4 research outputs found
Optimization and Analysis of Wireless Networks Lifetime using Soft Computing for Industrial Applications
Recently, wireless networks are applied in various engineering and industrial applications. One of the critical problems in wireless network system optimization in intelligent applications is obtaining an adequate energy fairness level. This issue can be resolved by applying effective cluster-based routing optimization with multi-hop routing. Hence a new network structure is developed that is derived from energy consumption architecture by applying soft computing strategies such as evolutionary operators in determining the exact clusters for optimizing energy consumption. The new effective evolutionary operators are tested in the optimization of a lifetime. The proposed method is simulated for different values of the routing factor, α, for different types of networks. The energy levels range from 0.4 to 0.8, achieving good results for nearly 2500 rounds. The proposed strategy optimizes the clusters, and its head is selected reliably. The optimization of cluster head choice has been done based on the base station distance, the energy of the node, and the node's energy efficiency. The reliability of the long-distance nodes is increased during the data transmission by modifying the size of the area of the candidate set of nodes in contrast the near-distance node's energy consumption is reduced. For the energy levels that range from 0.4 to 0.8, the higher network throughput is obtained at the same time network lifetime is optimized compared to other well-known approaches. The proposed model is expected for different industrial wireless network applications to optimize the systems during the long-run simulation and to achieve high reliability and sustainability
Deep Learning for Distributed Optimization: Applications to Wireless Resource Management
This paper studies a deep learning (DL) framework to solve distributed
non-convex constrained optimizations in wireless networks where multiple
computing nodes, interconnected via backhaul links, desire to determine an
efficient assignment of their states based on local observations. Two different
configurations are considered: First, an infinite-capacity backhaul enables
nodes to communicate in a lossless way, thereby obtaining the solution by
centralized computations. Second, a practical finite-capacity backhaul leads to
the deployment of distributed solvers equipped along with quantizers for
communication through capacity-limited backhaul. The distributed nature and the
nonconvexity of the optimizations render the identification of the solution
unwieldy. To handle them, deep neural networks (DNNs) are introduced to
approximate an unknown computation for the solution accurately. In consequence,
the original problems are transformed to training tasks of the DNNs subject to
non-convex constraints where existing DL libraries fail to extend
straightforwardly. A constrained training strategy is developed based on the
primal-dual method. For distributed implementation, a novel binarization
technique at the output layer is developed for quantization at each node. Our
proposed distributed DL framework is examined in various network configurations
of wireless resource management. Numerical results verify the effectiveness of
our proposed approach over existing optimization techniques.Comment: to appear in IEEE J. Sel. Areas Commu