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Massive internet of things (IoT) data generated by IoT edge devices are shaping the data economy. Monetizing the stream of IoT data has enabled the development of IoT data trading systems, which allow individuals to sell and exchange data. This article presents a blockchain-based system for IoT data trading using fog computing. We propose crowdsourcing fog nodes on the edge network to communicate and collect data from IoT edge device owners. This paper focuses on developing a secure and dependable blockchain-based system that allows data providers and consumers to engage in data trading process. Through experimental results, we evaluate the performance of the proposed model with respect to transaction throughput, latency, and resource consumption metrics under varied scenarios and parameters using Hyperledger blockchain. [ABSTRACT FROM AUTHOR]

Data crowdsourcing is a promising paradigm that leverages the “wisdom” of a potentially large crowd of “workers” in many application domains. Quality-aware crowdsourcing is beneficial as it makes use of workers’ data quality to perform task allocation and data aggregation. However, a worker’s quality and data can be her private information that she may have incentive to misreport to the crowdsourcing requester. Moreover, a worker’s quality and data can depend on her sensitive information (e.g., location), which can be inferred from the outcomes of task allocation and data aggregation by an adversary. In this paper, we devise Privacy-preserving crowdsourcing mechanisms for truthful Data Quality Elicitation (PDQE). In these mechanisms, we design differentially private task allocation and data aggregation algorithms to prevent the inference of a worker’s quality and data from the outcomes of these algorithms. In the meantime, the mechanisms also incentivize workers to truthfully report their quality and data and make desired efforts. We first focus on the mechanisms for a single task (S-PDQE) and then extend it to the case of multiple tasks (M-PDQE). We further show that both the mechanisms achieve a bounded performance gap compared to the optimal strategy. We evaluate the proposed mechanisms using simulations based on real-world data, which corroborate their highly-desired properties on truthful data quality elicitation, data accuracy and privacy protection. [ABSTRACT FROM AUTHOR]

Incentive mechanism design and quality control are two key challenges in data crowdsourcing, because of the need for recruitment of crowd users and their limited capabilities. Without considering users’ social influences, existing mechanisms often result in low efficiency in terms of the platform’s cost. In this paper, we exploit social influences among users as incentives to motivate users’ participation, in order to reduce the cost of recruiting users. Based on social influences, we design incentive mechanisms with the goal of achieving high quality of crowdsourced data and low cost of incentivizing users’ participation. Specifically, we consider three scenarios. In the full information scenario, we design task assignment and user recruitment mechanisms to optimize the data quality while reducing the incentive cost. In the partial information scenario, users’ qualities and costs are unknown. We exploit the correlation between tasks to overcome the information asymmetry, for both cases of opportunistic crowdsourcing and participatory crowdsourcing. Further, in the dynamic social influence scenario, we investigate the dynamics of users’ social influences and design extra rewards for users to make full use of the social influence and achieve maximum cost saving. We evaluate the incentive mechanisms using numerical results, which demonstrate their effectiveness. [ABSTRACT FROM AUTHOR]

Crowdsourcing geographic information has been a massive phenomenon that took place over the last two decades and that changed dramatically the diffusion of geographic data in society. The availability and readiness of geographic information is paramount in practically all software systems and services and this is strongly determined by the drive of people putting energy in data collection and improvement. Understanding the motivations of people to participate in this process is fundamental to leverage the design of new systems able to increase satisfaction and productivity. The introduction of game mechanisms in geographic crowdsourcing is the theme that we face in this research. The so-called gamification of applications has been widely used as a stimulant for involving people in serious activities that are hidden behind the gaming façade. Cooperation and competition are key factors that are promoted through the game. We synthesize a conceptual model that collects the main gaming techniques tailored for geographic data and that is the basis for the development of a gamification library. As a proof of concept, we deploy an Android app for geometric data collection inside buildings and test it in a university setting.

Summary: The rise of the Internet of Things has generated high expectations about the improvement in people's lifestyles. In the last decade, we saw several examples of instrumented cities where different types of data were gathered, processed, and made available to inspire the next generation of scientists and engineers. In this framework, sensors and actuators became leading actors of technologically pervasive urban environments. However, in coastal areas, marine data crowdsourcing is difficult to apply due to the challenging operational conditions, extremely unstable network connectivity, and security issues in data movement. To fill this gap, we present a novel version of our DYNAMO transfer protocol (DTP), a platform‐independent data mover framework where data collected on board of vessels are stored locally and then moved from the edge to the cloud when the operating conditions are favorable. We evaluate the performance of DTP in a controlled environment with a private cloud by measuring the time it takes for the clouds ide to process and store a fixed amount of data while varying the number of microservice instances. We show that the time decreases exponentially when the number of microservice instances goes from 1 to 16 and it remains constant above that number. [ABSTRACT FROM AUTHOR]

Bioenergy is part of the solution to decarbonize energy systems and the economy, and to decrease greenhouse gases emissions drastically. The main goal of this work is to present a participatory database of bioenergy projects, initially based on information available on the International Energy Agency website. This new database aims at being updated over time through data crowdsourcing and being easily exportable in a spreadsheet for further processing. It provides numerous information about bioenergy projects around the world like the types of technology, inputs, outputs, financial information and project status. A detailed overview of the current database is presented, as well as the modus operandi suggested to improve over time this resource through voluntary contributions. The growing quality of this database will serve future research projects and analysis, while being a relevant tool to contribute to the success of the bioenergy sector.

Crowdsourcing geographic information has been a massive phenomenon that took place over the last two decades and that changed dramatically the diffusion of geographic data in society. The availability and readiness of geographic information is paramount in practically all software systems and services and this is strongly determined by the drive of people putting energy in data collection and improvement. Understanding the motivations of people to participate in this process is fundamental to leverage the design of new systems able to increase satisfaction and productivity. The introduction of game mechanisms in geographic crowdsourcing is the theme that we face in this research. The so-called gamification of applications has been widely used as a stimulant for involving people in serious activities that are hidden behind the gaming façade. Cooperation and competition are key factors that are promoted through the game. We synthesize a conceptual model that collects the main gaming techniques tailored for geographic data and that is the basis for the development of a gamification library. As a proof of concept, we deploy an Android app for geometric data collection inside buildings and test it in a university setting. [ABSTRACT FROM AUTHOR]

Crowdsourcing refers to solving large problems by involving human workers that solve component sub-problems or tasks. In data crowdsourcing, the problem involves data acquisition, management, and analysis. In this paper, we provide an overview of data crowdsourcing, giving examples of problems that the authors have tackled, and presenting the key design steps involved in implementing a crowdsourced solution. We also discuss some of the open challenges that remain to be solved. [ABSTRACT FROM PUBLISHER]