13,139 research outputs found
IDMoB: IoT Data Marketplace on Blockchain
Today, Internet of Things (IoT) devices are the powerhouse of data generation
with their ever-increasing numbers and widespread penetration. Similarly,
artificial intelligence (AI) and machine learning (ML) solutions are getting
integrated to all kinds of services, making products significantly more
"smarter". The centerpiece of these technologies is "data". IoT device vendors
should be able keep up with the increased throughput and come up with new
business models. On the other hand, AI/ML solutions will produce better results
if training data is diverse and plentiful.
In this paper, we propose a blockchain-based, decentralized and trustless
data marketplace where IoT device vendors and AI/ML solution providers may
interact and collaborate. By facilitating a transparent data exchange platform,
access to consented data will be democratized and the variety of services
targeting end-users will increase. Proposed data marketplace is implemented as
a smart contract on Ethereum blockchain and Swarm is used as the distributed
storage platform.Comment: Presented at Crypto Valley Conference on Blockchain Technology (CVCBT
2018), 20-22 June 2018 - published version may diffe
MaaSim: A Liveability Simulation for Improving the Quality of Life in Cities
Urbanism is no longer planned on paper thanks to powerful models and 3D
simulation platforms. However, current work is not open to the public and lacks
an optimisation agent that could help in decision making. This paper describes
the creation of an open-source simulation based on an existing Dutch
liveability score with a built-in AI module. Features are selected using
feature engineering and Random Forests. Then, a modified scoring function is
built based on the former liveability classes. The score is predicted using
Random Forest for regression and achieved a recall of 0.83 with 10-fold
cross-validation. Afterwards, Exploratory Factor Analysis is applied to select
the actions present in the model. The resulting indicators are divided into 5
groups, and 12 actions are generated. The performance of four optimisation
algorithms is compared, namely NSGA-II, PAES, SPEA2 and eps-MOEA, on three
established criteria of quality: cardinality, the spread of the solutions,
spacing, and the resulting score and number of turns. Although all four
algorithms show different strengths, eps-MOEA is selected to be the most
suitable for this problem. Ultimately, the simulation incorporates the model
and the selected AI module in a GUI written in the Kivy framework for Python.
Tests performed on users show positive responses and encourage further
initiatives towards joining technology and public applications.Comment: 16 page
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