<div>Localization is a fundamental task for optical internet</div><div>of underwater things (O-IoUT) to enable various applications</div><div>such as data tagging, routing, navigation, and maintaining link connectivity. The accuracy of the localization techniques for OIoUT greatly relies on the location of the anchors. Therefore, recently localization techniques for O-IoUT which optimize the anchor’s location are proposed. However, optimization of anchors location for all the smart objects in the network is not a useful solution. Indeed, in a network of densely populated smart objects, the data collected by some sensors are more valuable than the data collected from other sensors. Therefore, in this paper, we propose a three-dimensional accurate localization technique by optimizing the anchor’s location for a set of smart objects. Spectral graph partitioning is used to select the set of valuable</div><div>sensors.</div>
Objective:
Optical networks exploit the Wavelength Division Multiplexing (WDM) to meet
the ever-growing bandwidth demands of upcoming communication applications. This is achieved by dividing
the enormous transmission bandwidth of fiber into smaller communication channels. The major
problem with WDM network design is to find an optimal path between two end users and allocate an
available wavelength to the chosen path for the successful data transmission.
Methods:
This communication over a WDM network is carried out through lightpaths. The merging of all
these lightpaths in an optical network generates a virtual topology which is suitable for the optimal network
design to meet the increasing traffic demands. But, this virtual topology design is an NP-hard problem.
This paper aims to explore Mixed Integer Linear Programming (MILP) framework to solve this design
issue.
Results:
The comparative results of the proposed and existing mathematical models show that the
proposed algorithm outperforms with the various performance parameters.
Conclusion:
Finally, it is concluded that network congestion is reduced marginally in the overall performance
of the network.
An introduction to the mathematical tools used in the study of networks. Topics discussed include: the adjacency matrix; weighted, directed, acyclic, and bipartite networks; multilayer and dynamic networks; trees; planar networks. Some basic properties of networks are then discussed, including degrees, density and sparsity, paths on networks, component structure, and connectivity and cut sets. The final part of the chapter focuses on the graph Laplacian and its applications to network visualization, graph partitioning, the theory of random walks, and other problems.
MSP: Multiple Sub-graph Query Processing using Structure-based Graph Partitioning Strategy and Map-Reduce