187 research outputs found
Distributed Coverage Hole Prevention for Visual Environmental Monitoring with Quadcopters via Nonsmooth Control Barrier Functions
This paper proposes a distributed coverage control strategy for quadcopters
equipped with downward-facing cameras that prevents the appearance of
unmonitored areas in between the quadcopters' fields of view (FOVs). We derive
a necessary and sufficient condition for eliminating any unsurveilled area that
may arise in between the FOVs among a trio of quadcopters by utilizing a power
diagram, i.e. a weighted Voronoi diagram defined by radii of FOVs. Because this
condition can be described as logically combined constraints, we leverage
nonsmooth control barrier functions (NCBFs) to prevent the appearance of
unmonitored areas among a team's FOV. We then investigate the symmetric
properties of the proposed NCBFs to develop a distributed algorithm. The
proposed algorithm can support the switching of the NCBFs caused by changes of
the quadcopters composing trios. The existence of the control input satisfying
NCBF conditions is analyzed by employing the characteristics of the power
diagram. The proposed framework is synthesized with a coverage control law that
maximizes the monitoring quality while reducing overlaps of FOVs. The proposed
method is demonstrated in simulation and experiment.Comment: 17 pages, 18 figures, submitted to the IEEE Transactions on Robotic
Adaptive Algorithms for Coverage Control and Space Partitioning in Mobile Robotic Networks
We consider deployment problems where a mobile robotic network must optimize its configuration in a distributed way in order to minimize a steady-state cost function that depends on the spatial distribution of certain probabilistic events of interest. Three classes of problems are discussed in detail: coverage control problems, spatial partitioning problems, and dynamic vehicle routing problems. Moreover, we assume that the event distribution is a priori unknown, and can only be progressively inferred from the observation of the location of the actual event occurrences. For each problem we present distributed stochastic gradient algorithms that optimize the performance objective. The stochastic gradient view simplifies and generalizes previously proposed solutions, and is applicable to new complex scenarios, for example adaptive coverage involving heterogeneous agents. Finally, our algorithms often take the form of simple distributed rules that could be implemented on resource-limited platforms
Dynamic NOMA-Based Computation Offloading in Vehicular Platoons
Both the mobile edge computing (MEC) based and fog computing (FC) aided
Internet of Vehicles (IoV) constitute promising paradigms of meeting the
demands of low-latency pervasive computing. To this end, we construct a dynamic
NOMA-based computation offloading scheme for vehicular platoons on highways,
where the vehicles can offload their computing tasks to other platoon members.
To cope with the rapidly fluctuating channel quality, we divide the timeline
into successive time slots according to the channel's coherence time. Robust
computing and offloading decisions are made for each time slot after taking the
channel estimation errors into account. Considering a certain time slot, we
first analytically characterize both the locally computed source data and the
offloaded source data as well as the energy consumption of every vehicle in the
platoons. We then formulate the problem of minimizing the long-term energy
consumption by optimizing the allocation of both the communication and
computing resources. To solve the problem formulated, we design an online
algorithm based on the classic Lyapunov optimization method and block
successive upper bound minimization (BSUM) method. Finally, the numerical
simulation results characterize the performance of our algorithm and
demonstrate its advantages both over the local computing scheme and the
orthogonal multiple access (OMA)-based offloading scheme.Comment: 11 pages, 9 figure
Forever Young: Aging Control For Smartphones In Hybrid Networks
The demand for Internet services that require frequent updates through small
messages, such as microblogging, has tremendously grown in the past few years.
Although the use of such applications by domestic users is usually free, their
access from mobile devices is subject to fees and consumes energy from limited
batteries. If a user activates his mobile device and is in range of a service
provider, a content update is received at the expense of monetary and energy
costs. Thus, users face a tradeoff between such costs and their messages aging.
The goal of this paper is to show how to cope with such a tradeoff, by devising
\emph{aging control policies}. An aging control policy consists of deciding,
based on the current utility of the last message received, whether to activate
the mobile device, and if so, which technology to use (WiFi or 3G). We present
a model that yields the optimal aging control policy. Our model is based on a
Markov Decision Process in which states correspond to message ages. Using our
model, we show the existence of an optimal strategy in the class of threshold
strategies, wherein users activate their mobile devices if the age of their
messages surpasses a given threshold and remain inactive otherwise. We then
consider strategic content providers (publishers) that offer \emph{bonus
packages} to users, so as to incent them to download updates of advertisement
campaigns. We provide simple algorithms for publishers to determine optimal
bonus levels, leveraging the fact that users adopt their optimal aging control
strategies. The accuracy of our model is validated against traces from the
UMass DieselNet bus network.Comment: See also http://www-net.cs.umass.edu/~sadoc/agecontrol
Learning and Management for Internet-of-Things: Accounting for Adaptivity and Scalability
Internet-of-Things (IoT) envisions an intelligent infrastructure of networked
smart devices offering task-specific monitoring and control services. The
unique features of IoT include extreme heterogeneity, massive number of
devices, and unpredictable dynamics partially due to human interaction. These
call for foundational innovations in network design and management. Ideally, it
should allow efficient adaptation to changing environments, and low-cost
implementation scalable to massive number of devices, subject to stringent
latency constraints. To this end, the overarching goal of this paper is to
outline a unified framework for online learning and management policies in IoT
through joint advances in communication, networking, learning, and
optimization. From the network architecture vantage point, the unified
framework leverages a promising fog architecture that enables smart devices to
have proximity access to cloud functionalities at the network edge, along the
cloud-to-things continuum. From the algorithmic perspective, key innovations
target online approaches adaptive to different degrees of nonstationarity in
IoT dynamics, and their scalable model-free implementation under limited
feedback that motivates blind or bandit approaches. The proposed framework
aspires to offer a stepping stone that leads to systematic designs and analysis
of task-specific learning and management schemes for IoT, along with a host of
new research directions to build on.Comment: Submitted on June 15 to Proceeding of IEEE Special Issue on Adaptive
and Scalable Communication Network
Three more Decades in Array Signal Processing Research: An Optimization and Structure Exploitation Perspective
The signal processing community currently witnesses the emergence of sensor
array processing and Direction-of-Arrival (DoA) estimation in various modern
applications, such as automotive radar, mobile user and millimeter wave indoor
localization, drone surveillance, as well as in new paradigms, such as joint
sensing and communication in future wireless systems. This trend is further
enhanced by technology leaps and availability of powerful and affordable
multi-antenna hardware platforms. The history of advances in super resolution
DoA estimation techniques is long, starting from the early parametric
multi-source methods such as the computationally expensive maximum likelihood
(ML) techniques to the early subspace-based techniques such as Pisarenko and
MUSIC. Inspired by the seminal review paper Two Decades of Array Signal
Processing Research: The Parametric Approach by Krim and Viberg published in
the IEEE Signal Processing Magazine, we are looking back at another three
decades in Array Signal Processing Research under the classical narrowband
array processing model based on second order statistics. We revisit major
trends in the field and retell the story of array signal processing from a
modern optimization and structure exploitation perspective. In our overview,
through prominent examples, we illustrate how different DoA estimation methods
can be cast as optimization problems with side constraints originating from
prior knowledge regarding the structure of the measurement system. Due to space
limitations, our review of the DoA estimation research in the past three
decades is by no means complete. For didactic reasons, we mainly focus on
developments in the field that easily relate the traditional multi-source
estimation criteria and choose simple illustrative examples.Comment: 16 pages, 8 figures. This work has been submitted to the IEEE for
possible publication. Copyright may be transferred without notice, after
which this version may no longer be accessibl
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