Need and Relevance of Common Vocabularies and Ontologies in IoT Domain Arunima Sharma and Ramesh Babu Battula Abstract Data plays a foremost part in Future Communication. Internet of Things encompasses various sources of data of different type, by different name. The complexity and variance in data increase the criticalness of the model. Same data has different meaning in different applications. These variances make it difficult to apply any query on data. To resolve the issues in naming of data values and to create consistency Vocabulary and Ontologies are must. Data gathered on a big platform e.g. for IoT via cloud need to manage data in proper manner, which is spread all over the internet on different servers. The features and related research is explained in this chapter, having a brief introduction with application areas. Keywords Semantic web · Ontology · Vocabulary · Internet of thing · IoT · Smart city 1 Introduction Internet of Things (IoT) brings a heterogeneus of technologies and devices together to simplify the basic life of human beings. Smart Home, Traffic, Healthcare etc. are some of the major areas using different IoT components. On basis of applications we can consider IoT as a complex group of several devices, sensors and people who interacts with each other and controlled by a centre control station. This interaction or communication generates a large amount of data gathered from several source in different formats. This data has various formats of data related to each other. Due to complex relationship and variation this data is considered as heterogeneous data. Heterogeneous data is complex with perspective of management, access and collection. On web, data is linked together on basis of some relationship and similarity. The extension of World Wide Web which deal with related web pages, and extensions of similar data is known as Semantic Web. A. Sharma (B) · R. B. Battula Department of Computer Science and Engineering, Malviya National Institute of Technology, Jaipur, India e-mail: aru.92@rediffmail.com © Springer Nature Switzerland AG 2021 R. Pandey et al. (eds.), Semantic IoT: Theory and Applications, Studies in Computational Intelligence 941, https://doi.org/10.1007/978-3-030-64619-6_6 133 134 A. Sharma and R. B. Battula Complex linking in words on internet in interlinked with each other and creates a web of connections between words related to each other. Vocabulary and Ontology are two main characteristics of words which play most important role. The IoT assumes an ever-expanding job in empowering brilliant city applications. A philosophy based semantic methodology can help improve interoperability between an assortment of IoT-created just as reciprocal information expected to drive these applications. While various metaphysics indexes exist, utilizing them for IoT and brilliant city applications require critical measure of work. Semantic advancements propose an appropriate methodology for interoperability by sharing normal vocabularies, and furthermore empowering interoperable portrayal of construed information. IoT testbed suppliers have as of late began to add semantics to their structures permitting the creation of the semantic Sensor Web, which is an expansion of the current Web in which data is given all around characterized which means, empowering machine-to-machine interchanges and cooperations between items, gadgets and individuals. Semantics generally model the space ideas in incredible detail. Despite the fact that they can be applied for questioning nearly anything about articles, these unpredictable models are regularly hard to execute and utilize, particularly by non-specialists. They request extensive handling assets and along these lines they are viewed as unacceptable for compelled situations. Rather, IoT models ought to think about the limitations and dynamicity of the IoT situations, particularly with the new pattern towards incorporating semantics in obliged gadgets for example, M2M doors or cell phones. Simultaneously they have to show the connections and ideas that speak to and permit interoperability between IoT elements. In this manner, expressiveness versus intricacy is a test. It is essential to take note of that semantic models are most certainly not final results. They are ordinarily just piece of an answer also, ought to be straightforward to the end client. The semantic comment models ought to be offered with compelling strategies, Programming interface’s and instruments to process the semantics so as to extricate significant data from crude information. Question techniques, AI, thinking and information investigation methods ought to have the option to successfully utilize these semantics. Semantic demonstrating is just the underlying piece of the entire plan, and it needs to consider how the models will be utilized; how the explained information will be recorded and questioned with continuous information; and how to make the distribution reasonable for obliged conditions and enormous scope organizations when applications frequently require low inactivity and handling time. To permit a typical jargon to interoperate between various frameworks a scientific classification is required to portray the estimations of the gadgets as far as the amount sorts and units, for example, temperature and degrees Celsius. Need and Relevance of Common Vocabularies and Ontologies … 135 2 Vocabulary Vocabulary is the knowledge regarding word. It is the basic information required about a word. Controlled vocabularies provide a way to arrange data for successive information retrieval. Indexing, Heading, Thesauri, and other are collection of controlled data. This data is well defined selected for a category to belong on basis of knowledge, natural language and some restrictions. It is well formed set of information so that any query on data will give corrects outcome. It uses tagging system for words to get connected and retrieval becomes easy. 2.1 Vocabulary Tools 1. Subject Heading Subject heading helps to create the catalogue of data in categories and subcategories easily. It considers the whole document in pre-coordinated order. 2. Thesauri It is collection of words well organized in a sequence. It covers a specific portion of document in direct order. 3 Ontology Ontology is derived from Greek words means being logical discourse, in general like a dictionary. Ontology consist all the relations, rules, similarity and dissimilarity. It is the formal representation, categorization, and domain of data. All the ontologies are used to resolve the complex relationships. These ontologies are limited in number till there is no new requirement raised for a different sub category. Ontologies, for example, OWL-S and low-level determinations, for example, the TD or the oneM2M Base Ontology can be utilized together to depict IoT/WoT frameworks, cultivating interoperability. OWL-S helps programming operators to find the web administration that will satisfy a particular need. When found, OWL-S gives the vital language builds to depict how to conjure the administration. It permits portraying inputs what’s more, yields. Because of the elevated level portrayal of the administration, it is conceivable to form numerous administrations to perform increasingly complex errands. In OWL-S, administration depiction is composed into four regions: the procedure model, the profile, the establishing and the administration. In particular, the procedure model depicts how a help plays out its undertakings. It incorporates data about sources of info, yields, preconditions and results. 136 A. Sharma and R. B. Battula Additionally, the oneM2M Global Initiative characterizes a norm for machine-tomachine correspondence interoperability at the semantic level, the one M2M Base Ontology, which is a significant level metaphysics intended to encourage interoperability among different ontologies utilizing equivalences and arrangements. The TD is a focal structure obstruct in the W3C Web of Things (WoT) and can be considered as the passage purpose of a Thing. The TD comprises of semantic metadata for the Thing itself, an association model dependent on WoT’s Properties, Actions, and Events worldview, a semantic pattern to make information models machine-justifiable, and highlights for Web Linking to communicate relations among Things”. Both oneM2M Base Ontology and the metaphysics characterized in TD take a stab at interoperability among different IoT applications and stages, every one covering a huge arrangement of utilization cases, so there is additionally a work in progress to adjust the oneM2M philosophy to the TD cosmology. Symmetrical to these ontologies, the SOSA/SSN philosophy is a cosmology for depicting sensors and their perceptions. Among other ideas, it characterizes the class SOSA: Procedure, which “is a reusable work process, convention, plan, calculation, or then again computational strategy that can be utilized, among others, to indicate how a perception movement has been made, can be either a record of how the activation has been performed or then again a portrayal of how to associate with an actuator (i.e., the formula for performing incitations). Things utilizing ontologies in a way that permits us to legitimately convey the conduct usage. Work planned for displaying the conduct of Things utilizing FSMs and Web Ontology Language (OWL) exists in the writing. Unified Modeling Language (UML) FSMs utilizing OWL, playing out a very nearly balanced interpretation between UML ideas and OWL classes. In spite of the fact that their planning from UML to OWL takes into account a more machine-lucid data structure, its intricacy makes it unconventional to use. UML is utilized to indicate stage free route guides in web applications. They use OWL to portray a model for FSMs which fills in as a meta-model for semantic web portrayals of the route guides on the Semantic Web. There likewise exists some logical writing gave not exclusively to make a model to communicate the conduct of a help yet in addition to decipher furthermore, execute the conduct. The point is to build up a FSM that a unique server can peruse and mean executable elements. These executable substances are executed later by robots. They effectively fulfill their goal, their OWL FSM is space explicit and it incorporates properties that illuminate the multifaceted nature of their utilization case yet make the FSM excessively complex. 