Provenance has been instrumental in networked systems to solve issues related to data trustworthiness, network diagnostics, security, and forensic analysis. Existing provenance based solutions for the Internet of Things (IoT) are either device-centric or platform-centric, which address only application layer attacks. Whereas any stealthy attacks performed at MAC, network, and other sub-layers which alter the behavior of IoT devices often go undetected. Provenance based forensic solutions offer effective mechanisms for investigating link and network layer attacks in IoT networks. Realizing this potential, we propose ProvLink-IoT, a novel provenance model for link-layer forensic analysis in IoT networks. ProvLink-IoT employs PROV-DM and PROV-TEMPLATE standards to model the provenance of the network. Provenance graphs are generated under both benign and attack scenarios based on the provenance logs and network traffic collected from the network. ProvLink-IoT is implemented in a simulated environment with a case study on the 6TiSCH protocol stack. We implemented three link-layer attacks on TSCH and the 6top layers of the 6TiSCH network to study their impact and perform forensic analysis. Link-IoT, a comprehensive link-layer dataset, is generated from the network provenance, which can be used in the further incident and forensic analysis. The performance impact of ProvLink-IoT on IoT network is analyzed in terms of provenance growth rate and storage overhead. Experimental results showed the efficacy of the proposed solution in correlating evidence during incident analysis and its relevance to real-time scenarios.