Today, the Internet of Things (IoT) technology is applied everywhere, providing tremendous amounts of IoT service. The GS1, a non-profit international standards organization, has established standards for IoT technology. Especially, the GS1 standardized an Object Name Service (ONS) leveraging DNS's distributed and federated infrastructure, enables users to manage and discover IoT services such as the retail, food, healthcare, smart city, and so on. However, the ONS is vulnerable to the data tampering, privilege escalation, and service fault caused by DNS attacks including the man in the middle, cache poisoning, replay, hijacking, and denial of service attacks. Nowadays, IoT services are used in security-sensitive areas, such as finance and healthcare. Therefore, the security of ONS should be strengthened before causing severe problems such as data breach, economic loss, and social loss. In this paper, we propose a blockchain-based ONS with a tokenized authority called the BlockONS. The BlockONS provides strength in the data tampering and privilege escalation allowing a fault tolerance. The BlockONS consists of a content off-chain scaling, a data tampering validation method, a fault-tolerance method, and a Blockchain Token-Based Access Control (BTBAC) method. We designed the BlockONS into two parts: A BlockONS Node part manages the validation method and BTBAC model. A BlockONS Agent part manages the off-chain scaling and fault tolerance. Finally, we implemented the BlockONS leveraging a Hyperledger Sawtooth blockchain. We proved the proposed validation method, fault tolerance method, and BTBAC method through use cases and security analyses on attack situations. We deployed the BlockONS in the Daejeon city and evaluated the performance to show the feasibility of the BlockONS.

As supply chain has become extremely complicated, traditional supply chain management schemes reveal limitations in keeping track of resources for efficient risk management. Recently, blockchain-based solutions for supply chain have shown possibility to overcome those limits. However, numerous practical problems in this research domain are not fully explored up to date. Thus, we delve into one of the most significant problems among them: verification of business logic in transactions. In this paper, we propose a novel distributed ledger system for supply chain, enabled by anomaly detection framework that verifies semantic correctness of transactions based on business context data. Our blockchain data model is tailored to accurately represent events occurred in supply chain based on real-world business standards. In order to facilitate more efficient tracking of provenance, we leverage graph data model to represent supply chain network. On top of the data model, we present smart contract-based anomaly detection framework that verifies whether a transaction is anomalous. Generic rule-based and graph-based detection methods are devised. The feasibility of our proposed model is shown by the system implemented using Hyperledger Sawtooth. We show how our anomaly detection layer can be plugged into the system: how it interacts with other system components, and how overall system flow works with this new function. We evaluate our system with scenario-based simulations. For a number of use cases we synthesized, the correctness and effectiveness of our system are demonstrated.

In recent years, the connected automotive industry has grown explosively. Various connected car services are emerging, such as remote vehicle diagnostics, driver's health monitoring, infotainment, and vehicle safety management. As a result, the number and type of vehicle data are increasing tremendously day by day. However, existing connected automotive solutions have a limitation in that each company manages its own closed data silos. This restricts connected car services from using data sources in various domains. Hence, we propose the GS1 Connected Car, an integrated vehicle information platform, and its ecosystem. We suggest GS1-based automotive data standards for not only in-vehicle data but also all the automotive-related data generated during the lifecycle of vehicles. We provide standardized data collection to EPCIS, the discovery of global automotive services using ONS, IoT Mash-up service between an in-car dashboard platform and IoT devices, video infotainment called GS1 video, and automotive lifecycle management application. We have implemented our platform in a real car by developing an Android-based vehicle dashboard, including service discovery, Mash-up services, and GS1 Video. Also, a mobile application for lifecycle management and Amazon skills for collecting driver's information are developed. Our demonstration and case study show the feasibility of the proposed platform, widening the scope of future connected car services.