3.1 Components of Ontology 1. Individuals Initial object considered in starting phase of classification are known as individuals. It is the basic components of ontology which comprise real objects such Need and Relevance of Common Vocabularies and Ontologies … 137 Vehicle Two Wheeler Bicycle Scooter Four Wheeler Car Six Wheeler Jeep Truck Bus Fig. 1 Example of ontology as individuals, amphibians, books, plants, along with nonconcrete objects such as numbers and words. The overall purpose of ontology is to offer a way to categorizing entities, in different classes grouped together or correlated with each other. In Fig. 1 car, bicycle, truck are individuals. 2. Classes Categories in which Individuals are classified are known as classes. Classes are considered as type, category, sort, kind and extension. Classes are considered as abstract objects that are defined by aspect values or constraints to be member of a class. In Fig. 1 Vehicle is a class having two categories based on number of wheels. Classes have to fulfil criteria to belong to a specific group. Class can be a collection of other classes. Ontologies differ on the basis of conditions related to some conditions like classes can encompass supplementary classes, or can be applicable to itself, and if there is a universal class, etc. Occasionally some restrictions are also applied on classes to avoid inconsistencies. A class can be extensional if it is categorized only by its association or class is identical else it is an intentional class. Extensional classes have a systematic format and try to avoid any inconsistency in data. Most of the upper ontologies are considered as intentional classes having only necessary conditions for member association in a class. The amalgamation of compulsory and necessary conditions within a class is considered as fully defined. 3. Attributes Features or Properties of object which help them to categorize in particular type of class are known as attributes. Individual objects in ontology can be related to other things, on basis of some features or portions. These connected properties are their attributes, even though there can be present independent objects also. Every single attribute can be reflected as a class or individual or a sub class. The relationship among individuals can be explained on basis of attributes easily with their similarity and differences. For example numbers of wheels are classifying the vehicles in different categories as shown in Fig. 1. An attribute can be present as a complex data form, i.e. 138 A. Sharma and R. B. Battula on basis of wheels, sources, colour many vehicles get classify in different categories but they are having some common features and inter relationships. Ontologies are only strong if attributes are related to other attributes. If that is not the situation, then either taxonomy or controlled vocabulary is used. 4. Relations The way in which objects or classes are connected with each other based on a property is known as Relation between objects or classes. Relationships (relations) among entities in ontology identify how entities are interrelated to each other. For example, in Fig. 1 both truck and bus are six wheeler vehicles. Set of associations defines the semantics of the area. An important category of relation is the subsumption relation. This relationship describes which object belongs to which class. This helps to create a tree structure of objects which represents relation among objects having common attributes and properties. Another one is mereology relation, which represents association in a class or classes of objects. It uses a directed acyclic graph for data representation. Relation can be categories for relations among classes, individuals, single individual and one class, a object and a collection of objects and collections 5. Function terms Function terms are structure which is get developed on basis of a relation among individuals. It 6. Restrictions A limiting condition which used to satisfied for development of range and domain for individuals is Restriction on Individual. It restricts objects in a certain type of concept. 7. Rules Set of some conditions need to be fulfil by individual to qualify the minimum requirement to create a relation or to belong to a class. It is a logical statement 8. Axioms Prior Knowledge about the individuals, help to create Axioms. Axioms are set of rules known before and based on learned data. These assumptions plays most important role. The prior knowledge is necessary about the application to decide the axioms in ontology. These will help to initialize basic known category for data. This will reduce the data processing time and complexity of the processing. 9. Events The change of state of one individual to another based on some logic is known as event. It shows the transaction among the relationship or attributes of individuals on a input. Need and Relevance of Common Vocabularies and Ontologies … 139 3.2 Types of Ontology Ontologies help to represent information and theories in different forms at different levels of abstraction. These different domain ontologies are every so often incompatible with other ontologies. Different types of ontologies are as follow 1. Domain specific Ontology Individuals belong to a particular type of class comes under this category. The Domain specific ontology is classified on such sets which have no intersection and have different meanings for different classes. For example, “name”, can be “full name”, “last name”, “first name”… all these have different meanings when they get connected to different classes. Merging of different ontologies is complex and has to be done on basis of a common platform. 2. Hybrid Ontology Hybrid is mix up of different ontologies in one which doesn’t come in remaining categories. It is the most complex type of data which need variations in Axioms with changes of class for an individual. 3. Upper Ontology The general relationships among individuals which are well known and accepted belong to upper ontology. It considers linguistic rules and learned domain for categorization of individuals. Ontologies need to be designed for significant applications according to user requirements. It is required to be flexible enough for modification and reuse. Linked data, taxonomies, interoperability, axioms, vocabulary, need to be predefined and easy to use. Upper ontologies epitomize mutual notions and relations in range of domains and Domain ontologies characterize conceptions and relations of a specific. 3.3 IoT Ontologies A cosmology is characterized as “a formal, unequivocal detail of a common conceptualization” and is utilized to speak to information inside a space as a lot of ideas identified with one another. There are four fundamental parts that form a cosmology: Classes, relations, qualities and people. Classes are the primary ideas to depict. Each class can have one or a few kids, known as subclasses, used to characterize increasingly explicit ideas. Classes and subclasses have qualities that speak to their properties and attributes. People are occasions of classes or their properties. At long last, relations are the edges that interface all the introduced segments. IoT-based genuine world to be isolated into 3 layers: a physical layer, i.e., things; an data layer, i.e., information and metadata about information given by things; and a useful layer containing administrations gave by things. To coordinate our vision of the 140 A. Sharma and R. B. Battula genuine world and its portrayal by the Internet of Things, fabricating a metaphysics that really models every one of the three layers. Indeed, the physical layer is spoken to by a Device Metaphysics. The data and administration layers are spoken to by a (Physics and Mathematics Domain Ontology and Estimation Models Ontology. To portray the ontologies all the more decisively: 1. Gadget Ontology The Device Ontology models real equipment gadgets that may exist in the system. It tends to be viewed as the gadget depiction storehouse that can be gotten to for disclosure. 