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Performance Enhancement of Cognitive Radio in Wireless Network Using Modified LEACH Algorithm
1
Joyti Rajoria,
2Shailendra Yadav
1
M.Tech Student, BTIRT Sagar,
2Assistant Professor, BTIRT Sagar
1
[email protected],
2[email protected]
Abstract
Spectrum sensing or sharing is a base for the performance of all functions performed by the Cognitive Radio (CR). Issues related to single CR detection are overcome by cooperative detection using cluster head from nearby nodes in cognitive radio. Low Energy Adaptive Clustering Hierarchy (LEACH) is a hierarchical protocol in which most nodes transmit to cluster heads, and the cluster heads aggregate and compresses the data and forward it to the base station (sink) which increases cognitive radio performance A non cooperative spectrum sensing algorithm may not work well so Cooperative Spectrum Sensing (CSS) algorithms by utilizing multi-user diversity resolves with numerical results it can be observed that average throughput is achieved through optimization which have been investigated through an average Throughput vs number of cooperative secondary users for different fusion rule.
a radio which autonomously detects and exploits empty spectrum to increase your file transfer rate.
Suppose this same radio could remember the locations where your calls tend to drop and arrange for your call to be serviced by a different carrier for those locations. These are some of the ideas motivating the development of cognitive radio by LEACH. In effect, a cognitive radio is a software radio whose control processes leverage situational knowledge and intelligent processing to work towards achieving some goal related to the needs of the user, application, and/or network. Arising from a logical evolution of the control processes of a software radio, cognitive radio presents the possibility of numerous revolutionary applications, foremost of which is opportunistic spectrum utilization. Cognitive Radio Technologies (CRT).
Keyword Cognitive Radio Technologies (CRT).
Cooperative Spectrum Sensing (CSS)
1.1 INTRODUCTION
The cognitive radio is an intelligent wireless communication system that is aware about its surrounding physical environment and under a certain methodology is able to use available spectrum momentarily without interfering with the primary user who paid to be served in that area.
An example
Let’s imagine a portable radio that is able to communicate to its base which is relatively close.
Let’s call this pair the secondary system and picture it as a relative local service. Assume the system is working at the same spectrum of the cellular phone system, which is the primary system. These secondary systems should work in a kind of opportunistic way to borrow spectrum without interfering with the primary users or degrading the quality of its service.Cognitive radio should be able to scan and sense the spectrum around and find any available spot in frequency to establish its communication that has to be released when primary user comes back while claiming the spot.
What is the definition adopted by the FCC: “A cognitive radio is a system thatsenses its operational electromagnetic environment and can dynamically and autonomously adjust its radio operating parameters to modify system operation, such as maximize throughput, mitigate interference, facilitate interoperability, access secondary markets”
IJSMER201724 376 | P a g e 1.2 TYPES OF COGNITIVE RADIO
NETWORKS
Figure 1.1: Types of cognitive radio 2.1 SIMULATION SETUP
Matlab may be a software package that helps you to do arithmetic and computation, analyze information, develop algorithms, do simulation and modeling, and turn out graphical displays and graphical user interfaces. Typical uses include:
Math and computation
Algorithm development
Data acquisition
Modeling, simulation, and prototyping
Data analysis, examination, and apparition
Scientific and engineering graphics
Application development, including graphical user interface building
MATLAB will be thought of as a library of programs that may prove terribly helpful in determination several engineering science process issues. MATLAB is a perfect tool for numerically helping you in getting answers that may be a major goal of engineering analysis and style. This program is incredibly helpful in circuit analysis, device style, signal process, filter style, system analysis, antenna style, microwave engineering, photonics engineering, pc engineering, and every one alternative sub-fields of engineering science.
