TY - GEN
T1 - Trust computational heuristic for social internet of things
T2 - 2020 IEEE International Conference on Communications, ICC 2020
AU - Sagar, Subhash
AU - Mahmood, Adnan
AU - Sheng, Quan Z.
AU - Zhang, Wei Emma
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
KW - Machine Learning
KW - Social Internet of Things
KW - Trust Management
UR - http://www.scopus.com/inward/record.url?scp=85089423325&partnerID=8YFLogxK
U2 - 10.1109/ICC40277.2020.9148767
DO - 10.1109/ICC40277.2020.9148767
M3 - Conference proceeding contribution
AN - SCOPUS:85089423325
T3 - IEEE International Conference on Communications
SP - 1
EP - 6
BT - 2020 IEEE International Conference on Communications, ICC 2020 - Proceedings
PB - Institute of Electrical and Electronics Engineers (IEEE)
CY - Piscataway, NJ
Y2 - 7 June 2020 through 11 June 2020
ER -