4,741 research outputs found
Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey
Wireless sensor networks (WSNs) consist of autonomous and resource-limited
devices. The devices cooperate to monitor one or more physical phenomena within
an area of interest. WSNs operate as stochastic systems because of randomness
in the monitored environments. For long service time and low maintenance cost,
WSNs require adaptive and robust methods to address data exchange, topology
formulation, resource and power optimization, sensing coverage and object
detection, and security challenges. In these problems, sensor nodes are to make
optimized decisions from a set of accessible strategies to achieve design
goals. This survey reviews numerous applications of the Markov decision process
(MDP) framework, a powerful decision-making tool to develop adaptive algorithms
and protocols for WSNs. Furthermore, various solution methods are discussed and
compared to serve as a guide for using MDPs in WSNs
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
Energy Efficient and Reliable Wireless Sensor Networks - An Extension to IEEE 802.15.4e
Collecting sensor data in industrial environments from up to some tenth of
battery powered sensor nodes with sampling rates up to 100Hz requires energy
aware protocols, which avoid collisions and long listening phases. The IEEE
802.15.4 standard focuses on energy aware wireless sensor networks (WSNs) and
the Task Group 4e has published an amendment to fulfill up to 100 sensor value
transmissions per second per sensor node (Low Latency Deterministic Network
(LLDN) mode) to satisfy demands of factory automation. To improve the
reliability of the data collection in the star topology of the LLDN mode, we
propose a relay strategy, which can be performed within the LLDN schedule.
Furthermore we propose an extension of the star topology to collect data from
two-hop sensor nodes. The proposed Retransmission Mode enables power savings in
the sensor node of more than 33%, while reducing the packet loss by up to 50%.
To reach this performance, an optimum spatial distribution is necessary, which
is discussed in detail
Wireless Backhaul Node Placement for Small Cell Networks
Small cells have been proposed as a vehicle for wireless networks to keep up
with surging demand. Small cells come with a significant challenge of providing
backhaul to transport data to(from) a gateway node in the core network. Fiber
based backhaul offers the high rates needed to meet this requirement, but is
costly and time-consuming to deploy, when not readily available. Wireless
backhaul is an attractive option for small cells as it provides a less
expensive and easy-to-deploy alternative to fiber. However, there are multitude
of bands and features (e.g. LOS/NLOS, spatial multiplexing etc.) associated
with wireless backhaul that need to be used intelligently for small cells.
Candidate bands include: sub-6 GHz band that is useful in non-line-of-sight
(NLOS) scenarios, microwave band (6-42 GHz) that is useful in point-to-point
line-of-sight (LOS) scenarios, and millimeter wave bands (e.g. 60, 70 and 80
GHz) that are recently being commercially used in LOS scenarios. In many
deployment topologies, it is advantageous to use aggregator nodes, located at
the roof tops of tall buildings near small cells. These nodes can provide high
data rate to multiple small cells in NLOS paths, sustain the same data rate to
gateway nodes using LOS paths and take advantage of all available bands. This
work performs the joint cost optimal aggregator node placement, power
allocation, channel scheduling and routing to optimize the wireless backhaul
network. We formulate mixed integer nonlinear programs (MINLP) to capture the
different interference and multiplexing patterns at sub-6 GHz and microwave
band. We solve the MINLP through linear relaxation and branch-and-bound
algorithm and apply our algorithm in an example wireless backhaul network of
downtown Manhattan.Comment: Invited paper at Conference on Information Science & Systems (CISS)
201
Adaptive Controller Placement for Wireless Sensor-Actuator Networks with Erasure Channels
Wireless sensor-actuator networks offer flexibility for control design. One
novel element which may arise in networks with multiple nodes is that the role
of some nodes does not need to be fixed. In particular, there is no need to
pre-allocate which nodes assume controller functions and which ones merely
relay data. We present a flexible architecture for networked control using
multiple nodes connected in series over analog erasure channels without
acknowledgments. The control architecture proposed adapts to changes in network
conditions, by allowing the role played by individual nodes to depend upon
transmission outcomes. We adopt stochastic models for transmission outcomes and
characterize the distribution of controller location and the covariance of
system states. Simulation results illustrate that the proposed architecture has
the potential to give better performance than limiting control calculations to
be carried out at a fixed node.Comment: 10 pages, 8 figures, to be published in Automatic
Deploy-As-You-Go Wireless Relay Placement: An Optimal Sequential Decision Approach using the Multi-Relay Channel Model
We use information theoretic achievable rate formulas for the multi-relay
channel to study the problem of as-you-go deployment of relay nodes. The
achievable rate formulas are for full-duplex radios at the relays and for
decode-and-forward relaying. Deployment is done along the straight line joining
a source node and a sink node at an unknown distance from the source. The
problem is for a deployment agent to walk from the source to the sink,
deploying relays as he walks, given that the distance to the sink is
exponentially distributed with known mean. As a precursor, we apply the
multi-relay channel achievable rate formula to obtain the optimal power
allocation to relays placed along a line, at fixed locations. This permits us
to obtain the optimal placement of a given number of nodes when the distance
between the source and sink is given. Numerical work suggests that, at low
attenuation, the relays are mostly clustered near the source in order to be
able to cooperate, whereas at high attenuation they are uniformly placed and
work as repeaters. We also prove that the effect of path-loss can be entirely
mitigated if a large enough number of relays are placed uniformly between the
source and the sink. The structure of the optimal power allocation for a given
placement of the nodes, then motivates us to formulate the problem of as-you-go
placement of relays along a line of exponentially distributed length, and with
the exponential path-loss model, so as to minimize a cost function that is
additive over hops. The hop cost trades off a capacity limiting term, motivated
from the optimal power allocation solution, against the cost of adding a relay
node. We formulate the problem as a total cost Markov decision process,
establish results for the value function, and provide insights into the
placement policy and the performance of the deployed network via numerical
exploration.Comment: 21 pages. arXiv admin note: substantial text overlap with
arXiv:1204.432
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