Path Planning Method Based on the Location Uncertainty of Water Surface Nodes in Underwater Sensor Network

Author(s):  
Jian Zhou ◽  
Jingwen Yang ◽  
Fu Xiao ◽  
Xiaoyong Yan ◽  
Linfeng Liu
2019 ◽  
Vol 17 (12) ◽  
pp. 947-954
Author(s):  
Kamal Kumar Gola ◽  
Bhumika Gupta

As deployment process is one of the major tasks in underwater sensor network due to its constraint like: acoustic communication, energy, processing speed, cost and memory and dynamic nature of water. As many researchers have proposed many algorithms for the deployment of nodes in underwater sensor network. It was always a great issue in WSN as well as underwater sensor networks. This work proposes a node deployment technique based on depth. This work consists the following major components: (i) sensor nodes to sense the phenomena in underwater sensor networks, (ii) multiple surface station on the water surface. Use of multiple surface station provides better area coverage and connectivity in the networks. This work is divided into three phase like: initialization where nodes are randomly deployed at water surface and from 2D network topology, second phase is depth calculation for all the nodes and third is to distribute the depth to each node and send them to their designated depth to expand the 2D network into the 3D network. The proposed technique is simulated on Matlab for the analysis of area coverage and connectivity. Simulation results show better performance in terms of area coverage and connectivity as compared to ADAN-BC.


Author(s):  
Yu Zhou

This paper introduces a novel distributive path planning method, the bending beam method, for mobile robots moving in environments monitored by wireless sensor networks. The proposed method is inspired by the deflection analysis of bending beams. The initial and goal positions of a mobile robot are connected by a virtual beam. The in-between obstacles are replaced with the effective loads acting on the beam. The resulting robot path is represented by the deflection curve of the beam under those loads. Following the principle of superposition, the beam deflection under all the loads is equal to the sum of the deflections caused by the individual loads acting on the beam separately. In an environment covered by a wireless sensor network, each sensor node monitors the obstacles (stationary and moving) in its neighborhood. By letting each sensor node compute the deflections of the virtual beam caused by only those neighboring obstacles, the computation load for the global robot path planning can be distributed among the sensor nodes. Thus, the path planning becomes a highly parallel computation procedure. The robot only needs to collect the results from the sensor nodes and sum them up to generate its path. Moreover, the robot path can be dynamically modified by the sensor nodes in the case of moving obstacles. As a result, the proposed method may substantially reduce the time complexity of the sensor-based motion planning for mobile robots in dynamic environments, with the assistance from sensor networks.


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