4,429 research outputs found
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
Communication and Control in Collaborative UAVs: Recent Advances and Future Trends
The recent progress in unmanned aerial vehicles (UAV) technology has
significantly advanced UAV-based applications for military, civil, and
commercial domains. Nevertheless, the challenges of establishing high-speed
communication links, flexible control strategies, and developing efficient
collaborative decision-making algorithms for a swarm of UAVs limit their
autonomy, robustness, and reliability. Thus, a growing focus has been witnessed
on collaborative communication to allow a swarm of UAVs to coordinate and
communicate autonomously for the cooperative completion of tasks in a short
time with improved efficiency and reliability. This work presents a
comprehensive review of collaborative communication in a multi-UAV system. We
thoroughly discuss the characteristics of intelligent UAVs and their
communication and control requirements for autonomous collaboration and
coordination. Moreover, we review various UAV collaboration tasks, summarize
the applications of UAV swarm networks for dense urban environments and present
the use case scenarios to highlight the current developments of UAV-based
applications in various domains. Finally, we identify several exciting future
research direction that needs attention for advancing the research in
collaborative UAVs
A Comprehensive Review of AI-enabled Unmanned Aerial Vehicle: Trends, Vision , and Challenges
In recent years, the combination of artificial intelligence (AI) and unmanned
aerial vehicles (UAVs) has brought about advancements in various areas. This
comprehensive analysis explores the changing landscape of AI-powered UAVs and
friendly computing in their applications. It covers emerging trends, futuristic
visions, and the inherent challenges that come with this relationship. The
study examines how AI plays a role in enabling navigation, detecting and
tracking objects, monitoring wildlife, enhancing precision agriculture,
facilitating rescue operations, conducting surveillance activities, and
establishing communication among UAVs using environmentally conscious computing
techniques. By delving into the interaction between AI and UAVs, this analysis
highlights the potential for these technologies to revolutionise industries
such as agriculture, surveillance practices, disaster management strategies,
and more. While envisioning possibilities, it also takes a look at ethical
considerations, safety concerns, regulatory frameworks to be established, and
the responsible deployment of AI-enhanced UAV systems. By consolidating
insights from research endeavours in this field, this review provides an
understanding of the evolving landscape of AI-powered UAVs while setting the
stage for further exploration in this transformative domain
Supporting UAVs with Edge Computing: A Review of Opportunities and Challenges
Over the last years, Unmanned Aerial Vehicles (UAVs) have seen significant
advancements in sensor capabilities and computational abilities, allowing for
efficient autonomous navigation and visual tracking applications. However, the
demand for computationally complex tasks has increased faster than advances in
battery technology. This opens up possibilities for improvements using edge
computing. In edge computing, edge servers can achieve lower latency responses
compared to traditional cloud servers through strategic geographic deployments.
Furthermore, these servers can maintain superior computational performance
compared to UAVs, as they are not limited by battery constraints. Combining
these technologies by aiding UAVs with edge servers, research finds measurable
improvements in task completion speed, energy efficiency, and reliability
across multiple applications and industries. This systematic literature review
aims to analyze the current state of research and collect, select, and extract
the key areas where UAV activities can be supported and improved through edge
computing
Machine Learning-Aided Operations and Communications of Unmanned Aerial Vehicles: A Contemporary Survey
The ongoing amalgamation of UAV and ML techniques is creating a significant
synergy and empowering UAVs with unprecedented intelligence and autonomy. This
survey aims to provide a timely and comprehensive overview of ML techniques
used in UAV operations and communications and identify the potential growth
areas and research gaps. We emphasise the four key components of UAV operations
and communications to which ML can significantly contribute, namely, perception
and feature extraction, feature interpretation and regeneration, trajectory and
mission planning, and aerodynamic control and operation. We classify the latest
popular ML tools based on their applications to the four components and conduct
gap analyses. This survey also takes a step forward by pointing out significant
challenges in the upcoming realm of ML-aided automated UAV operations and
communications. It is revealed that different ML techniques dominate the
applications to the four key modules of UAV operations and communications.
While there is an increasing trend of cross-module designs, little effort has
been devoted to an end-to-end ML framework, from perception and feature
extraction to aerodynamic control and operation. It is also unveiled that the
reliability and trust of ML in UAV operations and applications require
significant attention before full automation of UAVs and potential cooperation
between UAVs and humans come to fruition.Comment: 36 pages, 304 references, 19 Figure
Integration of Data Driven Technologies in Smart Grids for Resilient and Sustainable Smart Cities: A Comprehensive Review
A modern-day society demands resilient, reliable, and smart urban
infrastructure for effective and in telligent operations and deployment.
However, unexpected, high-impact, and low-probability events such as
earthquakes, tsunamis, tornadoes, and hurricanes make the design of such robust
infrastructure more complex. As a result of such events, a power system
infrastructure can be severely affected, leading to unprecedented events, such
as blackouts. Nevertheless, the integration of smart grids into the existing
framework of smart cities adds to their resilience. Therefore, designing a
resilient and reliable power system network is an inevitable requirement of
modern smart city infras tructure. With the deployment of the Internet of
Things (IoT), smart cities infrastructures have taken a transformational turn
towards introducing technologies that do not only provide ease and comfort to
the citizens but are also feasible in terms of sustainability and
dependability. This paper presents a holistic view of a resilient and
sustainable smart city architecture that utilizes IoT, big data analytics,
unmanned aerial vehicles, and smart grids through intelligent integration of
renew able energy resources. In addition, the impact of disasters on the power
system infrastructure is investigated and different types of optimization
techniques that can be used to sustain the power flow in the network during
disturbances are compared and analyzed. Furthermore, a comparative review
analysis of different data-driven machine learning techniques for sustainable
smart cities is performed along with the discussion on open research issues and
challenges
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