114,006 research outputs found
Knowledge Discovery in Smart City Digital Twins
Despite the abundance of available urban data and the potential for reaching enhanced capabilities in the decision-making and management of city infrastructure, current data-driven approaches to knowledge discovery from city data often lack the capacity for collective data exploitation. Loosely defined data interpretation components, or disciplinary isolated interpretations of specific datasets make it easy to overlook necessary domain expertise, often resulting in speculative decision-making. Smart City Digital Twins are designed to overcome this barrier by integrating a more holistic analytics and visualization approach into the real-time knowledge discovery process from heterogeneous city data. Here, we present a spatiotemporal knowledge discovery framework for the collective exploitation of city data in smart city digital twins that incorporates both social and sensor data, and enables insights from human cognition. This is an initial step towards leveraging heterogeneous city data for digital twin-based decision-making
AutoDRIVE: A Comprehensive, Flexible and Integrated Cyber-Physical Ecosystem for Enhancing Autonomous Driving Research and Education
Prototyping and validating hardware-software components, sub-systems and
systems within the intelligent transportation system-of-systems framework
requires a modular yet flexible and open-access ecosystem. This work presents
our attempt towards developing such a comprehensive research and education
ecosystem, called AutoDRIVE, for synergistically prototyping, simulating and
deploying cyber-physical solutions pertaining to autonomous driving as well as
smart city management. AutoDRIVE features both software as well as
hardware-in-the-loop testing interfaces with openly accessible scaled vehicle
and infrastructure components. The ecosystem is compatible with a variety of
development frameworks, and supports both single and multi-agent paradigms
through local as well as distributed computing. Most critically, AutoDRIVE is
intended to be modularly expandable to explore emergent technologies, and this
work highlights various complementary features and capabilities of the proposed
ecosystem by demonstrating four such deployment use-cases: (i) autonomous
parking using probabilistic robotics approach for mapping, localization, path
planning and control; (ii) behavioral cloning using computer vision and deep
imitation learning; (iii) intersection traversal using vehicle-to-vehicle
communication and deep reinforcement learning; and (iv) smart city management
using vehicle-to-infrastructure communication and internet-of-things
City Data Fusion: Sensor Data Fusion in the Internet of Things
Internet of Things (IoT) has gained substantial attention recently and play a
significant role in smart city application deployments. A number of such smart
city applications depend on sensor fusion capabilities in the cloud from
diverse data sources. We introduce the concept of IoT and present in detail ten
different parameters that govern our sensor data fusion evaluation framework.
We then evaluate the current state-of-the art in sensor data fusion against our
sensor data fusion framework. Our main goal is to examine and survey different
sensor data fusion research efforts based on our evaluation framework. The
major open research issues related to sensor data fusion are also presented.Comment: Accepted to be published in International Journal of Distributed
Systems and Technologies (IJDST), 201
Sensing as a Service Model for Smart Cities Supported by Internet of Things
The world population is growing at a rapid pace. Towns and cities are
accommodating half of the world's population thereby creating tremendous
pressure on every aspect of urban living. Cities are known to have large
concentration of resources and facilities. Such environments attract people
from rural areas. However, unprecedented attraction has now become an
overwhelming issue for city governance and politics. The enormous pressure
towards efficient city management has triggered various Smart City initiatives
by both government and private sector businesses to invest in ICT to find
sustainable solutions to the growing issues. The Internet of Things (IoT) has
also gained significant attention over the past decade. IoT envisions to
connect billions of sensors to the Internet and expects to use them for
efficient and effective resource management in Smart Cities. Today
infrastructure, platforms, and software applications are offered as services
using cloud technologies. In this paper, we explore the concept of sensing as a
service and how it fits with the Internet of Things. Our objective is to
investigate the concept of sensing as a service model in technological,
economical, and social perspectives and identify the major open challenges and
issues.Comment: Transactions on Emerging Telecommunications Technologies 2014
(Accepted for Publication
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
Empowering citizens' cognition and decision making in smart sustainable cities
© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Advances in Internet technologies have made it possible to gather, store, and process large quantities of data, often in real time. When considering smart and sustainable cities, this big data generates useful information and insights to citizens, service providers, and policy makers. Transforming this data into knowledge allows for empowering citizens' cognition as well as supporting decision-making routines. However, several operational and computing issues need to be taken into account: 1) efficient data description and visualization, 2) forecasting citizens behavior, and 3) supporting decision making with intelligent algorithms. This paper identifies several challenges associated with the use of data analytics in smart sustainable cities and proposes the use of hybrid simulation-optimization and machine learning algorithms as an effective approach to empower citizens' cognition and decision making in such ecosystemsPeer ReviewedPostprint (author's final draft
Challenges and opportunities to develop a smart city: A case study of Gold Coast, Australia
With the rapid growth of information and communication technologies, there is a growing interest in developing smart cities with a focus on the knowledge economy, use of sensors and mobile technologies to plan and manage cities. The proponents argue that these emerging technologies have potential application in efficiently managing the environment and infrastructure, promoting economic development and actively engaging the public, thus contributing to building safe, healthy, sustainable and resilient cities. However, are there other important elements in addition to technologies which can contribute to the creation of smart cities? What are some of the challenges and opportunities for developing a smart city?
This paper aims to answer these questions by developing a conceptual framework for smart cities. The framework is then applied to the city of Gold Coast to identify challenges and opportunities for developing the city into a ‘smart city’. Gold Coast is a popular tourist city of about 600,000 populations in South East Queensland, Australia, at the southern end of the 240km long coastal conurbation that is centred by Brisbane. Recently, IBM has nominated Gold Coast as one of the three cities in Australia for its Smarter Cities Challenge Grant. The grant will provide the Gold Coast City Council with the opportunity to collaborate with a group of experts from IBM to develop strategies for enhancing its ICT arrangements for disaster response capabilities. Gold Coast, meanwhile, has potential to diversify its economy from being centred on tourism to a knowledge economy with focus on its educational institutions, investments in cultural precincts and high quality lifestyle amenities. These provide a unique opportunity for building Gold Coast as an important smart city in the region. As part of the research methodology, the paper will review relevant policies of the council. Finally, lessons will be drawn from the case study for other cities which seek to establish themselves as smart cities
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