Natural gas losses are observed in all states of the world and in this regard “Washington, US department of energy” records state-wise natural gas annual supply & disposition. UFG losses among the states are from 0.6% to 5.8% [23]. The natural gas balancing item indicates the difference between the number of natural gas supply components and the sum of natural gas disposition components. Each state calculates it as a result of a correlation between total recorded supply and total recorded disposition [23].

Xu [27] offers an idea of the smart—city concept in which every house is equipped with smart meters that transmit its readings to the home gateway, which in turn information to the community center in a secure and privacy-preserving manner via the community network. It was a floating concept, and hence smart metering started. In a report published in June 2014 entitled “benchmarking smart metering implementation in the EU-27,” the European Commission (EC) found that only 16 nations within the EU block expected to deploy smart meters of electricity by 2020, while the remainder had not proved it. In the meantime, twelve nations have agreed that these smart meters cannot be justified and that only five of the twenty-seven nations (Ireland, Italy, Luxembourg, the Netherlands, and the UK) are moving them forward by 2020. Both meters of electricity and gas have vital criteria for measuring success, and the EC study also showed a broad range of values among member states [28].

Gas leakages, measurement errors, and suspected meters or theft are factors that contribute to UFG losses [21]. UFG usually varies from 0.2% to 0.5% in Italian networks. UFG’s average annual price in the US over the past ten years is 0.6 percent. The ruling body for defining the UFG benchmark is the oil and gas regulatory authority (OGRA) in Pakistan. Unlike the 2% international benchmark, the UFG benchmark for Fiscal year (FY) 2015–16 identified by OGRA was 4.5%. However, Sui northern gas pipeline limited (SNGPL) actual UFG for FY 2015–16 was 10.97%, resulting in volumetric 4.28 BCF and a financial loss of 2.49 trillion Pakistan Rupees. The values above show the importance of addressing the issue of managing UFG losses in SNGPL due to a substantial divergence from local and international standards. This analysis, therefore, focuses on assessing UFG in gas networks [29]. Litvinov et al. [30] Introduces new data management and analytical technologies in the oil and gas enterprise business processes, describes the technological characteristics of current Radio-frequency identification (RFID) systems, RFID implementation guidelines, and some applications in oil and gas supply chains. Tab. 1 shows a summary of year-wise related work progress in gas-related management in the field of IoT and to date, there is no research work on interoperability in natural gas management.

In different fields of life, several proposed ways assist in interoperability. One of the suggested solutions is semantic web interoperability technology in various information systems running via sensors and sensing systems. Such technologies operate alone or with semantic sensor web [31] to combine, manage, and request information from sensors. World wide web consortium (W3C) has developed these technologies. Its methods for linking different resources on the internet are RDF, RDF/XML, Rdf schema (RDFs), and Ontology web language (OWL). The RDF [32] semantic web technology is a language that helps to provide explanations of the web’s tools. It is the most commonly used data model to describe semantic sensor data using triples. Triples are the format consisting of subject, property, object, and various syntaxes that help to write and serialize data in RDF [33]. Smart home plus smart environments are its examples. Zhang et al. [34] focused primarily on converting sensor data to RDF and Satterfield et al. [35] worked on smart home and used RDF triples for sensor data throughout systems, as shown in Tab. 2.

SenML lets IoT devices link to the internet at the level of data exchange. Resource-limited device develops SenML for its explanation uses a single base object composed mainly of an array of entries as well as attributes. Furthermore, all submissions include the sensor parameter name and parameters such as the measurement time and current value [36].

Tab. 3 shows the use of SenML with RDF for SI has been achieved successfully in the submarine system [36] and the smart campus system [37]. Tab. 4 shows the features of RDF, SenML, and advantages of transformation from SenML to RDF. The resource-limited device uses the SenML prototype by extending the custom attributes, such as using a Resource type (RT) attribute. These features include semantic information, whereas preserving descriptions of SenML easy. The key approach is to allow a mapping of the RDF model, i.e., a marked, directed graph, between SenML components. First, SenML does not use URIs to about the same degree as RDF; second, URIs are a simple component of the RDF key; the non-literal object of information has its own URI. The conversion allows IoT sensors enabled by SenML to be attached to information-based systems with reduced code, as shown in Tab. 5.

The change allows the connection of IoT sensors enabled by SenML to information-based systems with comparatively low code conversions. Sensors might use SenML deprived of certain extra computing to ready the data, and a quick analyzing element for conversion SenML into RDF is part of an information-based system. The conversion enables IoT sensors allowed by SenML to access knowledge-based systems with reduced modifications. Sensors can use SenML to prepare the data without any additional computation and uses a simple parsing feature for translating SenML into RDF on a knowledge-based framework. This part may be physically installed on an IoT systems gateway or server machine. To implement this, an algorithm must have the following steps for transforming SenML to RDF [36–38].

