer devices execute an application for navigation which is location-based. It includes information of location, amount of traffic, weather and map. Consumers of this application need a navigation device which is stimulated with need based requirements. (E.g. Mobile phone or internet tablet). Different providers are needed for these different conditions. So their services may have differences in terms of QOS and Cost. So to provide a better quality assured system there is a need to follow some proper framework of quality assurance which governs not only the individual service but also their composition problems.
VII. C ONCLUSIONES Y T RABAJO A F UTURO
En este art´ıculo se propone un Modelo de Calidad para la Evaluaci´on de Servicios Web Geogr´aficos en la IDEuy. El mismo se construy´o en base a caracter´ısticas de calidad identificadas en requerimientos del contexto uruguayo (p. ej. en la IDEuy), IDEs de referencia y trabajo relacionado en el ´area. El modelo propuesto consta de 7 dimensiones, 24 factores y 61 m´etricas, las cuales pueden utilizarse para medir los distintos objetos contemplados por el modelo: instituci´on, nodo, servicio, m´etodo y capa. El trabajo tambi´en propone la definici´on de perfiles para realizar la evaluaci´on de la calidad de dichos objetos y presenta un prototipo que implementa m´etodos de medici´on para algunas de las m´etricas.
• Provide the basis for an integrated software quality management product. SQO-OSS is based around three distinct processing subsystems that share a common data store. The data acquisition subsystem processes unstructured project data and feeds the resultant structured data to the analysis stages. The user interaction subsystem presents analysis results to the user and ac- cepts input to affect the analysis parameters. The components of the data acquisition subsystem are responsible for extracting useful data for analysis from the raw data that is available from the range of sources within software development projects. Metric analysis of source code is well-known and an important aspect of this system. Repository analysis will perform examine the commit behaviour of developers in response to user requests and security issues. The information extraction component will extract structured informa- tion from mailing lists and other textual source in order to feed higher-level analyses.