| Title | Trust Computational Heuristic for Social Internet of Things: A Machine Learning-Based Approach |
| Publication Type | Conference Paper |
| Year of Publication | 2020 |
| Authors | Sagar, Subhash, Mahmood, Adnan, Sheng, Quan Z., Zhang, Wei Emma |
| Conference Name | ICC 2020 - 2020 IEEE International Conference on Communications (ICC) |
| Date Published | jun |
| Keywords | Aggregates, Computational modeling, human factors, Internet of Things, machine learning, Mathematical model, Measurement, pubcrawl, Scalability, Social Agents, social Internet of Things, Task Analysis, Trust management |
| Abstract | The Internet of Things (IoT) is an evolving network of billions of interconnected physical objects, such as, numerous sensors, smartphones, wearables, and embedded devices. These physical objects, generally referred to as the smart objects, when deployed in real-world aggregates useful information from their surrounding environment. As-of-late, this notion of IoT has been extended to incorporate the social networking facets which have led to the promising paradigm of the `Social Internet of Things' (SIoT). In SIoT, the devices operate as an autonomous agent and provide an exchange of information and services discovery in an intelligent manner by establishing social relationships among them with respect to their owners. Trust plays an important role in establishing trustworthy relationships among the physical objects and reduces probable risks in the decision making process. In this paper, a trust computational model is proposed to extract individual trust features in a SIoT environment. Furthermore, a machine learning-based heuristic is used to aggregate all the trust features in order to ascertain an aggregate trust score. Simulation results illustrate that the proposed trust-based model isolates the trustworthy and untrustworthy nodes within the network in an efficient manner. |
| DOI | 10.1109/ICC40277.2020.9148767 |
| Citation Key | sagar_trust_2020 |
Trust Computational Heuristic for Social Internet of Things: A Machine Learning-Based Approach