MSIoT: Mobile Social Internet of Things, A New Paradigm

2020 ◽  
Author(s):  
Ali Mamourian Esfahani ◽  
Amir Masoud Rahmani ◽  
Ahmad Khademzadeh
Keyword(s):  
Internet Of Things ◽  
New Paradigm ◽  
Social Internet Of Things

scholarly journals Social Internet of Things: vision, challenges, and trends

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Mozhgan Malekshahi Rad ◽  
Amir Masoud Rahmani ◽  
Amir Sahafi ◽  
Nooruldeen Nasih Qader
Keyword(s):  
Social Networks ◽  
Internet Of Things ◽  
Trust Management ◽  
Process Time ◽  
New Paradigm ◽  
Simulation Tools ◽  
Social Internet Of Things ◽  
Common Goals ◽  
Main Components ◽  
Evaluation Parameters

AbstractIoT describes a new world of billions of objects that intelligently communicate and interact with each other. One of the important areas in this field is a new paradigm-Social Internet of Things (SIoT), a new concept of combining social networks with IoT. SIoT is an imitation of social networks between humans and objects. Objects like humans are considered intelligent and social. They create their social network to achieve their common goals, such as improving functionality, performance, and efficiency and satisfying their required services. Our article’s primary purpose is to present a comprehensive review article from the SIoT system to analyze and evaluate the recent works done in this area. Therefore, our study concentrated on the main components of the SIoT (Architecture, Relation Management, Trust Management, web services, and information), features, parameters, and challenges. To gather enough information for better analysis, we have reviewed the articles published between 2011 and December 2019. The strengths and weaknesses of each article are examined, and effective evaluation parameters, approaches, and the most used simulation tools in this field are discussed. For this purpose, we provide a scientific taxonomy for the final SIoT structure based on the academic contributions we have studied. Ultimately we observed that the evaluation parameters are different in each element of the SIoT ecosystem, for example for Relation Management, scalability 29% and navigability 22% are the most concentrated metrics, in Trust Management, accuracy 25%, and resiliency 25% is more important, in the web service process, time 23% and scalability 16% are the most mentioned and finally in information processing, throughput and time 25% are the most investigated factor. Also, Java-based tools like Eclipse has the most percentage in simulation tools in reviewed literature with 28%, and SWIM has 13% of usage for simulation.

Enabling smart objects discovery via constructing hypergraphs of heterogeneous IoT interactions

2016 ◽  
Vol 44 (1) ◽  
pp. 110-124 ◽  
Author(s):  
Jooik Jung ◽  
Sejin Chun ◽  
Xiongnan Jin ◽  
Kyong-Ho Lee
Keyword(s):  
Internet Of Things ◽  
Social Relationships ◽  
Social Relations ◽  
Smart Home ◽  
New Paradigm ◽  
Sensors And Actuators ◽  
Smart Objects ◽  
The Social ◽  
Social Internet Of Things ◽  
The Internet Of Things

Recent advances in the Internet of Things (IoT) have led to the rise of a new paradigm: Social Internet of Things (SIoT). However, the new paradigm, as inspired by the idea that smart objects will soon have a certain degree of social consciousness, is still in its infant state for several reasons. Most of the related works are far from embracing the social aspects of smart objects and the dynamicity of inter-object social relations. Furthermore, there is yet to be a coherent structure for organising and managing IoT objects that elicit social-like features. To fully understand how and to what extent these objects mimic the behaviours of humans, we first model SIoT by scrutinising the distinct characteristics and structural facets of human-centric social networks. To elaborate, we describe the process of profiling the IoT objects that become social and classify various inter-object social relationships. Afterwards, a novel discovery mechanism, which utilises our hypergraph-based overlay network model, is proposed. To test the feasibility of the proposed approach, we have performed several experiments on our smart home automation demo box built with various sensors and actuators.

scholarly journals Attribute based Weighted Mechanism for Community Detection in Social Internet of Things

2021 ◽  
Author(s):  
Subash Rajendran ◽  
Jebakumar R
Keyword(s):  
Internet Of Things ◽  
Community Detection ◽  
Large Scale ◽  
Local Community ◽  
Rate Function ◽  
User Preferences ◽  
Computational Time ◽  
New Paradigm ◽  
Social Internet Of Things ◽  
Real World Datasets

