Social Internet of Things (SIoT)

IoT is an interconnected structure of computing devices, humans and digital objects, with the ability to communicate over the Internet. Conventionally, the term 'things' refer to interactions in cyber space, cognitive reasoning,


Scope of CPSS

Applications of CPSS



Field of Application

Scale of Application









  • • Monitor health condition of the patients
  • • Suggest proper treatment through smart medical devices.

Sheth, Anantharamand Henson (2013), Gunes et al. (2014)


Intelligent transportation



• Improve safety and traffic management skills in real time.

Elmaghraby and Losavio (2014), Han, Duan and Li (2017)


Robot utilization



• Semi-automatic or fully automatic type utilized for human welfare.

Gunes et al. (2014), Ning et al. (2016)


Vehicular networking systems



• To monitor the conditions of roads and their issues.

Xiong et al. (2015), Meenalaxmi, Abinaya and Suseela (2018)


Education based on mobile


• Affordable e-learning as an effective way of acquiring knowledge

Ning et al. (2016), Guo et al. (2017)


Defence Systems


• To monitor military aviation systems and their traffic regularities

Wang, Tomgren and Onori (2015), Han, Duan and Li (2017)


Complex infrastructure - smart grids



• To contribute to the welfare of treasured properties and safety of nation

(Wang, Torngren and Onori (2015), Liu etal. (2017)





• Utilized for next generation building automation to control heating, cooling and electricity consumption

Rajkumar et al. (2010), Baig etal. (2017)


Smart homes



• Utilized to enhance safety and comfort of home and society at large

Li et al. (2011), Elmaghraby and Losavio (2014)

TABLE 12.2

Challenges in CPSS




Problem Delineation




Attack happens during transmission of data.

Guo et al. (2017)




Higher power is consumed by the cyber components during operation.

Shakshuki, Malik and Sheltami (2014)




Errors in input or during execution lead to robustness issues.

Wang, Torngren and Onori (2015), Sisinni et al. (2018)



Problem arises when an operating system crashes, where there are networking issues

Meenalaxmi, Abinayaand Suseela (2018), Guo et al. (2017)



Timing inaccuracies for capturing sensor readings

Gunes et al. (2014), Ashibani and Mahmoud (2017)



Certain components paves way for continuous repairing

Gunes et al. (2014), Lee, Bagheri and Kao (2015)



Certain attacks that may occur in cyber components leads to the non-availability of the system when required.

Ashibani and Mahmoud (2017), Lu (2012)



May harm when it is operated due to the lack of precise monitoring of the system, affecting the processing in reality

Rajkumar et al. (2010), Sisinni et al. (2018)



As CPS includes many components to perform both in the physical and cyber aspect, the control flow between the components may vary, leading to a reduction in reliability factor.

Xiong et al. (2015)



Unpredictable environmental occurrences

Gao et al. (2013), Ashibani and Mahmoud (2017)

physical perceptions and social relations. The interactions between the components in this system can be remotely monitored and controlled. They also possess a high degree of independence from human intervention. Furthermore, an interfusion of the physical environment and the virtual world of information can be achieved, utilizing IoT (Yang et al. 2018). The term IoT was conceived by Kevin Ashton in 1999.

IoT facilitates the wireless communication of physical objects, such as sensors, gadgets, automobiles and buildings, with the cyber world, consisting of computers and networks with minimal human intervention. IoT wirelessly connects smart devices, involving sensors, to the cyber world, using Internet (Li et al. 2011; Baig et al. 2017). This interlink, while being beneficial in many aspects, leads to security and privacy issues (Harel et al. 2017). Security solely permits the authorized users to decrypt confidential information and ensures integrity of service, whereas privacy refers to the share-ability of personal information, such as photographs, activities and locations with third-party service providers (Elmaghraby et al. 2014). A deficit in security or privacy leads to vulnerabilities in the system and large-scale breaches in data confidentiality of service users.

In this digital age, social networks are a ubiquitous mode of communication. In addition to publicizing information by virtually connecting people, social networks help to shape and sway user opinions. When conjoined with IoT, the Social Internet of Things (SIoT) offers a multitude of benefits, such as instantaneous decision making and enhanced data access at economical processing costs. SIoT can be envisaged as a unique subset of IoT, wherein numerous interconnected, heterogeneous IoT devices socialize and cooperate with each other in order to accomplish specific tasks in a collaborative manner. The salient feature of SIoT is the formation of social relationships between the heterogeneous IoT devices to provide autonomous services. Such relationships are achieved by means of a service-oriented architecture and lead to a multitude of IoT-powered applications, namely smartgrids, communities and cities (Wang, et al. 2015). In particular, International Business Machines (IBM) relates smart cities consisting of humans and various components, with being intelligent and interconnected with instrumentation (Elmaghraby et al. 2014). In smart communities, IoT sensors are deployed effectively to monitor and respond to changes in their functional environment in realtime (Baig et al. 2017).

