Joint Optimization of Multicast Energy in Delay-Constrained Mobile Wireless Networks - 2018


This Project studies the matter of optimizing multicast energy consumption in delay-constrained mobile wireless networks, where data from the supply desires to be delivered to all or any the k destinations among an imposed delay constraint. Most existing works merely focus on deriving transmission schemes with the minimum transmitting energy, overlooking the energy consumption at the receiver aspect. Thus, during this Project, we tend to propose ConMap, a completely unique and general framework for economical transmission theme design that jointly optimizes each the transmitting and receiving energy. In doing thus, we formulate our drawback of designing minimum energy transmission theme, called DeMEM, as a combinatorial optimization one, and prove that the approximation ratio of any polynomial time algorithm for DeMEM can't be higher than (one/4) lnk. Aiming to produce additional efficient approximation schemes, the proposed ConMap initial converts DeMEM into a similar directed Steiner tree downside through making auxiliary graph gadgets to capture energy consumption, then maps the computed tree back into a transmission theme. The advantages of ConMap are threefolded: one) Generality- ConMap exhibits sturdy applicability to a big selection of energy models; 2) Flexibility- Any algorithm designed for the matter of directed Steiner tree can be embedded into our ConMap framework to achieve totally different performance guarantees and complexities; 3) Potency- ConMap preserves the approximation ratio of the embedded Steiner tree algorithm, to which only slight overhead will be incurred. The three options are then empirically validated, with ConMap additionally yielding close to-optimal transmission schemes compared to a brute-force actual algorithm. To our best information, this is the primary work that jointly considers each the transmitting and receiving energy in the design of multicast transmission schemes in mobile wireless networks.

Did you like this research project?

To get this research project Guidelines, Training and Code... Click Here

PROJECT TITLE : CANet Cross-Disease Attention Network for Joint Diabetic Retinopathy and Diabetic Macular Edema Grading ABSTRACT: One in three people who are working-age and have diabetes will go blind due to diabetic retinopathy
PROJECT TITLE : Depth Restoration From RGB-D Data via Joint Adaptive Regularization and Thresholding on Manifolds ABSTRACT: By integrating the properties of local and non-local manifolds that offer low-dimensional parameterizations
PROJECT TITLE : Depth Super-Resolution via Joint Color-Guided Internal and External Regularizations ABSTRACT: Many real-world applications make heavy use of depth information. In practise, however, depth maps tend to have a lower
PROJECT TITLE : Graph-based Joint Dequantization and Contrast Enhancement of Poorly Lit JPEG Images ABSTRACT: The lossy compression of JPEG images results in images with low contrast and coarse quantization artefacts in low-light
PROJECT TITLE : Graph-Regularized Locality-Constrained Joint Dictionary and Residual Learning for Face Sketch Synthesis ABSTRACT: For digital entertainment and police enforcement, face sketch synthesis is a critical issue It's