12,253 research outputs found
Wireless Network Design for Control Systems: A Survey
Wireless networked control systems (WNCS) are composed of spatially
distributed sensors, actuators, and con- trollers communicating through
wireless networks instead of conventional point-to-point wired connections. Due
to their main benefits in the reduction of deployment and maintenance costs,
large flexibility and possible enhancement of safety, WNCS are becoming a
fundamental infrastructure technology for critical control systems in
automotive electrical systems, avionics control systems, building management
systems, and industrial automation systems. The main challenge in WNCS is to
jointly design the communication and control systems considering their tight
interaction to improve the control performance and the network lifetime. In
this survey, we make an exhaustive review of the literature on wireless network
design and optimization for WNCS. First, we discuss what we call the critical
interactive variables including sampling period, message delay, message
dropout, and network energy consumption. The mutual effects of these
communication and control variables motivate their joint tuning. We discuss the
effect of controllable wireless network parameters at all layers of the
communication protocols on the probability distribution of these interactive
variables. We also review the current wireless network standardization for WNCS
and their corresponding methodology for adapting the network parameters.
Moreover, we discuss the analysis and design of control systems taking into
account the effect of the interactive variables on the control system
performance. Finally, we present the state-of-the-art wireless network design
and optimization for WNCS, while highlighting the tradeoff between the
achievable performance and complexity of various approaches. We conclude the
survey by highlighting major research issues and identifying future research
directions.Comment: 37 pages, 17 figures, 4 table
Control of Connected and Automated Vehicles: State of the Art and Future Challenges
Autonomous driving technology pledges safety, convenience, and energy
efficiency. Challenges include the unknown intentions of other road users:
communication between vehicles and with the road infrastructure is a possible
approach to enhance awareness and enable cooperation. Connected and automated
vehicles (CAVs) have the potential to disrupt mobility, extending what is
possible with driving automation and connectivity alone. Applications include
real-time control and planning with increased awareness, routing with
micro-scale traffic information, coordinated platooning using traffic signals
information, eco-mobility on demand with guaranteed parking. This paper
introduces a control and planning architecture for CAVs, and surveys the state
of the art on each functional block therein; the main focus is on techniques to
improve energy efficiency. We provide an overview of existing algorithms and
their mutual interactions, we present promising optimization-based approaches
to CAVs control and identify future challenges
Fog Computing in IoT Aided Smart Grid Transition- Requirements, Prospects, Status Quos and Challenges
Due to unfolded developments in both the IT sectors viz. Intelligent
Transportation and Information Technology contemporary Smart Grid (SG) systems
are leveraged with smart devices and entities. Such infrastructures when
bestowed with the Internet of Things (IoT) and sensor network make a universe
of objects active and online. The traditional cloud deployment succumbs to meet
the analytics and computational exigencies decentralized, dynamic cum
resource-time critical SG ecosystems. This paper synoptically inspects to what
extent the cloud computing utilities can satisfy the mission-critical
requirements of SG ecosystems and which subdomains and services call for fog
based computing archetypes. The objective of this work is to comprehend the
applicability of fog computing algorithms to interplay with the core centered
cloud computing support, thus enabling to come up with a new breed of real-time
and latency free SG services. The work also highlights the opportunities
brought by fog based SG deployments. Correspondingly, we also highlight the
challenges and research thrusts elucidated towards the viability of fog
computing for successful SG Transition.Comment: 13 Pages, 1 table, 1 Figur
Towards Massive Machine Type Cellular Communications
Cellular networks have been engineered and optimized to carrying
ever-increasing amounts of mobile data, but over the last few years, a new
class of applications based on machine-centric communications has begun to
emerge. Automated devices such as sensors, tracking devices, and meters - often
referred to as machine-to-machine (M2M) or machine-type communications (MTC) -
introduce an attractive revenue stream for mobile network operators, if a
massive number of them can be efficiently supported. The novel technical
challenges posed by MTC applications include increased overhead and control
signaling as well as diverse application-specific constraints such as ultra-low
complexity, extreme energy efficiency, critical timing, and continuous data
intensive uploading. This paper explains the new requirements and challenges
that large-scale MTC applications introduce, and provides a survey on key
techniques for overcoming them. We focus on the potential of 4.5G and 5G
networks to serve both the high data rate needs of conventional human-type
communications (HTC) subscribers and the forecasted billions of new MTC
devices. We also opine on attractive economic models that will enable this new
class of cellular subscribers to grow to its full potential.Comment: accepted and to appear in the IEEE Wireless Communications Magazin
Greening Geographical Power Allocation for Cellular Networks
Harvesting energy from nature (solar, wind etc.) is envisioned as a key
enabler for realizing green wireless networks. However, green energy sources
are geographically distributed and the power amount is random which may not
enough to power a base station by a single energy site. Burning brown energy
sources such as coal and crude oil, though companied with carbon dioxide
emission, provides stable power. In this paper, without sacrificing
communication quality, we investigate how to perform green energy allocation to
abate the dependence on brown energy with hybrid brown and green energy
injected in power networks. We present a comprehensive framework to
characterize the performance of hybrid green and brown energy empowered
cellular network. Novel performance metric "bits/ton\ce{CO2}/Hz" is proposed to
evaluate the greenness of the communication network. As green energy is usually
generated from distributed geographical locations and is time varying, online
geographical power allocation algorithm is proposed to maximize the greenness
of communication network considering electricity transmission's physical laws
i.e., Ohm's law and Kirchhoff's circuit laws. Simulations show that
geographically distributed green energy sources complement each other by
improving the communication capacity while saving brown energy consumption.