Cyber Physical Social Systems (CPSS) consider the virtual locations of the users in addition to their physical locations. Hence, CPSS can be regarded as the third evolution from online social networks. The first generation of broadly utilized social networks, namely Linkedln, Facebook and Orkut, forged connections between users solely based on actual, existing relationships, such as family, friends and existing business relationships. New connections were created through explicit requests from users. A review of the literature indicated that less than 10% of connections were established between users hitherto unfamiliar with each other. With sustained innovation, the second generation of social networks, also referred to as location- based social networks (LBSN), considered the geolocations of the users. In addition to explicit requests, new contacts could be established between users based on shared locations. Examples of such networks include Facebook Places, Skout and Foursquare. Evolution then led to the third generation of social networks, which utilized the physical and virtual context of the users. Whereas geolocations of the users contribute to the physical context, the data gathered from user behaviour, such as current interests, history of information searched online, liked content and frequently viewed videos or webpages visited, contribute to the virtual context. Due to this interaction between the cyber and physical world, the third generation is often referred to as CPSS (Weth et al. 2017).

Community Detection

A community is generally defined as a social unit of entities having shared, common thoughts or identities. The size of the community is contingent on the number of members sharing similar interests.

Need for Communities to be Detected

In a social network, community detection is an essential procedure to detect individual nodes that overlap on the basis of functional subunits. Links between communities in biological networks, comprising metabolic networks and protein-protein interactions, demonstrate that large social networks consist of community structures that are organized in a hierarchical manner (Ahn et al. 2010).

Smart Communities

Smart communities refer to the interdependence between humans and physical entities. In particular, 'smart' refers to the ability of a system to acquire knowledge about its surroundings and to employ this vital information to enhance the quality of life of its inhabitants. This interaction can be achieved through an amalgamation of social computing techniques with CPS. A smart community can also be visualized as a multi-hop network of wirelessly interconnected homes (Li et al. 2011). Social networks are typically built on a multitude of relationships, such as business associations, peer groups, friends and familial connections. Several social networking applications have harnessed the aforementioned relationships to create virtual communities. Such communities enable information sharing and widespread dissemination among its members (users). The use of this information by computers, networked via the Internet to interact with the physical world by means of sensors, monitors and controls, is the genesis of smart communities (Xia et al. 2011). In essence, smart communities can be envisaged as social objects, such as humans and physical entities communicating with each other and delivering multifaceted services, utilizing social intelligence and a cyberphysical network. In a community, data on user behaviour is gathered from multiple sources. A user can therefore be visualized as a logic unit with attributes, such as location in the physical space, roles assumed in the community and digital information published in the cyberspace. Assimilation of data from discrete users in a community is complex and requires analysis on multiple variables and dimensions. This necessitates link prediction techniques to utilize community-based classification of discrete user activities in order to rationalize analysis of big data from large community-based networks (Zhang et al. 2014).

Numerous techniques, such as pattern mining, clustering and graph- based approaches, have been utilized for analyzing communities and detecting smart, cohesive clusters or subgroups that are closely interconnected in a network. In the pattern mining technique, a community evaluation function is optimized to identify densely connected nodes (Atzmueller 2014). Predictions based on extensive information gathered from community activity are significantly valuable owing to the reasons mentioned hereunder.

  • • Reduced complexity of analysis: A single model can be created based on big data gathered from communities, comprising numerous individuals, grouped by common behavioural traits. Analyzing this model is uncomplicated, compared with interpreting data of all individuals in a network.
  • • Trade-off of efficiency vs accuracy: Community-based models can expeditiously discover and predict the demands of a majority of the population in a community. However, the accuracy of such models is a contentious issue. In essence, a study of the population requires a more efficient prediction, rather than an accurate prediction, of community activities (Zhang et al. 2014).

A smart city is a class of a smart community, utilizing data and insights from IoT systems, to provide enhanced services to its inhabitants. The framework for a smart city comprises of a network of sensors and actuators spread over the city terrain, constantly communicating with mobile devices via cloud- based Internet services. The information disseminated through this cyber physical system assists in monitoring and controlling a city's resources and services, such as smart parking and street lighting, air-water quality, surveillance and product delivery using unmanned aerial vehicles, structural health monitoring, real-time traffic monitoring and smart electricity grids (Cassandras 2016). The U.S. National Institute of Standards and Technology (NIST) specifies a globally accepted standard, comprising six categories of smart city functions, namely environment, mobility, economy, governance, people and living (Baig et al. 2017). A smart environment can be defined as one which acquires and utilizes the knowledge of the surroundings to enhance the environmental circumstances of its inhabitants (Cook et al. 2007).

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