Key Technologies Of The Constrained Deployment And Data Acquisition Of Sensors For Internet Of Things

Access to information in the physical world is the basis of various applications and services provided by the Internet of Things(IoTs).Most of current research on sensor sensing is mostly based on the assumption of ideal sensing environment or sufficient number of sensors.In fact,the deployment of sensors and data acquisition is often constrained in many application scenarios of the Internet of Things.Due to various factors such as the sensor's sensing ability,deployment cost,and physical environment,it is difficult for the Internet of Things to fully cover the monitoring region.Research on improving the perception of the Internet of Things in the case of constrained sensor deployment and data acquisition can expand the application range of the Internet of Things and become a hotspot in the research of IoTs perception in recent years.In order to improve the sensing capability of the Internet of Things under the constraints of sensor deployment and data acquisition,some key issues in sensor deployment optimization and acquisition mechanism are studied comprehensively and systelatically in this paper.The main content is divided into two parts:sensor deployment strategy and data acquisition key technologies.The former mainly studies sensor deployment of event-aware monitoring network and sensor sparse deployment of data-space analysis sensor network;the latter mainly studies data acquisition technology of mobile robot in wide area monitoring area and data space reconstruction technology of entire monitoring regions.These two parts are progressive and interrelated.The main contents and innovations of this paper are as follows:Firstly,the deployment optimization of event-aware sensors(such as surveillance cameras)with incomplete coverage is studied.This section focuses on the case that the change of physical quantity with spatial position is frequent in the complex environment of monitoring region.When the number of sensors deployed in the monitoring region is limited or given,in order to improve the capture rate of events,we adopt a weight-aware sparse deployment strategy.We centrally deploy limited sensing devices in key areas.In such Internet of Things application scenarios,the key is to calculate the weight of each place in the monitoring region.This article grids the entire monitoring region.The weights of sensing grids are calculated from three aspects:the probability of occurrence of anomalous events,the impact of anomalous events and the tolerance time of anomalous events.In order to facilitate calculation and analysis,this paper proposes a method of seesaw mapping to calculate the weights of the grids.Then sensors are deployed in the grid center according to the weights.Combining the sensing accuracy and coverage of sensors,and considering the weights of the sensing grids,a model of sensing reliability of the wireless sensor networks is proposed to ensure reliable and efficient abnormal event perception.Secondly,the research is about the problem of sensor sparse deployment for data reconstruction.When the sensors cannot cover the monitoring region completely,that is,the area of the monitoring region is much larger than the sensing area of all sensors,this paper proposed a sparse sensor deployment algorithm based on iterative dividing sub-regions,taking full advantage of the spatial correlation between the physical quantities of the monitoring region.This method is beneficial to improve the reconstruction accuracy in the process of interpolation reconstruction.Experiments show that the proposed sparse deployment method based on iterative sub-regions can improve the accuracy of reconstruction of the data space when using inverse distance interpolation.The proposed iterative quartile sparse deployment method makes the deployment more uniform and has higher reconstruction accuracy.Thirdly,the robust strategy of data space reconstruction of the locations where the sensors are not deployed under incomplete data acquisition of wireless sensor networks is studied in this paper.This paper focuses on the data reconstruction problem in the case of some sensor data loss or distortion in the monitoring region.Because of the spatial correlation of the physical quantities monitored,this article uses the information of sensor-deployed location closest to the location where the sensor is not deployed to reconstruct its information.Wireless Sensor networks are usually deployed in harsh environments,and information loss or errors are inevitable.In this paper,we use the robustness of neural networks and propose the concept of learning operator.We introduce inverse distance interpolation algorithm into artificial neural network.Using arctangent function to harmonize multiple predictions,a robust data reconstruction method is obtained.The simllation results show that our algorithm has higher reconstruction accuracy than the inverse distance interpolation algorithm.Our algorithm has strong robustness.Fourthly,this paper studies the energy and time-aware mobile acquisition problem.When collecting data for Inobile robots,the energy of the robot is limited,and the time for the robot to perform tasks is also limited.How to maximize the amount of information collected under the constraints of energy and time for robots is facing technical challenges.This paper proposed an energy and time-aware mobile acquisition strategy.We transform energy and time-aware mobile acquisition into a multi-objective optimization problem.We use the maximum and minimum method to normalize each target quantity in the multi-objective,so as to integrate the multi-objective into a single-objective problem for optimal solution.In this paper,the popular particle swarm optimization(PSO)algorithm is used to re-plan and optimize the acquisition path.We improved the particle swarm optimization algorithm and proposed a mixed cognitive particle swarm optimization algorithm.In order to obtain optimized results,we also proposed a layer-by-layer screening method for the particle survival of the fittest.In this way,the search space of PSO is enlarged and the convergence rate is faster.Through simulation experiments,we can see that our improved algorithm can achieve earlier convergence and wider optimization solution space than some popular particle swarm optimization algorithms.It makes the robot collect more information,encounter less obstacle probability and travel shorter total distance under energy and time constraints.