Integrating On-chain and Off-chain Governance for Supply Chain Transparency and Integrity

Integrating on-chain and off-chain data storage for decentralised and distributed information systems, such as blockchain, presents specific challenges for providing transparency of data governance and ensuring data integrity through stakeholder engagement. Current research on blockchain-based supply chains focuses on using on-chain governance rules developed for cryptocurrency blockchains to store some critical data points without designing tailored on-chain governance mechanisms and disclosing off-chain decision-making processes on data governance. In response to this research gap, this paper presents an integrated data governance framework that coordinates supply chain stakeholders with inter-linked on-chain and off-chain governance to disclose on-chain and off-chain rules and decision-making processes for supply chain transparency and integrity. We present a Proof-of-Concept (PoC) of our integrated data governance approach and suggest future research to strengthen scaling up and supply chain-based use cases based on our learnings.Comment: The 5th Symposium on Distributed Ledger Technolog

Integration of Next Generation IIoT with Blockchain for the Development of Smart Industries

In modern era, a wide range of smart industries is being focus on automation-based applications. Various technologies are rapidly implementing in Industrial Internet of Things (IIoT) for manufacturing sectors that helping to achieve advanced schedule production framework and on time delivery of products. The integration of IIoT platforms with the blockchain are challenging service in manufacturing system. The primary objective of this article is to characterize various issues and challenges that are implementing IIoT and blockchain in industries. The proposed work is an integration of IIoT and blockchain in industrial processes for solving the security issues in real-time. Also, identifying various enablers of blockchain and issues of IIoT from smart industries manufacturing using a survey tool is formed in the form of questionnaire. Based on these responses Decision Making Trial and Evaluation Laboratory (DEMATEL) technique has been implemented for categorizing these challenges into cause and effect. In this paper, we introduce the general layout with their key issues and challenges of IIoT and blockchain that signifies the safety requirements to design the IIoT and blockchain. Further, we describe how IIoT can be integrated to the blockchain for smart Industrial applications. Finally, various recommendations are the proposed to upcoming IIoT and blockchain developments. The proposed work will be highly beneficial for the smart industries to develop a next generation IIoT and blockchain based framework

Secure Decentralized Decisions in Consolidated Hospital Systems: Intelligent Agents and Blockchain

Shared decision making has become a very important solution in order to build a consolidated healthcare system. While there is some research in the healthcare literature discussing the advantages and disadvantages of the shared decision making, its efficiency has not been addressed quantitatively. In this thesis, we propose a universal decentralized decision-making architecture utilizing the Blockchain Technology and Machine Learning (predictive and prescriptive analytics) to address the compelling need for coordination among healthcare providers and patients in an efficient and integrated manner. The healthcare process considered is the assignment of a patient to the best physician and hospital in consolidated hospital systems. After designing Decentralized Patients Assignment System (DPAS), the model is simulated using Agent-based models (ABM). The ABM consist of 4 agents including patient, physician, hospital and miner (assignment algorithms) which interact inside a decentralized integrated system. The proposed mechanism introduces the importance of interoperability between healthcare agents in the decision making process created by Blockchain Technology. To illustrate the model efficiency, two scenarios have been simulated and the results are compared. The results demonstrate the proposed model efficiency in terms of the assignment rate, computational time, and cost

Internet of robotic things : converging sensing/actuating, hypoconnectivity, artificial intelligence and IoT Platforms

The Internet of Things (IoT) concept is evolving rapidly and influencing newdevelopments in various application domains, such as the Internet of MobileThings (IoMT), Autonomous Internet of Things (A-IoT), Autonomous Systemof Things (ASoT), Internet of Autonomous Things (IoAT), Internetof Things Clouds (IoT-C) and the Internet of Robotic Things (IoRT) etc.that are progressing/advancing by using IoT technology. The IoT influencerepresents new development and deployment challenges in different areassuch as seamless platform integration, context based cognitive network integration,new mobile sensor/actuator network paradigms, things identification(addressing, naming in IoT) and dynamic things discoverability and manyothers. The IoRT represents new convergence challenges and their need to be addressed, in one side the programmability and the communication ofmultiple heterogeneous mobile/autonomous/robotic things for cooperating,their coordination, configuration, exchange of information, security, safetyand protection. Developments in IoT heterogeneous parallel processing/communication and dynamic systems based on parallelism and concurrencyrequire new ideas for integrating the intelligent “devices”, collaborativerobots (COBOTS), into IoT applications. Dynamic maintainability, selfhealing,self-repair of resources, changing resource state, (re-) configurationand context based IoT systems for service implementation and integrationwith IoT network service composition are of paramount importance whennew “cognitive devices” are becoming active participants in IoT applications.This chapter aims to be an overview of the IoRT concept, technologies,architectures and applications and to provide a comprehensive coverage offuture challenges, developments and applications

Report from GI-Dagstuhl Seminar 16394: Software Performance Engineering in the DevOps World

This report documents the program and the outcomes of GI-Dagstuhl Seminar 16394 "Software Performance Engineering in the DevOps World". The seminar addressed the problem of performance-aware DevOps. Both, DevOps and performance engineering have been growing trends over the past one to two years, in no small part due to the rise in importance of identifying performance anomalies in the operations (Ops) of cloud and big data systems and feeding these back to the development (Dev). However, so far, the research community has treated software engineering, performance engineering, and cloud computing mostly as individual research areas. We aimed to identify cross-community collaboration, and to set the path for long-lasting collaborations towards performance-aware DevOps. The main goal of the seminar was to bring together young researchers (PhD students in a later stage of their PhD, as well as PostDocs or Junior Professors) in the areas of (i) software engineering, (ii) performance engineering, and (iii) cloud computing and big data to present their current research projects, to exchange experience and expertise, to discuss research challenges, and to develop ideas for future collaborations

Storage Solutions for Big Data Systems: A Qualitative Study and Comparison

Big data systems development is full of challenges in view of the variety of application areas and domains that this technology promises to serve. Typically, fundamental design decisions involved in big data systems design include choosing appropriate storage and computing infrastructures. In this age of heterogeneous systems that integrate different technologies for optimized solution to a specific real world problem, big data system are not an exception to any such rule. As far as the storage aspect of any big data system is concerned, the primary facet in this regard is a storage infrastructure and NoSQL seems to be the right technology that fulfills its requirements. However, every big data application has variable data characteristics and thus, the corresponding data fits into a different data model. This paper presents feature and use case analysis and comparison of the four main data models namely document oriented, key value, graph and wide column. Moreover, a feature analysis of 80 NoSQL solutions has been provided, elaborating on the criteria and points that a developer must consider while making a possible choice. Typically, big data storage needs to communicate with the execution engine and other processing and visualization technologies to create a comprehensive solution. This brings forth second facet of big data storage, big data file formats, into picture. The second half of the research paper compares the advantages, shortcomings and possible use cases of available big data file formats for Hadoop, which is the foundation for most big data computing technologies. Decentralized storage and blockchain are seen as the next generation of big data storage and its challenges and future prospects have also been discussed