Abstract A new paradigm of Internet of Things (IoT) is emerging rapidly by socializing the smarter physical devices called as Social Internet of Things (SIoT). Social relationships established between these objects make them autonomously connected for services, without any human intervention. Since SIoT is a large-scale network with huge data involved, the content spreading behaviour need to be exploited. In order to ensure the growth of the content spread, the large-scale SIoT network is divided into several communities based on the social attributes in this work. We first divided the SIoT network into high quality Sociality based Weighted Communities (SWC). Social attributes like user preferences, social similarities, and mutual friends’ degrees are main metrics for achieving the best rate function. The weighted method based on these social attributes determine the nodes to be present in their respective communities. Also, the controlling of the local community augmentation using cluster concepts is done in our approach. Finally, a Credential Acclaimed Information Spreading (CAIS) mechanism is proposed which selects the best node with the maximum credential to surge the content spreading behaviour in the detected communities of SIoT network. The proposed social-driven attribute based weighted mechanism for community detection is validated using three diverse real-world datasets such as CASAS, MIT and ARAS dataset containing 427 sensors. Investigational outcomes validate that the overall performance of the proposed method overwhelms the conventional community detection algorithms like Louvain, Girvan Newman, Bron Kerbosch, Infomax and the recent state-of-art-approaches interms of spreading outcomes, NMI, modularity, F-measure, precision, recall and computational time.

Trust Management in Social Internet of Things: Architectures, Recent Advancements and Future Challenges

2020 ◽  
pp. 1-1
Author(s):  
Wazir Zada Khan ◽  
Qurat-ul-Ain Arshad ◽  
Saqib Hakak ◽  
Muhammad Khurram Khan ◽  
Saeed-Ur-Rehman
Keyword(s):  
Internet Of Things ◽  
Trust Management ◽  
Social Internet Of Things ◽  
Future Challenges

scholarly journals Mobile Networks and Internet of Things Infrastructures to Characterize Smart Human Mobility

Smart Cities ◽  
2021 ◽  
Vol 4 (2) ◽  
pp. 894-918
Author(s):  
Luís Rosa ◽  
Fábio Silva ◽  
Cesar Analide
Keyword(s):  
Internet Of Things ◽  
Mobile Networks ◽  
Smart Cities ◽  
Human Mobility ◽  
Rapid Development ◽  
Disruptive Technology ◽  
New Paradigm ◽  
Design And Optimization ◽  
High Level

The evolution of Mobile Networks and Internet of Things (IoT) architectures allows one to rethink the way smart cities infrastructures are designed and managed, and solve a number of problems in terms of human mobility. The territories that adopt the sensoring era can take advantage of this disruptive technology to improve the quality of mobility of their citizens and the rationalization of their resources. However, with this rapid development of smart terminals and infrastructures, as well as the proliferation of diversified applications, even current networks may not be able to completely meet quickly rising human mobility demands. Thus, they are facing many challenges and to cope with these challenges, different standards and projects have been proposed so far. Accordingly, Artificial Intelligence (AI) has been utilized as a new paradigm for the design and optimization of mobile networks with a high level of intelligence. The objective of this work is to identify and discuss the challenges of mobile networks, alongside IoT and AI, to characterize smart human mobility and to discuss some workable solutions to these challenges. Finally, based on this discussion, we propose paths for future smart human mobility researches.

scholarly journals Graph-based service recommendation in Social Internet of Things

2021 ◽  
Vol 17 (4) ◽  
pp. 155014772110090
Author(s):  
Yuanyi Chen ◽  
Yanyun Tao ◽  
Zengwei Zheng ◽  
Dan Chen
Keyword(s):  
Internet Of Things ◽  
Social Relationships ◽  
Latent Variable ◽  
Smart Cities ◽  
Knowledge Graph ◽  
Service Recommendation ◽  
Variable Model ◽  
Completion Problem ◽  
Social Internet Of Things ◽  
Object Usage

While it is well understood that the emerging Social Internet of Things offers the capability of effectively integrating and managing massive heterogeneous IoT objects, it also presents new challenges for suggesting useful objects with certain service for users due to complex relationships in Social Internet of Things, such as user’s object usage pattern and various social relationships among Social Internet of Things objects. In this study, we focus on the problem of service recommendation in Social Internet of Things, which is very important for many applications such as urban computing, smart cities, and health care. We propose a graph-based service recommendation framework by jointly considering social relationships of heterogeneous objects in Social Internet of Things and user’s preferences. More exactly, we learn user’s preference from his or her object usage events with a latent variable model. Then, we model users, objects, and their relationships with a knowledge graph and regard Social Internet of Things service recommendation as a knowledge graph completion problem, where the “like” property that connects users to services needs to be predicted. To demonstrate the utility of the proposed model, we have built a Social Internet of Things testbed to validate our approach and the experimental results demonstrate its feasibility and effectiveness.

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