Sensor Search Techniques for Sensing as a Service Architecture for The Internet of Things

The Internet of Things (IoT) is part of the Internet of the future and will comprise billions of intelligent communicating "things" or Internet Connected Objects (ICO) which will have sensing, actuating, and data processing capabilities. Each ICO will have one or more embedded sensors that will capture potentially enormous amounts of data. The sensors and related data streams can be clustered physically or virtually, which raises the challenge of searching and selecting the right sensors for a query in an efficient and effective way. This paper proposes a context-aware sensor search, selection and ranking model, called CASSARAM, to address the challenge of efficiently selecting a subset of relevant sensors out of a large set of sensors with similar functionality and capabilities. CASSARAM takes into account user preferences and considers a broad range of sensor characteristics, such as reliability, accuracy, location, battery life, and many more. The paper highlights the importance of sensor search, selection and ranking for the IoT, identifies important characteristics of both sensors and data capture processes, and discusses how semantic and quantitative reasoning can be combined together. This work also addresses challenges such as efficient distributed sensor search and relational-expression based filtering. CASSARAM testing and performance evaluation results are presented and discussed.Comment: IEEE sensors Journal, 2013. arXiv admin note: text overlap with arXiv:1303.244

Data Commons

Publicly available data from open sources (e.g., United States Census Bureau (Census), World Health Organization (WHO), Intergovernmental Panel on Climate Change (IPCC)) are vital resources for policy makers, students and researchers across different disciplines. Combining data from different sources requires the user to reconcile the differences in schemas, formats, assumptions, and more. This data wrangling is time consuming, tedious and needs to be repeated by every user of the data. Our goal with Data Commons (DC) is to help make public data accessible and useful to those who want to understand this data and use it to solve societal challenges and opportunities. We do the data processing and make the processed data widely available via standard schemas and Cloud APIs. Data Commons is a distributed network of sites that publish data in a common schema and interoperate using the Data Commons APIs. Data from different Data Commons can be joined easily. The aggregate of these Data Commons can be viewed as a single Knowledge Graph. This Knowledge Graph can then be searched over using Natural Language questions utilizing advances in Large Language Models. This paper describes the architecture of Data Commons, some of the major deployments and highlights directions for future work

Remodelling media: The urgent search for new media business models

One of the most contentious and pressing issues concerning media in the early twenty-first century is identifying viable business models, with widespread reports that twentieth-century business models underpinning press, radio and television are collapsing because of 'audience fragmentation' driven by an ever-widening range of choice in media content and sources on the internet. Some scholars, media proprietors and content producers see announcements by Rupert Murdoch's News Corporation and the New York Times that they will increasingly charge for news and other content as a harbinger of the new mediascape and a resolution to media decline. However, a number of reader surveys and industry analyses warn that many contemporary media users will not pay for content and will further abandon traditional media if 'paywalls' are erected. A number of other potential business models are being touted in business and industry circles, but remain under-researched and under-explored in scholarly literature. This article reviews scholarly studies that do exist, as well as business and industry studies and media data, to identify the range of options available for funding journalism and other media content in future. Identification of sustainable media business models is an urgent priority, as continuing decline in audiences and collapse of media organisations pose a major threat to journalism and society, with scholars agreeing that further erosion of quality journalism threatens democracy. Future media business models also have major implications for the advertising industry and a wide range of content producers