Social Internet Of Things Paradigm Research Articles

A Social Internet of Things Smart City Solution for Traffic and Pollution Monitoring in Cagliari

In the last years, the smart city (SC) paradigm has been deeply studied to support sustainable mobility and to improve human living conditions. In this context, a new SC based on the Social Internet of Things paradigm is presented in this article. Starting from the tracking of all vehicles (that is, private and public) and pedestrians, integrated with air quality measurements (that is, in real time by mobile and fixed sensors), the system aims to improve the viability of the city, both for pedestrian and vehicular users. A monitoring network based on sensors and devices hosted on board in local public transport allows real-time monitoring of the most sensitive areas both from traffic congestion and from an environmental point of view. The proposed solution is equipped with an appropriate intelligence that takes into account instantaneous speed, type of traffic, and instantaneous pollution data, allowing to evaluate the congestion and pollution condition in a specific moment. Moreover, specific tools support the decisions of public administration facilitating the identification of the most appropriate actions for the implementation of effective policies relating to mobility. All collected data are elaborated in real time to improve traffic viability suggesting new directions and information to citizens to better organize how to live in the city.

Trust-Related Attacks and Their Detection: A Trust Management Model for the Social IoT

The integration of social networking concepts into the Internet of Things (IoT) has led to the so called Social Internet of Things paradigm, according to which the objects are capable of establishing social relationships in an autonomous way with respect to their owners. Within this scenario, “things” interact opportunistically with their peers to seek needed services. However, attacks and malfunctions in the IoT can outweigh any of its benefits if not handled adequately. In this article, we focus on the possible types of trust attacks that can affect the IoT and propose a trust management model able to overcome all the analyzed attacks. Simulations show how the proposed model can effectively isolate almost any malicious nodes in the network at the expense of an increase in the number of transactions needed for the model to converge.

Trust-Aware sensing Quality estimation for team Crowdsourcing in social IoT

In the Internet of Things (IoT), the mobile smart devices with powerful sensing capability help mobile crowdsourcing become an important paradigm to sense environment information. The social Internet of Things paradigm can be exploited for complex task crowdsourcing by forming a collaborative team of socially connected nodes (i.e., smart devices). Few existing team crowdsourcing studies have ever satisfied requirements of trustworthy sensing data and collaborative communication among team members.In this paper, we design TAQ-Crowd (Trust-Aware sensing Quality estimation for team Crowdsourcing), a social team crowdsourcing framework for Social Internet of Things systems. Within TAQ-Crowd, we first incorporate the consideration of trustworthy relationships between nodes into sensing data quality evaluation for TAQ model design. Then, we design a task assignment algorithm CS-Selection, in which the sensing quality guides the participant selection to maximize the overall task valuation under a budget constraint. Meanwhile, we consider a variant of the classic Traveling Salesman Problem (TSP) to extract a tree-structured routing network for team communication. Solving the team crowdsourcing problem concerns participating device selection and task cooperation, which involves two coupling NP-hard problems. The two coupling problems can be transformed into an essentially submodular cost submodular knapsack problem to be solved by the greedy task assignment strategy. Finally, extensive simulation experiments are conducted. The results show that TAQ-Crowd significantly outperforms state-of-art approaches in team formation with at least (1−1∕e)∕2 approximation ratio. Furthermore, the achieved superior performance can validate our proposed trust-aware sensing quality estimation.

A Query based Information search in an Individual’s Small World of Social Internet of Things

Recently, the consolidation of Social Network and the Internet of Things (IoT) acquiring appreciation and consideration among researchers due to its immense and adaptable behavior. Social Internet of Things (SIoT) comprehends the adequacy to support the inventive applications and network services with adept and persuasive demeanors. The author’s exertion for this research provides the subsistence to the SIoT amelioration Currently, the SIoT technique proven to be competent, but the exponential growth of heterogeneous smart devices may arise a challenging scenario. Searching the right object, service, or best path to the informative nodes in such highly convoluted networks is always a challenging task. This research strengthen the SIoT paradigm by consolidating the properties of the individual’s Small World. In addition, this research also advises a Smart Social Agent (SSA) to minimize the human intervention. The absolute objective of this paper is two-fold, (i) transformation of SIoT network into Individual’s Small World SIoT network, (ii) searching the right information efficiently by exploiting SSA. To accomplish the goal we have proposed an efficient Query based Searching algorithm which exploits Service Oriented properties of Individual’s Small World SIoT network and searches information. First result shows the significant alleviation in the network complexity, reduces the average path length, and network diameter. Eventually, it improves the network scalability and navigability. While second result shows that the proposed Query based searching algorithm proves to be efficient with its mean execution time not more than 2.66 ms for complete network on two different machines. Whereas, for Individual’s small world network the mean execution time is not more than 2.47 ms for both machine.

