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:  Ontologies before SSN Ontology 
1. Abstract of Cited Paper (BIB001):  Abstract - This paper describes the approach to building OntoSensor: a prototype sensor knowledge repository compatible with evolving Semantic Web infrastructure. OntoSensor includes definitions of concepts and properties adopted in part from SensorML, extensions to IEEE SUMO and references to ISO 19115. Simple queries have been developed and tested using Protege 2000 and Prolog. Although OntoSensor is in the early development stage, it presents a practical approach to building a sensor knowledge repository. It is proposed that OntoSensor may serve as a component in comprehensive applications that include more advanced inference mechanisms. Such comprehensive applications will be used for synergistic fusion of heterogeneous data in a network-centric environment. Keywords: Data Fusion, Semantic Web, Sensor Ontology 1.0 Introduction The assessment of situations and strategies in complex environments requires the fusion of information from heterogeneous data sources including sensors [1]. Synergistic fusion of data from multiple sensors ranging in complexity from simple acoustic sensors to sophisticated imaging equipment such as Forward Looking Infrared (FLIR) sensors will lead to the extraction of knowledge that cannot be perceived or inferred using individual sensors alone. For example, information from diverse sources such as sensors on an Unmanned Aerial Vehicle (UAV), reconnaissance reports and satellite imagery can be integrated to obtain high-level knowledge of objects in an area under surveillance including their spatial and temporal interrelationships; and to generate predictions of their intentions, positions and alignments in future states. 
2. Abstract of Cited Paper (BIB002):  It is important to provide adaptive data processing in wireless sensor networks in order to deal with various applications. In this paper, we propose a WIreless Sensor Networks Ontology (WISNO) for flexible modeling of sensor data. WISNO contains two-tier ontologies, a front-end for coarse-grained analysis and a back-end for high-level fine-grained data processing. We also describes the WISNO reasoning rules that adopts description logic and SWRL for managing data automatically. 
3. Abstract of Cited Paper (BIB003):  In this paper we introduce a new agent-based approach for middleware in mixed mode environments. Mixed mode environments have different dimensions of heterogeneity: devices with widely differing capabilities, different software, and different communication technologies. To deal with these, each node in the network is seen as an independent entity: a device agent. These device agents are abstractions of the network nodes and they offer services corresponding to nodes' capabilities and also use services offered by other agents. Interoperability is achieved through an agent interaction interface. For the description of device types, capabilities and services a predefined but extensible ontology is used. This approach combines and adopts results from the areas of multi-agent systems and heterogeneous ad hoc networks. 
4. Abstract of Cited Paper (BIB004):  Sensor-mission assignment involves the allocation of sensor and other information-providing resources to missions in order to cover the information needs of the individual tasks in each mission. This is an important problem in the intelligence, surveillance, and reconnaissance (ISR) domain, where sensors are typically over-subscribed, and task requirements change dynamically. This paper approaches the sensor-mission assignment problem from a Semantic Web perspective: the core of the approach is a set of ontologies describing mission tasks, sensors, and deployment platforms. Semantic reasoning is used to recommend collections of types of sensors and platforms that are known to be "fit-for-purpose" for a particular task, during the mission planning process. These recommended solutions are used to constrain a search for available instances of sensors and platforms that can be allocated at mission execution-time to the relevant tasks. An interface to the physical sensor environment allows the instances to be configured to operate as a coherent whole and deliver the necessary data to users. Feedback loops exist throughout, allowing re-planning of the sensor-task fitness, reallocation of instances, and reconfiguration of the sensor network. 
5. Abstract of Cited Paper (BIB005):  This paper discusses an OWL ontology for specifying sensors. The ontology is intended as the basis for the semantic representation of sensors and as the formal description for reasoning about sensors and observations. The paper describes the ontology, presents two example sensor descriptions and shows how standard reasoning and querying techniques can be used to perform tasks including classification and composition. In conjunction with the technical material the trade-offs required to express complex material in OWL is also discussed. 
6. Abstract of Cited Paper (BIB006):  Abstract We describe a semantic data validation tool that is capable of observing incoming real-time sensor data and performing reasoning against a set of rules specific to the scientific domain to which the data belongs. Our software solution can produce a variety of different outcomes when a data anomaly or unexpected event is detected, ranging from simple flagging of data points, to data augmentation, to validation of proposed hypotheses that could explain the phenomenon. Hosted on the Jena Semantic Web Framework, the tool is completely domain-agnostic and is made domain-aware by reference to an ontology and Knowledge Base (KB) that together describe the key resources of the system being observed. The KB comprises ontologies for the sensor packages and for the domain; historical data from the network; concepts designed to guide discovery of internet resources unavailable in the local KB but relevant to reasoning about the anomaly; and a set of rules that represent domain expert knowledge of constraints on data from different kinds of instruments as well as rules that relate types of ecosystem events to properties of the ecosystem. We describe an instance of such a system that includes a sensor ontology, some rules describing coastal storm events and their consequences, and how we relate local data to external resources. We describe in some detail how a specific actual event—an unusually high chlorophyll reading—can be deduced by machine reasoning to be consistent with being caused by benthic diatom resuspension, consistent with being caused by an algal bloom, or both. 
7. Abstract of Cited Paper (BIB007):  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.

Literature Review:
Before SSN ontology, a lot of sensor ontologies have been developed. These ontologies are mainly divided into two categories: the ontologies of specific application, and the general ontologies of the sensor network. The ontologies of specific application are CESN  , A3ME BIB004 , SWAMO  , MMI  , OOSTethys  and ISTAR BIB005 etc. The CESN ontology is developed in the Coastal Environmental Sensing Networks project for sensor networks for coastal observing. The MMI ontology and the OOSTethys ontology were intended for marine equipment interoperability and focus on the system structure and the observation process and results. The A3ME ontology was developed for classifying the heterogeneous network equipment and resource-constrained devices. The SWAMO ontology for intelligent software agents describes the physical equipment, the process model and tasks. While the ISTAR ontology was used for selecting sensor to task allocation automatically. The ontologies of the specific applications have a narrow scope of application, and they have no universality. Therefore, the general ontologies of the sensor network have been developed. The functions of the original general ontology are simple, such as Avancha BIB001 and WISNO BIB003 ontology. They are developed for providing general ontology of adaptive data processing to meet various applications. While OntoSensor BIB002 , CESN BIB007 , CSIRO BIB006 cover a wider range of ontology concepts. OntoSensor BIB002 ontology is a generic sensor knowledge base which is used for querying and reasoning. CESN BIB007 ontology can inference domain knowledge from data. CSIRO BIB006 is a general ontology that can describe sensor and its deployment. In all these ontologies, the combination of OntoSensor and CSIRO represent the general semantic and expression power of sensor. However, problems still exist, e.g., how to express the correct structure and the scope of the sensor ontology, how to better express process and the system composition, and how to follow a fixed modeling process and reuse the existing standards BIB008 . SSN ontology which develops from the CSIRO, MMI and OOTethys ontology is a general ontology and full of expressive power. It can solve the existing problems in the original ontologies.