And Research On Low Energy Consumption Of Wireless Sensor Network In Agricultural Greenhouse Based On Intelligent Algorithm

As a big agricultural country,China’s farming methods have experienced thousands of years of development and changes,completing the transition from traditional agriculture to mechanical agriculture to intelligent agriculture.In the process of transformation of agricultural farming methods,the technology and means of agricultural production are also continuously improved.At present,the booming modern agriculture has shifted from the traditional way of relying on manual farming to the intelligent way of monitoring and management using automatic control technology and network technology.Using wireless sensor network technology to improve or create environmental factors that adapt to crop growth in greenhouses is a model of modern smart agriculture,providing suitable environmental conditions for the growth of crops,such as temperature,humidity and light.However,the energy consumption of wireless sensor networks is not only a key breakthrough point in the practical application of wireless sensor networks,but also a hot issue in current research.Aiming at the energy consumption of wireless sensor network,this thesis proposes an improved intelligent algorithm to optimize the energy consumption of wireless sensor network in agricultural greenhouses,improve the quality of network communication,ensure the healthy operation of the network,and reduce the energy consumption of inspection.In order to reduce the communication energy consumption of wireless sensor network in agricultural greenhouses,firstly,a low-energy clustering scheme is designed to reduce the communication energy consumption generated when nodes in agricultural greenhouses transmit data,and improve the energy consumption utilization rate.A new Elite Clone Genetic Algorithm(ECGA)is proposed,a clustering model is designed,and an objective function is proposed to calculate the communication energy consumption of wireless sensor networks in agricultural greenhouses,a new clustering coding method is designed to Improve performance of low energy clustering.New elite operators and clone operators are added to the traditional genetic algorithm,which improves the convergence speed of the algorithm and prevents the algorithm from falling into premature convergence.In the simulation experiments,for the low-energy clustering problem of wireless sensor networks in agricultural greenhouses,the proposed ECGA clustering scheme is compared with the Shuffled Frog Leaping Algorithm(SFLA)and the Simulated Annealing(SA).It is proved that the proposed scheme can effectively reduce the communication energy consumption of the wireless sensor network in agricultural greenhouses,improve the energy utilization efficiency of the network,and prolong the network life cycle.Secondly,optimize the QoS routing of the wireless sensor network in agricultural greenhouses,and find the optimal routing path while ensuring the quality of data transmission,thereby reducing the communication energy consumption during data transmission.Aiming at the QoS routing problem of wireless sensor networks under the condition of limited energy,an improved Clone Adaptive Whale Optimization Algorithm(CAWOA)is proposed to optimize QoS routing.The elite operator and the adaptive operator are designed to speed up the convergence speed of the algorithm and effectively improve the optimization ability of the algorithm.The CAWOA optimization scheme is compared with the traditional Whale Optimization Algorithm(WOA)and SA.The results show that the proposed CAWOA can find the optimal routing path faster and reduce the communication energy consumption of the wireless sensor network in agricultural greenhouses.Finally,in order to ensure the communication quality of the wireless sensor network in agricultural greenhouses and reduce the energy consumption of inspection,automated robots are used to conduct regular inspections of sensor nodes in the greenhouse to ensure the healthy operation of the network.And plan the inspection path of the automated robot,so as to find the shortest inspection route and reduce the energy consumption of the robot inspection.Aiming at the problem of robot inspection path planning,a new Adaptive Immune Ant Colony Optimization(AIACO)is proposed to optimize the inspection path planning.In addition,new adaptive operators and immune operators are designed to prevent the algorithm from falling into local optimum and improve the optimization ability.To verify the performance of the algorithm,the algorithm is compared with the Immune Cloning Algorithm(ICA)and Genetic Algorithm(GA).The results show that the path length optimized by the proposed algorithm is smaller than the path length optimized by the other two algorithms,so AIACO can effectively reduce the energy consumption of the inspection of the wireless sensor network system in agricultural greenhouses.