Supply chain management enhanced by Internet of Things (IoT) solutions integrates special tags (e.g., RFID, NFC, QR-codes) with products to create Smart Tags, which, in addition to storing supplemental information about a product, are also used to track products during their lifecycle. However, a product consumer has to implicitly trust the Smart Tag creator and other stakeholders within the supply chain that they are providing authentic data within a product’s tag. The DL-Tags solution steps into this environment to offer a decentralized, privacy-preserving and verifiable management of Smart Tags during a product’s lifecycle. The solution is based on Distributed Ledger Technology (DLT) and uses the Ethereum blockchain to mediate interactions between stakeholders during a product’s exchange process. By reaching a consensus on the product’s description and state logged on the blockchain, all involved stakeholders and product consumers can verify product’s authenticity without revealing their identity. The paper describes the DL-Tags solution and includes a cost analysis of all implemented transactions on the Ethereum blockchain. The proposed solution provides evidence of the product’s origin and its journey across the supply chain while preventing tag duplication and manipulation. It is among the first documented practical solutions using DLT and IoT for supply chain management which is designed to be distributed ledger agnostic.

Fog computing emerged as a concept that responds to the requirements of upcoming solutions requiring optimizations primarily in the context of the following QoS parameters: latency, throughput, reliability, security, and network traffic reduction. The rapid development of local computing devices and container-based virtualization enabled the application of fog computing within the IoT environment. However, it is necessary to utilize algorithm-based service scheduling that considers the targeted QoS parameters to optimize the service performance and reach the potential of the fog computing concept. In this paper, we first describe our categorization of IoT services that affects the execution of our scheduling algorithm. Secondly, we propose our scheduling algorithm that considers the context of processing devices, user context, and service context to determine the optimal schedule for the execution of service components across the distributed fog-to-cloud environment. The conducted simulations confirmed the performance of the proposed algorithm and showcased its major contribution—dynamic scheduling, i.e., the responsiveness to the volatile QoS parameters due to changeable network conditions. Thus, we successfully demonstrated that our dynamic scheduling algorithm enhances the efficiency of service performance based on the targeted QoS criteria of the specific service scenario.