Step 1. Transform SenML elements, usually URIs and literals, into their corresponding unique defined elements. The names of elements should be concatenated when it specifies any prefix or base name in the SenML text. It affirms that with comprehensive demonstrations, resources, properties, types, and preset values.

It is common to transform SenML description into multiple RDF statements for assigning the URIs to unambiguous identifiers of SenML. It ensures that full representations of resources as well as properties their types, and given values. Resource type is an obligatory element of a SenML description. It is for the reason that resource type has a significant role when SenML descriptions alter into RDF statements. It specifies the type of device that produces the data, that is, the type of subject in the RDF statement. It can be mapped to RDF: type, and it will be connected to a class name when applying ontology logic to sensor data in an ontology. The namespace for IoT applications and Sensors explanation handles sensor data. RDF has a much greater expressive potential than SenML, but interpreting action details when using the full power of RDF is also difficult for several resource-constrained IoT tools [36–38].

Ontology is a set of objects and relationships which can define and represent a field of concern in IoT. They reflect an abstraction infrastructure aimed at hiding the heterogeneity of IoT entities, serving as an intermediary between IoT service providers and consumers, and facilitating their semantic matchmaking [39]. Though, it’s challenging to define the abstraction level needed for an IoT ontology. There are several initiatives nowadays that define the models that represent IoT devices and the environment around them. Nonetheless, finding one that satisfies all the criteria is always difficult. Ontology attains Syntactic and SI among platforms [39]. In the context of IoT, several ontologies such as the W3C Semantic sensor network (SSN), IoT-Ontology, SAREF and open IoT have been proposed [39].

Cloud services are still in their early days in SI. Here are some of the prominent achievements discussed. Doukas et al. [25] demonstrated in 2010 that together mobile application and cloud computing would allow better sharing, storage, upgrading, and retrieval of electronic healthcare data. This concept was introduced as a mobile application in android format. The cloud service Amazon S3 has been used to test the framework created. All work in [36,37] transforms SenML to RDF using Tab. 5. On the bases of these projects, an ontology for a specific solution is a technical task. In the gas domain, a specific ontology tracks the consumer using SenML and RDF.

To achieve intelligent interoperability [40] computational intelligence approaches like the Fuzzy system [41], Neural Network [42], DELM [42,43] and SVM [44] are robust candidate solutions in the field of semantic web technologies and smart city [43].

In the gas domain user interface is consisted of gas companies and users of the company. The users of the company are 1. Industry consumers, 2. Commercial consumers, 3. Special domestic consumers 4. Transmission consumers, and 5. Internal users of the gas company. The gas company is itself a consumer of natural gas reservoirs; therefore, the user interface shows it. In this study, only industry consumers are significant because significant UFG occurs in the industry in the shape of gas theft or leakage of gas. In UI, both gas companies and consumers communicate with IoT devices. In a gas company, gas main server (GMS) keeps a record of all incoming gas data from reservoirs of gas and outgoing data to the consumers. It cannot calculate UFG on its own due to different protocols and heterogeneous smart devices. GMS such as sensors and UI sends data directly to SI.

In the SI phase, there are many data producers like sensors, observing systems, data aggregators, and data archiver. Here sensors are the smart meters of consumers and GMS of the company and intermediate sensor to control the gas flows like Town border station (TBS). In SI data producers data is collected through a secure communications system via SMS within the form of SenML, i.e., every smart device has some id and properties. SI phase first collects data and then convert it into RDF based on the following ontology using Tab. 5.

Ontology for UFG depends on the scenario. The current scenario is that the company supplies gas to industrial, commercial, domestic, special domestic, transmission consumers, and internal departments of the gas company shown in Fig. 1 and the main issues of UFG occur in the industry department. Gas wells provide gas to GMS, and it further provides gas to the industrial area as shown in Fig. 2. The GMS has all input volumes that are sent to the industry but still, losses occur because there is no correlation of billing system and if the input of gas from GMS is 100 units then billing should be of 100 units instead of 80 or 50 units.

New ontology to reduce gas theft is as follows:

There are smart meters on Town Border Station (TBS).

If at a certain level of TBS, the value of input and output does not match then the billing and history of consumers of that area are reconciled to check the discrepancy, in this whole procedure only devices work with RDF, which is a machine-understandable format. It is recommended that there should be one TBS for a small group of industries. For suspected industries, another smart meter may be installed, which should not be in reach of the consumer.

Data readings of the sensor’s

{“e”:[{ “n”:“temperature”,“u”:“cel”,“v”:22}], {“e”:[{ “n”:“unit consumption”,“u”:“UC”,“v”:units}],

Consumer 1 unit consumption reading by smart meter consumer 1 unit consumption reading by smart meter

“bn”:“http://iot.ga/o#temsensor012”/ “bn”:“http://iot.ga/o#industryabc”/

“rt”:“gassensingnode”“rt”:

“pr”:“http://iot.ga/o#”

This smart sensor data is converted into RDF/XML using Tab. 5.

