Location Algorithm For Wireless Sensor Networks Research Articles

Wireless sensor network location algorithm based on SDN network structure

In the past, it is difficult for people to locate wireless sensors, which is easy to cause problems such as information loss and information islands. However, under the SDN network structure, wireless sensors can be located by specific algorithms. This paper mainly introduces the basic concept of SDN network structure and the localization difficulties of wireless sensor networks, and then designs the network localization system. In the process, the localization algorithm is proposed, and finally the system operation is simulated and tested. Through research, relying on the SDN network structure, the system can locate the wireless sensor, and the accuracy of the location results is up to standard.

Research of localization algorithm for wireless sensor network based on DV-Hop

Wireless sensor network (WSN) possesses very broad application prospect in many fields, where the node location technology is one of the key technologies of WSN. Distance vector hop (DV-Hop) localization algorithm is a widely used algorithm in this technology, and it uses routing exchange protocol to make unknown nodes obtain beacon node information which will be used for coordinate calculation, therefore there exists certain error for the algorithm itself. Aiming at the disadvantage of large error existing in the traditional wireless sensor network location algorithm based on DV-Hop, an improved DV-Hop algorithm based on hop thinning and distance correction is proposed. The minimum hop is corrected by introducing received signal strength indication (RSSI) ranging technology, and the average hop distance is corrected by weighted average value of hop distance error and estimated distance error. Subsequently, the overall improvement on the location performance of the Hop-DV location algorithm is realized, and the location error is reduced. Under the Matlab simulation environment, the simulation experiment on the improved algorithm is carried out. The experimental results show that the improved algorithm reduces the location error and has higher location accuracy.

Optimal path selection algorithm for mobile beacons in sensor network under non-dense distribution

Abstract When the traditional anchor aided location algorithm is used to select the mobile beacon path in the sensor network, there is no analysis of the energy imbalance of nodes in non-dense conditions, the optimal network node cannot be selected, and the selection error of the optimal path of the beacon is larger. A path selection algorithm for mobile beacons in a sensor network under non-dense distribution is proposed. Using the mobile beacon based wireless sensor network location algorithm, the weighted centroid algorithm and the extended Kalman filter (EKF) are used to obtain the accurate location results of the unknown nodes around the mobile beacon in the sensor network under non-dense distribution condition. The optimal node energy partition of the unknown node is obtained by the chaotic differential evolution method, and the optimal location of the optimal energy node in the wireless sensor network is calculated using the dynamic escape particle swarm optimization method, and the optimal beacon path is extracted. The experimental results show that the proposed algorithm can enhance the clustering performance of the optimal node in the wireless sensor network and has a better performance of dynamic node selection in wireless sensor network, and the convergence speed is faster and the operation time is shorter.

A wireless sensor network location algorithm based on insufficient fingerprint information

To realize sensor network-based positioning, the use of conventional techniques based on fingerprints has considerable cost due to the need of establishing in an offline manner a fingerprint database. Besides, it is also time-consuming when searching the database for calculating the localization solution. To address these drawbacks, we first propose to utilize the inverse distance weighted (IDW) interpolation method to improve the spatial resolution of the fingerprint database. The genetic algorithm learning machine (GAELM) is introduced to speed up the database lookup while enhancing the positioning accuracy of the fingerprint-based localization. Experiments show that the proposed Extreme Learning Location algorithm based on Insufficient Fingerprint information (ELL-IF) offers improved positioning performance over the BP-neural network (BP-NN)-based and extreme learning machine (ELM)-based methods.

Wireless Sensor Network Dynamic Mathematics Modeling and Node Localization

With the rapid development of wireless sensor network (WSN) technology and its localization method, localization is one of the basic services for data collection in WSN. The localization accuracy often depends on the accuracy of distance estimation. Because of the constraint in size, power, and cost of sensor nodes, the investigation of efficient location algorithms which satisfy the basic accuracy requirement for WSN meets new challenges. This paper proposes a novel intelligent node localization algorithm in WSN based on beacon nodes to improve the precision in location estimation. Firstly, system model of WSN node localization is constructed according to the WSN environment. Then traditional WSN node localization methods such as DV‐HOP, GA, and PSO are studied. Localization algorithm of WSN is proposed by using dynamic mathematics modeling. And the result of simulation, which is compared to the traditional algorithm, indicated that this algorithm is better to improve the accuracy and coverage of WSN. The simulation results show that the performance of the proposed WSN location algorithm is better than the traditional localization algorithms.