A tailored Q- Learning for routing in wireless sensor networks

Sharma, V.; Shukla, Shiv Shankar; Singh, Varun

doi:10.1109/pdgc.2012.6449899

Cited by 21 publications

(10 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We can then re-estimate the values of Q π (s t , a t ) through either ( 13) or (14), and continue to improve the policy using (15). When the process converges, we reach an optimal policy π * (and its corresponding value functions Q π * (s t , a t )).…”

Section: Conventional Q-learningmentioning

confidence: 99%

Scalable Deep Reinforcement Learning for Routing and Spectrum Access in Physical Layer

Cui

2021

IEEE Trans. Commun.

View full text Add to dashboard Cite

Section: Conventional Q-learningmentioning

confidence: 99%

Scalable Deep Reinforcement Learning for Routing and Spectrum Access in Physical Layer

Cui

2021

IEEE Trans. Commun.

View full text Add to dashboard Cite

“…Indeed, network's elements, even the resource-constrained ones, have been upgraded with different degrees of smartness and provided with new capabilities of computation, reasoning and learning through AI-related and data mining techniques. In particular, several researches efforts have been focused on optimizing the node's task scheduling, routing paths, and computationrelated aspects [10]- [12]. However, it has been also largely studied that most of node's energy expenditure is due to the radio activity (transmitting one bit may consume as much as executing a few thousands instructions), thus suggesting that communication should be traded for computation [13].…”

Section: Related Workmentioning

confidence: 99%

Lightweight Reinforcement Learning for Energy Efficient Communications in Wireless Sensor Networks

et al. 2019

View full text Add to dashboard Cite

High-density communications in wireless sensor networks (WSNs) demand for new approaches to meet stringent energy and spectrum requirements. We turn to reinforcement learning, a prominent method in artificial intelligence, to design an energy-preserving MAC protocol, with the aim to extend the network lifetime. Our QL-MAC protocol is derived from Q-learning, which iteratively tweaks the MAC parameters through a trial-and-error process to converge to a low energy state. This has a dual benefit of 1) solving this minimization problem without the need of predetermining the system model and 2) providing a self-adaptive protocol to topological and other external changes. QL-MAC self-adjusts the WSN node duty-cycle, reducing energy consumption without detrimental effects on the other network parameters. This is achieved by adjusting the radio sleeping and active periods based on traffic predictions and transmission state of neighboring nodes. Our findings are corroborated by an extensive set of experiments carried out on off-the-shelf devices, alongside large-scale simulations. INDEX TERMS Wireless sensor network, artificial intelligence, reinforcement learning, energy-efficient network, medium access control.

show abstract

“…However, path quality estimation provides incorrect path quality value due to dissimilar path delay and bandwidth always have variable trends. Authors also investigated soft computing based approaches [20][21][22][29][30] to optimize network performance in wireless network. However, Thang and Tao [31] investigated the IPv6 routing protocol performance for Wireless Sensor Networks (WSN).…”

Section: Related Workmentioning

confidence: 99%

New Delay-based Fast Retransmission Policy for CMT-SCTP

Verma¹,

Sharma²,

Kumar³

2018

IJISA

View full text Add to dashboard Cite

Concurrent Multipath Transfer (CMT) uses multi-homing feature of Stream Control Transmission Protocol (SCTP) to transfer data concurrently over the multiple paths. CMT provides bandwidth aggregation, fault tolerance, and reliability in multipath data transfer. In multipath data transmission, each path has different delay and bandwidth. Therefore, destination receives unordered data which causes receiver buffer blocking and unwanted congestion window (cwnd) reduction. Both the problem degrades the CMT performance significantly. Thus, this paper proposes a new delay-based fast retransmission policy to adjust the transmission rate of each path according to path delay. Simulation results show that the proposed approach achieves better throughput, reduces the number of the timeout and improves the cwnd growth. The proposed approach improved throughput up to 16% in variable packet loss and 18% in variable network delay environment.

show abstract

A tailored Q- Learning for routing in wireless sensor networks

Cited by 21 publications

References 4 publications

Scalable Deep Reinforcement Learning for Routing and Spectrum Access in Physical Layer

Scalable Deep Reinforcement Learning for Routing and Spectrum Access in Physical Layer

Lightweight Reinforcement Learning for Energy Efficient Communications in Wireless Sensor Networks

New Delay-based Fast Retransmission Policy for CMT-SCTP

Contact Info

Product

Resources

About