For example, the use of some sort of standards to announce each printer and its attributes is one way to elevate such an issue and there are so many such attributes.
Today, each vertical industry comes along with its protocol and specifications bodies to develop their data models. For example, in the industrial automation industry, organizations like OPC are working on data models and objects which can be used on the shop floor. In the automotive industry, ETSI’s Intelligent Transport Systems technical committee is working collaboratively to define messages and data models for communication between cars. Many IoT applications also involve several partners in a distributed value chain. For instance, an intelligent application for an industrial plant might automatically order feedstock from one or more partners for its production line. Supplies are typically ordered and delivered by several partners. It is easy to see how this scenario can end up in up in an “island of things” configuration since different partners in the value chain belong to different verticals, each with their specific data models. It is thus desired to make sure the cross-availability of IoT devices, services, and data for the growth of new business and the emergence of opportunities. This can assist managing data from multiple sources, generate new avenues, and innovate suitable solutions for the existing service providers to scale new markets.
1.5.2 Semantic Interoperability (SI)
The last decade witnessed a many-fold increase in a host of heterogeneous devices, actuators, sensors, etc. with varied applications in the IoT platform. To cope up with the smart environment, an efficient distribution, monitor, support, coordination, control, and communication among these sensors remains essential that gives rise to the term interoperability. The interoperability can be achieved with the following major layers as shown in Figure 1.12.
Technical interoperability is concerned with the communicability among the things or objects in IoT domain using the software and hardware. On achieving the suitable connectivity, the syntactic interoperability deals with the data models, data formats, data encoding, communication protocols, and serialization techniques using certain specified standards. Finally, Semantic interoperability establishes the desired meaning to the content and assists to comprehend of the shared unambiguous meaning of data. The interoperability concept can be better visualized using the five major perspectives and is given in Table 1.2.
Figure 1.12 Different layers of interoperability.
Table 1.2 Taxonomy of interoperability: major perspectives.
| Taxonomy of interoperability | Attributes |
| Device interoperability [19] | Involves both the low and high-end devices High-end devices are Raspberry Pi, smartphones, etc. with good computational abilities and resources Low-end devices are low-cost sensors, actuators, RFID tags, Arduino, OpenMote, etc. with resource-crunch, communication, low energy, and processing abilities. It aims for better integration and communication among several heterogeneous devices in advanced IoT platforms. |
| Network interoperability [20] | The network remains is multi-service, multi-vendor, largely distributed and, heterogeneous. It facilitates the better transfer of data among several smart systems using efficient networking systems. It can alleviate issues such as addressing, resource optimization, routing, security, QoS, mobility support, etc. |
| Syntactical interoperability [21] | It allows interoperation of the format and structure of the data during communication among heterogeneous IoT devices, entities, domains, systems, etc. It includes the syntactic set rules in the same or some different grammar It is significant in the case of disparities between the encode and decode rules involving the source and the end-user. |
| Semantic interoperability [22] | It allows the meaningful exchange of knowledge and information among agents, services, and applications. It is significant when the automatic interoperation of IoT information or data models is not materialized due to the difficulties in descriptions and understandings of operational resources or procedures. |
| Platform interoperability [21] | The need arises with the advancement of diverse and versatile operating systems, programming languages, data structures, IoT architectures, access mechanisms, etc. Different mechanisms are developed for efficient data management involving several IoT platforms. Similarly, cross-platform and cross-domain in different heterogeneous domains are addressed. |
1.5.3 Semantic Interoperability (SI) Security
The semantic IoT is considered as a black hole in a nebulous term. It must have a security policy that is comprehensive with expansive visibility. Further, several decades-old IoT technologies require be managing or segmenting effectively along with emerging technologies. For example, the oldest IoT iteration concept is to involve multi-function printers with both copying and scanning abilities. Nevertheless, the security concerned often ignores these which pose a threat to the domain as anybody can easily route or access these from a workplace or other places. The proliferation of security corresponding to smart systems can lead to potential threats to put infrastructure, or an entire city and its traffic, lighting, and power grid systems having millions of IoT users. However, as these smart systems work in the cloud, the organization can develop several usage patterns. The integration and coordination of smart IoT devices such as wired or unwired equipment, cameras, biometric access, gesture or face identification models, keypad, etc. with interoperability, it becomes possible to place the right people at a right place. For example, the huge meshing network structure present in a smart city environment warrants a robust security traffic system, secured wired or wireless sensors, parking meters, to prevent the proliferation of the IoT system. The security system must contain visibility to maintain awareness