A delay-aware spectrum handoff scheme for prioritized time-critical industrial applications with channel selection strategy

2019 ◽  
Vol 144 ◽  
pp. 112-123
Author(s):  
Stephen S. Oyewobi ◽  
Gerhard P. Hancke ◽  
Adnan M. Abu-Mahfouz ◽  
Adeiza J. Onumanyi
Keyword(s):  
Channel Selection ◽  
Industrial Applications ◽  
Selection Strategy ◽  
Spectrum Handoff ◽  
Time Critical

scholarly journals An Effective Spectrum Handoff Based on Reinforcement Learning for Target Channel Selection in the Industrial Internet of Things

Sensors ◽  
2019 ◽  
Vol 19 (6) ◽  
pp. 1395 ◽  
Author(s):  
Stephen Oyewobi ◽  
Gerhard Hancke ◽  
Adnan Abu-Mahfouz ◽  
Adeiza Onumanyi
Keyword(s):  
Reinforcement Learning ◽  
Spectrum Sensing ◽  
Dynamic Spectrum Access ◽  
Network Performance ◽  
Channel Selection ◽  
Selection Strategy ◽  
Spectrum Access ◽  
Network Resources ◽  
Spectrum Handoff ◽  
Industrial Internet

The overcrowding of the wireless space has triggered a strict competition for scare network resources. Therefore, there is a need for a dynamic spectrum access (DSA) technique that will ensure fair allocation of the available network resources for diverse network elements competing for the network resources. Spectrum handoff (SH) is a DSA technique through which cognitive radio (CR) promises to provide effective channel utilization, fair resource allocation, as well as reliable and uninterrupted real-time connection. However, SH may consume extra network resources, increase latency, and degrade network performance if the spectrum sensing technique used is ineffective and the channel selection strategy (CSS) is poorly implemented. Therefore, it is necessary to develop an SH policy that holistically considers the implementation of effective CSS, and spectrum sensing technique, as well as minimizes communication delays. In this work, two reinforcement learning (RL) algorithms are integrated into the CSS to perform channel selection. The first algorithm is used to evaluate the channel future occupancy, whereas the second algorithm is used to determine the channel quality in order to sort and rank the channels in candidate channel list (CCL). A method of masking linearly dependent and useless state elements is implemented to improve the convergence of the learning. Our approach showed a significant reduction in terms of latency and a remarkable improvement in throughput performance in comparison to conventional approaches.

scholarly journals A Dynamic Channel Selection Strategy for Dense-Array ERP Classification

2009 ◽  
Vol 56 (4) ◽  
pp. 1040-1051 ◽  
Author(s):  
Srinivas Kota ◽  
Lalit Gupta ◽  
Dennis L. Molfese ◽  
Ravi Vaidyanathan
Keyword(s):  
Channel Selection ◽  
Selection Strategy ◽  
Dense Array ◽  
Dynamic Channel

scholarly journals Data-Centric Knowledge Discovery Strategy for a Safety-Critical Sensor Application

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Nilamadhab Mishra ◽  
Hsien-Tsung Chang ◽  
Chung-Chih Lin
Keyword(s):  
Knowledge Discovery ◽  
Time Frame ◽  
Industrial Applications ◽  
Sensor Data ◽  
Sensors And Actuators ◽  
Safety Critical ◽  
Sensor Applications ◽  
Hard Time ◽  
Collection Data ◽  
Time Critical

In an indoor safety-critical application, sensors and actuators are clustered together to accomplish critical actions within a limited time constraint. The cluster may be controlled by a dedicated programmed autonomous microcontroller device powered with electricity to perform in-network time critical functions, such as data collection, data processing, and knowledge production. In a data-centric sensor network, approximately 3–60% of the sensor data are faulty, and the data collected from the sensor environment are highly unstructured and ambiguous. Therefore, for safety-critical sensor applications, actuators must function intelligently within a hard time frame and have proper knowledge to perform their logical actions. This paper proposes a knowledge discovery strategy and an exploration algorithm for indoor safety-critical industrial applications. The application evidence and discussion validate that the proposed strategy and algorithm can be implemented for knowledge discovery within the operational framework.

scholarly journals Analysis and Implementation  of Reinforcement Learning  on a GNU Radio Cognitive  Radio Platform

2021 ◽  
Author(s):  
◽  
Yu Ren
Keyword(s):  
Cognitive Radio ◽  
Success Rate ◽  
Large Scale ◽  
Channel Selection ◽  
Packet Transmission ◽  
Selection Strategy ◽  
Gnu Radio ◽  
Selection Scheme ◽  
Q Learning ◽  
Transmission Success

<p>Spectrum today is regulated based on fixed licensees. In the past radio operators have been allocated a frequency band for exclusive use. This has become problem for new users and the modern explosion in wireless services that, having arrived late find there is a scarcity in the remaining available spectrum. Cognitive radio (CR) presents a solution. CRs combine intelligence, spectrum sensing and software reconfigurable radio capabilities. This allows them to opportunistically transmit among several licensed bands for seamless communications, switching to another channel when a licensee is sensed in the original band without causing interference. Enabling this is an intelligent dynamic channel selection strategy capable of finding the best quality channel to transmit on that suffers from the least licensee interruption. This thesis evaluates a Q-learning channel selection scheme using an experimental approach. A cognitive radio deploying the scheme is implemented on GNU Radio and its performance is measured among channels with different utilizations in terms of its packet transmission success rate, goodput and interference caused. We derive similar analytical expressions in the general case of large-scale networks. Our results show that using the Q-learning scheme for channel selection significantly improves the goodput and packet transmission success rate of the system.</p>

Deep learning approach on channel selection strategy for minimizing co-channel interference in unlicensed channels

2020 ◽  
Vol 105 ◽  
pp. 113558
Author(s):  
Lei Yen ◽  
Abebe Belay Adege ◽  
Hsin-Piao Lin ◽  
Ching-Huai Ho ◽  
Ken Lever
Keyword(s):  
Deep Learning ◽  
Channel Selection ◽  
Selection Strategy ◽  
Learning Approach ◽  
Channel Interference

Priority Based Centralized Scheduling for Time Slotted Channel Hopping Based Multihop IEEE 802.15.4 Networks

2020 ◽  
Vol 17 (1) ◽  
pp. 363-372
Author(s):  
K. M. Martin ◽  
B. Seetha Ramanjaneyulu
Keyword(s):  
Low Power ◽  
Ieee 802.15.4 ◽  
Scheduling Algorithms ◽  
Industrial Applications ◽  
Mac Protocols ◽  
Access Methods ◽  
Wireless Applications ◽  
Channel Hopping ◽  
Time Critical ◽  
Ieee 802.15.4E

To meet the growing demands of low power and determinism in Industrial Wireless applications, IEEE defined IEEE 802.15.4e amendment that includes many channel access methods. Time Slotted Channel Hopping protocol is one of the most popular MAC protocols under IEEE 802.15.4e. However, scheduling of time slots for time slotted channel hopping, was not part of the protocol and so different scheduling algorithms were proposed by researchers. A new time slotted channel hopping scheduling mechanism that considers priorities to meet the time critical industrial applications is proposed in this work. Latency improvements of about 40 percentage are obtained here, for slot allocations to higher priority devices, when compared with the conventional queuing methods.

An Optimal Channel Selection Strategy via Cognitive Radio Nodes

2012 ◽  
Vol 10 (8) ◽  
pp. 1682-1689
Author(s):  
R. Kaniezhil ◽  
C. Chandrasekar
Keyword(s):  
Cognitive Radio ◽  
Channel Selection ◽  
Selection Strategy
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