Layout Optimization for Greenhouse WSN Based on Path Loss Analysis

When wireless sensor networks (WSN) are deployed in the vegetablegreenhouse with dynamic connectivity and interference environment, it is necessary to increase the node transmit power to ensure the communication quality,which leads to serious network interference. To offset the negative impact, thetransmit power of other nodes must also be increased. The result is that the network becomes worse and worse, and node energy is wasted a lot. Taking intoaccount the irregular connection range in the cucumber greenhouse WSN, wemeasured the transmission characteristics of wireless signals under the 2.4 Ghzoperating frequency. For improving network layout in the greenhouse, a semiempirical prediction model of signal loss is then studied based on the measureddata. Compared with other models, the average relative error of this semi-empiricalsignal loss model is only 2.3%. Finally, by combining the improved networktopology algorithm and tabu search, this paper studies a greenhouse WSN layoutthat can reduce path loss, save energy, and ensure communication quality. Giventhe limitation of node-degree constraint in traditional network layout algorithms,the improved algorithm applies the forwarding constraint to balance network energyconsumption and constructs asymmetric network communication links. Experimentalresults show that this research can realize the energy consumption optimization ofWSN layout in the greenhouse.

Intelligent Recommendation and Matching Method for Agricultural Knowledge Based on Context-Aware Models

The personalized recommendation of the cloud platform for agricultural knowledge and agricultural intelligent service is one of the core technologies for the development of smart agriculture.Revealing the implicit laws and dynamic characteristics of agricultural knowledge demand is a key problem to be solved urgently.In order to enhance the matching ability of knowledge recommendation and service in human-computer interaction of cloud platform,the mechanism of agricultural knowledge intelligent recommendation service integrated with context-aware model was analyzed.By combining context data acquisition,data analysis and matching,and personalized knowledge recommendation,a framework for agricultural knowledge recommendation service is constructed to improve the ability to extract multidimensional information features and predict sequence data.Using the cloud platform for agricultural knowledge and agricultural intelligent service,this research aims to deliver interesting video service content to users in order to solve key problems faced by farmers,including planting technology,disease control,expert advice,etc.Then the knowledge needs of different users can be met and user satisfaction can be improved.

Multi-Task Timing Assignment Algorithm for Intelligent Production of Vegetables in Open Field

Vegetable production in the open field involves many tasks,such as soil preparation,ridging,and transplanting/sowing.Different tasks require agricultural machinery equipped with different agricultural tools to meet the needs of the operation.Aiming at the coupling multi-task in the intelligent production of vegetables in the open field,the task assignment method for multiple unmanned tractors based on consistency alliance is studied.Firstly,unmanned vegetable production in the open field is abstracted as a multi-task assignment model with constraints of task demand,task sequence,and the distance traveled by an unmanned tractor.The tight time constraints between associated tasks are transformed into time windows.Based on the driving distance of the unmanned tractor and the replacement cost of the tools,an expanded task cost function is innovatively established.The task assignment model of multiple unmanned tractors is optimized by the consensus based bundle algorithm(CBBA)with time windows.Experiments show that the method can effectively solve task conflict in unmanned production and optimize task allocation.A basic model is provided for the cooperative task of multiple unmanned tractors for vegetable production in the open field.