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

Solving Fixed Channel Allocation using Hybrid Evolutionary Method

Assigning channels to cells in wireless networks is an NP-hard problem. There are different soft computing strategies are applied to solve fixed channel allocation with the interference constraints of the mobile network. This research focuses on applying the new genetic operators with the local search and heuristic strategies to obtain the near optimal solution. This hybrid evolutionary method is implemented on some of the benchmark instances. Near optimal solution is obtained in the minimal complexity and the results are found to be better than the existing methods

Solving Fixed Channel Allocation using Hybrid Evolutionary Method

Assigning channels to cells in wireless networks is an NP-hard problem. There are different soft computing strategies are applied to solve fixed channel allocation with the interference constraints of the mobile network. This research focuses on applying the new genetic operators with the local search and heuristic strategies to obtain the near optimal solution. This hybrid evolutionary method is implemented on some of the benchmark instances. Near optimal solution is obtained in the minimal complexity and the results are found to be better than the existing methods

Design and Analysis of a Cluster-Based Intelligent Hybrid Recommendation System for E-Learning Applications

Recently, different recommendation techniques in e-learning have been designed that are helpful to both the learners and the educators in a wide variety of e-learning systems. Customized learning, which requires e-learning systems designed based on educational experience that suit the interests, goals, abilities, and willingness of both the learners and the educators, is required in some situations. In this research, we develop an intelligent recommender using split and conquer strategy-based clustering that can adapt automatically to the requirements, interests, and levels of knowledge of the learners. The recommender analyzes and learns the styles and characteristics of learners automatically. The different styles of learning are processed through the split and conquer strategy-based clustering. The proposed cluster-based linear pattern mining algorithm is applied to extract the functional patterns of the learners. Then, the system provides intelligent recommendations by evaluating the ratings of frequent sequences. Experiments were conducted on different groups of learners and datasets, and the proposed model suggested essential learning activities to learners based on their style of learning, interest classification, and talent features. It was experimentally found that the proposed cluster-based recommender improves the recommendation performance by resulting in more lessons completed when compared to learners present in the no-recommender cluster category. It was found that more than 65% of the learners considered all criteria to evaluate the proposed recommender. The simulation of the proposed recommender showed that for learner size values of L, the recommendation list size, and the attributes of learners. The learners were also satisfied with the accuracy and speed of the recommender. For the sample dataset considered, a significant difference was observed in the standard deviation σ and mean μ of parameters, in terms of the Recall (List, User) and Ranking Score (User) measures, compared to other methods. The devised method performed well concerning all the considered metrics when compared to other methods. The simulation results signify that this recommender minimized the mean absolute error metric for the different clusters in comparison with some well-known methods

Solving channel allocation problem using new genetic operators – An experimental approach

Channel allocation schemes are required in mobile networks to allocate bandwidth and channels to mobile stations. The main objective of channel allocation is to achieve maximum efficiency by means of channel reuse by avoiding adjacent and co-channel interferences among nearby cells or networks that share the bandwidth. Channel allocation problem, an NP-hard problem, which means an exact solution cannot be found in polynomial time. Evolutionary and heuristic algorithms can be applied to find near optimal solutions to channel allocation. The main objective of this research is to design a new constraint based genetic crossover and mutation operators with the effective heuristic initialization to solve channel allocation with minimal computational complexity. The performances of the proposed genetic operators are compared with existing methods. It has been found that the proposed method significantly reduces the computational complexity

New approximation algorithms for solving graph coloring problem – An experimental approach

Some of the real world applications require the solution to graph coloring problem, an NP-hard combinatorial optimization problem. χ(G), the chromatic number of a graph G, the minimum number of colors required to color the vertex set V(G) with adjacent vertices assigned with different color can also be obtained using evolutionary methods. This paper exhibits two new approximation methods of solving graph coloring based on μ½(G), the median of the degrees V(G). In the first method, a heuristic procedure is designed to color V(G) which works in two stages. In the first stage, to minimize the conflicting edges, the vertices of V(G) whose degrees are ≥μ½(G) are colored. Then the remaining vertices are colored through a heuristic procedure. The second method is implemented using divide & conquer strategy. These new approximation algorithms are exhibited on some of the small, intermediate and large benchmark graphs and the results are compared. The proposed algorithms significantly reduce the computational complexity in obtaining the near optimal solution and also the results are found to be better than the existing approaches

Optimization and Analysis of Wireless Networks Lifetime using Soft Computing for Industrial Applications

135-141Recently, 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

New hybrid decentralized evolutionary approach for DIMACS challenge graph coloring & wireless network instances

The Graph Coloring Problem is an NP-hard combinatorial optimization problem, and it is being used in different real-world environments. The chromatic integer is determined using different probabilistic methods. This paper explores a new hybrid decentralized evolutionary approach that applies the fixed colors and reduces the edge conflicts iteratively using greedy, split and conquer strategies. This article explores a new hybrid decentralized stochastic methodology for solving graph coloring. The method is constructed by combining the following strategies: Greedy heuristics, split & conquer, and decentralized strategy with an advanced & enhanced global search evolutionary operator. These hybrid design strategies are exhibited on complex DIMACS challenge benchmark graphs and wireless network instances. The proposed approach minimizes the complexity and converges to the optimal solution within a minimal time. The minimum percentage of successful runs obtained for the DIMACS benchmarks lies in (82%, 85%) except for the difficult instance latin_square_10.col, the vertices count n = 900 and edges count m = 307350. For the latin_square_10.col graph, the minimum color is reduced to 97 compared to other methods with less successful runs percentage. For the difficult instance flat1000_76_0.col graph, the minimum color is reduced to 76 compared to other methods, resulting in a better successful run. The method obtains the minimum color as χ(G) for the difficult instances le.col and flat.col graphs compared to other methods. The time taken to execute the developed technique is compared with the competing methods, and the proposed method outperforms very competitively in finding the minimum color for large graphs and also in finding the better solution with the high frequency of convergence (> 98%) in the channel allocation of wireless networks compared to the current methods

Multi-Layered Non-Local Bayes Model for Lung Cancer Early Diagnosis Prediction with the Internet of Medical Things

The Internet of Things (IoT) has been influential in predicting major diseases in current practice. The deep learning (DL) technique is vital in monitoring and controlling the functioning of the healthcare system and ensuring an effective decision-making process. In this study, we aimed to develop a framework implementing the IoT and DL to identify lung cancer. The accurate and efficient prediction of disease is a challenging task. The proposed model deploys a DL process with a multi-layered non-local Bayes (NL Bayes) model to manage the process of early diagnosis. The Internet of Medical Things (IoMT) could be useful in determining factors that could enable the effective sorting of quality values through the use of sensors and image processing techniques. We studied the proposed model by analyzing its results with regard to specific attributes such as accuracy, quality, and system process efficiency. In this study, we aimed to overcome problems in the existing process through the practical results of a computational comparison process. The proposed model provided a low error rate (2%, 5%) and an increase in the number of instance values. The experimental results led us to conclude that the proposed model can make predictions based on images with high sensitivity and better precision values compared to other specific results. The proposed model achieved the expected accuracy (81%, 95%), the expected specificity (80%, 98%), and the expected sensitivity (80%, 99%). This model is adequate for real-time health monitoring systems in the prediction of lung cancer and can enable effective decision-making with the use of DL techniques