Data Mining of Regional Economic Analysis Based on Mobile Sensor Network Technology

With the continuous development of regional economy, the difference of regional economy has also aroused the attention of all walks of life. Due to the limitations of the traditional research methods, the research results are relatively simple and unable to conduct a more comprehensive analysis. The traditional methods include the following: (1) analyze the evolution of regional logistics based on the location Gini coefficient and location quotient of GIS, and reflect the situation of industrial agglomeration from the annual change curve of the location Gini coefficient; (2) use SPSS12.0 software to perform multivariate or event factors, and analyze and calculate the factor score to sum up several principal component factors; and (3) the production function analysis method is used to measure the economies of scale and agglomeration. As an extension, the relationship between the estimated total output and the agglomeration index of the factor input to measure the uniform state of the industrial distribution department is an effective measurement method for the agglomeration economy. In order to promote the sustainable development of regional economy, this paper analyzes the regional economy comprehensively based on the emerging mobile sensor network technology and data mining technology. Firstly, this paper analyzes the location technology of mobile sensor networks based on sequential Monte Carlo, selects the C -means clustering method which is suitable for economic large-sample clustering analysis, and constructs a complete data mining model. Then, the model is used to analyze the economic, social, natural, and educational science and technology indicators of a certain region from 2015 to 2019. The results show that the first principal component weight of economic indicators is the highest proportion of fiscal revenue, which is 0.986. This shows that the role of fiscal revenue in economic indicators is greater. The main index of urban consumption is 72.0, which is the highest. This shows that the population growth rate and the average consumption of urban households in social indicators play a greater role. The first principal component of natural index has the highest weight of pollution emission, which is 0.47, while the second principal component has the highest weight of total energy consumption, which is 0.48. This shows that the pollution emissions and total energy consumption in the natural indicators play a greater role. In the educational science and technology index, the first principal component weight is the highest, which is 0.61. This shows that the education funds play an important role in educational science and technology indicators. Therefore, the data mining model based on mobile sensor network technology can comprehensively and accurately analyze various indicators of regional economy.

Study of Node Distribution and Density Optimization in Mobile Sensor Network 3D Space

Mobile sensor network is applied in information collection in emergencies. As the mobile sensor network in real environment is widely deployed with different height and the redundancy of the sensor node needs to be as low as possible, therefore, it is necessary to effectively deploy mobile sensor nodes in the 3D space to have reasonable layout and optimized density. To this end, we established the optimization model of mobile sensor network deployment and solved the model with chemical reaction optimization (CRO). The experimental results have shown that compared with traditional particle swarm optimization (PSO), CRO algorithm can achieve reasonable deployment more rapidly and enhance the network performance evaluation value effectively. The reasonable deployment of mobile sensor network node is very significant to information collecting, postperiod decision-making, and rapid rescuing work in emergencies.

LSTM-Enabled Level Curve Tracking in Scalar Fields Using Multiple Mobile Robots

In this work, we investigate the problem of level curve tracking in unknown scalar fields using a limited number of mobile robots. We design and implement a long short term memory (LSTM) enabled control strategy for a mobile sensor network to detect and track desired level curves. Based on the existing work of cooperative Kalman filter, we design an LSTM-enhanced Kalman filter that utilizes the sensor measurements and a sequence of past fields and gradients to estimate the current field value and gradient. We also design an LSTM model to estimate the Hessian of the field. The LSTM enabled strategy has some benefits such as it can be trained offline on a collection of level curves in known fields prior to deployment, where the trained model will enable the mobile sensor network to track level curves in unknown fields for various applications. Another benefit is that we can train using larger resources to get more accurate models, while utilizing a limited number of resources when the mobile sensor network is deployed in production. Simulation results show that this LSTM enabled control strategy successfully tracks the level curve using a mobile multi-robot sensor network.

Developing an Energy Efficient Ubiquitous Agriculture Mobile Sensor Network Based Threshold Built-In Mac Routing Protocol (TBMP)

Smart agriculture has been a promising model with the intention of supervising farms by means of contemporary wireless technologies to enhance the quantity and quality of yield at the same time as minimizing the individual labor requirement. In addition the effective utilization of the Sensors as communication components that is the key one to monitor and manage soil, water, light, humidity, temperature. A Mobile Ad-hoc sensor node comprises sensors to gather real time environment from the agricultural land with the wireless communication technology and process the data before sharing information with other nodes in the network. On the other hand, the challenges have been enormously high path loss and lack of communication range under the environment when passing through soil, sand, water and other climatic conditions. As Wireless Sensor Networks (WSNs) has self-organized and adhoc wireless capability to monitor physical or environmental conditions, it can be used effectively in smart agriculture. As sensor nodes have been limited itself by means of power to be in active mode always, the design of such energy efficient Agriculture WSN is a paramount issue. Hence it has been planned to utilize the WSN as well as Ubiquitous technology for the smart agriculture with energy efficiency. With the purpose of build up a model, a Ubiquitous agriculture Mobile Sensor Network based Threshold built-in MAC Routing protocol (TBMP) has been proposed to make it fit for minimal resource utilization by comparing with the existing protocols IMR and PTSR. In addition, the testing will be done to monitor changes in environmental surroundings in the agricultural land smartly in order to obtain maximum usage of Ubiquitous concept by applying existing and proposed protocols.