Data Transmission Reliability Analysis of Wireless Sensor Networks for Social Network Optimization

Abstract

With the rapid development of the Internet in recent years, people are using the Internet less and less frequently. People publish and obtain information through various channels on the Internet, and online social networks have become one of the most important channels. Many nodes in social networks and frequent interactions between nodes create great difficulties for privacy protection, and some of the existing studies also have problems such as cumbersome computational steps and low efficiency. In this paper, we take the complex environment of social networks as the research background and focus on the key issues of mobile wireless sensor network reliability from the mobile wireless sensor networks that apply to large-scale, simpler information, and delay tolerance. By introducing intelligent learning methods and swarm intelligence bionic optimization algorithms, we address reliability issues such as mobile wireless sensor network fault prediction methods and topology reliability assessment methods in industrial application environments, the impact of mobile path optimization of mobile wireless sensor networks on data collection efficiency and network reliability, reliable data transmission based on data fusion methods, and intelligent fault tolerance strategies for multipath routing to ensure mobile wireless sensor networks operate energy-efficiently and reliably in complex industrial application environments.

Reliability Evaluation for Chain Routing Protocols in Wireless Sensor Networks Using Reliability Block Diagram

“…It is essential to calculate WSN reliability, to minimize the cost, minimize the node power consumption, and maximize the network lifetime, as unreliable WSNs may fail to accomplish the tasks, resulting in inefficient use of sensor resources [6]. The reliability of the WSN can be evaluated using various methods such as Markov chain theory, universal generating function (UGF), a Monte Carlo (MC) simulation approach, a reliability block diagram (RBD), fault tree (FT) [7], and a signal strength and trust model [8].…”

Variable-Length Multiobjective Social Class Optimization for Trust-Aware Data Gathering in Wireless Sensor Networks

“…This method starts with the initial setting of random solutions, and it determines the heuristic interactions between the solutions for reaching a set of nondominated solutions after a set of iterations [ 12 ]. Multiobjective optimization is evaluated from various perspectives, namely, the Pareto front, hypervolume, delta metric, and generation distance [ 13 ]. The quality of the solutions is assessed from various perspectives including domination, diversity, and enabling more choices for the decision-maker [ 14 ].…”