2. Space Ontology The (Physics and Mathematics) Space Ontology models data about genuine world physical ideas and their relations among one another. It tends to be viewed as the fundamental archive to access for administration creation. 3. Estimation Ontology The Estimation Ontology contains data about various estimation models (“straight introduction”, “Kalman channel”, “gullible Bayesian learning”, and so on.), the conditions that drive them, the administrations that execute them, etc. It tends to be for the most part viewed as the archive portraying the gadget’s nature of administration, and gives data expected to support structure. This cosmology to be utilized as a source of perspective by any middleware or application requiring IoT administrations, i.e., administrations gave by genuine world things. Those administrations, much of the time, produce inexact be that as it may, never 100% exact results. Most existing philosophy work concentrated on demonstrating either gadgets as done, e.g., in MMI cosmology 2, or material science independently. The curiosity of our methodology is that it joins and takes favorable circumstances of the three ontologies by connecting, all together, the area of information for detecting, activating, and handling assignments and this present reality portrayal through IoT administrations, that know about their condition. A significant commitment is the degree of deliberation at which things, permit clients to portray gadgets in an expressive way while as yet staying away from complex subtleties. Truth be told, as target adaptability, consider straightforwardness in displaying information to be a basic models. 3.4 Advantages of Ontology One of the main advantages of ontologies is that, by having information only regarding the necessary relationships between objects helps to identify information about data easily. Such relationship makes it is easy to implement for graphical data. Ontologies function helps to depict the human perspective about data and interlinked concepts among objects. In addition ontologies make available a extra rational and tranquil navigation for operators in the ontology organization. Need and Relevance of Common Vocabularies and Ontologies … 141 Ontologies are tranquil to outspread as associations and notion equivalently easy to connect with the present ontologies. It also provides the meaningful significance of datum in different format; even data is unstructured, semi-structured or structured, making data amalgamation simpler with text quarrying, and information based analysis. 3.5 Restrictions of Ontology Ontologies offer an opulent group of tools and techniques for information modelling, but it comes by way of some limitations also. One such restriction is the existing property paradigms. For instance, while providing dominant class, the most recent version of the Web Ontology Language—OWL2 has limited set of property paradigms. Another limitation is that they specify how data has to be structured and avoid addition of extra data. Frequently, data introduced from a new source would be operationally inconsistent with the constraints set. Therefore, this new data need to be modified before being incorporated with existing data. 4 Need of Vocabulary and Ontology Efficient data storage is must needed to keep relevant multimedia data connected and organized. This data is having lots of variance with large volume and velocity. IoT is going to gather data from different applications developed and used by different people is going to have inconsistency in naming methodology. If a similar vocabulary is used it become easy to gather relevant data. Query processing and decision making processes will also become an easy task for end users to gather important information and used it further. 5 Ontology Quality Methodology For evaluation and validation ontologies, ontology quality methodology [1] is used. It has following categories to measure the quality of data (1) Serialization Serialization is the sequential process to convert data of an individual object into form of binary values. Bit data is easy to save and send during communication. Its main aim is to save all different states of data, which make its serial retrieval easy. The inverse process of serialization is called deserialization. Serialization allows user to save sequential states of an individual object and re-construct it also by retracing. It provide data storage and data exchange simple among distributed applications. With 142 A. Sharma and R. B. Battula flexibility of serialization, user can perform different actions like sending the object by using a web service, object passing from one domain to another or through a firewall etc. Serialization helps to maintain security or user-specific shared information across different heterogeneous applications. (2) Syntactic validation Syntax validation is the method of to check the syntax of a program is free of any error. There are so many tools available to check the syntax of language. These validators work both online and offline. There are syntax validators, know as linters, for programming language Syntax validators can check syntax, fading common errors such as dividing by zero. Syntax validators highlight code style. Most of the search engines on web use online code checkers. (3) Interlinking Linked data is structured data which is inter-related with each other so it becomes more valuable through semantic queries. It is based on standard Web technologies but rather than using those to use as web pages only it used to share information which is automatically accessed by digital devices. One part of the visualization of linked data is Internet which is source of global database. (4) Documentation Data documentation helps to keeping data organised easy to collect. The resulting data libraries include information and method how to process it. Brief and clear documentation help users to understand the framework of data. It increases the probability of data usage. (5) Visualization Ontology Visualization is representation of data in form of graph as node link diagram having edges and vertices for visual representation of data. Usually vertices contain semantic information and attributes. Sometimes external features are also used, like colour shape and codes to support user visualization flexibility. (6) Availability of resources The resource availability describe how ontologies availability changes over time and how user and developer observe those changes in ontologies. These attributes include longevity and observability. Attributes of individuals captures whether the availability of ontology is directly observable, partially observable, or unobservable to various entities. (7) Discoverability Ontologies can be used to determine information from Big Data. Based on these distinct projections, efforts can be on the way to exploration of the likelihood to use ontological knowledge for the requirement of Big data and meta data as a underpinning to proficiently determine useful statistics for exploration. Need and Relevance of Common Vocabularies and Ontologies … 143 (8) Ontology Design What’s, why’s and where’s organise the ontology in data set to identify the best place for data. Ontology designers need to find correct place to classify data in ontology with its relationship with rest. Matching problems doesn’t provide optimal solution which makes need of ontology design. Domain and task are two major entities in designing of ontologies. Domain establishes a relationship and information of individuals and task makes them identify able in the set. Selection of Ontology has to be based on some parameters are as follow: 1. Goal of ontology Sharing common understanding of the structure of data is one of the major goals of ontology. For example, if different Web sites contain medical data it becomes difficult to share it or correlate it with other sites data. If these Web domains share and distribute the similar ontology, data extraction and aggregation becomes easy. The users can practice this combined statistics to reply enquiries or as feedback into other applications. Permitting reprocess of domain information was unique and powerful services after recent outpouring in ontology exploration. For instance, models used for representation of dissimilar time in different domains. This comprises the concepts of time pauses among domains, points in time, comparative time measurement, and much more. If we requires a large ontology, integration of several present ontologies help to create better and enormous province. Production of unambiguous domain norms creates it easy on the way to alter if there is any changes occurred. Separating the domain describe mechanisms rendering to the mandatory measurement and implement it independently. 