Research On Gateway Deployment And Scheduling Algorithms Of Roadside Parking Detection IoTs System

With the acceleration of urbanization,the number of vehicles has increased year by year,and the problem of parking difficulties has become more and more serious.Finding an available on-street parking space can often take a long time during peak hours,causing additional traffic congestion and air pollution.The Internet of Things(Io Ts)system can monitor the status and parking time of parking spaces in real time,and can be used for parking guidance,parking billing,reverse car search and illegal parking detection,etc.The intelligent roadside parking management system based on the Io Ts is one of the important ways to alleviate the parking problem.Roadside parking detection Io Ts relies on gateways to realize bidirectional connection between sensor nodes and remote servers.Reasonable deployment and scheduling of gateway is the key to ensuring network connectivity.However,there are still the following problems in the research of gateway deployment and scheduling: 1)In the roadside parking detection Io Ts,the deployment location of the gateway is limited(usually deployed on the roadside lamppost),and the gateway coverage object——sensor nodes along the road The distribution is linear and the location is known.Most of the existing deployment algorithms do not consider such characteristics,and cannot be well applied to roadside parking scenarios,resulting in too much coverage redundancy for gateway deployment.2)In the roadside parking detection Io Ts,the change of the parking space state will affect the communication link between the sensor nodes and the gateway.Since the communication link between gateways and sensor nodes is easily interfered by vehicles(vehicles can be regarded as large metal objects),when the parking space changes from free to occupied,the number of gateways that sensor nodes can communicate with will decrease,and when parking As bits change from occupied to free,the number of gateways the sensor node can communicate with increases.The existing gateway scheduling algorithm does not take this feature into consideration,which leads to the fact that when the number of gateways that sensor nodes can communicate with decreases,it cannot be guaranteed that all sensor nodes are connected by gateways,and when the number of gateways that sensor nodes can communicate with increases,the energy consumption of gateways is high,the load is too heavy.This paper conducts research work on the above questions:(1)Research on gateway deployment algorithm for roadside parking detection Io Ts system.Considering the characteristics of linear distribution of sensor nodes in the roadside parking detection Io Ts gateway deployment and the limited deployment location of gateways(usually deployed on street lamps),this gateway deployment problem is first modeled as a set cover problem.Then,a linear deployment algorithm is proposed,which converts the sensor nodes and gateway candidate points in the road network into a linear sequence and adopts an iterative search strategy in sequence to reduce coverage redundancy,ensure full coverage of sensor nodes,and seek the smallest gateway set to reduce deployment costs.Finally,experiments are conducted to compare the algorithm with multiple existing algorithms,and the results show that the linear deployment algorithm under different scales of sensors can effectively reduce the deployment cost by 7.05% on average,reduces the coverage redundancy by 3.95%,and reduces the time complexity by an average of 99.3%.(2)Research on the gateway scheduling algorithm of roadside parking detection Io T system.Aiming at the impact of the status change of parking spaces on the number of gateways that sensor nodes can communicate with in the gateway scheduling of roadside parking detection Io T,a genetic scheduling algorithm is proposed.The improved genetic algorithm optimizes the energy consumption of the gateway and balances the load of the gateway,and dynamically schedules the gateway.Connection relationship with sensor nodes.Specific improvements: a)A heuristic algorithm is used to initialize the population to ensure that each individual meets the requirement that all sensor nodes are connected to the gateway;b)For individuals that do not meet the full connection,repair operations are performed to make them fully connected requirements.This can reduce the number of unqualified individuals in the population,thereby improving the performance of the algorithm and search efficiency.Finally,compared with the existing algorithms,the results show that the genetic scheduling algorithm can effectively reduce the energy consumption of the gateway by 5.6%,balance the network load,and increase the life cycle of the gateway by 29.3%.(3)Design a gateway deployment and scheduling system based on GIS(Geographic Information System).This system uses the actual collected gateway and sensor node information,and based on the gateway deployment and scheduling algorithm proposed in the above two research contents,it realizes the actual scene Available gateway deployment and scheduling systems.