Joint Optimization of Multicast Energy in Delay-Constrained Mobile Wireless Networks

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Joint Optimization of Multicast Energy in Delay-Constrained Mobile Wireless Networks

Joint Optimization of Multicast Energy in Delay-Constrained Mobile Wireless Networks

Abstract

This Joint Optimization of Multicast Energy in Delay-Constrained Mobile paper looks at the problem of optimizing energy consumption in delayed mobile wireless networks, where information about the source should be delivered within imposed time constraints in all destinations.

Most of the works already under way simply aim to derive transmission schemes with the minimum energy transfer overlooking the recipient’s energy consumption.

In this paper, we therefore propose ConMap, new general framework for the design of an efficient transmission system which jointly optimizes energy transmission and energy consumption.

We are thereby making our problem of designing DeMEM as combinatorial optimization system, minimum energy transmission scheme and demonstrating that any polynomial time algorithm is not approximating better than ln k.

The proposed ConMaps first convert DeMEM into Steiner tree problem, creating auxiliary graph gadgets to capture energy consumption, and then map the tree into system of transmission to ensure more efficient approximation schemes.

Conclusion

In this article, we examined the issue of optimizing the use of multicast energy in delayed mobile networks.First, we formulated our problem called DeMEM as combinatorial optimisation problem and then proved that DeMEM can’t have more than ln approximation rate of any polynomial time algorithms.As DeMEM’s approximation strength implies its NP hardness, we have proposed a new approximation framework-ConMap-to offer efficient approximation schemes.

Initially, the proposed ConMap will convert DeMEM to an equivalent directed problem of the Steiner Bree by creating auxiliary graph gadgets that capture energy consumption.Through theoretical analysis and extensive simulations on real-life network data sets, we demonstrated the generality, flexibility and efficiency of ConMap framework.