Energy enhancement using Multiobjective Ant colony optimization with Double Q learning algorithm for IoT based cognitive radio networks

Vimal, S.; Khari, Manju; Crespo, Rubén González; Kalaivani, L.; Dey, Nilanjan; Kaliappan, M.

doi:10.1016/j.comcom.2020.03.004

Cited by 128 publications

(54 citation statements)

References 28 publications

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“…e most obvious idea is to construct a mixed reward function that returns a combined result according to the different objectives [161,259,370]. Another possible way is to combine multiobjective ant colony optimization methods with RL techniques like deep reinforcement learning or double Q-learning algorithms [83,142].…”

Section: Distributed and Parallel Methodsmentioning

confidence: 99%

The Applicability of Reinforcement Learning Methods in the Development of Industry 4.0 Applications

2021

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Reinforcement learning (RL) methods can successfully solve complex optimization problems. Our article gives a systematic overview of major types of RL methods, their applications at the field of Industry 4.0 solutions, and it provides methodological guidelines to determine the right approach that can be fitted better to the different problems, and moreover, it can be a point of reference for R&D projects and further researches.

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Section: Distributed and Parallel Methodsmentioning

confidence: 99%

The Applicability of Reinforcement Learning Methods in the Development of Industry 4.0 Applications

2021

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“…Moreover, the maintenance cost of multicast forwarding topologies is costlier and requires the application of certain multicast protocol [18]. Some other models available in the literature are [19][20][21].…”

Section: Related Workmentioning

confidence: 99%

A Novel Hybrid Optimization for Cluster‐Based Routing Protocol in Information-Centric Wireless Sensor Networks for IoT Based Mobile Edge Computing

Vaiyapuri

Velmurugan

Sivaram

et al. 2021

Wireless Pers Commun

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In present days, the utilization of mobile edge computing (MEC) and Internet of Things (IoT) in mobile networks offers a bottleneck in the evolving technological requirements. Wireless Sensors Network (WSN) become an important component of the IoT and is the major source of big data. In IoT enabled WSN, a massive amount of data collection generated from a resource-limited network is a tedious process, posing several challenging issues. Traditional networking protocols offer unfeasible mechanisms for large-scaled networks and might be applied to IoT platform without any modifications. Information-Centric Networking (ICN) is a revolutionary archetype which that can resolve those big data gathering challenges. Employing the ICN architecture for resource-limited WSN enabled IoT networks may additionally enhance the data access mechanism, reliability challenges in case of a mobility event, and maximum delay under multihop communication. In this view, this paper proposes an IoT enabled cluster based routing (CBR) protocol for information centric wireless sensor networks (ICWSN), named CBR-ICWSN. The proposed model undergoes a black widow optimization (BWO) based clustering technique to select the optimal set of cluster heads (CHs) effectively. Besides, the CBR-ICWSN technique involves an oppositional artificial bee colony (OABC) based routing process for optimal selection of paths. A series of simulations take place to verify the performance of the CBR-ICWSN technique and the results are examined under several aspects. The experimental outcome of the CBR-ICWSN technique has outperformed the compared methods interms of network lifetime and energy efficiency.

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“…Zhang et al [38] applied double Q-learning to the speed control of autonomous vehicle. Vimal et al [39] applied double Q-learning to improve energy efficiency of cognitive radio networks. Zhang et al [40] applied double Q-learning to the energy-saving scheduling of edge computing.…”

Section: Routing Strategy Based On Reinforcement Learningmentioning

confidence: 99%

Adaptive Routing Strategy Based on Improved Double Q-Learning for Satellite Internet of Things

Zhou

Gong

Sun

et al. 2021

Security and Communication Networks

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Satellite Internet of Things (S-IoT), which integrates satellite networks with IoT, is a new mobile Internet to provide services for social networks. However, affected by the dynamic changes of topology structure and node status, the efficient and secure forwarding of data packets in S-IoT is challenging. In view of the abovementioned problem, this paper proposes an adaptive routing strategy based on improved double Q-learning for S-IoT. First, the whole S-IoT is regarded as a reinforcement learning environment, and satellite nodes and ground nodes in S-IoT are both regarded as intelligent agents. Each node in the S-IoT maintains two Q tables, which are used for selecting the forwarding node and for evaluating the forwarding value, respectively. In addition, the next hop node of data packets is determined depending on the mixed Q value. Second, in order to optimize the Q value, this paper makes improvements on the mixed Q value, the reward value, and the discount factor, respectively, based on the congestion degree, the hop count, and the node status. Finally, we perform extensive simulations to evaluate the performance of this adaptive routing strategy in terms of delivery rate, average delay, and overhead ratio. Evaluation results demonstrate that the proposed strategy can achieve more efficient and secure routing in the highly dynamic environment compared with the state-of-the-art strategies.

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Energy enhancement using Multiobjective Ant colony optimization with Double Q learning algorithm for IoT based cognitive radio networks

Cited by 128 publications

References 28 publications

The Applicability of Reinforcement Learning Methods in the Development of Industry 4.0 Applications

The Applicability of Reinforcement Learning Methods in the Development of Industry 4.0 Applications

A Novel Hybrid Optimization for Cluster‐Based Routing Protocol in Information-Centric Wireless Sensor Networks for IoT Based Mobile Edge Computing

Adaptive Routing Strategy Based on Improved Double Q-Learning for Satellite Internet of Things

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