Optimization And Fusion Of Wireless Sensor Networks And Crowdsensing Networks

Sensing data in Wireless Sensor Networks?WSNs?is accurate and stable,and can be used as reliable data source and validation data for data sensing.But it has some problems,such as high maintenance cost,expansion hardness and so on.In recent years,the emergence of Crowdsensing network has provided effective support for data sensing with the flexibility of networking and the diversity of tasks.Therefore,it can be integrated with WSNs to improve sensing tasks.This thesis mainly studies how to optimize the coverage tasks of WSNs and perception tasks of Crowdsensing networks,meanwhile,using respective advantage of WSNs and Crowdsensing networks at the same time can expand and optimize the sensing capabilities of this hybrid network.Firstly,this thesis studies the coverage problem of WSNs,focusing on barrier multi-coverage problem in mobile sensor networks.We propose a new coverage problem called K line coverage.The K line coverage problem requires that object be perceived by at least K sensors.How to make the mobile sensors move as little as possible to meet the specific coverage requirements is the main objective of the coverage problem in the deployment of mobile wireless sensors.Because the energy consumption on moving greatly exceeds the energy consumption of sensing,calculation and transmission in the order of magnitude.For this reason,we propose an optimal algorithm to achieve barrier multi-coverage by minimizing the total moving distance of the mobile sensors.This thesis focuses on mobile sensor networks,because mobility makes the networks more flexible and challenging,and brings configurability for the improvement of network performance.This provides the research preparation and foundation for the study of Crowdsensing networks which have more flexibility.We propose two non-optimal but time-efficient algorithms,LK-KM and LK-KM+.They are all based on the famous Hungarian algorithm?KM algorithm?.Then an optimal layer-based strategy algorithm LLK-MinMovs is proposed,which has polynomial time complexity.This thesis gives the proof of its optimality.Furthermore,we present a general version of the layer-based algorithm GenLLK-MinMovs under the open placement assumption.It solves a serious problem which exists in the algorithm MinSum proposed in the paper^[1].Secondly,how to integrate Crowdsensing networks with sensor networks is an open problem.According to the characteristics of WSNs and Crowdsensing networks,firstly,we use the crowd-sourcing characteristics in Crowdsensing networks to assist WSNs to achieve crowdsourcing-aided positioning.We consider a crowdsourcing-aided GPS positioning framework for WSNs,and pro-pose two optimization objectives for recruiting participants,which are minimum participants and time-efficient.We formulate these two problems as integer linear programming problems,and point out their optimization objectives which are aimed to set function with sub-modulus cost.An al-gorithm based on greedy idea is proposed to solve these two problems,and the correctness of this algorithm is proved and compared in experiments.Secondly,at data level,Crowdsensing networks and WSNs are integrated for better data sensing.The Bayesian method of calibration of sensing data is theoretically analyzed through probabilistic modeling on mutual calibration of data quality,accuracy and confidence.Finally,We also conduct trusted data sensing under incomplete information based on the data accuracy of confidence intervals and the reliability of validation.We use bias and variance to model the worker's quality in crowdsensing networks.For the classical dilemma of exploration and exploiting,we introduce an improved Multi-Armed Bandit algorithm to solve it.A data integration scheme based on Bayesian estimation is proposed,which can better calculate the ground truth of the target location task.In addition,we state that expectation sensing errors can be limited to an upper bound,which is deduced from the bound of expectation regret of Multi-Armed Bandit.In the simulation experiment,we use real-world data sets to verify the theoretical results of our algorithm,and compare the algorithm with the baselines under different settings,and draw the conclusion that the performance of our proposed algorithm is better.Based on the above research,this thesis proposes an effective scheme for optimization and fusion of WSNs and Crowdsensing networks.The main research directions,including mobile WSNs coverage,Crowdsensing Aided positioning,data calibration and worker selection,are studied in depth.Through model building,problem definition,algorithm design,theoretical analysis and simulation experiments,the expected goal and effect of the optimization and fusion of WSNs and Crowdsensing networks are achieved.In addition,this thesis also contains a review about the WSNs coverage problem with uncertainty from 2007 to 2017.The results of surveys provide a theoretical and comparative basis for the study of WSNs and Crowdsensing networks,which is a kind of network with obvious uncertainties.The research results of this thesis provide theoretical preparation and practical basis for further optimization and fusion of WSNs and Crowdsensing networks.