2. Size Large size of ontology increases its efficiency as well as complexity. The small size ontologies are not enough to perform any operation, so it gets integrated with parent class but still remain as an individual class. The big ontology requires optimal search and update algorithms whereas classification in such data sets is more accurate. Interrelationship makes it difficult to manage and find a relation individually among objects or classes. 3. Documentation Proper documentation of ontology makes it easy to use and access. Usually for developer it is easy to extend and integrate different data values with the help of documentation. The proper management of ontology make easy to identify an error. 4. Avability on Web Avability of ontology on web make it easy to update frequently having more precise and updated knowledge. The linking of data also becomes easy to categorized data because of it. If an unknown user having less or no information will edit data it will decrease its performance and quality. Identification of such faults is too 144 A. Sharma and R. B. Battula difficult in such cases. Avability on web make ontologies to easily available anytime anywhere. The queries and correlation of data become easy in this case. It will remain updated in real time with multi user accessibility. 5. Popularity Popularity is the most important aspect to improve the quality of words and it plays an important role in data set. Unpopular words get vanished from daily conversation and rarely used. The elimination of this data is not a good decision because it helps to identify miscellaneous hidden information. But it has to give low priority for optimal identification. Most of the least used words represent unauthorized data with respective of forensics. Arrangement of ontologies on basis of popularity helps to make frequently used data easily available. It makes operations less time consuming. System optimality gets increased with this feature. 6. Maintenance with time Maintenance of data allows speeding up query process by illuminating the inherent relations which are authenticated and approved through the semantics of ontology. The complication of reasoning within ontology, is in this manner moved from demand time to apprise time. 7. Meta data Vocabulary “Data about Data” or set of words denoting the features of resources is known as Meta data. Ontology is a metadata vocabulary. Generally, metadata vocabularies are domain precise. Controlled vocabularies are homogeneous and ordered words provide a dependable way to define data. Metadata designers allocate terms from vocabularies to simplify information recovery. Commonly vocabularies contain subject headings, lists, files, and thesauri. Controlled vocabularies can be organised in alphabetical lists with a hierarchical arrangement of data. Thesauri also comprise synonyms, associated data, scope and notes, data antiquity, alternative idioms, or numerical data. Ontologies include even more specification, such as description, concepts, hierarchy and relationships with other values. 6 Application Smart City [2] requires interoperable system, to process data securely, and manages services. Secure data access will reduce the cost of operation. Some famous Ontologies for Smart Cities [1] are KM4City (Knowledge Model for City), Semantic Traffic Analytics and Reasoning for CITY(STAR-CITY), SCOnt (Smart City Ontology), and CityPulse Agriculture and Food supply [3] is also needed a dictionary o track entities in system and to manage information from seeding to selling of crops. It helps to manage data of farm items and to keep transaction easy. Smart City, Smart Home, Need and Relevance of Common Vocabularies and Ontologies … 145 And Smart Weather [4] have dependency among each other for various tasks and management. Health Sector [5] shows how LOV4IoT will help to manage complex distributed data having different type of media contents but having same vocabulary can be easily manages. the data access will be easy due to inter linking from different departments. Transportation and Logistics [6] will also become easy to manage in real time. Time required to share information will get reduced and different representations of outcome can be done easily. 7 Background Gyrard et al. [2] projected a semantic web search application for IoT based cities with case study of three procedure cases FIESTA-IOT EU, Machine-to-Machine Measurement (M3), and VITAL EU scheme. This project is combination of web based data from IoT to Semantic based but not suitable for real time interoperability practically. Kamilaris et al. [3] proposed Agri-IoT, an IoTbased smart farming applications over web which supports big data analysis, event detection, interoperability, online information and linked datasets accessible to end uses always. But it doesn’t standardized data specifically used for agriculture and semantic web axioms. Noura et al. [4] created a corpus and discussed application of semantic web in Smart City, Smart Home, and Smart Weather. It created Knowledge Extraction for the Web of Things (KE4WoT) a set of ontologies based on some specific domain. It is efficient one if domain to which word belong is considered else it put word in unrelated category or discard it. This causes outlier data which can be useful in ignored data category. Gyrard et al. [1] suggest some techniques to make ontologies more effective with combined ontology sets for IoT and Smart city LOV, READY4SmartCities, Open Sensing City (OSC), in addition to LOV4IoT. Gyrard et al. [7] raise the requirement of interoperability of data needed for semantic web [8] and proposed a framework Machine-to-Machine Measurement (M3). But it still lacks combination of different domains. It is difficult to combine these frameworks together. Cross domain applications [9] can be useful to collect similar type of data from different applications but requires domain base knowledge. It provides a set of linked open rules which can be used generally for IoT applications having cross domain data. Bermudez-Edo et al. [10] states that semantic techniques upsurge the complexity and processing time which makes them unsuitable for IoT. To resolve this issue they proposed IoT-Lite for semantic sensor networks but it lacks interoperability. Linked Open Vocabularies for IoT (LOV4IoT) [11] overcome the challenges of classification, re-engineering and designing of interoperability. This shows evolved better results and easy to establish technique. It is up to the mark but doesn’t consider previous established classification and classes for ontologies. IERC Cluster Semantic (IERC AC4) [12] resolve four interoperability issues Technical, Syntactical, Semantic and Organizational. IERC AC4 has some shortcomings also like reuse of methodologies, validation and evaluation of ontologies, and a well-designed structure. Machine-to-Machine Measurement 146 A. Sharma and R. B. Battula (M3) framework [13] tries to simplify domain specific issues. It highlighted uniform nomenclature requirement to improve performance of Ontology. In perspective of security M3 framework is not secure because it is accessible by third party easily. IoT-O [14] an extension of M2M is proposed to use known ontologies and defines some concepts relevant to IoT. It is based on principal of indexing of resources data; still semantic data interoperability is unachieved. 8 IoT-Related Ontologies The space of ontologies is divided, paying little heed to the area of intrigue. The more extravagant a metaphysics is, the bigger zone it ranges. Subsequently, uniqueness and crossing points with different ontologies become increasingly manysided and complex. Web of Things traverses tremendous number of areas, and extends with the developing prominence of “shrewd gadgets”. Utilization of ontologies in the IoT mirrors this extensiveness. There are numerous ontologies that speak to models applicable to the IoT, including, in any case, not restricted to, gadgets, units of estimation, information streams, information preparing, geo-location, information provenance, PC equipment, techniques for correspondence, and so forth. Highlight of the IoT is a brilliant gadget fit for correspondence. From this point of view, ontologies that catch the possibility of a gadget, and are entrenched in the IoT space: SSN, SAREF, oneM2M Base Ontology, IoT-Lite, and OpenIoT. Every one of them takes an alternate way to deal with demonstrating the IoT space at the same time, in spite of the distinctions in conceptualization, they spread converging sections of the IoT scene. Beneath, we talk about disparity, oppositeness and covers between these ontologies. SSN, or “Semantic Sensor Network” [15] is a metaphysics based on sensors and perceptions. It is an accepted augmentation of the SensorML language. SSN centers around estimations and perceptions, ignoring equipment data about the gadget. In particular, it portrays sensors as far as abilities, execution, use conditions, perceptions, estimation forms, and organizations. It is profoundly measured and extendable. Truth be told, it relies upon other ontologies in key territories (for example time, area, units) and, for every