mobile wireless sensor
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2022 ◽  
Vol 2022 ◽  
pp. 1-12
Author(s):  
Xia Xu ◽  
Jin Tang ◽  
Hua Xiang

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.


Author(s):  
Valery Romaniuk ◽  
Olexandr Lysenko ◽  
Valery Novikov ◽  
Ihor Sushyn

Background. The article presents the results of a study of methods of positioning, localization and data collection from nodes of a mobile wireless sensor network using intelligent adaptive telecommunication air platforms. To implement the study of this research topic, an analysis of literary sources on this topic was carried out. Based on a fairly rich bibliographic material, this work has the main task of examining, analyzing and systematizing already known approaches to positioning objects in wireless sensor networks using intelligent adaptive telecommunication air platforms and suggesting options for their development. Objective. The aim of the work is to improve the methods of direct data collection of TA from the nodes of BSM, the general directions of synthesis of which are defined in the work. Methods. Methods of cluster analysis (network clustering), graph theory (research of analytical models of BSM with TA functioning, construction of cluster topology), theory of telecommunication networks (when calculating bandwidth in BSM with TA radio channels) and theory were used to solve the formulated problem. (when developing a positioning model for telecommunications air platforms) Results. A technique for evaluating the effectiveness of methods for collecting data from wireless sensor networks using intelligent adaptive telecommunication air platforms is proposed. Conclusions. The method of collecting TA monitoring data from the main nodes of clustered BSM has been improved. The method of estimation of efficiency of methods of data collection with BSM by telecommunication air platforms is offered.


2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Pin-Jiao Zhao ◽  
Guo-Bing Hu ◽  
Liang-Tian Wan

For tacking and localizing sources in the mobile wireless sensor network, underdetermined direction of arrival (DOA) estimation with high-accuracy is a crucial issue. In this paper, a novel sparse array configuration is developed for accurate DOA estimation from the perspective of sum-difference coarray (SDCA). As compared with most of the existing sparse array configurations, the proposed array can effectively reduce the overlap between difference coarray (DCA) and sum coarray (SCA) and can achieve more consecutive degrees of freedom (DOF), more sources can be resolved accordingly. Additionally, the proposed array has hole-free DCA and SDCA. Then, the concept of coarray redundancy ratio (CRR) is introduced for evaluating the coarray overlap quantitatively and the closed-form CRR expressions of the proposed array are derived in detail. Based on the good properties of the proposed array, vectorized conjugate augmented MUSIC (VCAM) is adopted for underdetermined DOA estimation. The theoretical propositions and numerical simulations demonstrate the superior performance of the proposed array in terms of CRR, consecutive DOF, and DOA estimation accuracy.


2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Giji Kiruba ◽  
Benita

Abstract The energy performance of IoT-MWSNs may be augmented by using a suitable clustering technique for integrating IoT sensors. Clustering, on the other hand, requires additional overhead, such as determining the cluster head and cluster formation. Environmental Energy Attentive Clustering with Remote Nodes is a unique environmental energy attentive clustering approach for IoT-MWSNs proposed in this study methodology (E2ACRN). Cluster head (CH) in E2ACRN is entirely determined by weight. The residual energy of each IoT sensor and the local average energy of all IoT sensors in the cluster are used to calculate the weight. Inappropriately planned allocated clustering techniques might result in nodes being too far away from CH. These distant nodes communicate with the sink by using more energy. The ambient average energy, remoteness among IoT sensors, and sink are used to determine whether a distant node transmits its information to a CH in the previous cycle or to sink in order to lengthen lifetime. The simulation results of the current technique revealed that E2ACRN performs better than previous clustering algorithms.


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