Research On Information Transportability And New Transmission Mechanism In Decentralized Crowd Sensing Networks

As an emerging field,crowdsensing networks have gained more and more attention as its wide range of intersections with traditional areas such as the Internet of Things,so-cial networks,sensor networks,and delay-tolerant networks,and its application of new technologies,such as blockchain,edge computing,etc..However,it also brings new challenges.For example,the cooperation problem for transmission,sensing,etc.Tradi-tional centralized sensing and information processing method becomes less applicable.Aiming at the limitation of current approaches borrowed from traditional networks,this paper analyzes the network reachability and the foundamentals of transmission behind such networks,and proposes new mechanism and solutions for information transmis-sion and remote sensing.Regarding decentralized delay tolerant Crowdsensing networks,this paper ana-lyzes the network reachability via casual entropy theory.Specifically,this paper pro-pose an information-dynamics based transmission mechanism based on the mathemat-ical principle of causal entropy.Different from traditional approaches that need ad-ditional information such as locations,this paper takes historical reachable nodes into account to predict the diversity of network evolution.By maximizing the production of causal entropy of the network,the transmission mechanism could thus maximize its evolution diversity in future.During this study,network reachability and transmis-sion performance are analyzed theoretically,and experimental verification is carried out in multiple data sets,which covers various kinds of networks.The proposed model also shows the power to explain the validity and limitations of traditional best effort strategies such as degree and betweenness from another perspective..The experimen-tal study on various real-world datasets shows the power of diversity,an evolution-ary force and new evluation metric,in network optimization.The proposed diversity based design and optimization achieves lower latency and greater delivery ratio with almost same communication overhead under datasets with greater social attribute,and achieves lower latency and greater delivery ratio with less communication overhead under datasets without social attribute(IoV of Taxis).This research proposes a matrix recovery based computational storage and trans-mission technology for distributed Crowdsensing networks.Based on low-rank ma-trix recovery theory and low-rank of sensed data in spatial and temporal domain,we solved both of the following questions:how to compress and gather the large volume data effectively,and how to keep various time/spacescale event readings unaltered in the gathered data.According to state-of-the-art,either problem can be solved well but never both at the same time.Sensed data with event readings can hardly keep sparsity but can keep low-rank in time domain which allow better tolerance for event readings of our method.We showed via theoretical analysis and experimental study on real-world datasets that our method outperforms the original CS method in terms of compression ratio,outperforms Matrix Completion based method in term of communication over-head and achieves 10db to 20db(SNR)better than typical CS based method in recovery quality.A good deal of sensing content in Crowdsensing networks is multi-media data which result in greater volume of data than that of traditional WSNs.Considering that a considerable part of the data in crowdsensing networks is mul-timedia data,which needs high capacity channel for transmission compared to the tra-ditional wireless sensor network.This paper focuses on fast-lane network transmission service of media-rich content in Crowdsensing networks.Considering dynamic topol-ogy of due to failure of nodes or links,this paper piles up redundant resources and ability among sensors based on temporal and spatial diversity of network content/service ac-cess.With source Erasure Coding and unconscious collaboration of Fog Nodes,we provide a bottom-up approach to speedup the network by leveraging a group of nodes,(fog nodes)capacity for fastlane service.In time domain we take full advantage of small pieces of resources and achieves better tolerance for failure of nodes and links.Splitting of data stream via multi-path makes better efficiency through load-balance.