pragmatic reason, should be stretched out before genuine execution of a SSN-based IoT framework. SSN, detailed on head of DUL, is an ontological reason for the IoT, as it attempts to cover any utilization of sensors in the IoT. SAREF or “The Smart Appliances REFerence” [16], metaphysics covers the zone of shrewd gadgets in houses, workplaces, open spots, and so forth. It doesn’t concentrate on any mechanical or logical usage. The gadgets are described overwhelmingly by the function(s) they perform, orders they acknowledge, and states they can be in. Those three classifications fill in as building squares of the semantic depiction in SAREF. Components from each can be consolidated to deliver complex depictions of multi-practical gadgets. The portrayal is supplemented by gadget benefits that offer capacities. A critical module of SAREF is the vitality and force profile that got significant consideration, not long after its inception. SAREF utilizes WGS84 for geolocation and characterizes its own estimation units. oneM2M Base Ontology Need and Relevance of Common Vocabularies and Ontologies … 147 (oneM2M BO) is an as of late made philosophy, with first non-draft discharge in August 2016. It is generally little, readied for the discharge 2.0 of oneM2M details, and planned with the expectation of giving a common ontological base, to which different ontologies would adjust. It is like the SSN, since any solid framework fundamentally needs to expand it before execution. It depicts gadgets in an expansive degree, empowering (in an overall sense) particular of gadget usefulness, organizing properties, activity and administrations. The way of thinking behind this methodology was to empower revelation of semantically differentiated assets utilizing an insignificant arrangement of ideas. It is a base cosmology, as it doesn’t expand some other base models, (for example, DUL or Dublin Core). Notwithstanding, arrangements to different ontologies are known. IoT-Lite [10] is a “launch” of the SSN, for example an immediate augmentation of a few of its modules. It is a negligible cosmology, to which a large portion of the provisos of the SSN apply. In particular: center around sensors and perceptions, dependence on other ontologies (for example time or units ontologies), high measured quality and extendability. The thought behind the IoT-Lite was to make a little/light semantic model that would be less burdening (than other, increasingly verbose and more extensive models) on gadgets that process it. Simultaneously, it expected to cover enough ideas to be valuable. The metaphysics portrays gadgets, articles, frameworks and administrations. The principle augmentation of the SSN, in the IoT-Lite, lies also of actuators (to supplement sensors, as a gadget type) and an inclusion property. It expressly utilizes ideas from a geo-location cosmology to divide gadget inclusion and arrangement area. OpenIoT [17] philosophy was created inside the OpenIoT venture. In any case, here, we utilize the expression “OpenIoT” to allude to the metaphysics. It is a nearly enormous model that reuses and joins different ontologies. Those incorporate all modules of the SSN (the principle reason for the OpenIoT), SPITFIRE (counting sensor systems), Event Model-F, PROV-O, WGS84, CloudDomain, SIOC, Association Ontology and others, including littler ontologies created at the DERI (at present, Insight Center). It likewise utilizes ontologies that give premise to those listed before, for example DUL. Other than ideas from the SSN, OpenIoT, utilizes an enormous number of SPITFIRE ideas, for example system and sensor arrange depictions. While some referenced ontologies are definitely not imported by the OpenIoT unequivocally, they show up in all models, documentation, what’s more, venture expectations. Consequently, one can treat OpenIoT as a blend of portions of those. Essentially to the SSN, OpenIoT doesn’t characterize its own area ideas and doesn’t unequivocally import geolocation ontologies. It depends on different ontologies for that yet, rather than the SSN, it plainly shows LinkedGeoData and WGS84 of geolocation portrayals. It characterizes a constrained set of units of measure (for example temperature, wind speed), however just when they were pertinent to the OpenIoT venture pilot usage. The rich set-up of utilized ontologies implies that OpenIoT gives rich depiction of gadgets, their functionalities, capacities, provenance, estimations, arrangements and position, vitality, pertinent occasions, clients and numerous others. Strikingly enough, it doesn’t unequivocally portray actuators or impelling properties/capacities. 148 A. Sharma and R. B. Battula It tends to be seen that the expansive extent of the cosmology makes it rather entangled. This is likewise in light of the fact that, it isn’t recorded all around ok, for example the detail level and straightforward entry of the documentation don’t coordinate the range of inclusion of ideas in the model. Besides, it isn’t plainly and expressly modularized, notwithstanding being an expansion of the SSN. Let us note that, while there are other IoT models of expected intrigue (such as OGC Sensor Things, FAN FPAI, UniversAAL ontologies, IoT Ontology3, M3 Vocabulary), we won’t consider them here. This is a result of space constraint, and the way that they have produced substantially less “general intrigue”. In any case, we intend to remember these ontologies for resulting work. Let us presently think about the choose ontologies [18] one next to the other. Chosen key angles, or classifications, straightforwardly relating to the IoT; set the first segment of Table 1. Notwithstanding, due to complexities and different ways of thinking behind thought about ontologies, every classification should be further researched. Every one of considered ontologies proposes an alternate way to deal with demonstrating the IoT space [19]. The greatest contrasts are in the subtleties. (a) OneM2M BO proposes a little base cosmology, like upper ontologies that gives just an insignificant set of profoundly unique elements. This takes into consideration a wide arrangement of space ontologies to be effortlessly lined up with it. It likewise implies that the BO itself isn’t sufficient to model any solid issue Table 1 IoT ontologies comparison (a), (b) (a) Sub Domain Thing Gadget Gadget sending Gadget properties and capacities Gadget Capacity and vitality administration SSN ✓ ✓ ✓ ✓ ✓ SAREF ✓ ✓ ✓ OneM2M ✓ BO ✓ ✓ IoT-Lite ✓ ✓ ✓ OpenIoT ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ (b) Sub Domain Detecting and sensor properties Perception SSN ✓ ✓ SAREF ✓ ✓ OneM2M BO Impelling and actuator properties ✓ ✓ ✓ IoT-Lite ✓ OpenIoT ✓ ✓ ✓ Conditionals Need and Relevance of Common Vocabularies and Ontologies … (b) (c) (d) (e) 149 (or arrangement) in the IoT. Moreover, it doesn’t catch a few angles that are normal in different ontologies. OpenIoT contrasts this way of thinking by giving a nitty gritty model to a particular issue (for example pilot usage from the OpenIoT venture) that can be additionally applied in an increasingly broad case, or in different arrangements. Its overwhelming utilization of outside ontologies gives high semantic interoperability by structure. SSN is a created model of the IoT when all is said in done, however with solid spotlight on sensors. It depends on DUL, what’s more, is unmistakably modularized, which makes it a decent contender for augmentations into solid frameworks and executions. This is prove by the way that other ontologies, assessed here, utilize it. With regards to particularity, it places itself in the center between oneM2M BO and OpenIoT. IoT-Lite is an expansion of chose SSN modules, essentially to incorporate actuators. Or maybe than concentrating on giving a point by point portrayal of a delimited issue space inside the IoT, it moves toward the displaying issue from the point of view of a usage gadget. It plans to convey a little, yet complete, model in request to rearrange preparing of semantic data. This is additionally its particular attributes. SAREF is a model with a solid spotlight on its own region—of brilliant apparatuses. Despite the fact that mappings to different norms exist, SAREF was created without any preparation to speak to a particular zone of use of the IoT. In this region, it conveys a solid and point by point base, that is additionally clear and simple to comprehend. Simultaneously, it is sufficiently general to be utilized when stretched out to different areas, or arrangements. Curiously, every one of these ontologies totally ignore equipment determinations. It appears that the “place” of a gadget in an IoT framework is significantly more imperative to philosophy engineers than its equipment detail and coming about abilities. 