A Survey on Semantic Sensor Web: Sensor Ontology, Mapping and Query <s> Conclusion and Future Work <s> In this article, I introduce the exciting paradigm of citizen sensing enabled by mobile sensors and human computing - that is, humans as citizens on the ubiquitous Web, acting as sensors and sharing their observations and views using mobile devices and Web 2.0 services. <s> BIB001 </s> A Survey on Semantic Sensor Web: Sensor Ontology, Mapping and Query <s> Conclusion and Future Work <s> Sensors have begun to infiltrate people's everyday lives. They can provide information about a car's condition, can enable smart buildings, and are being used in various mobile applications, to name a few. Generally, sensors provide information about various aspects of the real world. Online social networks, another emerging trend over the past six or seven years, can provide insights into the communication links and patterns between people. They have enabled novel developments in communications as well as transforming the Web from a technical infrastructure to a social platform, very much along the lines of the original Web as proposed by Tim Berners-Lee, which is now often referred to as the Social Web. In this position paper, we highlight some of the interesting research areas where sensors and social networks can fruitfully interface, from sensors providing contextual information in context-aware and personalized social applications, to using social networks as "storage infrastructures" for sensor information. <s> BIB002 </s> A Survey on Semantic Sensor Web: Sensor Ontology, Mapping and Query <s> Conclusion and Future Work <s> People are on the verge of an era in which the human experience can be enriched in ways they couldn't have imagined two decades ago. Rather than depending on a single technology, people progressed with several whose semantics-empowered convergence and integration will enable us to capture, understand, and reapply human knowledge and intellect. Such capabilities will consequently elevate our technological ability to deal with the abstractions, concepts, and actions that characterize human experiences. This will herald computing for human experience (CHE). The CHE vision is built on a suite of technologies that serves, assists, and cooperates with humans to nondestructively and unobtrusively complement and enrich normal activities, with minimal explicit concern or effort on the humans' part. CHE will anticipate when to gather and apply relevant knowledge and intelligence. It will enable human experiences that are intertwined with the physical, conceptual, and experiential worlds (emotions, sentiments, and so on), rather than immerse humans in cyber worlds for a specific task. Instead of focusing on humans interacting with a technology or system, CHE will feature technology-rich human surroundings that often initiate interactions. Interaction will be more sophisticated and seamless compared to today's precursors such as automotive accident-avoidance systems. Many components of and ideas associated with the CHE vision have been around for a while. Here, the author discuss some of the most important tipping points that he believe will make CHE a reality within a decade. <s> BIB003 </s> A Survey on Semantic Sensor Web: Sensor Ontology, Mapping and Query <s> Conclusion and Future Work <s> Provenance, from the French word “provenir”, describes the lineage or history of a data entity. Provenance is critical information in the sensors domain to identify a sensor and analyze the observation data over time and geographical space. In this paper, we present a framework to model and query the provenance information associated with the sensor data exposed as part of the Web of Data using the Linked Open Data conventions. This is accomplished by developing an ontology-driven provenance management infrastructure that includes a representation model and query infrastructure. This provenance infrastructure, called Sensor Provenance Management System (PMS), is underpinned by a domain specific provenance ontology called Sensor Provenance (SP) ontology. The SP ontology extends the Provenir upper level provenance ontology to model domain-specific provenance in the sensor domain. In this paper, we describe the implementation of the Sensor PMS for provenance tracking in the Linked Sensor <s> BIB004 </s> A Survey on Semantic Sensor Web: Sensor Ontology, Mapping and Query <s> Conclusion and Future Work <s> Concept diagrams were introduced for precisely specifying ontologies in a manner more readily accessible to developers and other stakeholders than symbolic notations. In this paper, we present a case study on the use of concept diagrams in visually specifying the Semantic Sensor Networks (SSN) ontology. The SSN ontology was originally developed by an Incubator Group of the W3C. In the ontology, a sensor is a physical object that implements sensing and an observation is observed by a single sensor. These, and other, roles and concepts are captured visually, but precisely, by concept diagrams. We consider the lessons learnt from developing this visual model and show how to convert description logic axioms into concept diagrams. We also demonstrate how to merge simple concept diagram axioms into more complex axioms, whilst ensuring that diagrams remain relatively uncluttered. <s> BIB005 </s> A Survey on Semantic Sensor Web: Sensor Ontology, Mapping and Query <s> Conclusion and Future Work <s> This paper describes a semantic modelling scheme, a naming convention and a data distribution mechanism for sensor streams. The proposed solutions address important challenges to deal with large-scale sensor data emerging from the Internet of Things resources. While there are significant numbers of recent work on semantic sensor networks, semantic annotation and representation frameworks, there has been less focus on creating efficient and flexible schemes to describe the sensor streams and the observation and measurement data provided via these streams and to name and resolve the requests to these data. We present our semantic model to describe the sensor streams, demonstrate an annotation and data distribution framework and evaluate our solutions with a set of sample datasets. The results show that our proposed solutions can scale for large number of sensor streams