As the advance of the Internet of Things (IoT), more M2M sensors and devices are connected to the Internet. These sensors and devices generate sensor-based big data and bring new business opportunities and demands for creating and developing sensor-oriented big data infrastructures, platforms and analytics service applications. Big data sensing is becoming a new concept and next technology trend based on a connected sensor world because of IoT. It brings a strong impact on many sensor-oriented applications, including smart city, disaster control and monitor, healthcare services, and environment protection and climate change study. This paper is written as a tutorial paper by providing the informative concepts and taxonomy on big data sensing and services. The paper not only discusses the motivation, research scope, and features of big data sensing and services, but also exams the required services in big data sensing based on the state-of-the-art research work. Moreover, the paper discusses big data sensing challenges, issues, and needs.
In this paper, we study the model-checking problem of linear-time properties in multi-valued systems. Safety properties, invariant properties, liveness properties, persistence and dual-persistence properties in multi-valued logic systems are introduced. Some algorithms related to the above multi-valued linear-time properties are discussed. The verification of multi-valued regular safety properties and multi-valued ω-regular properties using lattice-valued automata are thoroughly studied. Since the law of non-contradiction (i.e., a ∧ ¬a = 0) and the law of excluded-middle (i.e., a ∨ ¬a = 1) do not hold in multi-valued logic, the lineartime properties introduced in this paper have new forms compared to those in classical logic. Compared to those classical model-checking methods, our methods to multi-valued model checking are accordingly more direct: We give an algorithm for showing TS | = P for a model TS and a linear-time property P, which proceeds by directly checking the inclusion Traces(TS) ⊆ P instead of Traces(TS) ∩ ¬P = ∅. A new form of multi-valued model checking with membership degree is also introduced. In particular, we show that multi-valued model checking can be reduced to classical model checking. The related verification algorithms are also presented. Some illustrative examples and a case study are also provided.
The recent advance in mobile network technologies (3G/4G) provides an efficient mobile network infrastructure supporting mobile users to access large volumes of mobile data. Now, many mobile applications are developed based on mobile databases on devices and conventional databases. Due to the limitations of mobile devices, using mobile databases on mobile devices encounters certain scalability issues in mobile data accesses and storage. One alternative approach is to use cloud-based databases to support mobile users and applications on mobile devices. This also runs into the several challenges, such as mobility support, localization, energy consumption, and performance concerns. This paper presents a mobile enabled cloud database solution, called MCloudDB, to address the related issues and needs. MCloudDB provides a mobile enabled cloud database infrastructure with a framework, which can be used as a bridge to connect and integrate mobile applications with back-end cloud databases to support mobile cloud data management and access services. The paper presents the design of this framework and the related key solutions for essential features in mobile cloud databases, such as multi-tenancy, elasticity, seamless connectivity and disaster recovery. A simple application example and related experiment results are reported.
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