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
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