It is additionally a strong graphic and visualization tool. The primary step in victimization MATLAB is to understand a way to decision it. It’s vital to recollect that though the front-end and also the interfacing for machines with completely different in operation systems area unit generally different, once
you're within MATLAB, all programs and routines area unit written within the same manner. solely those few commands that area unit for file management and for interfacing with external devices like printers could also be totally different for various in operation systems. MATLAB has evolved over a amount of years with input from several users. In university environments, it's the quality tutorial tool for preliminary and superior courses in arithmetic, engineering, and science. In trade, MATLAB is that the tool of alternative for high-productivity analysis, development, and analysis.
MATLAB (MATrix LABoratory) is superior interacting data-intensive software package surroundings for high-efficiency engineering and scientific numerical calculations. Applications include: heterogeneous simulations and data-intensive analysis of terribly complicated systems and signals, comprehensive matrix and arrays manipulations in numerical analysis, finding roots of polynomials, 2 and three-dimensional plotting and graphics for various coordinate systems, integration and differentiation, signal process, control, identification, symbolic calculus, improvement, etc.
The goal of MATLAB is to modify the users to unravel a large spectrum of analytical and numerical issues victimization matrix-based strategies, attain glorious interfacing and interactive capabilities, compile with high-level programming languages, guarantee hardiness in data-intensive analysis and heterogeneous simulations, give quick access to and easy implementation of progressive numerical algorithms, guarantee powerful graphical options, etc. thanks to high flexibility and flexibility, the MATLAB surroundings has been considerably increased and developed throughout recent years.
This provides users with advanced newest algorithms, monumental data-handling talents, and powerful programming tools.
MATLAB relies on a high-level matrix array language with management flow statements,
IJSMER201724 377 | P a g e functions, data\structures, input/output, and object-
oriented programming options
3.1RESULT DISCUSSION
Low Energy Adaptive Clustering Hierarchy (LEACH) is a hierarchical protocol in which most nodes transmit to cluster heads, and the cluster heads aggregate and compresses the data and forward it to the base station (sink) which increases cognitive radio performance. Each node uses a stochastic at each round to determine whether it will become a cluster head in this round. LEACH assumes that each node has a cognitive radio powerful enough to directly reach the base station or the nearest cluster head, but that using this Cognitive radio at full power all the time would waste energy.Nodes that have been cluster heads cannot become cluster heads again for P rounds, where P is the desired percentage of cluster heads.
Thereafter, each node has a 1/P probability of becoming a cluster head again. At the end of each round, each node that is not a cluster head selects the closest cluster head and joins that cluster. The cluster head then creates a schedule for each node in its cluster to transmit its data.All nodes that are not cluster heads only communicate with the cluster head in a TDMA fashion, according to the schedule created by the cluster head.
They do so using the minimum energy needed to reach the cluster head, and only need to keep their cognitive radios on during their time slot.LEACH also uses CDMA so that each cluster uses a different set of CDMA codes, to minimize interference between clusters.
Shows 10 nodes round up performance in cognitive radio
The operation of LEACH is broken up into Rounds where each round begins with a set-up phase, when the clusters are organized, followed by a steady-state phase, when data transfers to the base station occur.
In order to minimize overhead, the steady-state phase is long compared to the set-up phase. The 100 nodes having 1.0 joule initial energy each and 5% of the nodes having double the energy i.e. 2.0 joule. The figure depicts the comparison between an uncompromised network and a network under selective forwarding attack. In the uncompromised network, all the nodes drain out their whole energy after 3000 rounds approximately
Shows 100 nodes round up performance in cognitive radio
IJSMER201724 378 | P a g e While in the compromised node, a few nodes are left
with substantial energy, but with no use, as the malicious node won be forwarding the nodes to the base-station. The network administrator will be in stateof confusion as it would appear that a few nodes have energy left but no information is received at the base-station. The adversary would intelligently forward information at timely intervals so as to avoid the risk of getting caught by the neighboring nodes.Figure shows the comparison between a homogeneous network and a heterogeneous network following LEACH as their routing protocol.
