Application of Random Walk Model in Mobile Communication Data Optimization

With the continuous development of society and economy, the development and application of mobile communications have become more and more popular, and more people have begun to use mobile devices for office, study and life. The quality of mobile communications network signals directly affects people’s experience of use. The scope of actual application has gradually increased with the renewal of the technology itself. Based on the random walk model, this paper constructs a mobile communication data optimization plan. Through data storage, transmission delay, and information total control, the optimization form of wireless network mobile communication data transmission performance is studied. Practice shows that this scheme can effectively improve communication quality and transmission efficiency.

Data Optimization in IoT-Assisted Sensor Networks on Cloud Platform

This article presents a new scheme for data optimization in IoT assister sensor networks. The various components of IoT assisted cloud platform are discussed. In addition, a new architecture for IoT assisted sensor networks is presented. Further, a model for data optimization in IoT assisted sensor networks is proposed. A novel Membership inducing Dynamic Data Optimization (MIDDO) algorithm for IoT assisted sensor network is proposed in this research. The proposed algorithm considers every node data and utilized membership function for the optimized data allocation. The proposed framework is compared with two stage optimization, dynamic stochastic optimization and sparsity inducing optimization and evaluated in terms of performance ratio, reliability ratio, coverage ratio and sensing error. It was inferred that the proposed MIDDO algorithm achieves an average performance ratio of 76.55%, reliability ratio of 94.74%, coverage ratio of 85.75% and sensing error of 0.154.

GenACO a multi-objective cached data offloading optimization based on genetic algorithm and ant colony optimization

Background Data exchange and management have been observed to be improving with the rapid growth of 5G technology, edge computing, and the Internet of Things (IoT). Moreover, edge computing is expected to quickly serve extensive and massive data requests despite its limited storage capacity. Such a situation needs data caching and offloading capabilities for proper distribution to users. These capabilities also need to be optimized due to the experience constraints, such as data priority determination, limited storage, and execution time. Methods We proposed a novel framework called Genetic and Ant Colony Optimization (GenACO) to improve the performance of the cached data optimization implemented in previous research by providing a more optimum objective function value. GenACO improves the solution selection probability mechanism to ensure a more reliable balancing of the exploration and exploitation process involved in finding solutions. Moreover, the GenACO has two modes: cyclic and non-cyclic, confirmed to have the ability to increase the optimal cached data solution, improve average solution quality, and reduce the total time consumption from the previous research results. Result The experimental results demonstrated that the proposed GenACO outperformed the previous work by minimizing the objective function of cached data optimization from 0.4374 to 0.4350 and reducing the time consumption by up to 47%.

Study on Multi-Objective Bio-Inspired Algorithms for Feature Selection

With the era of big data, the problems of data size and data optimization have become more diversified and complicated, thus the optimization method has become the focus of people's attention. Algorithm is used to solve practical problems in various fields. In this paper, we studied different techniques of feature selection for big data using optimization algorithm.