Fig. 4 shows the full industrial area graph showing how billing data is moving from industry to GMS with the help of the protege graph. SPARQL, W3C’s proposed structured query language, is designed to query RDF data. Because SPARQL queries appear as query graphs, a SPARQL question can be answered by matching the graph format across RDF graphs.

All specific consumer information is semantically annotated with the RDF graph to supply matching information that excludes the hardware specifics of the vendors. Records of specific users from the RDF graph can extract using SPARQL request in a text format. The following query helps to extract a record from RDF data

select? variable a? variable b? solution variable

where

{

?variable 1? solution Variable a ? variable b.

}

In semantization, after data collection, ontology converts SenML to RDF Semantic Engine. In SI data is visualized using the GMS portal.

All user’s data, ontology, and required operations data is a store with the help of cloud services. Cloud technology enables information sharing with authorized company’s employees. Cloud storage offers easy and efficient access to stored data on-demand. Thus gradation of the digital infrastructure occurs with minimal interruption of operation. The ontology services can exist anywhere and anywhere using mobile and cloud computing together. Cloud computing has almost infinite storage space.

In the NGMSIM system, the following algorithm measures the user unit values, as shown in Fig. 5.

Let “£ 1 ” is TBS1, “£ 2 ” TBS2, and “£ N ” is TBS N having users “ α ” is as follows:

N=∑α=1N⁡i

So, at

GMS = £1 + £2 … + £N

if £ unit consumption does not match the GMS units, then the profile of users from the cloud will be checked by GMS automatically. In case GMS billing <£1 + £2 + … + £N ontology is shown in Fig. 5.

That is the calculation of units is done at individual TBS. Let “uc” is user consumption then “£ 1 ” total consumption is

(2) 1=∑α=1N⁡{ucα,1+ucα,2…+ucα,n}+γ

Here γis a constant value of UFG in the form of line losses of gas.

for £ 2 and £ N, it is as follows:

(3) 2=∑α=1N⁡{ucα,1+ucα,2…+ucα,n}+γ

(4) N=∑α=1N⁡{ucα,1+ucα,2…+ucα,n}+γ

After simplification the above equations, total consumption is calculated and finds the discrepancy at the root level.

A TBS under which four industries are working is shown in Fig. 6. This TBS is different from other TBS as it has Wi-Fi, smart meter and solar panel for smart working.

The TBS gets smart meter data from industries and saves it on the cloud after semantization to calculate UFG with the help of the billing department procedure. According to ontology, first units are calculated which are received by the TBS from GMS. Tab. 7 shows the gas consumption week wise in four industries. After the creation of the synthetic data semantization process starts.

Data coming from Sensor’s

{“e”:[{ “n”:“temperature”,“u”:“cel”,“v”:22}], {“e”:[{ “n”:“unit consumption”,“u”:“UC”,“v”:10895}],

Consumer 1 unit consumption reading by smart meter Consumer 1 unit consumption reading by smart meter

“bn”:“http://iot.ga/o#temSensor012”/ “bn”:“http://iot.ga/o#Industryabc”/

“rt”:“GasSensingNode”“rt”:

“pr”:“http://iot.ga/o#”

Example of a sensor’s SenML data

In the above example, a smart meter sensor gives SenML data, this displays device ID is industry abc (with “bn”) and gas sensing node (with “rt”) is the resource type. A prefix component (“pr”: “http:/iot.ga/o#”) establishes the namespace of this sensor to solve any possible conflict in global IoT systems.

Representation of SenML data by RDF/XML

<rdf:RDFxml:base=“http://iot.ga/o”xmlns=“http://iot.ga/o#”

xmlns:owl=http://www.w3.org/2002/07/owl#

xmlns:rdf=“http://www.w3.org/1999/02/22-rdf-syntax-ns#”

xmlns:xml=“http://www.w3.org/XML/1998/namespace”

xmlns:xsd=“http://www.w3.org/2001/XMLSchema#”

xmlns:rdfs=“http://www.w3.org/2000/01/rdf-schema#”>

<i:GasINdrdf:ID=“Industryabc 2”/>

<i:value>10895.0309</i:value>

<iotterms:unitsrdf:resource=http://iot.ga/units/cu.ft.>

</i:GasSensingNode>

</rdf:RDF>

Example: Representation of SenML data by RDF/XML

The above shows the RDF/XML representation of SenML into RDF.

The user information is processed via shared cloud storage of Amazons Optimized capacity. The cluster xlarge helps in the cloud implementation of the proposed device. Administration, audit departments and government agencies may access this information. One of the essential tasks of the program is to recover the pattern of users’ use.

The usage time of the gas in the industry can be helpful to find UFG utilizing the information received from the smart meters RDF code. It helps in gaining consumption patterns that recognize which consumer uses gas on weekdays, as shown in Fig. 7.