with different types of data and various attributes. <s> BIB006 </s> A Survey on Semantic Sensor Web: Sensor Ontology, Mapping and Query <s> Conclusion and Future Work <s> As computing systems evolve and mature, they are also expected to grow in size and complexity. With the continuing paradigm shift towards cloud computing, these systems have already reached the stage where the human effort required to maintain them at an operational level is unsupportable. Therefore, the development of appropriate mechanisms for run-time monitoring and adaptation is essential to prevent cloud platforms from quickly dissolving into a non-reliable environment. In this paper we present our approach to enable cloud application platforms with self-managing capabilities. The approach is based on a novel view of cloud platforms as networks of distributed data sources - sensors. Accordingly, we propose utilising techniques from the Sensor Web research community to address the challenge of monitoring and analysing continuously flowing data within cloud platforms in a timely manner. <s> BIB007 </s> A Survey on Semantic Sensor Web: Sensor Ontology, Mapping and Query <s> Conclusion and Future Work <s> Internet of Things (IoT) will comprise billions of devices that can sense, communicate, compute and potentially actuate. Data streams coming from these devices will challenge the traditional approaches to data management and contribute to the emerging paradigm of big data. This paper discusses emerging Internet of Things (IoT) architecture, large scale sensor network applications, federating sensor networks, sensor data and related context capturing techniques, challenges in cloud-based management, storing, archiving and processing of sensor data. <s> BIB008 </s> A Survey on Semantic Sensor Web: Sensor Ontology, Mapping and Query <s> Conclusion and Future Work <s> To realize sharing and reuse of sensor data and improve interoperability, semantic sensor web (SSW) is proposed to add semantic inf ormation to existing sensor networks by utilizing domain, spatial and temporal anthologies and other related semantic technology. This paper presents state of the art of SSW from various aspects from the basic concepts, characters of SSW and the core technologies of SSW to the representative real world applications. Besides, it presents the challenges and the possible future research areas in SSW. <s> BIB009 </s> A Survey on Semantic Sensor Web: Sensor Ontology, Mapping and Query <s> Conclusion and Future Work <s> The reach of dominating social media like Facebook and Twitter in the current population is enormous, and these media have long been leveraged for diverse applications. In particular, for some citizen science projects, existing social media increasingly become platforms on which participants interact and contribute. These user contributions, often termed User-Generated Content (UGC), can be a mix bag of posts, comments, images, and other media. We report in this paper a work-in-progress in formalizing user contributions from a large Facebook group (more than 4,000 users) established for biodiversity observation. A major part of our work is to extract structured datasets with welldefined semantics from unstructured UGC collections. We use common vocabularies from Darwin Core (DwC), Friendof-a-friend (FOAF), Semantically-Interlinked Online Communities (SIOC), Semantic Sensor Network (SSN), among <s> BIB010
In this paper, the current research progress of Semantic Sensor Web was summarized. The introductions of this paper are expected to promote the focus and research on this emerging technology by the related scholars. We expect that the future development directions of Semantic Sensor Web include the following aspects BIB009 BIB006 . (1) Researching in the cloud platform In the real environment, the data size of sensor network is very huge. The integration between sensor network and cloud computing is one of the current development trend. The combination of semantic Web, big data technology and cloud computing technology can conduct distributed storage for ontology instance knowledge. The combination can explore how to improve the interaction of heterogeneous sensor network data, enhance the intelligent ways and means of the sensor network application system, and provide new idea and theory basis for building intelligent and efficient decision-making application system BIB007 BIB008 . (2) Linking with the open data source The data in Semantic Sensor Web are the linked data. The link scope is broader, the search scope is bigger. Therefore, that the domain ontology connects with the existing linked open data sources such as linked geodata and DBpedia is needed. The combination can make the deducibility semantic description of sensor information and a extensible data model will be obtained. The model can be used to conduct query in sensor data and context connection data. SPITFIRE FP7 project which was developed by Myriam Leggieri  devotes to this research. (3) Combining with social network Society is a big sensor community made up of all human, and each person is an intelligent sensor. These sensors move autonomously in the social and the natural environment. They can perceive, interpret, and integrate information, and can conduct the mutual exchange of information through social networks. Therefore, the combination of the Semantic Sensor Web and social network is a development trend of the future BIB001 BIB003 BIB010 BIB002 . (4) Combining with artificial intelligence Semantic Sensor Web can also combine with artificial intelligence. The combination will establish question-answering system for the application query of Semantic Sensor Web, understand and recognize the natural language, and conduct intelligent query. All these will be the future research directions. (5) The visualization of Semantic Sensor Web The visualization research of the query results or the real query of Semantic Sensor Web is also a research direction in the future. Query results are displayed on the electronic map. When click the place on the map, you can get the specific query information or the implicit information. The data visualization technology can also used to automatically map data into charts, and enables users to easily find the implicit information in the sensor network BIB005 BIB004 .