Constrained Surface-Level Gateway Placement for Underwater Acoustic Wireless Sensor Networks

2010 ◽  
pp. 46-57 ◽  
Author(s):  
Deying Li ◽  
Zheng Li ◽  
Wenkai Ma ◽  
Hong Chen
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Wireless Sensor ◽  
Underwater Acoustic ◽  
Surface Level ◽  
Gateway Placement

scholarly journals Constrained surface-level gateway placement for underwater acoustic wireless sensor networks

2012 ◽  
Vol 447 ◽  
pp. 100-106 ◽  
Author(s):  
Deying Li ◽  
Zheng Li ◽  
Wenkai Ma ◽  
Hong Chen ◽  
Wenping Chen
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Wireless Sensor ◽  
Underwater Acoustic ◽  
Surface Level ◽  
Gateway Placement

Scheduling Real-Time Traffic in Underwater Acoustic Wireless Sensor Networks

2016 ◽  
pp. 150-162 ◽  
Author(s):  
Rodrigo Santos ◽  
Javier Orozco ◽  
Matías Micheletto ◽  
Sergio F. Ochoa ◽  
Roc Meseguer ◽  
...  
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Real Time ◽  
Wireless Sensor ◽  
Underwater Acoustic ◽  
Real Time Traffic

A Neural-Q-Learning Based Approach for Delay Tolerant Underwater Acoustic Wireless Sensor Networks

2014 ◽  
Vol 548-549 ◽  
pp. 1530-1535
Author(s):  
Lin Zou ◽  
De Feng Huang ◽  
Roberto Togneri
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Learning Algorithm ◽  
Wireless Sensor ◽  
Underwater Acoustic Communications ◽  
Frequency Channel ◽  
Underwater Acoustic ◽  
Continuous Space ◽  
Q Learning ◽  
Delay Tolerant

Delay tolerance is a major design concern for supporting underwater acoustic wireless sensor networks (UA-WSNs) to carry out tasks in harsh subsea environments. Due to the great difference between the underwater acoustic channel and the radio frequency channel, most of the existing routing protocols developed for terrestrial wireless sensor networks perform poorly in underwater acoustic communications. In this work, we present a Neural-Q-Learning algorithm based delay tolerant protocol for UA-WSNs. Due to the advantages of the artificial neural network along with the Q-Learning algorithm, the ferry node is capable of determining an optimal route in a two-dimensional continuous space to relay packets effectively and efficiently between sensors. Simulation results show that the delivery delay and delivery cost of the network significantly decrease by maximizing the meeting probability between the ferry node and sensors.

Bi-criteria Gateway Placement Problem in Wireless Sensor Networks

2010 ◽  
Vol 56 (3) ◽  
pp. 215-222 ◽  
Author(s):  
Maria Czajko ◽  
Jacek Wojciechowski
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Cost Effective ◽  
Wireless Sensor ◽  
Placement Problem ◽  
Connection Management ◽  
Gateway Placement ◽  
Network Operation ◽  
Random Manner ◽  
Gateway Deployment

Bi-criteria Gateway Placement Problem in Wireless Sensor NetworksOver the last few years, wireless sensor networks (WSNs) have started to play more and more important role in civil and military applications. A typical sensor network consists of resource-constrained sensing nodes, which monitor environment and send the data to more powerful gateway nodes. The goal of gateway nodes is to aggregate, process and send the data to other gateways or directly to sink nodes. The proper placement of nodes is needed to provide good network operation. Sensing nodes are often placed in a random manner unlike gateways. Gateways, due to their role and cost, are installed rather in a controlled way. In this paper, a bi-criteria gateway placement problem is introduced. The problem is shown to be NP-hard. We formulate it as a linear programming problem, then we develop the Multi-criteria Simulated Allocation (MSAL) heuristic algorithm, for the purpose of cost-effective gateway deployment and power-effective wireless connection management. Finally, we evaluate the efficiency of the algorithm by comparison with the exact method.

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