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

2020 ◽  
Vol 163 ◽  
pp. 176-185
Author(s):  
Abdul Rehman ◽  
Anand Paul ◽  
Awais Ahmad
Sensors ◽  
2019 ◽  
Vol 19 (9) ◽  
pp. 2007 ◽  
Author(s):  
Farhan Amin ◽  
Rashid Abbasi ◽  
Abdul Rehman ◽  
Gyu Sang Choi

The Internet of Things (IoT) is a recent evolutionary technology that has been the primary focus of researchers for the last two decades. In the IoT, an enormous number of objects are connected together using diverse communications protocols. As a result of this massive object connectivity, a search for the exact service from an object is difficult, and hence the issue of scalability arises. In order to resolve this issue, the idea of integrating the social networking concept into the IoT, generally referred as the Social Internet of Things (SIoT) was introduced. The SIoT is gaining popularity and attracting the attention of the research community due to its flexible and spacious nature. In the SIoT, objects have the ability to find a desired service in a distributed manner by using their neighbors. Although the SIoT technique has been proven to be efficient, heterogeneous devices are growing so exponentially that problems can exist in the search for the right object or service from a huge number of devices. In order to better analyze the performance of services in an SIoT domain, there is a need to impose a certain set of rules on these objects. Our novel contribution in this study is to address the link selection problem in the SIoT by proposing an algorithm that follows the key properties of navigability in small-world networks, such as clustering coefficients, path lengths, and giant components. Our algorithm empowers object navigability in the SIoT by restricting the number of connections for objects, eliminating old links or having fewer connections. We performed an extensive series of experiments by using real network data sets from social networking sites like Brightkite and Facebook. The expected results demonstrate that our algorithm is efficient, especially in terms of reducing path length and increasing the average clustering coefficient. Finally, it reflects overall results in terms of achieving easier network navigation. Our algorithm can easily be applied to a single node or even an entire network.


Author(s):  
Wazir Zada Khan ◽  
Qurat-ul-Ain Arshad ◽  
Saqib Hakak ◽  
Muhammad Khurram Khan ◽  
Saeed-Ur-Rehman

2021 ◽  
Vol 17 (4) ◽  
pp. 155014772110090
Author(s):  
Yuanyi Chen ◽  
Yanyun Tao ◽  
Zengwei Zheng ◽  
Dan Chen

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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