Blockchain is a promising technology for establishing trust in IoT networks, where network nodes do not necessarily trust each other. Cryptographic hash links and distributed consensus mechanisms ensure that the data stored on an immutable blockchain can not be altered or deleted. However, blockchain mechanisms do not guarantee the trustworthiness of data at the origin. We propose a layered architecture for improving the end-to-end trust that can be applied to a diverse range of blockchain-based IoT applications. Our architecture evaluates the trustworthiness of sensor observations at the data layer and adapts block verification at the blockchain layer through the proposed data trust and gateway reputation modules. We present the performance evaluation of the data trust module using a simulated indoor target localization and the gateway reputation module using an end-to-end blockchain implementation, together with a qualitative security analysis for the architecture.
Distributed trust methods have been proposed @cite_4 whereby multiple observer nodes within spatial or temporal proximity independently corroborate nearby observations, yet these methods have been so far considered independently from auditability, transparency, and trust mechanisms of the blockchain. conducted a survey on IoT trust management mechanisms and their objectives in @cite_16 . More recent works focus on integrating the trust and reputation management mechanisms into blockchain-based IoT applications. The structure of blockchain-based applications require decentralization of trust and reputation management mechanisms. Furthermore, since blockchain technology has been applied to a diverse range of IoT applications with different network topologies, rules of participation and governance, and interactions between nodes, current trust and reputation management proposals are application-specific. These proposals can be categorized based on the layer at which the reputation and trust mechanisms work as: IoT data capture and blockchain node interactions.
@cite_16 @cite_4
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We propose a dynamic trust management protocol for Internet of Things (IoT) systems to deal with misbehaving nodes whose status or behavior may change dynamically. We consider an IoT system being deployed in a smart community where each node autonomously performs trust evaluation. We provide a formal treatment of the convergence, accuracy, and resilience properties of our dynamic trust management protocol and validate these desirable properties through simulation. We demonstrate the effectiveness of our dynamic trust management protocol with a trust-based service composition application in IoT environments. Our results indicate that trust-based service composition significantly outperforms non-trust-based service composition and approaches the maximum achievable performance based on ground truth status. Furthermore, our dynamic trust management protocol is capable of adaptively adjusting the best trust parameter setting in response to dynamically changing environments to maximize application performance. Managing trust is a problem of particular importance in peer-to-peer environments where one frequently encounters unknown agents. Existing methods for trust management, that are based on reputation, focus on the semantic properties of the trust model. They do not scale as they either rely on a central database or require to maintain global knowledge at each agent to provide data on earlier interactions. In this paper we present an approach that addresses the problem of reputation-based trust management at both the data management and the semantic level. We employ at both levels scalable data structures and algorithms that require no central control and allow to assess trust by computing an agents reputation from its former interactions with other agents. Thus the meethod can be implemented in a peer-to-peer environment and scales well for very large numbers of participants. We expect that scalable methods for trust management are an important factor, if fully decentralized peer-to-peer systems should become the platform for more serious applications than simple file exchange. The continual advancement in semiconductor technology has enabled vendors to integrate an increasing number of sensors, e.g., accelerometer, gyroscope, digital compass, high resolution cameras and others, in modern mobile phones. Combining with the ever expanding market penetration of mobile phones, the onboard sensors are driving the sensing community towards a new paradigm called participatory sensing in which ordinary citizens voluntarily collect and share information from their local environment using personal mobile devices. The inherent openness of this platform is a double-edged sword. While encouraging user participation on the one hand, it also makes it easy to contribute corrupted data. As such, data trustworthiness becomes a key issue which needs to be addressed to ensure sustainable development of this emerging paradigm. In this paper, we propose a reputation system in which a reputation score is calculated for each device as a reflection of the trustworthiness of its sensor data. We adopt the Gompertz function as a fundamental building block of our system, since it is better suited to deal with the unique characteristics of participatory sensing. We present an application-agnostic implementation of the system, which can work with a wide variety of participatory sensing applications. We evaluate the performance of our reputation system in the context of two real-world participatory sensing applications. Our results show that the proposed reputation system outperforms existing solutions by up to a factor of six. The prevalence of IoT devices makes them an ideal target for attackers. To reduce the risk of attacks vendors routinely deliver security updates (patches) for their devices. The delivery of security updates becomes challenging due to the issue of scalability as the number of devices may grow much quicker than vendors' distribution systems. Previous studies have suggested a permissionless and decentralized blockchain-based network in which nodes can host and deliver security updates, thus the addition of new nodes scales out the network. However, these studies do not provide an incentive for nodes to join the network, making it unlikely for nodes to freely contribute their hosting space, bandwidth, and computation resources. In this paper, we propose a novel decentralized IoT software update delivery network in which participating nodes referred to as distributors) are compensated by vendors with digital currency for delivering updates to devices. Upon the release of a new security update, a vendor will make a commitment to provide digital currency to distributors that deliver the update; the commitment will be made with the use of smart contracts, and hence will be public, binding, and irreversible. The smart contract promises compensation to any distributor that provides proof-of-distribution, which is unforgeable proof that a single update was delivered to a single device. A distributor acquires the proof-of-distribution by exchanging a security update for a device signature using the Zero-Knowledge Contingent Payment (ZKCP) trustless data exchange protocol. Eliminating the need for trust between the security update distributor and the security consumer (IoT device) by providing fair compensation, can significantly increase the number of distributors, thus facilitating rapid scale out.