An apprenticeship learning scheme based on expert demonstrations for cross-layer routing design in cognitive radio networks

Du, Yihang; Xue, Lei; Xu, Ying; Liu, Zunyang

doi:10.1016/j.aeue.2019.05.041

Cited by 8 publications

(7 citation statements)

References 24 publications

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“…Du et al. [13] used reinforcement learning in CR for the cognition engine construction. It addresses two challenges that includes: First, interacting with the environment takes a long time before making smart decisions.…”

Section: Related Workmentioning

confidence: 99%

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An IoT and machine learning‐based routing protocol for reconfigurable engineering application

Natarajan¹,

Srihari

Chandragandhi³

et al. 2021

IET Communications

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With new telecommunications engineering applications, the cognitive radio (CR) networkbased internet of things (IoT) resolves the bandwidth problem and spectrum problem. However, the CR-IoT routing method sometimes presents issues in terms of road finding, spectrum resource diversity and mobility. This study presents an upgradable cross-layer routing protocol based on CR-IoT to improve routing efficiency and optimize data transmission in a reconfigurable network. In this context, the system is developing a distributed controller which is designed with multiple activities, including load balancing, neighbourhood sensing and machine-learning path construction. The proposed approach is based on network traffic and load and various other network metrics including energy efficiency, network capacity and interference, on an average of 2 bps/Hz/W. The trials are carried out with conventional models, demonstrating the residual energy and resource scalability and robustness of the reconfigurable CR-IoT. INTRODUCTIONWireless networks reconfigurable (RWN) is mainly an adaptive network firmware developed to satisfy the demands of modern applications, changing network topologies and changing network conditions. In particular, the RWM can be reconfiguredThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Section: Related Workmentioning

confidence: 99%

“…The cross-layered routing for CR-IoTs [13][14][15][16][17][18] is often developed with dynamic spectrum allocation and its utility. It often increases the throughput of CR-IoT by optimal routing by customizing scheduling, spectral resource and transmit power control.…”

Section: Introductionmentioning

confidence: 99%

An IoT and machine learning‐based routing protocol for reconfigurable engineering application

Natarajan¹,

Srihari

Chandragandhi³

et al. 2021

IET Communications

View full text Add to dashboard Cite

show abstract

“…Also, in this section, the optimal routing strategy of routing decisions is discussed in several protocols relating to cross-layered architecture. For cognitive engine development, Du et al [9] used CRN reinforcement learning. It tackles two challenges, including: firstly, it takes a long time before it is intelligent to interact with the surroundings.…”

Section: Related Workmentioning

confidence: 99%

“…The network conjunction with the MAC provides a routing solution that is imperative [8]. Often with dynamic alloca-tion and its value, CRNs [9][10][11][12][13][14] are cross-layered. It often boosts customised programming, spectrum resources and power control by improving the optimum routing of the CRN.…”

Section: Introductionmentioning

confidence: 99%

SHANN: an IoT and machine-learning-assisted edge cross-layered routing protocol using spotted hyena optimizer

Dhiman¹,

Sharma

2021

Complex Intell. Syst.

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In the case of new technology application, the cognitive radio network (CRN) addresses the bandwidth shortfall and the fixed spectrum problem. The method for CRN routing, however, often encounters issues with regard to road discovery, diversity of resources and mobility. In this paper, we present a reconfigurable CRN-based cross-layer routing protocol with the purpose of increasing routing performance and optimizing data transfer in reconfigurable networks. Recently developed spotted hyena optimizer (SHO) is used for tuning the hyperparameters of machine-learning models. The system produces a distributor built with a number of tasks, such as load balance, quarter sensing and the development path of machine learning. The proposed technique is sensitive to traffic and charges, as well as a series of other network metrics and interference (2bps/Hz/W average). The tests are performed with classic models that demonstrate the residual energy and strength of the resistant scalability and resource.

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“…In [23], an apprenticeship learning based cross-layer routing scheme was proposed for cognitive radio networks. Apprenticeship learning is a way for apprentice nodes to learn strategies from nearby expert nodes.…”

Section: B Other Rl-based Macmentioning

confidence: 99%

Multi-Agent Reinforcement-Learning-Based Time-Slotted Channel Hopping Medium Access Control Scheduling Scheme

Park

Kim

et al. 2020

IEEE Access

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Time-slotted channel hopping (TSCH) is a medium access control technology that realizes collision-free wireless network communication by coordinating the media access time and channel of network devices. Although existing TSCH schedulers have suitable application scenarios for each, they are less versatile. Scheduling without collisions inevitably lowers the throughput, whereas contention-based scheduling achieves high-throughput but it may induces to frequent collisions in densely deployed networks. Therefore, a TSCH scheduler that can be used universally, regardless of the topology and data collection characteristics of the application scenario, is required to overcome these shortcomings. To this end, a multiagent reinforcement learning (RL)-based TSCH scheduling scheme that allows contention but minimizes collisions is proposed in this study. RL is a machine-learning method that gradually improves actions to solve problems. One specific RL method, Q-Learning (QL), was used in the scheme to enable the TSCH scheduler to become a QL agent that learns the best transmission slot. To improve the QL performance, reward functions tailored for the TSCH scheduler were developed. Because the QL agent runs on multiple nodes concurrently, changes in the TSCH schedule of one node also affect the performance of the TSCH schedules of other nodes. The use of action peeking is proposed to overcome this non-stationarity problem in a multi-agent environment. The experimental results indicate that the TSCH scheduler consistently performs well in various types of applications, compared to other schedulers.INDEX TERMS Internet of Things (IoT), Time-Slotted Channel Hopping (TSCH) scheduling, wireless sensor networks.

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An apprenticeship learning scheme based on expert demonstrations for cross-layer routing design in cognitive radio networks

Cited by 8 publications

References 24 publications

An IoT and machine learning‐based routing protocol for reconfigurable engineering application

An IoT and machine learning‐based routing protocol for reconfigurable engineering application

SHANN: an IoT and machine-learning-assisted edge cross-layered routing protocol using spotted hyena optimizer

Multi-Agent Reinforcement-Learning-Based Time-Slotted Channel Hopping Medium Access Control Scheduling Scheme

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