3-D-SIS: A 3-D-Social Identifier Structure for Collaborative Edge Computing Based Social IoT

• Published in: IEEE transactions on computational social systems 2022-02, Vol.9 (1), p.313-323
• Main Authors: Ibrar, Muhammad, Wang, Lei, Akbar, Aamir, Jan, Mian Ahmad, Shah, Nadir, Abid, Shahbaz Akhtar, Segal, Michael
• Format: Article
• Language: English
• Subjects: Collaboration; Collaborative edge computing (CEC); Computation offloading; Computational modeling; Computer architecture; Edge computing; Internet of Things; Peer-to-peer computing; Performance enhancement; Physical layer; social device-to-device (social D2D) communication; social Internet of Things (IoT) (SIoT); Social networking (online); Social networks; Three dimensional models
• ISSN: 2329-924X; 2329-924X; 2373-7476
• DOI: 10.1109/TCSS.2021.3064716

The social Internet of Things (IoT) (SIoT) helps to enable an autonomous interaction between the two architectures that have already been established: social networks and the IoT. SIoT also integrates the concepts of social networking and IoT into collaborative edge computing (CEC), the so-called CEC-based SIoT architecture. In closer proximity, IoT devices self-organize into a CEC-based SIoT computing cluster and provide social device-to-device (S-D2D) services, such as computation offloading, service discovery, and content delivery. In the CEC-based SIoT, however, cooperation based on social connections leads to a problem called social and spatial physical trade-off . This problem is also referred to as the mismatch problem, which arises because the spatial neighbors in the social layer cannot always be related. The spatial distance thus calls for additional multi-hop transmissions. This work presents a novel solution called 3-D-social identifier structure (3-D-SIS) model. The 3-D-SIS model is based on 3-D social space (3-D-SS) and considers social ties and physical connections (i.e., intra-neighbor) of the SIoT devices and utilizes a 3-D structure to evaluate that relationship. Moreover, it minimizes the end-to-end delay and communication cost to address the mismatch problem. To validate the performance of the (3-D-SIS) model, we use the real traces of social networks (INFOCOM06) . The results show that the 3-D-SIS selects the best neighbor in S-D2D communication and improves performance in terms of end-to-end delay and throughput.