As a typical instance of human–computer interaction, the notion of decentralized autonomous organization (DAO) represents an organization constructed by automatically executed rules, such as via smart contracts, incorporating features of the permissionless committee, transparent proposals, and fair contributions by stakeholders. As of May 2023, DAO has impacted over $24.3B market caps. However, there are limited studies focused on this emerging field. To fill the gap, we start from the ground truth by empirically studying the breadth and depth of the DAO markets in mainstream public chain ecosystems in this article. We dive into the most widely adoptable DAO launchpad,Snapshot, which covers 95% of the wild DAO projects for data collection and analysis. By integrating extensively enrolled DAOs and corresponding data measurements, we explore statistical resources from Snapshot and analyze data from 581 DAO projects, encompassing 16 246 proposals over the course of 3+ years. Our empirical research has uncovered a multitude of previously unknown facts about DAOs, spanning topics such as their status, features, performance, threats, and ways of improvement. We have distilled these findings into a series of key insights and takeaway messages, emphasizing their significance. Notably, our study is the first of its kind to comprehensively examine the DAO ecosystem with a focus on scale and scope of data, real-time relevance, practical implementations, and comprehensive metrics, addressing critical gaps in the current literature.

Across industries, there is an ever-increasing rate of data sharing for collaboration and innovation between organizations and their customers, partners, suppliers, and internal teams. However, many enterprises are restricted from freely sharing data due to regulatory restrictions across different regions, performance issues in moving large volume data, or requirements to maintain autonomy. In such situations, the enterprise can benefit from the concept of federated learning, in which machine learning models are constructed at various geographic sites. In this paper, we introduce a general framework, namely BDSP, to share data among enterprises based on Blockchain and federated learning techniques. Specifically, we propose a transparency contribution accounting mechanism to estimate the valuation of data and implement a proof-of-concept for further evaluation. The extensive experimental results show that the proposed BDSP has a competitive performance with higher training accuracy, an increase of over 5%, and lower communication overhead, reducing 3 times, compared to baseline approaches.

Decentralized autonomous organizations (DAOs) are critical to the blockchain ecosystem as they enable decentralized decision-making and governance, and facilitate the creation of decentralized applications (DApps) and organizations. However, despite significant importance, there is currently a lack of a comprehensive overview and detailed understanding of DAOs. To address the gap, this work presents a primary investigation of DAOs (35+). We category, examine and evaluate existing DAOs regarding their operational features, (non-)functionalities and real-world performance. In addition, we provide a consolidated exploration of DAOs by conducting a literature review [1] and an empirical study on mainstream projects, particularly Snapshot [2]. Our research contributes to a better understanding of DAOs and their potential impact on the blockchain ecosystem.

Urban areas are negatively impacted by Carbon Dioxide (CO2 ) and Nitrogen Oxide (NOx) emissions. In order to achieve a cost-effective reduction of greenhouse gas emissions and to combat climate change, the European Union (EU) introduced an Emissions Trading System (ETS) where organizations can buy or receive emission allowances as needed. The current ETS is a centralized one, consisting of a set of complex rules. It is currently administered at the organizational level and is used for fixed-point sources of pollution such as factories, power plants, and refineries. However, the current ETS cannot efficiently cope with vehicle mobility, even though vehicles are one of the primary sources of CO2 and NOx emissions. In this study, we propose a new distributed Blockchain-based emissions allowance trading system called B-ETS. This system enables transparent and trustworthy data exchange as well as trading of allowances among vehicles, relying on vehicle-to-vehicle communication. In addition, we introduce an economic incentive-based mechanism that appeals to individual drivers and leads them to modify their driving behavior in order to reduce emissions. The efficiency of the proposed system is studied through extensive simulations, showing how increased vehicle connectivity can lead to a reduction of the emissions generated from those vehicles. We demonstrate that our method can be used for full life-cycle monitoring and fuel economy reporting. This leads us to conjecture that the proposed system could lead to important behavioral changes among the drivers

Mobile devices with embedded sensors for data collection and environmental sensing create a basis for a cost-effective approach for data trading. For example, these data can be related to pollution and gas emissions, which can be used to check the compliance with national and international regulations. The current approach for IoT data trading relies on a centralized third-party entity to negotiate between data consumers and data providers, which is inefficient and insecure on a large scale. In comparison, a decentralized approach based on distributed ledger technologies (DLT) enables data trading while ensuring trust, security, and privacy. However, due to the lack of understanding of the communication efficiency between sellers and buyers, there is still a significant gap in benchmarking the data trading protocols in IoT environments. Motivated by this knowledge gap, we introduce a model for DLT-based IoT data trading over the narrowband Internet-of-Things (NB-IoT) system, intended to support massive environmental sensing. We characterize the communication efficiency of three basic DLT-based IoT data trading protocols via NB-IoT connectivity in terms of latency and energy consumption. The model and analyses of these protocols provide a benchmark for IoT data trading applications.