9 Semantic Web The Semantic Web is an expansion of the World Wide Web [20] through norms set by the World Wide Web Consortium (W3C). The objective of the Semantic Web is to make Internet information machine-decipherable. To empower the encoding of semantics with the information, innovations, for example, Resource Description Framework (RDF) and Web Ontology Language (OWL)are utilized. These advancements are utilized to officially speak to metadata. For instance, metaphysics can portray ideas, connections among elements, and classifications of things. These implanted semantics offer critical points of interest, for example, thinking over information and working with heterogeneous information sources. 150 A. Sharma and R. B. Battula These principles advance regular information configurations and trade conventions on the Web, on a very basic level the RDF. As per the W3C, “The Semantic Web gives a typical structure that permits information to be shared and reused across application, undertaking, and network boundaries.” The Semantic Web is in this way viewed as an integrator across various substance and data applications and frameworks. The term was authored by Tim Berners-Lee for a snare of information (or information web) that can be prepared by machines that is, one in which a significant part of the importance is machine-intelligible. While its faultfinders have scrutinized its possibility, advocates contend that applications in library and data science, industry, science and human sciences research have just demonstrated the legitimacy of the first concept. Berners-Lee initially communicated his vision of the Semantic Web in 1999 as follows: I have a fantasy for the Web [in which computers] become fit for investigating all the information on the Web—the substance, connections, and exchanges among individuals and PCs. A “Semantic Web”, which makes this potential, still can’t seem to develop, yet when it does, the everyday instruments of exchange, administration and our day by day lives will be dealt with by machines conversing with machines. The “astute specialists” individuals have promoted for a long time will at last materialize. 9.1 Difficulties A portion of the difficulties for the Semantic Web incorporate incomprehensibility, dubiousness, vulnerability, irregularity, and double dealing. Robotized thinking frameworks should manage these issues so as to convey on the guarantee of the Semantic Web. 1. Incomprehensibility: The World Wide Web contains a large number of pages. The SNOMED CT clinical wording philosophy alone contains 370,000 class names, and existing innovation has not yet had the option to kill all semantically copied terms. Any computerized thinking framework should manage really immense sources of info. 2. Unclearness: These are uncertain ideas like “youthful” or “tall”. This emerges from the unclearness of client inquiries, of ideas spoke to by content suppliers, of coordinating inquiry terms to supplier terms and of attempting to join diverse information bases with covering yet quietly various ideas. Fluffy rationale is the most well-known method for managing dubiousness. 3. Vulnerability: These are exact ideas with unsure qualities. For instance, a patient may introduce a lot of indications that relate to various diverse particular judgments each with an alternate likelihood. Probabilistic thinking strategies are commonly utilized to address vulnerability. Need and Relevance of Common Vocabularies and Ontologies … 151 4. Irregularity: These are consistent logical inconsistencies that will unavoidably emerge during the improvement of huge ontologies, and when ontologies from isolated sources are joined. Deductive thinking bombs disastrously when confronted with irregularity, since “anything follows from a logical inconsistency”. Defeasible thinking and paraconsistent thinking are two procedures that can be utilized to manage irregularity. 5. Trickery: This is the point at which the maker of the data is deliberately deceptive the customer of the data. Cryptography methods are right now used to lighten this danger. By giving a way to decide the data’s honesty, including what identifies with the personality of the substance that delivered or distributed the data, anyway validity gives despite everything must be tended to in instances of possible misleading. This rundown of difficulties is illustrative as opposed to comprehensive, and it centers around the difficulties to the “binding together rationale” and “evidence” layers of the Semantic Web. The World Wide Web Consortium (W3C) Incubator Group for Uncertainty Reasoning for the World Wide Web (URW3-XG) last report protuberances these issues together under the single heading of “uncertainty” [21]. Many of the procedures referenced here will expect expansions to the Web Ontology Language (OWL) for instance to comment on restrictive probabilities. This is a region of dynamic research. 9.2 Principles Normalization for Semantic Web with regards to Web 3.0 is under the consideration of W3C. 9.3 Parts The expression “Semantic Web” [22] is frequently utilized all the more explicitly to allude to the organizations and advancements that empower it. The assortment, organizing and recuperation of connected information are empowered by advances that give a conventional depiction of ideas, terms, and connections inside a given information area. These innovations are determined as W3C guidelines and include: • • • • • • • Resource Description Framework (RDF),an overall technique for depicting data RDF Schema (RDFS) Simple Knowledge Organization System (SKOS) SPARQL, a RDF question language Notation3 (N3), planned in view of human-coherence N-Triples, an arrangement for putting away and transmitting information Turtle (Terse RDF Triple Language) 152 A. Sharma and R. B. Battula • Web Ontology Language (OWL), a group of information portrayal dialects • Rule Interchange Format (RIF), a structure of web rule language lingos supporting principle trade on the Web. 9.4 The Semantic Web Stack The Semantic Web Stack [23] outlines the engineering of the Semantic Web. The capacities and connections of the parts can be summed up as follows: XML gives an essential punctuation to content structure inside records, yet connects no semantics with the significance of the substance contained inside. XML isn’t at present an important part of Semantic Web advancements as a rule, as option sentence structures exists, for example, Turtle. Turtle is an accepted norm, yet has not experienced a conventional normalization process. XML Schema is a language for giving and limiting the structure and substance of components contained inside XML records. RDF is a basic language for communicating information models, which allude to objects (“web assets”) and their connections. A RDF-based model can be spoken to in an assortment of language structures, e.g., RDF/XML, N3, Turtle, and RDFa. RDF is a key norm of the Semantic Web. RDF Schema expands RDF and is a jargon for portraying properties and classes of RDF-based assets, with semantics for summed up progressions of such properties and classes. OWL includes more jargon for portraying properties and classes: among others, relations between classes (for example disjointness), cardinality (for example “precisely one”), fairness, more extravagant composing of properties, qualities of properties (for example balance), and listed classes. SPARQL is a convention and inquiry language for semantic web information sources. RIF is the W3C Rule Interchange Format. It’s a XML language for communicating Web decides that PCs can execute. RIF gives different renditions, called tongues. It incorporates a RIF Basic Logic Dialect (RIF-BLD) and RIF Production Rules Dialect (RIF PRD). 9.5 Applications The goal is to improve the ease of use and convenience of the Web and its interconnected assets by making Semantic Web Services, for example, Servers that uncover existing information frameworks utilizing the RDF and SPARQL norms. Numerous converters to RDF exist from various applications. Relational databases are a significant source. The semantic web server connects to the current framework without influencing its activity. Need and Relevance of Common Vocabularies and Ontologies … 153 Records “increased” with semantic data. This could be machine-reasonable data about the human-justifiable substance of the archive, (for example, the designer, title, depiction, and so forth.) or it could be absolutely metadata speaking to a lot of realities, (for example, assets and administrations somewhere else on the site). Note that anything that can be related to a Uniform Resource Identifier (URI) can be portrayed, so the semantic web can reason about creatures, individuals, places, thoughts, and so forth. There are four semantic explanation arranges that can be utilized in HTML records; Microformat, RDFa, Microdata and JSON-LD. Semantic markup is frequently produced consequently, as opposed to physically. Basic metadata vocabularies (ontologies) and guides between vocabularies that permit record makers to realize how to increase their archives so operators can utilize the data in the provided metadata. Computerized specialists to perform undertakings for clients of the semantic web utilizing this information. Electronic administrations (frequently with operators of their own) to gracefully data explicitly to specialists, for instance, a Trust administration that an operator could inquire as to whether some online store has a background marked by helpless assistance or spamming. Such administrations could be helpful to open web indexes, or could be utilized for information the executives inside an association. Business applications include: • Encouraging the joining of data from blended sources • Dissolving ambiguities in corporate phrasing • Improving data recovery subsequently decreasing data over-burden and expanding the refinement and accuracy of the information retrieved • Distinguishing applicable data as for a given domain • Giving dynamic help. In a partnership, there is a shut gathering of clients and the administration can uphold organization rules like the selection of explicit ontologies and utilization of semantic explanation. Contrasted with the open Semantic Web there are lesser prerequisites on adaptability and the data circling inside an organization can be progressively confided when all is said in done; protection is less of an issue outside of treatment of client information. 