922 research outputs found
A Parallel Meta-Heuristic Approach to Reduce Vehicle Travel Time in Smart Cities
The development of the smart city concept and inhabitants’ need to reduce travel time, in addition to society’s awareness of the importance of reducing fuel consumption and respecting the environment, have led to a new approach to the classic travelling salesman problem (TSP) applied to urban environments. This problem can be formulated as “Given a list of geographic points and the distances between each pair of points, what is the shortest possible route that visits each point and returns to the departure point?”. At present, with the development of Internet of Things (IoT) devices and increased capabilities of sensors, a large amount of data and measurements are available, allowing researchers to model accurately the routes to choose. In this work, the aim is to provide a solution to the TSP in smart city environments using a modified version of the metaheuristic optimization algorithm Teacher Learner Based Optimization (TLBO). In addition, to improve performance, the solution is implemented by means of a parallel graphics processing unit (GPU) architecture, specifically a Compute Unified Device Architecture (CUDA) implementation.This research was supported by the Spanish Ministry of Science, Innovation and Universities and the Research State Agency under Grant RTI2018-098156-B-C54 co-financed by FEDER funds, and by the Spanish Ministry of Economy and Competitiveness under Grant TIN2017-89266-R, co-financed by FEDER funds
Deep Reinforcement Learning for Backscatter Communications: Augmenting Intelligence in Future Internet of Things
Backscatter communication (BC) technology offers sustainable solutions for
next-generation Internet-of-Things (IoT) networks, where devices can transmit
data by reflecting and adjusting incident radio frequency signals. In parallel
to BC, deep reinforcement learning (DRL) has recently emerged as a promising
tool to augment intelligence and optimize low-powered IoT devices. This article
commences by elucidating the foundational principles underpinning BC systems,
subsequently delving into the diverse array of DRL techniques and their
respective practical implementations. Subsequently, it investigates potential
domains and presents recent advancements in the realm of DRL-BC systems. A use
case of RIS-aided non-orthogonal multiple access BC systems leveraging DRL is
meticulously examined to highlight its potential. Lastly, this study identifies
and investigates salient challenges and proffers prospective avenues for future
research endeavors.Comment: 7,
Emerging approaches for data-driven innovation in Europe: Sandbox experiments on the governance of data and technology
Europe’s digital transformation of the economy and society is one of the priorities of the current Commission
and is framed by the European strategy for data. This strategy aims at creating a single market for data
through the establishment of a common European data space, based in turn on domain-specific data spaces
in strategic sectors such as environment, agriculture, industry, health and transportation. Acknowledging the
key role that emerging technologies and innovative approaches for data sharing and use can play to make
European data spaces a reality, this document presents a set of experiments that explore emerging
technologies and tools for data-driven innovation, and also deepen in the socio-technical factors and forces
that occur in data-driven innovation. Experimental results shed some light in terms of lessons learned and
practical recommendations towards the establishment of European data spaces
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