1,850 research outputs found
AMCTD: Adaptive Mobility of Courier nodes in Threshold-optimized DBR Protocol for Underwater Wireless Sensor Networks
In dense underwater sensor networks (UWSN), the major confronts are high
error probability, incessant variation in topology of sensor nodes, and much
energy consumption for data transmission. However, there are some remarkable
applications of UWSN such as management of seabed and oil reservoirs,
exploration of deep sea situation and prevention of aqueous disasters. In order
to accomplish these applications, ignorance of the limitations of acoustic
communications such as high delay and low bandwidth is not feasible. In this
paper, we propose Adaptive mobility of Courier nodes in Threshold-optimized
Depth-based routing (AMCTD), exploring the proficient amendments in depth
threshold and implementing the optimal weight function to achieve longer
network lifetime. We segregate our scheme in 3 major phases of weight updating,
depth threshold variation and adaptive mobility of courier nodes. During data
forwarding, we provide the framework for alterations in threshold to cope with
the sparse condition of network. We ultimately perform detailed simulations to
scrutinize the performance of our proposed scheme and its comparison with other
two notable routing protocols in term of network lifetime and other essential
parameters. The simulations results verify that our scheme performs better than
the other techniques and near to optimal in the field of UWSN.Comment: 8th International Conference on Broadband and Wireless Computing,
Communication and Applications (BWCCA'13), Compiegne, Franc
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
K-Means and Fuzzy based Hybrid Clustering Algorithm for WSN
Wireless Sensor Networks (WSN) acquired a lotof attention due to their widespread use in monitoring hostileenvironments, critical surveillance and security applications. Inthese applications, usage of wireless terminals also has grownsignificantly. Grouping of Sensor Nodes (SN) is called clusteringand these sensor nodes are burdened by the exchange of messagescaused due to successive and recurring re-clustering, whichresults in power loss. Since most of the SNs are fitted with nonrechargeablebatteries, currently researchers have been concentratingtheir efforts on enhancing the longevity of these nodes. Forbattery constrained WSN concerns, the clustering mechanism hasemerged as a desirable subject since it is predominantly good atconserving the resources especially energy for network activities.This proposed work addresses the problem of load balancingand Cluster Head (CH) selection in cluster with minimum energyexpenditure. So here, we propose hybrid method in which clusterformation is done using unsupervised machine learning based kmeansalgorithm and Fuzzy-logic approach for CH selection
K-Means and Fuzzy based Hybrid Clustering Algorithm for WSN
Wireless Sensor Networks (WSN) acquired a lotof attention due to their widespread use in monitoring hostileenvironments, critical surveillance and security applications. Inthese applications, usage of wireless terminals also has grownsignificantly. Grouping of Sensor Nodes (SN) is called clusteringand these sensor nodes are burdened by the exchange of messagescaused due to successive and recurring re-clustering, whichresults in power loss. Since most of the SNs are fitted with nonrechargeablebatteries, currently researchers have been concentratingtheir efforts on enhancing the longevity of these nodes. Forbattery constrained WSN concerns, the clustering mechanism hasemerged as a desirable subject since it is predominantly good atconserving the resources especially energy for network activities.This proposed work addresses the problem of load balancingand Cluster Head (CH) selection in cluster with minimum energyexpenditure. So here, we propose hybrid method in which clusterformation is done using unsupervised machine learning based kmeansalgorithm and Fuzzy-logic approach for CH selection
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