A DYNAMIC MANAGEMENT MECHANISM FOR OBJECT COOPERATION IN THE SOCIAL INTERNET OF THINGS

Abstract

The explosive growth in the number of heterogeneous objects in the Internet of Things (IoT) leads to issues such as scalability, integration, and navigability. To allevi- ate these issues, the concept of the Social Internet of Things (SIoT), as an integration of IoT with social networks, has been developed for the objects to mimic the social behaviour of human beings in society and thus effectively cooperate with one another without human intervention. The cooperation between these objects is ultimately de- termined by their respective owners, in terms of the type of services to be shared by the objects, the respective owners’ willingness to share these services, and the social relationship among these owners. Such decisive factors in determining cooperation have not been considered in most existing works that employ a symmetric friendship metric where the ratio of the common friends between two owners is equal for both of them. There is scope to consider different approaches to friendship, such as asymmet- ric friendship, which can be used to evaluate cooperativeness. This thesis presents a novel dynamic management mechanism for object coopera- tion that operates on behalf of the owners of objects. This consists of a novel abstract model of willingness to share, a novel asymmetric friendship evaluation model and a cooperativeness assessment model. The willingness to share SIoT services at differ- ent levels of privacy is abstracted by the abstract model of willingness to share; the asymmetric friendship among the owners is dynamically established and updated by the asymmetric friendship model; and the probability of cooperating with other objects for sharing services is assessed by the cooperativeness assessment model in which the abstracted value of willingness to share is discounted by the asymmetric friendship. Simulation experiments using the, commonly used for SIoT, SWIM simulator confirm that the proposed mechanism works in a similar way to the descriptive results from the real world and that it also works in different scenarios in a consistent manner compared with existing methods.

Date of Award	31 Dec 2023
Original language	English
Awarding Institution	The University of Manchester
Supervisor	Rizos Sakellariou (Supervisor) & Vasileios Pavlidis (Supervisor)