Resource allocation for relay-aided underwater acoustic sensor networks with energy harvesting

2019 ◽  
Vol 33 ◽  
pp. 241-248 ◽  
Author(s):  
Kang Song ◽  
Baofeng Ji ◽  
Chunguo Li
Keyword(s):  
Resource Allocation ◽  
Sensor Networks ◽  
Energy Harvesting ◽  
Acoustic Sensor ◽  
Underwater Acoustic Sensor Networks ◽  
Underwater Acoustic ◽  
Acoustic Sensor Networks

scholarly journals Deep Q-Network-Based Cooperative Transmission Joint Strategy Optimization Algorithm for Energy Harvesting-Powered Underwater Acoustic Sensor Networks

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6519
Author(s):  
Song Han ◽  
Luo Li ◽  
Xinbin Li
Keyword(s):  
Sensor Networks ◽  
Energy Harvesting ◽  
Cooperative Transmission ◽  
Superior Performance ◽  
Acoustic Sensor ◽  
Underwater Acoustic Sensor Networks ◽  
Underwater Acoustic ◽  
Acoustic Sensor Networks ◽  
Joint Strategy

Cooperative transmission is a promising technology for underwater acoustic sensor networks (UASNs) to ensure the effective collection of underwater information. In this paper, we study the joint relay selection and power allocation problem to maximize the cumulative quality of information transmission in energy harvesting-powered UASNs (EH-UASNs). First, we formulate the process of cooperative transmission with joint strategy optimization as a Markov decision process model. In the proposed model, an effective state expression is presented to better reveal interactive relationship between learning and environment, thereby improving the learning ability. Then, we further propose a novel reward function which can guide nodes to adjust power strategy adaptively to balance instantaneous capacity and long-term quality of service (QoS) under the dynamic unpredictable energy harvesting. More specifically, we propose a deep Q-network-based resource allocation algorithm for EH-UASNs to solve the complex coupled strategy optimization problem without any prior underwater environment information. Finally, simulation results verify the superior performance of the proposed algorithm in improving the cumulative network capacity and reducing outages.

scholarly journals Packet Flow Based Reinforcement Learning MAC Protocol for Underwater Acoustic Sensor Networks

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2284
Author(s):  
Ibrahim B. Alhassan ◽  
Paul D. Mitchell
Keyword(s):  
Sensor Networks ◽  
Mac Protocol ◽  
Propagation Delay ◽  
Basic Function ◽  
Acoustic Sensor ◽  
Underwater Acoustic Sensor Networks ◽  
Medium Access ◽  
Underwater Acoustic ◽  
Q Learning ◽  
Acoustic Sensor Networks

Medium access control (MAC) is one of the key requirements in underwater acoustic sensor networks (UASNs). For a MAC protocol to provide its basic function of efficient sharing of channel access, the highly dynamic underwater environment demands MAC protocols to be adaptive as well. Q-learning is one of the promising techniques employed in intelligent MAC protocol solutions, however, due to the long propagation delay, the performance of this approach is severely limited by reliance on an explicit reward signal to function. In this paper, we propose a restructured and a modified two stage Q-learning process to extract an implicit reward signal for a novel MAC protocol: Packet flow ALOHA with Q-learning (ALOHA-QUPAF). Based on a simulated pipeline monitoring chain network, results show that the protocol outperforms both ALOHA-Q and framed ALOHA by at least 13% and 148% in all simulated scenarios, respectively.

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