9.6 The Semantic Web Stack for the IoT The Semantic Web Technologies Stack presents the center Semantic Web Technologies utilized at various levels of an IoT framework [24]. The mix of Semantic Web innovations into IoT frameworks can be distinguished at three various levels. The “demonstrating level” gives a typical comprehension of Things’ qualities and abilities. It utilizes shared and basic acknowledged vocabularies and ontologies to encourage the reconciliation of information created by various frameworks (for example sensor ontologies). The “information preparing level” utilizes depiction rationales and OWL semantics so as to empower thinking and deduction over the 154 A. Sharma and R. B. Battula information. At last, the “IoT Administrations and Application” level uses particular portrayal furthermore, ontologies that empowers administration distribution, disclosure, piece and adjustment. 9.6.1 Models and Meta-models: Information Bases The general nature of the last help or application depends of the nature of each included layer. In this specific circumstance, the primary layer is worried about information readiness. Deciphering furthermore, understanding the information is the primary essential in this process. This layer deals with the semantic coordination and accumulation of information from an assortment of sources. Semantically explained information can be changed and displayed by explicit necessities. The Model speaks to the Thing and structures the Assertions Box (ABox), while the Meta-model depicts the jargon used to portray the Thing and structures the Terminological Box (TBox). A Knowledge Base is made by these two parts. At last, the meta-metamodel gives the build jargon to the TBox. 9.6.2 Information Preparing An IoT framework [18, 21] is, by its temperament, a conveyed framework and handling its information should be possible at various levels. While the restricted neighborhood data can give some essential translation furthermore, preparing in its area of intrigue, further understanding on the information is acquired at more elevated levels, when information from numerous sources are assembled, prepared and associated. We stress two distinct methodologies for preparing this information: (1) utilizing semantic reasoners and (2) utilizing Big Data explicit calculations (for example AI). (1) Reasoning and Inferences. Rules and semantic arrangements (for example owl:equivalentClass, owl:subClassOf, owl:sameAs) can be utilized to change and adjust the information to the pronounced ontologies. Contingent upon the expressiveness of the ontologies, thinking motors can additionally gather affiliations and connections into the information. For information transmission and putting away in a Semantic Web setting, JSONLD, a W3C suggestion from 2014, gives a advantageous approach to serialize RDF information. XML design is too accessible. Triplestores (for example Fuseki, StarDog) are utilized to store RDF significantly increases. The inquiry language for the Semantic Web is (SPARQL Protocol And RDF Query Language). It gives a helpful method to cross examine different triplestores over HTTP. (2) Big Data and AI calculations. Need and Relevance of Common Vocabularies and Ontologies … 155 Semantic Advances are a superb decision for IoT frameworks [19] for two reasons: (1) it permits sharing the information depiction through diagrams and ontologies and (2) it permits information encoding in ontologies by means of portrayal rationale develops. Anyway enormous amount of information joined with high expressive ontologies, can limit reasoners’ exhibition in their inductions. IoT frameworks are commonly used to screen, analyze, foresee and suggest activities. In metaphysics based frameworks, the information is depicted from the earlier, this makes them less adjusted to frameworks where the goal is to foresee and dissect practices of various conditions and clients. Starting here of view, the coordination of AI calculations with very much portrayed information could give better esteem administrations and applications. An model utilizing both measurable learning and ontologies to separate private client action is introduced. 9.6.3 IoT Services and Applications Ontologies and semantic comments can likewise upgrade the portrayal of the offered types of assistance. OWL-S gives semantic markup for web administrations. The Semantic Sensor Perception Service (SemSOS) and the utilization of ontologies for computerized organization are a few instances of semantic advances based applications and administrations in IoT conditions. 10 Ontologies for Smart Cities KM4City, an Italian national task, demonstrated a cosmology intended for amassing static or dynamic brilliant city information. The creators reuse ontologies, for example, OWL-time, DC terms, FOAF, WGS84, GoodRelations, and cosmology transportation systems (OTNs). The undertaking is adaptable since they handle 81 million triples with a development of 4 million triples for each month. It gives a connected information chart, perception and investigation device what’s more, administration map applications abusing the collected information. Semantic traffic examination and thinking for CITY (STARCITY), an IBM venture, is conveyed in four keen urban communities, such as Dublin, Bologna, Miami, and Rio de Janeiro. The venture is centered around planning ontologies to analyze and anticipate street gridlocks. Information preparing misuses six heterogeneous sources. (1) (2) (3) (4) (5) (6) Road climate conditions. Weather data. Dublin transport stream. Social media takes care of. Road works and support. City occasions. 156 A. Sharma and R. B. Battula Semantic Web Rule Language (SWRL) rules have been intended to characterize rules, for example, substantial traffic stream. Celebration IoT is a H2020 European undertaking. The Celebration IoT cosmology is intended to bring together existing IoTrelated ontologies to structure information created by testbeds. The SmartSantander city or even keen structures are testbeds creating genuine information, which is semantically commented on as per the philosophy. Imperative, a FP7 European undertaking, planned a cosmology to manage heterogeneous information streams produced by gadgets inside keen urban areas. The metaphysics models sensors and their, for IoT frameworks and administrations, and for keen city applications. Indispensable is creative since it gives a working framework to IoT to manage administration creation, coordination, and conventions. Fundamental gives the accompanying attributes: virtualization, measured quality, norms based (RDF and JSON-LD) and inexactly coupled, also, open-source. CityPulse, a FP7 European undertaking, gives the SAO to bring together savvy city datasets. SAO has been structured to address ongoing viewpoints. Brilliant city philosophy (SCO) is a metaphysics distributed in 2015. It reuses a few ontologies, for example, SKOS, yet it doesn’t reuse the SSN philosophy and absences of best practices. For example, the cosmology isn’t partaken in a appropriate way. Shrewd city SOFIA2 philosophy doesn’t expand SSN cosmology be that as it may, reuses IoT.est philosophy. PRISMA venture planned a philosophy which reuses WGS81, NeoGeo, and assortments ontologies. Notwithstanding, it makes reference to neither the utilization of information created by gadgets nor the utilization of SSN cosmology. The cosmology is predominantly intended to bind together heterogeneous information. (1) GeoData from the geographic data framework, information on lines, and stops of the open vehicle transport framework (REST Web administration in JSON group). (2) Public lighting framework for the upkeep of the city (XML document). (3) State of the streets, walkways, signs, and markings (Microsoft SQL Server database). (4) Historical information on city squander assortment (Microsoft Exceed expectations document). (5) Historical information on the urban flaw detailing administration (MySQL Server database). The venture gives the LODView instrument to a HTML portrayal of RDF assets and the LODLive apparatus to peruse