Social Security and Privacy for Social IoT Polymorphic Value Set: A Solution to Inference Attacks on Social Networks

Social Internet of Things (SIoT) integrates social network schemes into Internet of Things (IoT), which provides opportunities for IoT objects to form social communities. Existing social network models have been adopted by SIoT paradigm. The wide distribution of IoT objects and openness of social networks, however, make it more challenging to preserve privacy of IoT users. In this paper, we present a novel framework that preserves privacy against inference attacks on social network data through ranked retrieval models. We propose PVS, a privacy-preserving framework that involves the design of polymorphic value sets and ranking functions. PVS enables polymorphism of private attributes by allowing them to respond to different queries in different ways. We begin this work by identifying two classes of adversaries, authenticity-ignorant adversary, and authenticity-knowledgeable adversary, based on their knowledge of the distribution of private attributes. Next, we define the measurement functions of utility loss and propose PVSV and PVST that preserve privacy against authenticity-ignorant and authenticity-knowledgeable adversaries, respectively. We take into account the utility loss of query results in the design of PVSV and PVST. Finally, we show that PVSV and PVST meet the privacy guarantee with acceptable utility loss in extensive experiments over real-world datasets.

Assignment of Sensing Tasks to IoT Devices: Exploitation of a Social Network of Objects

The Social Internet of Things (SIoT) is a novel communication paradigm according to which the objects connected to the Internet create a dynamic social network that is mostly used to: route information and service requests, disseminate data, and evaluate the trust level of each member of the network. In this paper, the SIoT paradigm is applied to a scenario where geolocated sensing tasks are assigned to fixed and mobile devices, providing the following major contributions. The SIoT model is adopted to find the objects that can contribute to the IoT application by crawling the social network through the nodes profile and trust level. A new algorithm to address the resource management issue is proposed so that sensing tasks are fairly assigned to the objects in the SIoT. To this, an energy consumption profile is created per device and task, and shared among nodes of the same category through the SIoT. The resulting solution is also implemented in the SIoT-based Lysis platform. Emulations have been performed, which showed an extension of the time needed to completely deplete the battery of the first device of more than 40% with respect to alternative approaches.

Exploiting Small World Problems in a SIoT Environment

Internet of Things (IoT) has been at the center of attention among researchers for the last two decades. Their aim was to convert each real-world object into a virtual object. Recently, a new idea of integrating the Social Networking concept into the Internet of Things has merged and is gaining popularity and attention in the research society due to its vast and flexible nature. It comprises of the potential to provide a platform for innovative applications and network services with efficient and effective manners. In this paper, we provide the sustenance for the Social Internet of Things (SIoT) paradigm to jump to the next level. Currently, the SIoT technique has been proven to be efficient, but heterogeneous smart devices are growing exponentially. This can develop a problematic scenario while searching for the right objects or services from billions of devices. Small world phenomena have revealed some interesting facts and motivated many researchers to find the hidden links between acquaintances in order to reach someone across the world. The contribution of this research is to integrate the SIoT paradigm with the small world concept. By integrating the small world properties in SIoT smart devices, we empower the Smart Social Agent (SSA). The Smart Social Agent ensures the finding of appropriate friends (i.e., the IoT devices used by our friend circle) and services that are required by the user, without human intervention. The Smart Social Agent can be any smart device in SIoTs, e.g., mobile phones.

Social relationship paradigm applied to object interactions in industrial IoT

For Internet of Things (IoT) applications the interaction between heterogeneous objects, is mainly constrained by problems of interoperability, security and confidentiality. These issues are often related to classes of applications, and may limit scalability due to increase of connected things. A new approach to handle IoT device interactions has emerged known as Social Internet of Things (SIoT). It aims to provide socio inspired relationship between objects coming from social human behavior theories. The SIoT paradigm opens new perspectives to manage object interactions, with opportunities to extend the functionalities of Industrial IoT devices (IIoT) beyond the existing deterministic and constrained mechanisms that drive current industrial communications. By taking into account current research works, this paper contributes to the field by proposing a solid reference architecture for a Social Communicating Industrial Object, along with its associated properties and its socio-related interaction protocols. It aims at supporting a fully computerized management of social interaction between industrial assets. The potential application of SIoT paradigm in an industrial study case is analyzed, and the impacts are considered on product, resource and monetization of services.

Social Cloud-Based Cognitive Reasoning for Task-Oriented Recommendation

The social Internet of Things (SIoT) is promoted in the literature as enabling the integration of devices into users' daily lives. This integration can be achieved by exploiting the interconnectivity and the user friendliness offered by social network services (SNSs). The SIoT paradigm opens the door to studying the intelligence mechanisms required to enhance service adaptability. The authors study the integration of cognitive reasoning into the SIoT for providing recommendation of quotidian tasks in smart homes. Achieving situation characterization requires reasoning about physical as well as social aspects of context. Thus, as a service built on top of the social cloud (SoC), the authors propose an intelligent recommendation (InRe) framework that applies the reasoning mechanism on context elements that are represented using ontologies. Initial experiments with ThingsChat, a proof-of-concept prototype, indicate considerable improvement in adaptability of recommendation results to users' situations.

SIoT: Giving a Social Structure to the Internet of Things

The actual development of the Internet of Things (IoT) needs major issues related to things' service discovery and composition to be addressed. This paper proposes a possible approach to solve such issues. We introduce a novel paradigm of network of intelligent namely the Social Internet of Things (SIoT), based on the notion of social relationships among objects. Following the definition of a possible social structure among objects, a preliminary architecture for the implementation of SIoT is presented. Through the SIoT paradigm, the capability of humans and devices to discover, select, and use objects with their services in the IoT is augmented. Besides, a level of trustworthiness is enabled to steer the interaction among the billions of objects which will crowd the future IoT.