Gateway deployment optimization in wireless mesh backbone networks using directional antenna based on delaunay graph

2014 ◽  
Author(s):  
Chengxin Guo ◽  
Taoshen Li ◽  
Zhihui Ge
Keyword(s):  
Directional Antenna ◽  
Backbone Networks ◽  
Wireless Mesh ◽  
Deployment Optimization ◽  
Gateway Deployment

scholarly journals A Dynamic Surface Gateway Placement Scheme for Mobile Underwater Networks

Sensors ◽  
2019 ◽  
Vol 19 (9) ◽  
pp. 1993 ◽  
Author(s):  
Jun Liu ◽  
Wenxue Guan ◽  
Guangjie Han ◽  
Jun-Hong Cui ◽  
Lance Fiondella ◽  
...  
Keyword(s):  
Sensor Network ◽  
Optimization Problem ◽  
Sensor Nodes ◽  
Dynamic Surface ◽  
Surface Level ◽  
Optimization Framework ◽  
End To End Delay ◽  
Deployment Optimization ◽  
Gateway Placement ◽  
Gateway Deployment

Deployment of surface-level gateways holds potential as an effective method to alleviate high-propagation delays and high-error probability in an underwater wireless sensor network (UWSN). This promise comes from reducing distances to underwater nodes and using radio waves to forward information to a control station. In an UWSN, a dynamic energy efficient surface-level gateway deployment is required to cope with the mobility of underwater nodes while considering the remote and three-dimensional nature of marine space. In general, deployment problems are usually modeled as an optimization problem to satisfy multiple constraints given a set of parameters. One previously published static deployment optimization framework makes assumptions about network workload, routing, medium access control performance, and node mobility. However, in real underwater environments, all these parameters are dynamic. Therefore, the accuracy of performance estimates calculated through static UWSN deployment optimization framework tends to be limited by nature. This paper presents the Prediction-Assisted Dynamic Surface Gateway Placement (PADP) algorithm to maximize the coverage and minimize the average end-to-end delay of a mobile underwater sensor network over a specified period. PADP implements the Interacting Multiple Model (IMM) tracking scheme to predict the positions of sensor nodes. The deployment is determined based on both current and predicted positions of sensor nodes, which enables better coverage and shorter end-to-end delay. PADP uses a branch-and-cut approach to solve the optimization problem efficiently, and employs a disjoint-set data structure to ensure connectivity. Simulation results illustrate that PADP significantly outperforms a static gateway deployment scheme.

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