Limitations of sensors and the situation of a specific measurement can affect the quality of context information that is implicitly collected in pervasive environments. The lack of information about Quality of Context (QoC) can result in degraded performance of context-aware systems in pervasive environments, without knowing the actual problem. Context-aware systems can take advantage of QoC if context producers also provide QoC metrics along with context information.In this paper, we analyze QoC and present our model for processing QoC metrics. We evaluate QoC metrics considering the capabilities of sensors, circumstances of specific measurement, requirements of context consumer, and the situation of the use of context information. We also illustrate how QoC metrics can facilitate in enhancing the effectiveness and efficiency of different tasks performed by a system to provide context information in pervasive environments. IntroductionPervasive environments are characterized by a plethora of computing and communication enabled devices that diffuse themselves in everyday living and become invisible (Weiser, 1991). These devices implicitly sense and provide context, a core task in making a system adaptable, that is far more complicated than explicit input to the system (Gray & Salber, 2001;Mostefaoui et al., 2004). Quality of context information is deteriorated during this process and contrary to general assumption context information can be incomplete, inaccurate, and ambiguous (Dey, 2001;Henricksen et al., 2002). Inadequate quality of context information severely influences the adaptiveness of context-aware applications (Dey, 2001;Chen & Kotz, 2002;Henricksen & Indulska, 2004). Context-aware applications also perform extra effort to cope with uncertainty of context information (Ranganathan et al., 2004). Quality of Context (QoC), a measurable metric that provides information about the quality of context, can help resolving uncertain and conflicting situations about context information. Therefore, context-aware applications can take advantage of QoC if they are provided with usable QoC metrics that are evaluated considering their requirements regarding the collection, processing, and provision of context information. Currently, there is not only a lack of solutions that evaluate QoC metrics and pass them along with context information to context consumers, but also existing definitions of QoC ignore its multi-facetted nature and consider it as an objective term.In this paper we consider both objective and subjective views of QoC and redefine QoC. The objective view of QoC presents quality of context information independent of the requirements of a context consumer while the subjective view of QoC considers quality of context information as https://www.cambridge.org/core/terms. https://doi

Abstract. High quality context information plays a vital role in adapting a system to the rapidly changing situations. However, the diversity of the sources of context information and the characteristics of the computing devices strongly impact the quality of context information in pervasive computing environments. Quality of Context parameters can be used to characterize the quality of context information from different aspects. In this paper, we quantify the Quality of Context parameters to present them in a suitable form for use with the applications in pervasive environments. We also present a mechanism to tailor the Quality of Context parameters according to the needs of an application and then evaluate these parameters. Enrichment of context information with Quality of Context parameters enhances the capabilities of context-aware systems to effectively use the context information to adapt to the emerging situations in pervasive computing environments.

In complex emergency scenarios, teams from various emergency-response organizations must collaborate. These teams include both first responders, such as police and fire departments, and those operators who coordinate the effort from operational centers. The Workpad architecture consists of a front-and a back-end layer. The front-end layer is composed of several front-end teams of first responders, and the back-end layer is an integrated peer-to-peer network that lets front-end teams collaborate through information exchange and coordination. Team members at the front end carry PDAs, with team leaders' PDAs equipped with gateway communication technologies that let them communicate with the back-end centers

Data centers can go green by saving electricity in two major areas: computing and cooling. Servers in data centers require a constant supply of cold air from on-site cooling mechanisms for reliability. An increased computational load makes servers dissipate more power as heat and eventually amplifies the cooling load. In thermal-aware scheduling, computations are scheduled with the objective of reducing the data-center-wide thermal gradient, hotspots, and cooling magnitude. Complemented by heat modeling and thermal-aware monitoring and profiling, this scheduling is energy efficient and economical. A survey is presented henceforth of thermal-ware scheduling and associated techniques for green data centers.

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The Internet of Things (IoT) enables producers of context data like sensors to interact with remote consumers of context data like smart pervasive applications in an entirely decoupled way. However, two important issues are faced by context data distribution, namely providing context information with a sufficient level of quality-i.e. quality of context, QoC-while preserving the privacy of context owners. This article presents the solutions provided by the INCOME middleware framework for addressing these two potentially contradictory issues while hiding the complexity of context data distribution in heterogeneous and large-scale environments. Context producers and consumers not only express their needs in context contracts but also the guarantees they are ready to fulfil. These contracts are then translated into advertisement and subscription filters to determine how to distribute context data. Our experiments on a first open source prototype show that QoC-based filtering and privacy protection using attributed-based access control can be performed at a reasonable cost.