2,663 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
Statistical Analysis to Extract Effective Parameters on Overall Energy Consumption of Wireless Sensor Network (WSN)
In this paper, we use statistical tools to analysis dependency between
Wireless Sensor Network (WSN) parameters and overall Energy Consumption (EC).
Our approach has two main phases: profiling, and effective parameter
extraction. In former, a sensor network simulator is re-run 800 times with
different values for eight WSN parameters to profile consumed energy in nodes;
then in latter, three statistical analyses (p-value, linear and non-linear
correlation) are applied to the outcome of profiling phase to extract the most
effective parameters on WSN overall energy consumption.Comment: 5-pages. This paper has been accepted in PDCAT-2012 conference
(http://www.pdcat2012.org/
Coverage and Energy Analysis of Mobile Sensor Nodes in Obstructed Noisy Indoor Environment: A Voronoi Approach
The rapid deployment of wireless sensor network (WSN) poses the challenge of
finding optimal locations for the network nodes, especially so in (i) unknown
and (ii) obstacle-rich environments. This paper addresses this challenge with
BISON (Bio-Inspired Self-Organizing Network), a variant of the Voronoi
algorithm. In line with the scenario challenges, BISON nodes are restricted to
(i) locally sensed as well as (ii) noisy information on the basis of which they
move, avoid obstacles and connect with neighboring nodes. Performance is
measured as (i) the percentage of area covered, (ii) the total distance
traveled by the nodes, (iii) the cumulative energy consumption and (iv) the
uniformity of nodes distribution. Obstacle constellations and noise levels are
studied systematically and a collision-free recovery strategy for failing nodes
is proposed. Results obtained from extensive simulations show the algorithm
outperforming previously reported approaches in both, convergence speed, as
well as deployment cost.Comment: 17 pages, 24 figures, 1 tabl
Spatial prediction in mobile robotic wireless sensor networks with network constraints
© 2016 IEEE. In recent years mobile robotic wireless sensor networks have been a popular choice for modelling spatial phenomena. This research is highly demanding and non-trivial due to challenges from both network and robotic aspects. In this paper, we address the spatial modelling of a physical phenomena with the network connectivity constraints while the mobile robots are striving to achieve the minimum modelling mismatch in terms of root mean square error (RMSE). We have resolved it through Gauss markov random field based approach which is a computationally efficient implementation of Gaussian processes. In this strategy, the Mobile Robotic Wireless Sensor Node (MRWSN) are centrally controlled to maintain the connectivity while minimizing the RMSE. Once the number of MRWSNs reach their maximum coverage, a new MRWSN is requested at the most informative location. The experimental results are convincing and they show the effectiveness of the algorithm
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