RCAR: A Reinforcement-Learning-Based Routing Protocol for Congestion-Avoided Underwater Acoustic Sensor Networks

Jin, Zhigang; Zhao, Qinyi; Su, Yishan

doi:10.1109/jsen.2019.2932126

Cited by 89 publications

(43 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…since every node transmits its own packet and forwards the packets from all other nodes down the chain. Figure 23a shows the statistical distribution of the slot duration calculated using (14) in 10000 network realizations for every simulated source level. The slot duration is the shortest away.…”

Section: Simulation Setupmentioning

confidence: 99%

“…if the distance between any two nodes is less than the maximum connection range, there is a link between them) and to assume a fixed propagation speed of 1500 m/s, e.g. [13] [14]. Although this is a simple and intuitive approach that is useful for theoretical UAN protocol development, it oversimplifies the behaviour of a realistic UWA channel.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Channel Modeling for Underwater Acoustic Network Simulation

et al. 2020

View full text Add to dashboard Cite

Simulation forms an important part of the development and empirical evaluation of underwater acoustic network (UAN) protocols. The key feature of a credible network simulation model is a realistic channel model. A common approach to simulating realistic underwater acoustic (UWA) channels is by using specialised beam tracing software such as BELLHOP. However, BELLHOP and similar modeling software typically require knowledge of ocean acoustics and a substantial programming effort from UAN protocol designers to integrate it into their research. This paper is a distilled tutorial on UWA channel modeling with a focus on network simulation, providing a trade-off between the flexibility of low level channel modeling via beam tracing and the convenience of automated channel modeling, e.g. via the World Ocean Simulation System (WOSS). The tutorial is accompanied by our MATLAB simulation code that interfaces with BELLHOP to produce channel data for UAN simulations. As part of the tutorial, we describe two methods of incorporating such channel data into network simulations, including a case study for each of them: 1) directly importing the data as a look-up table, 2) using the data to create a statistical channel model. The primary aim of this paper is to provide a useful learning resource and modeling tool for UAN protocol researchers. Initial insights into the UAN protocol design and performance provided by the statistical channel modeling approach presented in this paper demonstrate its potential as a powerful modeling tool for future UAN research.

show abstract

Section: Simulation Setupmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Channel Modeling for Underwater Acoustic Network Simulation

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Fig. 23a shows the statistical distribution of the slot duration calculated using (14) in 10000 network realizations for every simulated source level. The slot duration is the shortest at 160 dB re 1 µPa @ 1m source level because in the vast majority of cases the maximum interference range is limited to 2 hops, thus eliminating the need to extend the guard interval to accommodate propagation delays to the nodes further away.…”

Section: Simulation Setupmentioning

confidence: 99%

Channel Modeling for Underwater Acoustic Network Simulation

Morozs¹,

Gorma²,

Henson³

et al. 2020

Preprint

View full text Add to dashboard Cite

This manuscript was submitted to IEEE Access on 12 Jun 2020.<div><br></div><div>Abstract:</div><div><br></div><div>Simulation forms an important part of the development and empirical evaluation of underwater acoustic network (UAN) protocols. The key feature of a credible network simulation model is a realistic channel model. A common approach to simulating realistic underwater acoustic (UWA) channels is by using specialised beam tracing software such as BELLHOP. However, BELLHOP and similar modeling software typically require knowledge of ocean acoustics and a substantial programming effort from UAN protocol designers to integrate it into their research. In this paper, we bridge the gap between low level channel modeling via beam tracing and automated channel modeling, e.g. via the World Ocean Simulation System (WOSS), by providing a distilled UWA channel modeling tutorial from the network protocol design point of view. The tutorial is accompanied by our MATLAB simulation code that interfaces with BELLHOP to produce channel data for UAN simulations. As part of the tutorial, we describe two methods of incorporating such channel data into network simulations, including a case study for each of them: 1) directly importing the data as a look-up table, 2) using the data to create a statistical channel model. The primary aim of this paper is to provide a useful learning resource and modeling tool for UAN protocol researchers. Initial insights into the UAN protocol design and performance provided by the statistical channel modeling approach presented in this paper demonstrate its potential as a powerful modeling tool for future UAN research.<br></div>

show abstract

“…A. Boukerche et al [16] proposed adjustable topology by moving void nodes to new depth in mobile UWSN, they also utilized greedy opportunistic forwarding for improving packet delivery ratio. Cross layer based reinforcement learning protocol proposed in [17] increases network complexity and reduce network life time. Rodolfo W. L. Coutinho et al [18] proposed routing protocol for UWSNs with adjustable topology named Geographic and opportunistic routing with Depth Adjustment (GEDAR).…”

Section: Related Workmentioning

confidence: 99%

Itinerary Aware Data Delivery Technique for Underwater Acoustic Sensor Networks

Bharathi¹,

Raja²,

Meenabavyakarthika³

et al. 2019

IJEAT

View full text Add to dashboard Cite

Monitoring and maintaining aquatic environment is the universal need and Underwater Acoustic Sensor Networks (UASN) is an emerging technology plays a major role in acoustic data acquistion. The data acquisition is challenging issue in UASN due to its communication characteristics. Though, there are several geo-opportunistic routing protocols were explored to improve the data acquisition it can be still improved by enhanced routing technique. The existing Geo-graphical depth adjustment routing (GEDAR) uses Global Positioning System(GPS) based notes for improving data acquisition, however it consumes more energy and increases overhead. We make an attempt to study about efficient data acquisition process and its path reliability. The proposed Itinerary aware routing protocol(IARP) acquires neighboring node’s information for constructing efficient and reliable link with minimum information which improves data delivery ratio with minimum energy consumption. The proposed IARP increases 11% packet delivery ratio and reduces delay by 13%, and energy consumption by 9% comparing with existing GEDAR based algorithm. IARP also performs better than Depth based routing (DBR).

show abstract

RCAR: A Reinforcement-Learning-Based Routing Protocol for Congestion-Avoided Underwater Acoustic Sensor Networks

Cited by 89 publications

References 35 publications

Channel Modeling for Underwater Acoustic Network Simulation

Channel Modeling for Underwater Acoustic Network Simulation

Channel Modeling for Underwater Acoustic Network Simulation

Itinerary Aware Data Delivery Technique for Underwater Acoustic Sensor Networks

Contact Info

Product

Resources

About