| As the largest special facility and site for storing radioactive uranium tailings in the nuclear fuel recycle system,the uranium tailings pond,which may cause potential radioactive pollution to its surrounding environment,is a major hazard and a long-term potential radioactive pollution source.In order to avoid and eliminate the radioactive pollution threat caused by uranium tailings ponds to surrounding environment,it is urgent to develop an energy efficient theoretical framework and method for intelligently monitoring the environmental radioactive pollution in the surrounding regions of uranium tailings ponds.In view of the shortcomings including low efficiency and Low intellectualization in existing radioactive pollution monitoring methods and theories,based on the theories of geostatistics and multi-source information fusion,this thesis studies a series of crucial problems on how to effectively monitor the radioactive pollution by fully exploits theadvantages of wireless sensor network and Internet of Things.This thesis mainly focuses on the issuses of WSN and Io T-based radioactive pollution target detection,coverage hole detection and coverage hole healing.We have proposed a data fusion based radiolgocial pollution target detection strategy,devised several energy-efficient confident information coverage hole detection alogrithms,and sovled the multi-modal confident information coverage hole healing problem.The main contributions of this thesis are listed as follows.(1)We propose an energy-efficient scheme for detecting radioactive pollution targets in surrounding uranium tailings pond based on data fusion theory.By fully exploiting the collaboration among sensor nodes,we can effectively detect the radioactive pollution targets and sources.The process of distributed cluster decision fusion includes the following steps.Firstly,each sensor makes a local-level decision based on its own measurement and a given local decision threshold.Secondly,the scattered sensors collect the decision results from their neighbors and make a cluster-level decision based on a K-out-of-N decision fusion rule.Finally,the base station of the network makes the network-level decision from the cluster-lever fusion results of these individual sensors.Experimental results show that the proposed strategy can effectively lower the false alarm rate of each monitoring data step by step,and significantly improve the reliability and accuracy of monitoring data.(2)We define the coverage hole detection problem in Internet of Things for uranium tailings pond environmental radioactive pollution monitoring based on the confident information coverage model(CIC),and study how to energy-efficiently detect the potential CIC holes.Firstly,the sensing field is partitioned into a series of reconstruction grids based on the spatial correlation and correlation range.Then each reconstruction grid will be scanned and detected based on the CIC model to be judged whether it is a hole.Finally,the boundary of the coverage hole will be exacted by image processing method.The experimental results show that both the proposed schemes can efficiently detect the locations and the number of the emerged coverage holes.(3)We study the localized confident information coverage hole detection(LCICHD)problem of environmental radioactive pollution monitoring for uranium tailings pond based on Internet of Things,and devise a family of localized coverage hole detection protocols taking the sensors' communication ability,communication radius and energy consumption into consideration.By making full use of the collaboration and communication capabilities of the neighbor sensor nodes,the locations and spatial distribution of sensor nodes and the spatial correlation of the monitored radioactive physical variables,we develop four heuristic CIC holes detection schemes including the LCHD,LCHDRL,Random and Random RL.Both the LCHD and LCHDRLschemes locally determine coverage status of each subregion and take the sensor communication ability into consideration.While the LCHDRL considers not only the sensor remaining energy but also the residual lifetime during the CIC hole detection.For comparison,both the Random and Random RL schemes arbitrarily select sensors within the sensing field to detect CIC holes,and the Random RL scheme takes the sensors' residual lifetime into consideration during the hole detection process.After acquiring the coverage status of each partitioned local subregion,the coverage hole boundary will be extracted by image processing techniques.Experimental simulations show that the proposed schemes can efficiently detect the emerged coverage holes including the locations and the number.(4)Based on the novel confident information coverage model,we provide a study on how to heal the emerged multi-modal confident information coverage holes(MCICH)in Io T for radiological pollution monitoring,and give two multi-modal mobile sensor dispatching schemas for healing the CIC coverage holes.We develop a family of heuristic schemes including the centralized C-MCICHH,the distributed D-MCICHH and Random by fully exploit the spatial correlation of monitoring environment variables and the coordination of sensor nodes,after proving its NP-completeness by reducing the MCICHH problem to the classical set partition problem.The C-MCICHH converts theMCICHH problem into the maximum weight maximum matching problem by constructing a complete weighted bipartite graph to acquire the optimal solution by utilizing the classical Hungarian strategy.The D-MCICHH greedily selects mobile nodes and sensing units according to the contribution index,and sends them to the holes through the competition mechanism.The Random scheme arbitrarily selects the multi-modal mobile Io T sensors to be dispatched to heal the existing multi-modal CIC holes in a centralized manner.Simulation results validate that the proposed solutions can effectively heal the multi-modal CIC holes,optimize the network coverage performance,improve the network operation efficiency and prolong network lifetime. |
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