27,456 research outputs found
Computing the -coverage of a wireless network
Coverage is one of the main quality of service of a wirelessnetwork.
-coverage, that is to be covered simultaneously by network nodes, is
synonym of reliability and numerous applicationssuch as multiple site MIMO
features, or handovers. We introduce here anew algorithm for computing the
-coverage of a wirelessnetwork. Our method is based on the observation that
-coverage canbe interpreted as layers of -coverage, or simply
coverage. Weuse simplicial homology to compute the network's topology and
areduction algorithm to indentify the layers of -coverage. Weprovide figures
and simulation results to illustrate our algorithm.Comment: Valuetools 2019, Mar 2019, Palma de Mallorca, Spain. 2019. arXiv
admin note: text overlap with arXiv:1802.0844
Movement-Efficient Sensor Deployment in Wireless Sensor Networks With Limited Communication Range.
We study a mobile wireless sensor network (MWSN) consisting of multiple
mobile sensors or robots. Three key factors in MWSNs, sensing quality, energy
consumption, and connectivity, have attracted plenty of attention, but the
interaction of these factors is not well studied. To take all the three factors
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 (or relocation) to optimize the sensing
quality with a limited communication range and a specific network lifetime
constraint. We derive necessary conditions for the optimal sensor deployment in
both homogeneous and heterogeneous MWSNs. According to our derivation, some
sensors are idle in the optimal deployment of heterogeneous MWSNs. Using these
necessary conditions, we design both centralized and distributed algorithms to
provide a flexible and explicit trade-off between sensing uncertainty and
network lifetime. The proposed algorithms are successfully extended to more
applications, such as area coverage and target coverage, via properly selected
density functions. Simulation results show that our algorithms outperform the
existing relocation algorithms
A Coverage Monitoring algorithm based on Learning Automata for Wireless Sensor Networks
To cover a set of targets with known locations within an area with limited or
prohibited ground access using a wireless sensor network, one approach is to
deploy the sensors remotely, from an aircraft. In this approach, the lack of
precise sensor placement is compensated by redundant de-ployment of sensor
nodes. This redundancy can also be used for extending the lifetime of the
network, if a proper scheduling mechanism is available for scheduling the
active and sleep times of sensor nodes in such a way that each node is in
active mode only if it is required to. In this pa-per, we propose an efficient
scheduling method based on learning automata and we called it LAML, in which
each node is equipped with a learning automaton, which helps the node to select
its proper state (active or sleep), at any given time. To study the performance
of the proposed method, computer simulations are conducted. Results of these
simulations show that the pro-posed scheduling method can better prolong the
lifetime of the network in comparison to similar existing method
K-coverage in regular deterministic sensor deployments
An area is k-covered if every point of the area is covered by at least k sensors. K-coverage is necessary for many applications, such as intrusion detection, data gathering, and object tracking. It is also desirable in situations where a stronger environmental monitoring capability is desired, such as military applications. In this paper, we study the problem of k-coverage in deterministic homogeneous deployments of sensors. We examine the three regular sensor deployments - triangular, square and hexagonal deployments - for k-coverage of the deployment area, for k ≥ 1. We compare the three regular deployments in terms of sensor density. For each deployment, we compute an upper bound and a lower bound on the optimal distance of sensors from each other that ensure k-coverage of the area. We present the results for each k from 1 to 20 and show that the required number of sensors to k-cover the area using uniform random deployment is approximately 3-10 times higher than regular deployments
Extremal Properties of Three Dimensional Sensor Networks with Applications
In this paper, we analyze various critical transmitting/sensing ranges for
connectivity and coverage in three-dimensional sensor networks. As in other
large-scale complex systems, many global parameters of sensor networks undergo
phase transitions: For a given property of the network, there is a critical
threshold, corresponding to the minimum amount of the communication effort or
power expenditure by individual nodes, above (resp. below) which the property
exists with high (resp. a low) probability. For sensor networks, properties of
interest include simple and multiple degrees of connectivity/coverage. First,
we investigate the network topology according to the region of deployment, the
number of deployed sensors and their transmitting/sensing ranges. More
specifically, we consider the following problems: Assume that nodes, each
capable of sensing events within a radius of , are randomly and uniformly
distributed in a 3-dimensional region of volume , how large
must the sensing range be to ensure a given degree of coverage of the region to
monitor? For a given transmission range, what is the minimum (resp. maximum)
degree of the network? What is then the typical hop-diameter of the underlying
network? Next, we show how these results affect algorithmic aspects of the
network by designing specific distributed protocols for sensor networks
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