The advent of Internet-of-Things (IoT) is creating an ecosystem of smart applications and services enabled by a multitude of sensors. The real value of these IoT smart applications comes from analyzing the information provided by these sensors. Information fusion improves information completeness/quality and, hence, enhances estimation about the state of things. Lack of trust and therefore, malicious activities renders the information fusion process and hence, IoT smart applications unreliable. Behavior-related issues associated with the data sources, such as trustworthiness, honesty, and accuracy, must be addressed before fully utilizing these smart applications. In this article, we argue that behavior trust modeling is indispensable to the success of information fusion and, hence, to smart applications. Unfortunately, the area is still in its infancy and needs further research to enhance information fusion. The aim of this article is to raise the awareness and the need of behavior trust modelling and its effect on information fusion. Moreover, this survey describes IoT architectures for modelling trust as well as classification of current IoT trust models. Finally, we discuss future directions towards trustworthy reliable fusion techniques.

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With the advent of IoT, storing, indexing and querying XML data efficiently is critical. To minimize the cost of querying XML data, researchers have proposed many indexing techniques. Nearly all the techniques, partition the XML data into a number of data-streams. To evaluate a query, existing twig pattern matching algorithms process a subset of the data-streams simultaneously. Processing many data-streams simultaneously results in some or all of the following four problems, namely, the accessing of many data nodes which don't appear in the final solution of a given query, the generation of duplicate results, the generation of huge number of intermediate results, and the cost of merging the generated intermediate results. To the best of our knowledge, all the existing twig pattern matching algorithms suffer from some or all of the above mentioned problems. This paper proposes a new twig pattern matching algorithm called MatchQTP which processes one data-stream at a time and avoids all the above mentioned four problems. It also proposes a new indexing technique called RLP-Index and a new XML node labeling scheme called RLP-Scheme, both of which are used by MatchQTP. Unlike the existing indexing techniques, RLP-Index stores a subset of the data nodes. The rest of the data nodes can be generated efficiently. This minimizes storage space utilization and query processing time and makes RLP-Index the first of its kind. Many experiments were conducted to study the performance of MatchQTP. The results show that MatchQTP is very efficient and highly scalable. It was also compared with four algorithms, three of which are used frequently in the literature to compare the performance of new algorithms and the fourth algorithm is the state-of-the-art algorithm. MatchQTP significantly and consistently outperformed all of them. INDEX TERMS IoT, XML indexing, twig queries, XML query processing, tree-pattern matching, node labeling.

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Existing architectures to handle big data in Oil & gas industry are based on industry-specific platforms and hence limited to specific tools and technologies. With these architectures, we are confined to big data single-provider solutions. The idea of multi-provider big data solutions is essential. When building up big data solutions, organizations should embrace the best-inclass technologies and tools that different providers offer. In this article, we hypothesize that the limitations of the proposed bigdata architectures for oil and gas industries can be addressed by a Service Oriented Architecture approach. In this article, we are proposing the idea of breaking complex systems to simple separate yet reliable distributed services. It should be noted that loose coupling exists between the interacting services. Thus, our proposed architecture enables petroleum industries to select the necessary services from the SOA-based ecosystem and create viable big data solutions.

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This paper describes the design, prototype implementation and performance characteristics of a DiseaseOutbreak Notification System (DONS). The prototype was implemented in a hybrid cloud environment as an online/real-time system. It detects potential outbreaks of both listed and unknown diseases. It uses data mining techniques to choose the correct algorithm to detect outbreaks of unknown diseases. Our experiments showed that the proposed system has very high accuracy rate in choosing the correct detection algorithm. To our best knowledge, DONS is the first of its kind to detect outbreaks of unknown diseases using data mining techniques.