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.
Highlights
With the deep development of Internet technology and computer technology, the emergence of social networks has changed the traditional way of human socialization, and social networks have gradually become the main place for people’s daily activities, and there are more and more participants in social networks, and information is spread in social networks at an unprecedented speed [1]
We propose to carry out research on mobile wireless sensor network reliability problems and conduct an in-depth and systematic study in mobile wireless sensor network reliability assessment and optimization by introducing intelligent optimization algorithms and swarm intelligent bionic optimization methods, to address mobile wireless sensor network node hardware and network failure prediction methods, network reliability assessment methods for mesh, tree, and ribbon topologies, mobile path optimization on data collection efficiency and network reliability, reliable data transmission based on data fusion methods, and intelligent fault tolerance of multipath routing and other reliability issues, to provide an effective way to build a reliable mobile wireless sensor network suitable for the complex environment of social networks
The software-defined network (SDN) path selection algorithm with dual impact factors based on the actual quality of experience (QoE) is proposed in the literature [18], which ensures link quality and load balancing by real-time state acquisition and dynamic adjustment of weights, while applying the ant colony algorithm to improve the transmission rate
Summary
With the deep development of Internet technology and computer technology, the emergence of social networks has changed the traditional way of human socialization, and social networks have gradually become the main place for people’s daily activities (entertainment and communication, online shopping, community Q&A, online education, etc.), and there are more and more participants in social networks, and information is spread in social networks at an unprecedented speed [1]. The network integrates information sensing, wireless communication, embedded computing, distributed information processing, and other technologies, the nodes preprocess the collected data and send it to the aggregation nodes in the form of multihop self-organization, and the aggregation nodes transmit the collected information to the monitoring centre, which performs appropriate processing and feeds the processed data back to the decision-makers and transmits it to the required users. We propose to carry out research on mobile wireless sensor network reliability problems and conduct an in-depth and systematic study in mobile wireless sensor network reliability assessment and optimization by introducing intelligent optimization algorithms and swarm intelligent bionic optimization methods, to address mobile wireless sensor network node hardware and network failure prediction methods, network reliability assessment methods for mesh, tree, and ribbon topologies, mobile path optimization on data collection efficiency and network reliability, reliable data transmission based on data fusion methods, and intelligent fault tolerance of multipath routing and other reliability issues, to provide an effective way to build a reliable mobile wireless sensor network suitable for the complex environment of social networks
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