You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: A Survey on Semantic Sensor Web: Sensor Ontology, Mapping and Query 
Chapter Title:  SSN Ontology 
1. Abstract of Cited Paper (BIB001):  Today, many sensor networks and their applications employ a brute force approach to collecting and analyzing sensor data. Such an approach often wastes valuable energy and computational resources by unnecessarily tasking sensors and generating observations of minimal use. People, on the other hand, have evolved sophisticated mechanisms to efficiently perceive their environment. One such mechanism includes the use of background knowledge to determine what aspects of the environment to focus our attention. In this paper, we develop an ontology of perception, IntellegO, that may be used to more efficiently convert observations into perceptions. IntellegO is derived from cognitive theory, encoded in set-theory, and provides a formal semantics of machine perception. We then present an implementation that iteratively and efficiently processes low level, heterogeneous sensor data into knowledge through use of the perception ontology and domain specific background knowledge. Finally, we evaluate IntellegO by collecting and analyzing observations of weather conditions on the Web, and show significant resource savings in the generation and storage of perceptual knowledge. 
2. Abstract of Cited Paper (BIB002):  Sensing devices are increasingly being deployed to monitor the physical world around us. One class of application for which sensor data is pertinent is environmental decision support systems, e.g. flood emergency response. However, in order to interpret the readings from the sensors, the data needs to be put in context through correlation with other sensor readings, sensor data histories, and stored data, as well as juxtaposing with maps and forecast models. In this paper we use a flood emergency response planning application to identify requirements for a semantic sensor web. We propose a generic service architecture to satisfy the requirements that uses semantic annotations to support well-informed interactions between the services. We present the SemSor- Grid4Env realisation of the architecture and illustrate its capabilities in the context of the example application. 
3. Abstract of Cited Paper (BIB003):  Sensor networks are often deployed with the purpose of providing data to large-scale information management and GIS systems, or to collect measurements for specific scientific experiments. The benefits of such use are clear and widely accepted. The reuse of observations in low-cost, lightweight, web applications and mashups is a further compelling use case for sensor networks, but requires provision of data through simple mechanisms, readily accessible, that are quick to develop with. To enable the latter while maintaining support for larger applications and, to increase information utility, links to and from other datasets, we propose a domain-driven approach that embodies REST and Linked Data principles using a common semantic framework that underpins a separation of concerns between domain models, sensor observation infrastructure, and Application Programming Interfaces (APIs) while maintaining information consistency. We describe a reusable, reconfigurable, web service that realises this design and can be deployed to provide access to multiple sources of sensor information, including databases and streaming data, with flexible semantic configuration of the API and domain mapping. 
4. Abstract of Cited Paper (BIB004):  Sensor network deployments are a primary source of massive amounts of data about the real world that surrounds us, measuring a wide range of physical properties in real time. However, in large-scale deployments it becomes hard to effectively exploit the data captured by the sensors, since there is no precise information about what devices are available and what properties they measure. Even when metadata is available, users need to know low-level details such as database schemas or names of properties that are specific to a device or platform. Therefore the task of coherently searching, correlating and combining sensor data becomes very challenging. We propose an ontology-based approach, that consists in exposing sensor observations in terms of ontologies enriched with semantic metadata, providing information such as: which sensor recorded what, where, when, and in which conditions. For this, we allow defining virtual semantic streams, whose ontological terms are related to the underlying sensor data schemas through declarative mappings, and can be queried in terms of a high level sensor network ontology. 
5. Abstract of Cited Paper (BIB005):  The W3C Semantic Sensor Network Incubator group (the SSN-XG) produced an OWL 2 ontology to describe sensors and observations - the SSN ontology, available at http://purl.oclc.org/NET/ssnx/ssn. The SSN ontology can describe sensors in terms of capabilities, measurement processes, observations and deployments. This article describes the SSN ontology. It further gives an example and describes the use of the ontology in recent research projects. 
6. Abstract of Cited Paper (BIB006):  The Internet of Things IoT has recently received considerable interest from both academia and industry that are working on technologies to develop the future Internet. It is a joint and complex discipline that requires synergetic efforts from several communities such as telecommunication industry, device manufacturers, semantic Web, and informatics and engineering. Much of the IoT initiative is supported by the capabilities of manufacturing low-cost and energy-efficient hardware for devices with communication capacities, the maturity of wireless sensor network technologies, and the interests in integrating the physical and cyber worlds. However, the heterogeneity of the "Things" makes interoperability among them a challenging problem, which prevents generic solutions from being adopted on a global scale. Furthermore, the volume, velocity and volatility of the IoT data impose significant challenges to existing information systems. Semantic technologies based on machine-interpretable representation formalism have shown promise for describing objects, sharing and integrating information, and inferring new knowledge together with other intelligent processing techniques. However, the dynamic and resource-constrained nature of the IoT requires special design considerations to be taken into account to effectively apply the semantic technologies on the real world data. In this article the authors review some of the recent developments on applying the semantic technologies to IoT. 
