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Rapid Deployment Indoor Localization Without Prior Human Participation, Han Xu, Zimu Zhou, Longfei Shangguan
Rapid Deployment Indoor Localization Without Prior Human Participation, Han Xu, Zimu Zhou, Longfei Shangguan
Research Collection School Of Computing and Information Systems
In this work, we propose RAD, a RApid Deployment localization framework without human sampling. The basic idea of RAD is to automatically generate a fingerprint database through space partition, of which each cell is fingerprinted by its maximum influence APs. Based on this robust location indicator, fine-grained localization can be achieved by a discretized particle filter utilizing sensor data fusion. We devise techniques for CIVD-based field division, graph-based particle filter, EM-based individual character learning, and build a prototype that runs on commodity devices. Extensive experiments show that RAD provides a comparable performance to the state-of-the-art RSSbased methods while relieving it …
A Localization Method For Underwater Wireless Sensor Networks Based On Mobility Prediction And Particle Swarm Optimization Algorithms, Ying Zhang, Jixing Liang, Shengming Jiang, Wei Chen
A Localization Method For Underwater Wireless Sensor Networks Based On Mobility Prediction And Particle Swarm Optimization Algorithms, Ying Zhang, Jixing Liang, Shengming Jiang, Wei Chen
Computer Science Faculty Research
Due to their special environment, Underwater Wireless Sensor Networks (UWSNs) are usually deployed over a large sea area and the nodes are usually floating. This results in a lower beacon node distribution density, a longer time for localization, and more energy consumption. Currently most of the localization algorithms in this field do not pay enough consideration on the mobility of the nodes. In this paper, by analyzing the mobility patterns of water near the seashore, a localization method for UWSNs based on a Mobility Prediction and a Particle Swarm Optimization algorithm (MP-PSO) is proposed. In this method, the range-based PSO …