23,721 research outputs found
Coverage Protocols for Wireless Sensor Networks: Review and Future Directions
The coverage problem in wireless sensor networks (WSNs) can be generally
defined as a measure of how effectively a network field is monitored by its
sensor nodes. This problem has attracted a lot of interest over the years and
as a result, many coverage protocols were proposed. In this survey, we first
propose a taxonomy for classifying coverage protocols in WSNs. Then, we
classify the coverage protocols into three categories (i.e. coverage aware
deployment protocols, sleep scheduling protocols for flat networks, and
cluster-based sleep scheduling protocols) based on the network stage where the
coverage is optimized. For each category, relevant protocols are thoroughly
reviewed and classified based on the adopted coverage techniques. Finally, we
discuss open issues (and recommend future directions to resolve them)
associated with the design of realistic coverage protocols. Issues such as
realistic sensing models, realistic energy consumption models, realistic
connectivity models and sensor localization are covered
Movement-efficient Sensor Deployment in Wireless Sensor Networks
We study a mobile wireless sensor network (MWSN) consisting of multiple
mobile sensors or robots. Two key issues in MWSNs - energy consumption, which
is dominated by sensor movement, and sensing coverage - have attracted plenty
of attention, but the interaction of these issues is not well studied. To take
both sensing coverage and movement energy consumption into consideration, we
model the sensor deployment problem as a constrained source coding problem. %,
which can be applied to different coverage tasks, such as area coverage, target
coverage, and barrier coverage. Our goal is to find an optimal sensor
deployment to maximize the sensing coverage with specific energy constraints.
We derive necessary conditions to the optimal sensor deployment with (i) total
energy constraint and (ii) network lifetime constraint. Using these necessary
conditions, we design Lloyd-like algorithms to provide a trade-off between
sensing coverage and energy consumption. Simulation results show that our
algorithms outperform the existing relocation algorithms.Comment: 18 pages, 10 figure
Sensor Deployment for Network-like Environments
This paper considers the problem of optimally deploying omnidirectional
sensors, with potentially limited sensing radius, in a network-like
environment. This model provides a compact and effective description of complex
environments as well as a proper representation of road or river networks. We
present a two-step procedure based on a discrete-time gradient ascent algorithm
to find a local optimum for this problem. The first step performs a coarse
optimization where sensors are allowed to move in the plane, to vary their
sensing radius and to make use of a reduced model of the environment called
collapsed network. It is made up of a finite discrete set of points,
barycenters, produced by collapsing network edges. Sensors can be also
clustered to reduce the complexity of this phase. The sensors' positions found
in the first step are then projected on the network and used in the second
finer optimization, where sensors are constrained to move only on the network.
The second step can be performed on-line, in a distributed fashion, by sensors
moving in the real environment, and can make use of the full network as well as
of the collapsed one. The adoption of a less constrained initial optimization
has the merit of reducing the negative impact of the presence of a large number
of local optima. The effectiveness of the presented procedure is illustrated by
a simulated deployment problem in an airport environment
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
Nonuniform Coverage Control on the Line
This paper investigates control laws allowing mobile, autonomous agents to
optimally position themselves on the line for distributed sensing in a
nonuniform field. We show that a simple static control law, based only on local
measurements of the field by each agent, drives the agents close to the optimal
positions after the agents execute in parallel a number of
sensing/movement/computation rounds that is essentially quadratic in the number
of agents. Further, we exhibit a dynamic control law which, under slightly
stronger assumptions on the capabilities and knowledge of each agent, drives
the agents close to the optimal positions after the agents execute in parallel
a number of sensing/communication/computation/movement rounds that is
essentially linear in the number of agents. Crucially, both algorithms are
fully distributed and robust to unpredictable loss and addition of agents
An evaluation of two distributed deployment algorithms for Mobile Wireless Sensor Networks
Deployment is important in large wireless sensor networks (WSN), specially because nodes may fall due to failure or battery issues. Mobile WSN cope with deployment and reconfiguration at the same time: nodes may move autonomously: i) to achieve a good area coverage; and ii) to distribute as homogeneously as possible. Optimal distribution is computationally expensive and implies high tra c load, so local, distributed approaches may be preferable. This paper presents an experimental evaluation of role-based and behavior based ones. Results show that the later
are better, specially for a large number of nodes in areas with obstacles.Universidad de Málaga. Campus de Excelencia Internacional AndalucĂa Tech
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
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