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Muhammad Shafi is an Associate Professor at the Faculty of Computing and Information Technology, Sohar University, Oman. His research interests are: Machine Learning Data Science Computer Vision Data Structures Theory of Computation Artificial Intelligence
The things in the Internet of Things are becoming more and more socially aware. What social means for these things (more often termed as “social objects”) is predominately determined by how and when objects interact with each other. In this paper, an agent-based model for Social Internet of Things is proposed, which features the realization of various interaction modalities, along with possible network structures and mobility modes, thus providing a novel model to ask interesting “what-if” questions. The scenario used, which is the acquisition of shared resources in a common spatial and temporal world, demands agents to have ad-hoc communication and a willingness to cooperate with others. The model was simulated for all possible combinations of input parameters to study the implications of competitive vs. cooperative social behavior while agents try to acquire shared resources/services in a peer-to-peer fashion. However, the main focus of the paper was to analyze the impact of profile-based mobility, which has an underpinning on parameters of extent and scale of a mobility profile. The simulation results, in addition to others, reveal that there are substantial and systematic differences among different combinations of values for extent and scale.
Kashif Zia; Umar Farooq; Muhammad Shafi; Muhammad Arshad. An Agent-Based Model of Task-Allocation and Resource-Sharing for Social Internet of Things. IoT 2021, 2, 187 -204.
AMA StyleKashif Zia, Umar Farooq, Muhammad Shafi, Muhammad Arshad. An Agent-Based Model of Task-Allocation and Resource-Sharing for Social Internet of Things. IoT. 2021; 2 (1):187-204.
Chicago/Turabian StyleKashif Zia; Umar Farooq; Muhammad Shafi; Muhammad Arshad. 2021. "An Agent-Based Model of Task-Allocation and Resource-Sharing for Social Internet of Things." IoT 2, no. 1: 187-204.
The latest manifestation of “all connected world" is the Internet of Things (IoT), and Internet of Vehicles (IoV) is one of the key examples of IoT these days. In Social IoV (SIoV), each vehicle is treated as a social object where it establishes and manages its own Social Network (SN). Incidentally, most of the SIoV research in the literature is related to proximity-based connectivity and interactions. In this paper, we bring people in the loop by incorporating their SNs. While emphasizing a recommendation scenario, in which vehicles may require recommendations from SNs of their owners (in addition to their own SIoV), we proposed an agent-based model of information sharing (for context-based recommendations) on a hypothetical population of smart vehicles. Some important hypotheses were tested using a realistic simulation setting. The simulation results reveal that a recommendation using weak ties is more valuable than a recommendation using strong ties in pure SIoV. The simulation results also demonstrate that recommendations using the most-connected person in the social network are not more valuable than recommendation using a random person in the social network. The model presented in this paper can be used to design a multi-scale recommendation system, which uses SIoV and a typical SN in combination.
Kashif Zia; Muhammad Shafi; Umar Farooq. Improving Recommendation Accuracy Using Social Network of Owners in Social Internet of Vehicles. Future Internet 2020, 12, 69 .
AMA StyleKashif Zia, Muhammad Shafi, Umar Farooq. Improving Recommendation Accuracy Using Social Network of Owners in Social Internet of Vehicles. Future Internet. 2020; 12 (4):69.
Chicago/Turabian StyleKashif Zia; Muhammad Shafi; Umar Farooq. 2020. "Improving Recommendation Accuracy Using Social Network of Owners in Social Internet of Vehicles." Future Internet 12, no. 4: 69.