scholarly journals Empowered Hybrid Parent Selection for Improving Network Lifetime, PDR, and Latency in Smart Grid

2021 ◽  
Vol 2021 ◽  
pp. 1-19
Author(s):  
Kanabadee Srisomboon ◽  
Tinnaphob Dindam ◽  
Wilaiporn Lee
Keyword(s):  
Cognitive Radio ◽  
Network Lifetime ◽  
High Energy ◽  
Single Parent ◽  
Transmission Range ◽  
Transmission Performance ◽  
Selection Scheme ◽  
Transmission Strategy ◽  
Multipath Transmission ◽  
Parent Selection

To support the constraints of smart meters—low power and memory—of AMI network, RPL is considered as the most suitable routing protocol to be implemented in practice. Network lifetime, PDR, and latency are the critical issues to be focused on and addressed. Generally, single parent selection scheme cannot satisfy all expected performance requirements of RPL based on AMI network due to tradeoff between workload balancing and transmission performance, PDR and latency. Moreover, the single parent also suffers from the package size and transmission range. Then, multiparent solution is proposed to overcome these demerits using multipath transmission strategy. Although the existing multiparent solutions, MELT and MAHP, overcome the issue of transmission performance, they present low network lifetime since multiparent solution consumes high energy in data transmission. In this paper, we propose an “empowered hybrid parent selection (EHPS)” that exploits the merits of multiparent solution and the single parent with cognitive radio technology in a hybridizing scheme. To split the data packet efficiently under multipath transmission strategy, a fuzzy AHP (FAHP) is adopted; therefore, EHPS balances the workload effectively and maximizes the network lifetime over long transmission range and large data size. Moreover, by exploiting cognitive radio, EHPS is flexible to the transmission range and data size since it achieves the highest transmission performance, highest PDR, and lowest latency among others, while maintaining high network lifetime.

Enhanced Adaptive Distributed Energy-Efficient Clustering (EADEEC) for Wireless Sensor Networks

2020 ◽  
Vol 13 (2) ◽  
pp. 168-172
Author(s):  
Ravi Kumar Poluru ◽  
M. Praveen Kumar Reddy ◽  
Syed Muzamil Basha ◽  
Rizwan Patan ◽  
Suresh Kallam
Keyword(s):  
Network Lifetime ◽  
Routing Protocols ◽  
Energy Efficient ◽  
High Energy ◽  
Base Station ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Distributed Energy ◽  
Stability Period ◽  
Energy Efficient Clustering

Background:Recently Wireless Sensor Network (WSN) is a composed of a full number of arbitrarily dispensed energy-constrained sensor nodes. The sensor nodes help in sensing the data and then it will transmit it to sink. The Base station will produce a significant amount of energy while accessing the sensing data and transmitting data. High energy is required to move towards base station when sensing and transmitting data. WSN possesses significant challenges like saving energy and extending network lifetime. In WSN the most research goals in routing protocols such as robustness, energy efficiency, high reliability, network lifetime, fault tolerance, deployment of nodes and latency. Most of the routing protocols are based upon clustering has been proposed using heterogeneity. For optimizing energy consumption in WSN, a vital technique referred to as clustering.Methods:To improve the lifetime of network and stability we have proposed an Enhanced Adaptive Distributed Energy-Efficient Clustering (EADEEC).Results:In simulation results describes the protocol performs better regarding network lifetime and packet delivery capacity compared to EEDEC and DEEC algorithm. Stability period and network lifetime are improved in EADEEC compare to DEEC and EDEEC.Conclusion:The EADEEC is overall Lifetime of a cluster is improved to perform the network operation: Data transfer, Node Lifetime and stability period of the cluster. EADEEC protocol evidently tells that it improved the throughput, extended the lifetime of network, longevity, and stability compared with DEEC and EDEEC.

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>

Nonparametric Bayesian Prediction of Primary Users' Air Traffics in Cognitive Radio Networks

2015 ◽  
pp. 770-791
Author(s):  
Ju Bin Song ◽  
Zhu Han
Keyword(s):  
Cognitive Radio ◽  
Cognitive Radio Networks ◽  
Feature Space ◽  
Gaussian Mixture ◽  
Radio Networks ◽  
Air Traffic ◽  
Traffic Patterns ◽  
Nonparametric Bayesian ◽  
Transmission Strategy ◽  
Primary Users

In cognitive radio networks a secondary user needs to estimate the primary users' air traffic patterns so as to optimize its transmission strategy. In this chapter, the authors describe a nonparametric Bayesian method for identifying traffic applications, since the traffic applications have their own distinctive air traffic patterns. In the proposed algorithm, the collapsed Gibbs sampler is applied to cluster the air traffic applications using the infinite Gaussian mixture model over the feature space of the packet length, the packet inter-arrival time, and the variance of packet lengths. The authors analyze the effectiveness of their proposed technique by extensive simulation using the measured data obtained from the WiMax networks.

Energy-Efficient Cooperative Spectrum Sensing for Cognitive Radio Networks

2015 ◽  
pp. 100-121
Author(s):  
Saud Althunibat ◽  
Sandeep Narayanan ◽  
Marco Di Renzo ◽  
Fabrizio Granelli
Keyword(s):  
Energy Consumption ◽  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Cognitive Radio Networks ◽  
Cooperative Spectrum Sensing ◽  
High Energy ◽  
Radio Networks ◽  
Participation Decision ◽  
Consumption Energy ◽  
Fusion Centre

One of the main problems of Cooperative Spectrum Sensing (CSS) in cognitive radio networks is the high energy consumption. Energy is consumed while sensing the spectrum and reporting the results to the fusion centre. In this chapter, a novel partial CSS is proposed. The main concern is to reduce the energy consumption by limiting the number of participating users in CSS. Particularly, each user individually makes the participation decision. The energy consumption in a CSS round is expected by the user itself and compared to a predefined threshold. The corresponding user will participate only if the expected amount of energy consumed is less than the participation threshold. The chapter includes optimizing the participation threshold for energy efficiency maximization. The simulation results show a significant reduction in the energy consumed compared to the conventional CSS approach.

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