5,729 research outputs found
Cooperative and Distributed Localization for Wireless Sensor Networks in Multipath Environments
We consider the problem of sensor localization in a wireless network in a
multipath environment, where time and angle of arrival information are
available at each sensor. We propose a distributed algorithm based on belief
propagation, which allows sensors to cooperatively self-localize with respect
to one single anchor in a multihop network. The algorithm has low overhead and
is scalable. Simulations show that although the network is loopy, the proposed
algorithm converges, and achieves good localization accuracy
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A Qualitative Analysis of Vehicle Positioning Requirements for Connected Vehicle Applications
Cooperative monocular-based SLAM for multi-UAV systems in GPS-denied environments
This work presents a cooperative monocular-based SLAM approach for multi-UAV systems that can operate in GPS-denied environments. The main contribution of the work is to show that, using visual information obtained from monocular cameras mounted onboard aerial vehicles flying in formation, the observability properties of the whole system are improved. This fact is especially notorious when compared with other related visual SLAM configurations. In order to improve the observability properties, some measurements of the relative distance between the UAVs are included in the system. These relative distances are also obtained from visual information. The proposed approach is theoretically validated by means of a nonlinear observability analysis. Furthermore, an extensive set of computer simulations is presented in order to validate the proposed approach. The numerical simulation results show that the proposed system is able to provide a good position and orientation estimation of the aerial vehicles flying in formation.Peer ReviewedPostprint (published version
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
Distributed localization of a RF target in NLOS environments
We propose a novel distributed expectation maximization (EM) method for
non-cooperative RF device localization using a wireless sensor network. We
consider the scenario where few or no sensors receive line-of-sight signals
from the target. In the case of non-line-of-sight signals, the signal path
consists of a single reflection between the transmitter and receiver. Each
sensor is able to measure the time difference of arrival of the target's signal
with respect to a reference sensor, as well as the angle of arrival of the
target's signal. We derive a distributed EM algorithm where each node makes use
of its local information to compute summary statistics, and then shares these
statistics with its neighbors to improve its estimate of the target
localization. Since all the measurements need not be centralized at a single
location, the spectrum usage can be significantly reduced. The distributed
algorithm also allows for increased robustness of the sensor network in the
case of node failures. We show that our distributed algorithm converges, and
simulation results suggest that our method achieves an accuracy close to the
centralized EM algorithm. We apply the distributed EM algorithm to a set of
experimental measurements with a network of four nodes, which confirm that the
algorithm is able to localize a RF target in a realistic non-line-of-sight
scenario.Comment: 30 pages, 11 figure
Multi-mode Tracking of a Group of Mobile Agents
We consider the problem of tracking a group of mobile nodes with limited
available computational and energy resources given noisy RSSI measurements and
position estimates from group members. The multilateration solutions are known
for energy efficiency. However, these solutions are not directly applicable to
dynamic grouping scenarios where neighbourhoods and resource availability may
frequently change. Existing algorithms such as cluster-based GPS duty-cycling,
individual-based tracking, and multilateration-based tracking can only
partially deal with the challenges of dynamic grouping scenarios. To cope with
these challenges in an effective manner, we propose a new group-based
multi-mode tracking algorithm. The proposed algorithm takes the topological
structure of the group as well as the availability of the resources into
consideration and decides the best solution at any particular time instance. We
consider a clustering approach where a cluster head coordinates the usage of
resources among the cluster members. We evaluate the energy-accuracy trade-off
of the proposed algorithm for various fixed sampling intervals. The evaluation
is based on the 2D position tracks of 40 nodes generated using Reynolds'
flocking model. For a given energy budget, the proposed algorithm reduces the
mean tracking error by up to in comparison to the existing
energy-efficient cooperative algorithms. Moreover, the proposed algorithm is as
accurate as the individual-based tracking while using almost half the energy.Comment: Accepted for publication in the 20th international symposium on
wireless personal multimedia communications (WPMC-2017
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