4,876 research outputs found
Multisensor Poisson Multi-Bernoulli Filter for Joint Target-Sensor State Tracking
In a typical multitarget tracking (MTT) scenario, the sensor state is either
assumed known, or tracking is performed in the sensor's (relative) coordinate
frame. This assumption does not hold when the sensor, e.g., an automotive
radar, is mounted on a vehicle, and the target state should be represented in a
global (absolute) coordinate frame. Then it is important to consider the
uncertain location of the vehicle on which the sensor is mounted for MTT. In
this paper, we present a multisensor low complexity Poisson multi-Bernoulli MTT
filter, which jointly tracks the uncertain vehicle state and target states.
Measurements collected by different sensors mounted on multiple vehicles with
varying location uncertainty are incorporated sequentially based on the arrival
of new sensor measurements. In doing so, targets observed from a sensor mounted
on a well-localized vehicle reduce the state uncertainty of other poorly
localized vehicles, provided that a common non-empty subset of targets is
observed. A low complexity filter is obtained by approximations of the joint
sensor-feature state density minimizing the Kullback-Leibler divergence (KLD).
Results from synthetic as well as experimental measurement data, collected in a
vehicle driving scenario, demonstrate the performance benefits of joint
vehicle-target state tracking.Comment: 13 pages, 7 figure
Robotic Wireless Sensor Networks
In this chapter, we present a literature survey of an emerging, cutting-edge,
and multi-disciplinary field of research at the intersection of Robotics and
Wireless Sensor Networks (WSN) which we refer to as Robotic Wireless Sensor
Networks (RWSN). We define a RWSN as an autonomous networked multi-robot system
that aims to achieve certain sensing goals while meeting and maintaining
certain communication performance requirements, through cooperative control,
learning and adaptation. While both of the component areas, i.e., Robotics and
WSN, are very well-known and well-explored, there exist a whole set of new
opportunities and research directions at the intersection of these two fields
which are relatively or even completely unexplored. One such example would be
the use of a set of robotic routers to set up a temporary communication path
between a sender and a receiver that uses the controlled mobility to the
advantage of packet routing. We find that there exist only a limited number of
articles to be directly categorized as RWSN related works whereas there exist a
range of articles in the robotics and the WSN literature that are also relevant
to this new field of research. To connect the dots, we first identify the core
problems and research trends related to RWSN such as connectivity,
localization, routing, and robust flow of information. Next, we classify the
existing research on RWSN as well as the relevant state-of-the-arts from
robotics and WSN community according to the problems and trends identified in
the first step. Lastly, we analyze what is missing in the existing literature,
and identify topics that require more research attention in the future
Optimizing Age of Information with Correlated Sources
We develop a simple model for the timely monitoring of correlated sources
over a wireless network. Using this model, we study how to optimize
weighted-sum average Age of Information (AoI) in the presence of correlation.
First, we discuss how to find optimal stationary randomized policies and show
that they are at-most a factor of two away from optimal policies in general.
Then, we develop a Lyapunov drift-based max-weight policy that performs better
than randomized policies in practice and show that it is also at-most a factor
of two away from optimal. Next, we derive scaling results that show how AoI
improves in large networks in the presence of correlation. We also show that
for stationary randomized policies, the expression for average AoI is robust to
the way in which the correlation structure is modeled. Finally, for the setting
where correlation parameters are unknown and time-varying, we develop a
heuristic policy that adapts its scheduling decisions by learning the
correlation parameters in an online manner. We also provide numerical
simulations to support our theoretical results.Comment: To be presented at ACM MobiHoc 202
Age of Information of a Server with Energy Requirements
We investigate a system with Poisson arrivals to two queues. One queue stores the status updates of the process of interest (or data packets) and the other handles the energy that is required to deliver the updates to the monitor. We consider that the energy is represented by packets of discrete unit. When an update ends service, it is sent to the energy queue and, if the energy queue has one packet, the update is delivered successfully and the energy packet disappears; however, in case the energy queue is empty, the update is lost. Both queues can handle, at most, one packet and the service time of updates is exponentially distributed. Using the Stochastic Hybrid System method, we characterize the average Age of Information of this system. Due to the difficulty of the derived expression, we also explore approximations of the average Age of Information of this systemJosu Doncel has received funding from the Department of Education of the Basque Government through the Consolidated Research Group MATHMODE (IT1294-19), from the Marie Sklodowska-Curie grant agreement No. 777778 and from from the Spanish Ministry of Science and Innovation with reference PID2019-108111RB-I00 (FEDER/AEI). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscrip
Timely Monitoring of Dynamic Sources with Observations from Multiple Wireless Sensors
Age of Information (AoI) has recently received much attention due to its
relevance in IoT sensing and monitoring applications. In this paper, we
consider the problem of minimizing the AoI in a system in which a set of
sources are observed by multiple sensors in a many-to-many relationship, and
the probability that a sensor observes a source depends on the state of the
source. This model represents many practical scenarios, such as the ones in
which multiple cameras or microphones are deployed to monitor objects moving in
certain areas. We formulate the scheduling problem as a Markov Decision
Process, and show how the age-optimal scheduling policy can be obtained. We
further consider partially observable variants of the problem, and devise
approximate policies for large state spaces. Our evaluations show that the
approximate policies work well in the considered scenarios, and that the fact
that sensors can observe multiple sources is beneficial, especially when there
is high uncertainty of the source states.Comment: Submitted for publicatio
Minimizing the Age of Information from Sensors with Common Observations
We study the average Age of Information (AoI) in a system where physical
sources produce independent discrete-time updates that are each observed by
several sensors. We devise a model that is simple, but still capable to capture
the main tradeoffs. Two sensor scheduling policies are proposed to minimize the
AoI of the sources; one in which the system parameters are assumed known, and
one in which they are learned. Both policies are able to exploit the common
sensor information to reduce the AoI, resulting in large reductions in AoI
compared to common schedules
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