CREnS: A Convolutional Coder-Based Encryption Algorithm for Tiny Embedded Cognitive Radio Sensor Node

2018 ◽  
pp. 233-243 ◽  
Author(s):  
S. Roy Chatterjee ◽  
S. Mukherjee ◽  
J. Chowdhury ◽  
M. Chakraborty
Keyword(s):  
Cognitive Radio ◽  
Sensor Node ◽  
Encryption Algorithm

scholarly journals Cognitive Radio Sensor Node Empowered Mobile Phone for Explosive Trace Detection

2011 ◽  
Vol 04 (01) ◽  
pp. 33-41 ◽  
Author(s):  
Swagata Roy Chatterjee ◽  
Mohuya Chakraborty ◽  
Jayanta Chakraborty
Keyword(s):  
Cognitive Radio ◽  
Mobile Phone ◽  
Sensor Node ◽  
Trace Detection

Cognitive Radio Sensor Node Empowered Mobile Phone for Explosive Trace Detection

2012 ◽  
Vol 4 (9) ◽  
pp. 29-37 ◽  
Author(s):  
P. Lokesh Kumar Reddy ◽  
B. Rama Bhupal Reddy ◽  
S. Rama Krishna
Keyword(s):  
Cognitive Radio ◽  
Mobile Phone ◽  
Sensor Node ◽  
Trace Detection

An Iterative Power Regulation Technique in Emergency Cognitive Radio Network based on Firefly Algorithm

2020 ◽  
Vol 10 ◽  
Author(s):  
Swagata Roy Chatterjee ◽  
Supriya Dhabal ◽  
Swati Chakraborti ◽  
Mohuya Chakraborty
Keyword(s):  
Cognitive Radio ◽  
Sensor Node ◽  
Firefly Algorithm ◽  
State Of The Art ◽  
Fitness Function ◽  
Base Station ◽  
Sensor Nodes ◽  
Comparative Performance ◽  
Optimal Power ◽  
Power Regulation

Background: Manmade disasters like explosion, toxic wastes, chemical spills etc. have become an imperative concern for our society. Manmade disasters are not everyday phenomenon. Thus, the allocation of separate resources is not realistic. The idea is to use existing cellular infrastructure to implement single hop cognitive radio sensor network for protecting human being from manmade disasters. Objective: The main objectives of this paper are as follows: (a) design of an efficient iterative power regulation algorithm based on Firefly Algorithm for the proposed network, (b) computation of sensor nodes’ optimal power for different position of the cellular user from the base station assuming that cellular user compromises power for its own sensor node, (c) computation of maximum number of sensor nodes coupled with single cellular user for different distances from the base station in worst channel condition, and (d) comparative performance analysis with state-of-the-art algorithms. Method: In presence of explosive and toxic gases, cognitive radio in sensor node establishes connection with the nearest base station to send pre-disaster alert signal utilizing cellular user’s resources. The power is distributed among sensor nodes maintaining the fundamental requirements of cellular users. Here an iterative power regulation mechanism is employed for distributing the power between sensor node and cellular user to achieve reliable utility of the network. The fitness function is designed under the constraints of interference and the designed algorithm is implemented in MATLAB platform towards the searching of optimal power of sensor nodes by maximizing the fitness function. Results: Comparative performance analysis demonstrates the effectiveness of proposed algorithm in terms of speed of convergence, position of mobile phone user from the base station, number of coupled sensor nodes with single cellular user, and Jain’s fairness factor. Conclusion: The proposed network controls the occurrence of manmade disasters and achieves reliable transmission of emergency information prior to disaster without disrupting the cellular phone users. Simulation results validate that the proposed IPRFA algorithm outperforms with respect to state-of-the-art methods in terms of sharing power and Jain’s fairness factor.

scholarly journals Spectrum Aware Cluster Formation Scheme for Cognitive Radio Sensor Network

2018 ◽  
Vol 7 (3.15) ◽  
pp. 105
Author(s):  
Noorhayati Mohamed Noor ◽  
Norashidah Md Din ◽  
Zolidah Kasiran
Keyword(s):  
Fuzzy Logic ◽  
Energy Consumption ◽  
Cognitive Radio ◽  
Sensor Network ◽  
Network Lifetime ◽  
Sensor Node ◽  
Cluster Formation ◽  
Common Channel ◽  
Cognitive Radio Sensor Network ◽  
Robust Cluster

In Cognitive Radio Sensor Network (CRSN), a cognitive radio sensor node operated on a dynamic spectrum allocation with limited computational and energy resource. A cognitive radio sensor node must vacate an occupied channel degrading its performance due to reclustering as the common channel no longer available. Furthermore, energy is mostly consumed during data transmission mechanism. Clustering is the best architecture model to minimize energy consumption among the nodes. With the objective of a robust cluster while maximizing network lifetime, a fuzzy logic technique is proposed. A metric named relative common channel is also proposed. The fuzzy logic combines two input parameters, the relative common channel and residual energy to elect the best suitable cluster head to minimize reclustering and maximize the network lifetime. The performance of the proposed algorithm is compared with LEACH, SAFCA and CogLEACH. The results show that the CRSN has more extended network lifetime and more balanced energy consumption attributed to the robust cluster formation. 

scholarly journals Sustainability Enhancement Multihop Clustering in Cognitive Radio Sensor Networks

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Jong-Hong Park ◽  
Yeonghun Nam ◽  
Jong-Moon Chung
Keyword(s):  
Cognitive Radio ◽  
Sensor Networks ◽  
Sensor Node ◽  
Base Station ◽  
Low Energy ◽  
Data Type ◽  
Transmission Range ◽  
Adaptive Clustering ◽  
Hybrid Data ◽  
Simulation Results

A cognitive radio based hybrid data-type clustering (CR-HDC) algorithm is proposed to maximize network energy efficiency of cognitive radio (CR) sensor networks (CRSNs). By analyzing the overall energy consumption of CRSNs under various conditions, the optimal transmission range of a sensor node can be obtained for both when spectrum handoff (SHO) is applied and when it is not. Simulation results show that CR-HDC achieves performance enhancements in terms of network lifetime and the number of packets received at the base station (BS) compared to when applying the centralized low energy adaptive clustering hierarchy (LEACH-C) or hybrid data-type clustering (HDC) to CRSN environments.

Principles of Cognitive Radio

2009 ◽  
Author(s):  
Ezio Biglieri ◽  
Andrea J. Goldsmith ◽  
Larry J. Greenstein ◽  
Narayan Mandayam ◽  
H. Vincent Poor
Keyword(s):  
Cognitive Radio
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