Reinforcement Learning-Based Data Forwarding in Underwater Wireless Sensor Networks with Passive Mobility

Chang, Haotian; Feng, Jing; Duan, Chaofan

doi:10.3390/s19020256

Cited by 26 publications

(25 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the candidate set selection phase, it takes a time complexity of O (h) at most to select the candidate nodes from neighbors, where h is the size of CS (n i ). The time complexity of the second phase is similar to the algorithm proposed in Reference [28], which is O(h(h-1)). As a result, the complexity of our algorithm can be represented as O (h 2 ).…”

Section: Discussionmentioning

confidence: 94%

“…Chang et al propose a reinforcement learning-based data forwarding algorithm in UWSNs with passive mobility [28]. The performance of both delay and energy consumption are improved in a specific dynamic scene.…”

Section: Related Workmentioning

confidence: 99%

“…Thus, it has great adaptability and flexibility in practical applications [25]. In recent years, several routing algorithms [26][27][28][29] based on Q-learning technique are implemented in UWSNs. The QELAR algorithm [26] applies machine-learning technique in the distributed underwater routing for the purpose of energy conservation and lifetime extension.…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, the frequent construction of routing paths brings about plenty of additional overheads and energy consumption. With AOA and RSS positioning mechanism, a reinforcement learning based data forwarding algorithm is proposed in Reference [28] to cope with the passive mobility of nodes in underwater routing. By adopting the Q-learning to build the mobile model of sensor nodes, it achieves a good performance in terms of energy consumption but has a low packet delivery ratio.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Energy-Efficient Depth-Based Opportunistic Routing with Q-Learning for Underwater Wireless Sensor Networks

Yongjie

Chen

et al. 2020

Sensors

View full text Add to dashboard Cite

Underwater Wireless Sensor Networks (UWSNs) have aroused increasing interest of many researchers in industry, military, commerce and academe recently. Due to the harsh underwater environment, energy efficiency is a significant theme should be considered for routing in UWSNs. Underwater positioning is also a particularly tricky task since the high attenuation of radio-frequency signals in UWSNs. In this paper, we propose an energy-efficient depth-based opportunistic routing algorithm with Q-learning (EDORQ) for UWSNs to guarantee the energy-saving and reliable data transmission. It combines the respective advantages of Q-learning technique and opportunistic routing (OR) algorithm without the full-dimensional location information to improve the network performance in terms of energy consumption, average network overhead and packet delivery ratio. In EDORQ, the void detection factor, residual energy and depth information of candidate nodes are jointly considered when defining the Q-value function, which contributes to proactively detecting void nodes in advance, meanwhile, reducing energy consumption. In addition, a simple and scalable void node recovery mode is proposed for the selection of candidate set so as to rescue packets that are stuck in void nodes unfortunately. Furthermore, we design a novel method to set the holding time for the schedule of packet forwarding base on Q-value so as to alleviate the packet collision and redundant transmission. We conduct extensive simulations to evaluate the performance of our proposed algorithm and compare it with other three routing algorithms on Aqua-sim platform (NS2). The results show that the proposed algorithm significantly improve the performance in terms of energy efficiency, packet delivery ratio and average network overhead without sacrificing too much average packet delay.

show abstract

Section: Discussionmentioning

confidence: 94%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Energy-Efficient Depth-Based Opportunistic Routing with Q-Learning for Underwater Wireless Sensor Networks

Yongjie

Chen

et al. 2020

Sensors

View full text Add to dashboard Cite

show abstract

“…Under the influence of ocean currents, tides and other factors, underwater sensor nodes exhibit dynamic displacements in their relative positions. Hence a mobility model of underwater sensor nodes [29] is here adopted. As shown in Fig.…”

Section: The Mobility Of Water Flowmentioning

confidence: 99%

A Trust Update Mechanism Based on Reinforcement Learning in Underwater Acoustic Sensor Networks

Han

Jiang

et al. 2022

IEEE Trans. on Mobile Comput.

View full text Add to dashboard Cite

Underwater acoustic sensor networks (UASNs) have been widely applied in marine scenarios, such as offshore exploration, auxiliary navigation and marine military. Due to the limitations in communication, computation, and storage of underwater sensor nodes, traditional security mechanisms are not applicable to UASNs. Recently, various trust models have been investigated as effective tools towards improving the security of UASNs. However, the existing trust models lack flexible trust update rules, particularly when facing the inevitable dynamic fluctuations in the underwater environment and a wide spectrum of potential attack modes. In this study, a novel trust update mechanism for UASNs based on reinforcement learning (TUMRL) is proposed. The scheme is developed in three phases. First, an environment model is designed to quantify the impact of underwater fluctuations in the sensor data, which assists in updating the trust scores. Then, the definition of key degree is given; in the process of trust update, nodes with higher key degree react more sensitively to malicious attacks, thereby better protecting important nodes in the network. Finally, a novel trust update mechanism based on reinforcement learning is presented, to withstand changing attack modes while achieving efficient trust update. The experimental results prove that our proposed scheme has satisfactory performance in improving trust update efficiency and network security.

show abstract

An efficient 3D localization algorithm for compensating stratification effect in underwater acoustic sensor network

Yadav

Khilar

2023

Trans Emerging Tel Tech

View full text Add to dashboard Cite

Communication and networking plays a crucial role in data transmission in UASN. The sensor nodes in UASN are intermittently deployed randomly in the three‐dimension scenario. As a consequence, the three‐dimension based localization algorithm is the need for an hour. To fulfill the objective, we have proposed a localization algorithm I‐LASP (Improvement of localization algorithm for compensating stratification effect based on extended improved particle swarm optimization technique) in three‐dimensional UASN, based on compensation of stratification effect for the improvement of the performance parameters such as localization accuracy, ranging accuracy, convergence rate and execution time. To compute the accurate position of target nodes, EIPSO (Extended improved PSO) technique is applied, and the degree of coplanarity is checked before the calculation of distance among nodes in order to get the accurate location of target nodes. The Centroid method is used to initialize the position of sensor nodes, and the ray theory method is used to compensate the stratification effect on the layered ocean water. The proposed algorithm is compared to the existing LASP, Std PSO, and GNA‐ESSP (Gauss‐newton algorithm‐extended sound speed profile) localization algorithm. The proposed algorithm provides 34.50%, 38.87%, and 42.66% of high accuracy in terms of localization with low density of target sensor nodes and 37.96%, 29.58%, and 50.77% high accuracy in terms of localization with a high density of target sensor nodes respectively. The proposed algorithm is compared with LASP, GNA‐ESSP, and TDOA to obtain 66.84%, 71.14%, and 86.13% of high accuracy in terms of ranging with low density of target sensor nodes and 42.34%, 89.00%, and 95.08% high accuracy in terms of ranging with a high density of target sensor nodes respectively. Experimental results represents that the proposed algorithm obtains better performance in terms of localization accuracy, ranging accuracy, root mean square error, normalized localization error, execution time and convergence rate.

show abstract

Reinforcement Learning-Based Data Forwarding in Underwater Wireless Sensor Networks with Passive Mobility

Cited by 26 publications

References 16 publications

Energy-Efficient Depth-Based Opportunistic Routing with Q-Learning for Underwater Wireless Sensor Networks

Energy-Efficient Depth-Based Opportunistic Routing with Q-Learning for Underwater Wireless Sensor Networks

A Trust Update Mechanism Based on Reinforcement Learning in Underwater Acoustic Sensor Networks

An efficient 3D localization algorithm for compensating stratification effect in underwater acoustic sensor network

Contact Info

Product

Resources

About