# A Distributed Energy-Throughput Efficient Cross-Layer Framework Using Hybrid Optimization Algorithm
## 3 Energy-Throughput Efficient Cross-Layer Solution Using MPNMRA for WUSNs
### DECMN Protocol Using MPNMRA for WUSN
The objective is to find the optimal combination of modulation function (\(m_{ij}\)) and channel coding scheme (\(c_{ij}\)) for each possible next-hop neighbor \(j\). Here, \(m_{ij}\in\mathcal{M}\) represents the adopted modulation technique. The chaotic code with length \(l_{ij}\in\mathcal{L}=[l_{min},\ l_{max}]\) is adopted for transmissions over link \(i-j\). By integrating these considerations and employing a distributed two-stage framework, DECMN aims to optimize the cross-layer design for WUSNs, considering application-driven objectives and environmental factors. The decision variables considered in DECMN include the channel coding scheme, the power level, the soil conductivity, the forwarder, the modulation scheme, the operating temperature, the DS-CDMA code length, and link throughput. DECMN combines non-cooperative game theory and distributed power management to address the near-far dilemma and optimize the link throughput in D$-CDMA systems. It utilizes a two-phase decision strategy to determine favored geographic routes with low signaling overhead and scalability, making it suitable for applications such as oil reservoir monitoring. The framework also incorporates a loss-tolerance random-access scheme to enhance the robustness of the system. In the DECMN framework, the sender divides the cross-layer optimization into two sub-problems, inspired by the geographical routing algorithm. These sub-problems are sequentially solved to achieve efficient energy and throughput in WUSNs. Minimizing link metric \(\mathit{EaT}_{ij}\) using MPNMRA: The first sub-problem focuses on minimizing the link metric \(\mathit{EaT}_{ij}\) for each link \(i-j\). The energy and throughput, i.e., \(\mathit{EaT}\) optimization, is performed using the MPNMRA (energy and next-hop multimetric routing algorithm) approach, which aims to minimize energy consumption while achieving the desired link throughput. Selection of the appropriate next-hop and optimum physical functionalities: The second sub-problem involves selecting the appropriate next-hop node \(j^{*}\) and determining the optimum physical functionalities (\(m^{*}_{ij^{*}}\); \(c^{*}_{ij}\)) for the chosen next-hop. This decision is made based on the results obtained from the first sub-problem. The objective is to identify the most suitable next-hop neighbor and the corresponding modulation function and channel coding scheme that optimize the link metric \(\mathit{EaT}_{ij}\). By solving these two sub-problems sequentially, the sender can optimize the cross-layer design for WUSNs. The first sub-problem focuses on finding the optimal combination of modulation and channel coding for each link, considering the power level and acceptable link throughput. The second sub-problem determines the appropriate next-hop neighbor and the optimum physical functionalities based on the results obtained from the first sub-problem. This approach allows for efficient resource allocation, energy management, and link optimization in WUSNs.