7. Abstract of Cited Paper (BIB007):  An abstraction is a representation of an environment derived from sensor observation data. Generating an abstraction requires inferring explanations from an incomplete set of observations (often from the Web) and updating these explanations on the basis of new information. This process must be fast and efficient. The authors' approach overcomes these challenges to systematically derive abstractions from observations. The approach models perception through the integration of an abductive logic framework called Parsimonious Covering Theory with Semantic Web technologies. The authors demonstrate this approach's utility and scalability through use cases in the healthcare and weather domains. 
8. Abstract of Cited Paper (BIB008):  The purpose of this paper is to review existing sensor and sensor network ontologies to understand whether they can be reused as a basis for a manufacturing perception sensor ontology, or if the existing ontologies hold lessons for the development of a new ontology. We develop an initial set of requirements that should apply to a manufacturing perception sensor ontology. These initial requirements are used in reviewing selected existing sensor ontologies. Additionally, we present our developed sensor ontology thus far that incorporates a refined list of requirements. This paper describes 1) extending and refining the requirements; 2) proposing hierarchical structures for verifying the purposes of the ontology; 3) choosing appropriate tools and languages (e.g., OWL (Web Ontology Language) [1] and SensorML (Sensor Markup Language) [2]) to support such an ontology; and 4) extending the Semantic Sensor Network (SSN) ontology with a set of refined requirements (i.e., our developed ontology). This work will be proposed as a standard within the IEEE (Institute of Electrical and Electronics Engineers) Robotics and Automation Society (RAS) Ontologies for Robotics Automation (ORA) Working Group [3]. 
9. Abstract of Cited Paper (BIB009):  Semantic modelling provides a potential basis for interoperating among different systems and applications in the Internet of Things (IoT). However, current work has mostly focused on IoT resource management while not on the access and utilisation of information generated by the ``Things''. We present the design of a comprehensive and lightweight semantic description model for knowledge representation in the IoT domain. The design follows the widely recognised best practices in knowledge engineering and ontology modelling. Users are allowed to extend the model by linking to external ontologies, knowledge bases or existing linked data. Scalable access to IoT services and resources is achieved through a distributed, semantic storage design. The usefulness of the model is also illustrated through an IoT service discovery method.

Literature Review:
To acquire the semantic interoperability of general scale, common semantic annotation framework, W3C SSN XG group BIB006 proposed a general, field-independent model: SSN ontology. SSN ontology provides an advanced schema to describe the sensor equipment, their measurement data and the relevant properties of the sensor processing. SSN ontology designs the Stimulus-Sensor-Observation (SSO) pattern to describe the relationships between the sensors, the stimulation and the observation. It can be seen from four main perspectives: the sensor perspective which focus on the sensor, the sense mode and the sense object, the observation perspective which focus on the observation data and the related metadata, the system perspective which describes systems of sensors and deployments and the feature and property perspective which concerns the sensor properties or observational properties BIB009 . SSN ontology builds several conceptual modules on the SSO pattern to cover key sensor concepts and includes forty one concepts and thirty nine properties. SSN ontology is the integration and upgrade of the original ontologies. It describes more clearly because it has more detailed classification. It has a wider range of generality, great extension space. But above all, it can be extended and reused as the basis to solve more complex problems. Therefore, many applications using SSN ontology have been appeared. SSN ontology is applied to many projects now1, such as IoT.est project BIB010 BIB007 , Semantic Perception BIB008 BIB002 , SECURE  , SemSorGrid4Env BIB001 BIB003 BIB004 , Swiss Experimen BIB005 , SPITFIRE project   , etc. In IoT. Est project, SSN ontology is used in the Internet of things to describe the resources, the observation and the measurement system of sensors. In Semantic Perception project, machine perception is researched. Perception ontology which reused SSN ontology is established. In the perception ontology, the key of perception explain: observation and terms of environmental knowledge is provided. SSN ontology is applied to the rescue environment In SECURE project. It describes observation data from the temperature sensors, carbon monoxide sensors, and carbon dioxide sensors, which can integrate and analyze the data, and concludes the events. In SemSorGrid4Env project, SSN ontology shows public data model of information space, and it is applied in environment monitoring system. In Swiss Experiment project, SSN ontology describes sensor meta-data and research collaborative environment and it connects with LOD cloud effectively in SPITFIRE project. In this project, it develops uniform concepts, methods and software infrastructure.