Because the received signal strength indicator (RSSI) IntroductionWith the rapid development of integrated circuit, sensor and wireless network technology, Wireless Sensor Networks (WSN) are more and more concerned by all the countries in the world. Wireless sensor network is mainly composed of the sensor node, the sink node and monitoring center, has the information processing and information communication function. Obtain accurate location information of sensor nodes and transmit them to the control center, is critical for WSN. According to whether need ranging, positioning algorithms are divided into range based localization algorithm (Range-based) and range free localization algorithm (Range-free). The Range-based algorithm measures distance or range parameters of unknown node to locate. It has the advantage of high positioning accuracy, but requires additional hardware support. The Range-free algorithm only depends on the connectivity of the network to achieve the positioning. Compared with the Range-based algorithm, this algorithm has the relatively small power consumption, and the positioning accuracy is lower than the former calculation. Because the above two types of localization algorithms have advantages and disadvantages, people care about energy consumption and precision positioning and hope to get the best service at minimum cost.Received Signal Strength Indicator (RSSI) location algorithm is a range based localization algorithm. It uses the relationship between communication distances and the received signal strength, to calculate the coordinates of unknown nodes. The cost of RSSI location algorithm is low, so the application is very extensive. At present, this algorithm has some shortcomings, for example, the measured position error is larger under different environment. Now, a lot of scholars and experts study the RSSI algorithm. S. Elango et al presented a low cost ZigBee based WSN implementation for indoor position monitoring in a home automation application. The real-time knowledge of the location of personnel, assets, and portable instruments can increase home automation control efficiency [1]. José Antonio Gómez Martin et al presented a research and a development of a fingerprint-indoor-positioning system using the Received Signal Strength Indication of a Wireless Sensor Network [2]. In this study,

Background: In the applications of wireless sensor network technology, three-dimensional node location technology is crucial. The process of node localization has some disadvantages, such as the uneven distribution of anchor nodes and the high cost of the network. Therefore, the mobile anchor nodes are introduced to effectively solve accurate positioning. Objective: Considering the estimated distance error, the received signal strength indication technology is used to optimize the measurement of the distance. At the same time, dynamic stiffness planning is introduced to increase virtual anchor nodes. Moreover, the bird swarm algorithm is also used to solve the optimal location problem of nodes. Method: Firstly, the dynamic path is introduced to increase the number of virtual anchor nodes. At the same time, the improved RSSI distance measurement technology is introduced to the node localization. Then, an intelligent three-dimensional node localization algorithm based on dynamic path planning is proposed. Finally, the proposed algorithm is compared with similar algorithms through simulation experiments. Results: Simulation results show that the node coordinates obtained by the proposed algorithm are more accurate, and the node positioning accuracy is improved. The execution time and network coverage of the algorithm are better than similar algorithms. Conclusion: The proposed algorithm significantly improves the accuracy of node positioning. However, the traffic of the algorithm is increased. A little increase in traffic in exchange for positioning accuracy is worthy of recognition. The simulation results show that the proposed algorithm is robust and can be implemented and promoted in the future.

Background: With the development of the Internet of things, WSN node positioning is particularly important due to its core technology. One of the most widely used algorithms, the DV-hop algorithm, has many advantages, such as convenient operation, use of no additional equipment, etc. At the same time, it also has some disadvantages, like large location error and insufficient robustness. Particle swarm optimization algorithm is advantageous in dealing with nonlinear optimization problems. Therefore, the improved particle swarm optimization algorithm is introduced to solve the problem of inaccurate positioning. Objective: This study aimed to determine the problem of large positioning error in three-dimensional node localization algorithm. Furthermore, this paper proposes an intelligent node localization algorithm based on hop distance adjustment. The algorithm is used to optimize the hop number of nodes and make the distance calculation more accurate. At the same time, particle swarm optimization is used to intelligently solve the problem of choosing the most valuable node position. Methods: Firstly, this paper analyzes the errors caused by the 3D DV-hop localization algorithms. Then, a new method of distance estimation and coordinate calculation is provided. At the same time, mutation factor and learning factor based on the particle swarm optimization algorithm are introduced. Then, a three-dimensional node localization algorithm based on ranging error correction and particle swarm optimization algorithm is proposed. Finally, the improved algorithm is simulated and compared with similar algorithms. Results: The simulation results show that the proposed algorithm has good convergence. It improves the positioning accuracy without additional hardware conditions and effectively solves the problem of inaccurate node positioning. The proposed algorithm creatively combines the hop number correction and particle swarm optimization algorithm to improve the accuracy of node positioning and robustness. However，the amount of computation is increased. Conclusion: Overall, it is within acceptable limits. It is worthwhile to improve the performance with a little increase in the amount of computation. The algorithm is worth popularizing.

Background: With the development of technology, the data amount has increased significantly. In data processing, multi table query is the most frequently operation. Because the join keys cannot correspond one by one, there will be much redundant data transmission, resulting in a waste of network bandwidth. Objective: In order to solve the problems of network overhead and low efficiency, this paper proposes a heuristic multi table join optimization method. By sharing information, the unconnected tuples are eliminated, so as to reduce the amount of data transmitting. This shortens response time and improves the execution performance. Method: Firstly, the join key information of one table is compressed by the algorithm to make the filtered information for sharing. Then, the concurrent execution is controlled according to the pancake parallel strategy. Finally, the selection strategy of multi table join order is proposed. Results/Discussion: The experiments show that the proposed algorithm can filter a large amount of useless data and improve query efficiency. At the same time, the proposed algorithm reduces a lot of network overhead, improves the algorithm performance, and better solves the problem of low efficiency of multi table join. Conclusion: This paper introduces the heuristic strategy to optimize the algorithm, so that it can perform the join tasks in parallel, which further improves the performance of multi table join. The algorithm creatively combines heuristic data filtering, which greatly improves the quality of data processing. The algorithm is worth popularizing and applying.

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