Besides, the penetration of green energy can also help reduce power loss on the
transmission breaches
Cyber-physical Control of Road Freight Transport
Freight transportation is of outmost importance for our society and is
continuously increasing. At the same time, transporting goods on roads accounts
for about 26% of all energy consumption and 18% of all greenhouse gas emissions
in the European Union. Despite the influence the transportation system has on
our energy consumption and the environment, road transportation is mainly done
by individual long-haulage trucks with no real-time coordination or global
optimization. In this paper, we review how modern information and communication
technology supports a cyber-physical transportation system architecture with an
integrated logistic system coordinating fleets of trucks traveling together in
vehicle platoons. From the reduced air drag, platooning trucks traveling close
together can save about 10% of their fuel consumption. Utilizing road grade
information and vehicle-to-vehicle communication, a safe and fuel-optimized
cooperative look-ahead control strategy is implemented on top of the existing
cruise controller. By optimizing the interaction between vehicles and platoons
of vehicles, it is shown that significant improvements can be achieved. An
integrated transport planning and vehicle routing in the fleet management
system allows both small and large fleet owners to benefit from the
collaboration. A realistic case study with 200 heavy-duty vehicles performing
transportation tasks in Sweden is described. Simulations show overall fuel
savings at more than 5% thanks to coordinated platoon planning. It is also
illustrated how well the proposed cooperative look-ahead controller for
heavy-duty vehicle platoons manages to optimize the velocity profiles of the
vehicles over a hilly segment of the considered road network
Wireless Access in Ultra-Reliable Low-Latency Communication (URLLC)
The future connectivity landscape and, notably, the 5G wireless systems will
feature Ultra-Reliable Low Latency Communication (URLLC). The coupling of high
reliability and low latency requirements in URLLC use cases makes the wireless
access design very challenging, in terms of both the protocol design and of the
associated transmission techniques. This paper aims to provide a broad
perspective on the fundamental tradeoffs in URLLC as well as the principles
used in building access protocols. Two specific technologies are considered in
the context of URLLC: massive MIMO and multi-connectivity, also termed
interface diversity. The paper also touches upon the important question of the
proper statistical methodology for designing and assessing extremely high
reliability levels.Comment: Invited paper, submitted for revie
Nonlinear Model Predictive Control of A Gasoline HCCI Engine Using Extreme Learning Machines
Homogeneous charge compression ignition (HCCI) is a futuristic combustion
technology that operates with a high fuel efficiency and reduced emissions.
HCCI combustion is characterized by complex nonlinear dynamics which
necessitates a model based control approach for automotive application. HCCI
engine control is a nonlinear, multi-input multi-output problem with state and
actuator constraints which makes controller design a challenging task. Typical
HCCI controllers make use of a first principles based model which involves a
long development time and cost associated with expert labor and calibration. In
this paper, an alternative approach based on machine learning is presented
using extreme learning machines (ELM) and nonlinear model predictive control
(MPC). A recurrent ELM is used to learn the nonlinear dynamics of HCCI engine
using experimental data and is shown to accurately predict the engine behavior
several steps ahead in time, suitable for predictive control. Using the ELM
engine models, an MPC based control algorithm with a simplified quadratic
program update is derived for real time implementation. The working and
effectiveness of the MPC approach has been analyzed on a nonlinear HCCI engine
model for tracking multiple reference quantities along with constraints defined
by HCCI states, actuators and operational limits.Comment: This paper was written as an extract from my PhD thesis (July 2013)
and so references may not be to date as of this submission (Jan 2015). The
article is in review and contains 10 figures, 35 reference
Joint Communication and Motion Energy Minimization in UGV Backscatter Communication
While backscatter communication emerges as a promising solution to reduce
power consumption at IoT devices, the transmission range of backscatter
communication is short. To this end, this work integrates unmanned ground
vehicles (UGVs) into the backscatter system. With such a scheme, the UGV could
facilitate the communication by approaching various IoT devices. However,
moving also costs energy consumption and a fundamental question is: what is the
right balance between spending energy on moving versus on communication? To
answer this question, this paper proposes a joint graph mobility and
backscatter communication model. With the proposed model, the total energy
minimization at UGV is formulated as a mixed integer nonlinear programming
(MINLP) problem. Furthermore, an efficient algorithm that achieves a local
optimal solution is derived, and it leads to automatic trade-off between
spending energy on moving versus on communication. Numerical results are
provided to validate the performance of the proposed algorithm.Comment: Proc. IEEE ICC'19, Shanghai, China, May 2019, 6 page
Small Cell Deployments: Recent Advances and Research Challenges
This paper summarizes the outcomes of the 5th International Workshop on
Femtocells held at King's College London, UK, on the 13th and 14th of February,
2012.The workshop hosted cutting-edge presentations about the latest advances
and research challenges in small cell roll-outs and heterogeneous cellular
networks. This paper provides some cutting edge information on the developments
of Self-Organizing Networks (SON) for small cell deployments, as well as
related standardization supports on issues such as carrier aggregation (CA),
Multiple-Input-Multiple-Output (MIMO) techniques, and enhanced Inter-Cell
Interference Coordination (eICIC), etc. Furthermore, some recent efforts on
issues such as energy-saving as well as Machine Learning (ML) techniques on
resource allocation and multi-cell cooperation are described. Finally, current
developments on simulation tools and small cell deployment scenarios are
presented. These topics collectively represent the current trends in small cell
deployments.Comment: 19 pages, 22 figure
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