the RDF diagram. This paper doesn’t concentrate on the depiction of the philosophy, however presents the need of this cosmology to give connected open information and executes Web administrations, SPARQL endpoints, browsable highlights, and representation on head of it. Brilliant city metaphysics (SCOnt) has been planned and utilized in a semantic-based system to control brilliant city information. In any case, the cosmology has not been shared online which ruins interoperability of shrewd city frameworks and the reuse of the philosophy. The cosmology reuses a populace philosophy, a geo-area metaphysics and the DBpedia metaphysics. Depictions with respect to the structure of the metaphysics Need and Relevance of Common Vocabularies and Ontologies … 157 and semantic planning are absent. The curiosity contrasted with existing brilliant city ventures isn’t clearly clarified. SCOnt is utilized to control keen city information in an engineering including four layers. (1) Data scratching layer assembles and refines information since duplication and inadequacy of meta-data and missing qualities issues are confronted. (2) Data adjustment layer gives cosmology displaying and semantic planning. (3) Data the board layer stores and records information inside a NoSQL database. Semantic Web administrations are referenced yet neither connection nor portrayals are given or on the other hand referenced. (4) Applications layer gives dashboards and APIs. Smart city ontologies are routinely upgraded which thwarts semantic interoperability. More ontologies identified with keen urban areas can be found on the LOV4IoT and OSC philosophy indexes. 10.1 Measures to Compare Smart Cities and IoT Ontologies For examination of keen city ontologies in a lot of measures to think about smart city ontologies which can likewise be applied to IoT ontologies. Those standards are primarily centered around the reusability of the ontologies. (1) Ontology objective ought to be obviously clarified. As a rule, the cosmology is intended for a task or an application. (2) Ontology size shows the profundity of the cosmology. Little or lightweight ontologies would be simpler to reuse. (3) Ontology documentation decreases the expectation to absorb information to comprehend and incorporate the metaphysics, and support its reusability. A well known practice is to give an on the web HTML documentation. A distribution, deliverable or any documentation is important to clarify in detail the metaphysics and its effect. (4) Ontology accessibility is emphatically empowered. Metaphysics ought to be shared on the Web to energize semantic interoperability. Philosophy planners should make an exertion in coordinating past ontologies and staying alert of the metaphysics constraints. (5) Ontology prominence shows the effect of the metaphysics and its genericity when the cosmology is utilized in different tasks. (6) Ontology support should be accomplished. Ordinarily, at the point when the undertakings are done, the metaphysics isn’t kept up. Be that as it may, cosmology creators may be responsive on the off chance that they keep on taking a shot at a similar exploration point. (7) Ontology metadata is basically required for building programmed instruments. 158 A. Sharma and R. B. Battula 11 Future Trends and Conclusion Ontology and Vocabulary both are essential components for IoT. The cross linking and interoperability of data make it difficult to create a standard set difficult. It requires timely maintenance with up gradation in rules. Security on data is one of the most needed aspects but similar use of individuals can make it risky. The interoperability of data is still not possible due to different vocabulary present for IoT which have no interconnection among each other. Each platform uses its own set of Axioms and programming languages which make integration difficult. Scrutinize the evaluation pattern of ontology is still undefined and not considered. References 1. Gyrard, A., Zimmermann, A., Sheth, A.: Building IoT-based applications for smart cities: how can ontology catalogs help? IEEE Internet Things J. 5(5), 3978–3990 (2018) 2. Gyrard, A., Serrano, M.: A unified semantic engine for internet of things and smart cities: from sensor data to end-users applications. In: 2015 IEEE International Conference on Data Science and Data Intensive Systems, pp. 718–725. IEEE (2015) 3. Kamilaris, A., Gao, F., Prenafeta-Boldu, F. X., Ali, M.I.: Agri-IoT: A semantic framework for internet of things-enabled smart farming applications. In: 2016 IEEE 3rd World Forum on Internet of Things (WF-IoT), pp. 442–447. IEEE (2016) 4. Noura, M., Gyrard, A., Heil, S., Gaedke, M.: Automatic knowledge extraction to build semantic web of things applications. IEEE Internet Things J. 6(5), 8447–8454 (2019) 5. Gyrard, A., Atemezing, G., Bonnet, C., Boudaoud, K., Serrano, M.: Reusing and unifying background knowledge for internet of things with LOV4IoT. In: 2016 IEEE 4th International Conference on Future Internet of Things and Cloud (FiCloud), pp. 262–269. IEEE (2016) 6. Ganzha, M., Paprzycki, M., Pawłowski, W., Szmeja, P., Wasielewska, K.: Semantic interoperability in the internet of things: an overview from the INTER-IoT perspective. J. Netw. Comput. Appl. 81, 111–124 (2017) 7. Gyrard, A., Datta, S.K., Bonnet, C., Boudaoud, K.: Cross-domain internet of things application development: M3 framework and evaluation. In: 2015 3rd International Conference on Future Internet of Things and Cloud, pp. 9–16. IEEE (2015) 8. Szilagyi, I., Wira, P.: Ontologies and semantic web for the internet of things-a survey. In: IECON 2016-42nd Annual Conference of the IEEE Industrial Electronics Society, pp. 6949–6954. IEEE (2016) 9. Gyrard, A., Bonnet, C., Boudaoud, K.: Enrich machine-to-machine data with semantic web technologies for cross-domain applications. In: 2014 IEEE World Forum on Internet of Things (WF-IoT), pp. 559–564. IEEE (2014) 10. Bermudez-Edo, M., Elsaleh, T., Barnaghi, P., Taylor, K.: IoT-Lite: a lightweight semantic model for the internet of things and its use with dynamic semantics. Pers. Ubiquit. Comput. 21(3), 475–487 (2017) 11. Gyrard, A., Bonnet, C., Boudaoud, K., Serrano, M.: LOV4IoT: a second life for ontologybased domain knowledge to build semantic web of things applications. In: 2016 IEEE 4th International Conference on Future Internet of Things and Cloud (FiCloud), pp. 254–261. IEEE (2016) 12. Gyrard, A., Serrano, M., Atemezing, G. A.: Semantic web methodologies, best practices and ontology engineering applied to Internet of Things. In: 2015 IEEE 2nd World Forum on Internet of Things (WF-IoT), pp. 412–417. IEEE (2015) Need and Relevance of Common Vocabularies and Ontologies … 159 13. Gyrard, A., Datta, S. K., Bonnet, C., Boudaoud, K. Standardizing generic cross-domain applications in Internet of Things. In: 2014 IEEE Globecom Workshops (GC Wkshps), pp. 589–594. IEEE (2014) 14. Alaya, M.B., Medjiah, S., Monteil, T., Drira, K.: Toward semantic interoperability in oneM2M architecture. IEEE Commun. Mag. 53(12), 35–41 (2015) 15. Compton, M., Barnaghi, P., Bermudez, L., GarcíA-Castro, R., Corcho, O., Cox, S., Graybeal, J., Hauswirth, M., Henson, C., Herzog, A., Huang, V.: The SSN ontology of the W3C semantic sensor network incubator group. J Web Semant. 17, 25–32 (2012) 16. Daniele, L., den Hartog, F., Roes, J.: Created in close interaction with the industry: the smart appliances reference (SAREF) ontology. In: International Workshop Formal Ontologies Meet Industries, pp. 100–112. Springer, Cham (2015) 17. Soldatos, J., Kefalakis, N., Hauswirth, M., Serrano, M., Calbimonte, J. P., Riahi, M., Aberer, K., Jayaraman, P.P., Zaslavsky, A., Žarko, I.P., Skorin-Kapov, L.: Openiot: Open source internetof-things in the cloud. In: Interoperability and open-source solutions for the internet of things, pp. 13–25. Springer, Cham (2015) 18. Bajaj, G., Agarwal, R., Singh, P., Georgantas, N., Issarny, V.: A study of existing Ontologies in the IoT-domain. arXiv preprint arXiv:1707.00112 (2017) 19. Lam, A.N., Haugen, Ø.: Applying semantics into service-oriented iot framework. In: 2019 IEEE 17th International Conference on Industrial Informatics (INDIN), vol. 1, pp. 206–213. IEEE (2019) 20. Mishra, S., Jain, S.: Ontologies as a semantic model in IoT. Int. J. Comput. Appl. 42(3), 233–243 (2020) 21. Ganzha, M., Paprzycki, M., Pawłowski, W., Szmeja, P., Wasielewska, K.: Towards common vocabulary for IoT ecosystems—preliminary considerations. In: Asian Conference on Intelligent Information and Database Systems, pp. 35–45. Springer, Cham (2017) 22. Venceslau, A., Andrade, R., Vidal, V., Nogueira, T., Pequeno, V.: IoT Semantic interoperability: a systematic mapping study. In: International Conference on Enterprise Information Systems, vol. 1, pp. 535–544. SciTePress (2019) 23. Rhayem, A., Mhiri, M.B.A., Gargouri, F.: Semantic web technologies for the internet of things: systematic literature review. Internet Things 100206 (2020) 24. Zgheib, R., Conchon, E., Bastide, R.: Semantic middleware architectures for IoT healthcare applications. In: Enhanced Living Environments, pp. 263–294. Springer, Cham (2019)