Design of energy-aware QoS routing algorithm in wireless sensor networks using reinforcement learning

Jafarzadeh, Sara Zafar; Moghaddam, Mohammad Hossein Yaghmaee

doi:10.1109/iccke.2014.6993408

Cited by 10 publications

(11 citation statements)

References 16 publications

(20 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Recently, significant research efforts have been made to apply RL to wireless sensor networks (WSN) and IoT. Several approaches were proposed, including the cooperative approach, which is one of the most used in many works, such as SSAR in [8], FROMS in [25], OPT-Q-Routing in [26], EQR-RL in [27], and the work by [28]. is approach is deemed to be suitable for multiagent RL models where agents are required to cooperate and work together for the same purpose.…”

Section: Related Workmentioning

confidence: 99%

“…However, they considered only the residual energy of devices; thus, the proposed protocol did not ensure a good balance for multihop communication in the long run. In [27], devices periodically broadcasted heartbeat packets that include the delivery ratio estimate and the sender's residual energy using EQR-RL.…”

Section: Related Workmentioning

confidence: 99%

“…End if (27) End if (28) End if (29) End for (30) End while ALGORITHM 1: Network set-up and cluster head election algorithm.…”

Section: Spop(s(i)) (26)mentioning

confidence: 99%

See 2 more Smart Citations

EER-RL: Energy-Efficient Routing Based on Reinforcement Learning

Mutombo

Lee

et al. 2021

Mobile Information Systems

View full text Add to dashboard Cite

Wireless sensor devices are the backbone of the Internet of things (IoT), enabling real-world objects and human beings to be connected to the Internet and interact with each other to improve citizens’ living conditions. However, IoT devices are memory and power-constrained and do not allow high computational applications, whereas the routing task is what makes an object to be part of an IoT network despite of being a high power-consuming task. Therefore, energy efficiency is a crucial factor to consider when designing a routing protocol for IoT wireless networks. In this paper, we propose EER-RL, an energy-efficient routing protocol based on reinforcement learning. Reinforcement learning (RL) allows devices to adapt to network changes, such as mobility and energy level, and improve routing decisions. The performance of the proposed protocol is compared with other existing energy-efficient routing protocols, and the results show that the proposed protocol performs better in terms of energy efficiency and network lifetime and scalability.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

EER-RL: Energy-Efficient Routing Based on Reinforcement Learning

Mutombo

Lee

et al. 2021

Mobile Information Systems

View full text Add to dashboard Cite

show abstract

“…After that, the node with the highest total payoff will be elected to forward the data packet to the next cooperative node group towards the sink node. More reinforcement learning-based routing protocols can be found in [ 100 , 101 ].…”

Section: The Cross-layer Design Methodsmentioning

confidence: 99%

A Survey on Underwater Acoustic Sensor Network Routing Protocols

Martínez-Ortega

Chaus

et al. 2016

Sensors

181

113

View full text Add to dashboard Cite

Underwater acoustic sensor networks (UASNs) have become more and more important in ocean exploration applications, such as ocean monitoring, pollution detection, ocean resource management, underwater device maintenance, etc. In underwater acoustic sensor networks, since the routing protocol guarantees reliable and effective data transmission from the source node to the destination node, routing protocol design is an attractive topic for researchers. There are many routing algorithms have been proposed in recent years. To present the current state of development of UASN routing protocols, we review herein the UASN routing protocol designs reported in recent years. In this paper, all the routing protocols have been classified into different groups according to their characteristics and routing algorithms, such as the non-cross-layer design routing protocol, the traditional cross-layer design routing protocol, and the intelligent algorithm based routing protocol. This is also the first paper that introduces intelligent algorithm-based UASN routing protocols. In addition, in this paper, we investigate the development trends of UASN routing protocols, which can provide researchers with clear and direct insights for further research.

show abstract

“…EQR-RL (Energy-aware Qos routing RL-based) -It is a routing protocol for energy consumption optimization in WSNs, while providing soft delivery delay guarantees [78]. Periodic Hello packets are broadcast and each data packet in the network incorporate the delivery delay estimate and the residual energy of the sender.…”

Section: R-crs (Natg) (Rl-based Cooperative Relay Selection) -mentioning

confidence: 99%

Reinforcement Learning Based Routing in Networks: Review and Classification of Approaches

Mammeri¹

2019

IEEE Access

173

View full text Add to dashboard Cite

Reinforcement learning (RL), which is a class of machine learning, provides a framework by which a system can learn from its previous interactions with its environment to efficiently select its actions in the future. RL has been used in a number of application fields, including game playing, robotics and control, networks, and telecommunications, for building autonomous systems that improve themselves with experience. It is commonly accepted that RL is suitable for solving optimization problems related to distributed systems in general and to routing in networks in particular. RL also has reasonable overheadin terms of control packets, memory and computation-compared to other optimization techniques used to solve the same problems. Since the mid-1990s, over 60 protocols have been proposed, with major or minor contributions in the field of optimal route selection to convey packets in different types of communication networks under various user QoS requirements. This paper provides a comprehensive review of the literature on the topic. The review is structured in a way that shows how network characteristics and requirements were gradually considered over time. Classification criteria are proposed to present and qualitatively compare existing RL-based routing protocols.INDEX TERMS Reinforcement learning, communication networks, routing protocols, path optimization, quality of service.

show abstract

Design of energy-aware QoS routing algorithm in wireless sensor networks using reinforcement learning

Cited by 10 publications

References 16 publications

EER-RL: Energy-Efficient Routing Based on Reinforcement Learning

EER-RL: Energy-Efficient Routing Based on Reinforcement Learning

A Survey on Underwater Acoustic Sensor Network Routing Protocols

Reinforcement Learning Based Routing in Networks: Review and Classification of Approaches

Contact Info

Product

Resources

About