Research On Key Technologies For Efficient Data Collection In Cyber Physical Systems

After computer,Internet,and mobile communication technologies,cyber physical system(CPS)is another core technology in the field of information technology.The essence of CPS is to build a networked closed-loop control system based on the automatic data flow between the cy-ber space and the physical world.Data is the soul of CPS.The perception of the state of the physical world through data collection is the starting point as well as the driving force for a closed loop of automatic data flow.Therefore,how to efficiently collect data has always been a hot research topic in CPS.However,due to factors like low user participation level,uneven user quality level,the ex-istence of malicious users,limited and unbalanced sensing resources,and unreliable transmission channels,it is quite difficult to achieve efficient data collection in CPS.To address the above-mentioned issues,along the main sequence of user incentivization,user selection,data sensing,and data transmission,this dissertation builds a framework of key technolo-gies to achieve efficient,reliable,and robust data collection in CPS.Specifically,considering the characteristics of user selfishness and diversity,the heterogeneity in sensing resources,and the spar-sity of sensing data in CPS,we respectively conduct research on personalized privacy-preserving incentive,trustworthy and cost-effective user selection,efficient data sensing in wide-area and het-erogeneous space,and reliable data transmission over lossy channels.The main contributions and innovations of this dissertation can be summarized as follows:1.We design a contract-based personalized privacy-preserving incentive mechanism,which provides personalized payments for users with different privacy demands as a compensation for the cost of privacy leakage,while ensuring high accuracy of data aggregation.The basic idea is that each user chooses to sign a contract with the data collector,which specifies a privacy-preserving level(PPL)and a payment,and then submits perturbed data with that PPL in return for that payment.In particular,we respectively derive a set of optimal contracts under both complete and incomplete information models,which could maximize the data aggregation accuracy while guaranteeing the budget feasibility,individual rationality,and incentive compatibility properties.Experiments on both synthetic and real-world datasets validate the feasibility and effectiveness of the proposed incentive mechanism.2.We propose a trustworthy and cost-effective user selection mechanism that takes sensing cost heterogeneity and malicious users into consideration simultaneously.To this end,we first propose to utilize an iterative statistical spatial interpolation technique to identify trust-worthy users with the help of a small portion of dedicated sensors.Furthermore,we employ the regularized mutual coherence(RMC)in compressive sensing(CS)theory to characterize the contribution to sensing accuracy of data submitted by different trustworthy users.Finally,the user selection strategy,which consumes the least sensing cost while satisfying a given sensing accuracy level,is determined via a RMC-constrained optimization problem.Exten-sive experiments on a real-world taxi GPS dataset demonstrate that the proposed approach can mitigate the adversarial effects of malicious users,and outperforms the baselines with less sensing cost for the same required sensing quality.3.We propose a compressive sensing(CS)based prejudiced random sensing strategy(PRSS)that explicitly considers the heterogeneous energy consumption of sensing nodes at different locations,in order to accurately attain a desired tradeoff between the overall energy con-sumption and the sensing accuracy.Specifically,each sensing node participates in sensing via distributed random access based on an assigned sensing probability,which is determined by its energy consumption in sending the sensed data and its contribution to sensing accuracy.We employ the statistical restricted isometry property(StRIP)as a practical indicator of the sensing accuracy and derive a sufficiently good recovery error bound based on it.Accord-ingly,we devise a novel convex optimization framework to find the most energy-efficient sensing probability assignment strategy with accuracy guarantee.We evaluate the PRSS us-ing real-world sea surface temperature data traces.Comparative simulations corroborate that the PRSS can significantly reduce energy consumption and prolong network lifetime without sacrificing sensing accuracy.4.We propose a double layer CS based data transmission framework over lossy channels.Specif-ically,the random data loss during transmission is first modeled as a linear dimension-reduced measurement process of CS.Then,in order to eliminate block data losses when adopting a large packet length,a simple CS-based source coding operation is employed at the sender node before transmission.Finally,the missing data are recovered from the re-ceived data using CS reconstruction techniques.Experimental results demonstrate that the proposed data transmission method could not only ensure the accuracy and reliability of data transmission,but could also lower the data transmission volume and reduce the energy con-sumption and transmission latency.