No abstract

Recently, artificial intelligence (AI) and blockchain have become two of the most trending and disruptive technologies. Blockchain technology has the ability to automate payment in cryptocurrency and to provide access to a shared ledger of data, transactions, and logs in a decentralized, secure, and trusted manner. Also with smart contracts, blockchain has the ability to govern interactions among participants with no intermediary or a trusted third party. AI, on the other hand, offers intelligence and decision-making capabilities for machines similar to humans. In this paper, we present a detailed survey on blockchain applications for AI. We review the literature, tabulate, and summarize the emerging blockchain applications, platforms, and protocols specifically targeting AI area. We also identify and discuss open research challenges of utilizing blockchain technologies for AI.

No abstract

No abstract

Big data production in industrial Internet of Things (IIoT) is evident due to the massive deployment of sensors and Internet of Things (IoT) devices. However, big data processing is challenging due to limited computational, networking and storage resources at IoT device-end. Big data analytics (BDA) is expected to provide operational-and customer-level intelligence in IIoT systems. Although numerous studies on IIoT and BDA exist, only a few studies have explored the convergence of the two paradigms. In this study, we investigate the recent BDA technologies, algorithms and techniques that can lead to the development of intelligent IIoT systems. We devise a taxonomy by classifying and categorising the literature on the basis of important parameters (e.g. data sources, analytics tools, analytics techniques, requirements, industrial analytics applications and analytics types). We present the frameworks and case studies of the various enterprises that have benefited from BDA. We also enumerate the considerable opportunities introduced by BDA in IIoT. We identify and discuss the indispensable challenges that remain to be addressed as future research directions as well.

No abstract

The essence of blockchain smart contracts lies in the execution of business logic code in a decentralized architecture in which the execution outcomes are trusted and agreed upon by all the executing nodes. Despite the decentralized and trustless architectures of the blockchain systems, smart contracts on their own cannot access data from the external world. Instead, smart contracts interact with off-chain external data sources, called oracles, whose primary job is to collect and provide data feeds and input to smart contracts. However, there is always risk of oracles providing corrupt, malicious, or inaccurate data. In this paper, we analyze and present the notion of trust in the oracles used in blockchain ecosystems. We analyze and compare trust-enabling features of the leading blockchain oracle approaches, techniques, and platforms. Moreover, we discuss open research challenges that should be addressed to ensure secure and trustworthy blockchain oracles.

No abstract