Shows 1000 nodes round up performance in cognitive radio
3.2 ALGORITHM
The algorithm for the Low Energy Adaptive Clustering Hierarchy (LEACH) implemented
Step1 Select Number of Nodes.
Step2 Select of cluster head from the nodes taking various rounds of selections.
Step3 Cluster head selection depends on Node performance.
Step 4.Cluster head compress data obtained from nodes.
Step 5 Compressed data again transmitted using CDMA.
Step 6 CDMA codifies data which decreases interference among Nodes.
Step 7 Cognitive radio performance increases when there is less interference.
Step 8 Less interference means less call drop.
4.1 RESULT COMPARISION.
The development of clustered sensor networks has recently been shown to decrease system delay, save energy while performing data aggregation and increase system throughput. These are strong comparison behind selecting LEACH as the baseline protocol for the analytical study. Also LEACH has a few but very significant disadvantages like it assumes all the nodes to have same energy, which is not the case always in real-time problems, its cannot be applied for mobile nodes, failure of cluster-heads creates a lot of problems and it doesn’t take into account that the systems might have multiple base stations. These problem is future research in LEACH 5.1 CONCLUSION:
We described LEACH, a clustering-based routing protocol that minimizes global energy usage by distributing the load to all the nodes at different points in time. LEACH outperforms static clustering algorithms by requiring nodes to volunteer to be high- energy cluster-heads and adapting the corresponding clusters based on the nodes that choose to be cluster- heads at a given time. At different times, each node has the burden of acquiring data from the nodes in the cluster, fusing the data to obtain an aggregate signal, and transmitting this aggregate signal to the base station. LEACH is completely distributed, requiring no control information from the base station, and the nodes do not re-quire knowledge of the global network in order for LEACH to operate.
Distributing the energy among the nodes in the network is effective in reducing energy dissipation from a global perspective and enhancing system lifetime. Specifically, our
Simulations show that:
LEACH reduces communication energy by as much as8x compared with direct transmission and minimum-transmission-energy routing.
The first node death in LEACH occurs over 8 times later than the first node death in direct transmission,
IJSMER201724 379 | P a g e minimum-transmission-energy routing, and a static
clustering protocol, and the last node death in LEACH occurs over 3 times later than the last node death in theother protocols. In order to verify our assumptions about LEACH, weare currently extending the network simulator
LEACH direct communication, and minimum transmission energy routing. This will verify our assumptions and give us a more accurate picture of the advantages and disadvantages of the different protocols. Based on our MATLAB simulations described above, we are confident that LEACH will outperform conventional communication protocols, in terms of energy dissipation, ease of configurations, and system lifetime/quality of the network. Providing such a low-energy, ad hoc, distributed protocol will help pave the way for future micro sensor network REFERENCE
[1]Lorenza Giupponi and Christian Ibars.Cooperative Cognitive Systems
[2] Najam ul Hasan. COOPERATIVE Spectrum sensing IN COGNITIVE RADIO NETWORKS, 2006- NUST-MS PhD-ComE
[3]http://enpub.fulton.asu.edu/PowerZone/FuzzyLogi c/chapter%201/frame1.htm
[4] L. Giupponi, Ana I. Pérez-Neira. Fuzzy-based Spectrum Handoff in Cognitive radio network, Centre Tecnològic de Telecomunicacions de Catalunya(CTTC), Universitat Politècnica de Catalunya (UPC).
[5] Dong Li, Xianhua Dai, Han Zhang. Joint Adaptive Modulation and Power Control in Cognitive Radio Networks, School of Information and Scienc Technology, Sun Yat-Sen University Guangzhou 510275, P. R. China.
[6] Nouha Baccour, Anis Koubˆaa, Habib Youssef, Maissa Ben Jamˆaa1,Denis do Ros´ario, M´ario Alves, and Leandro B. Becker.F-LQE: A
Fuzzy Link Quality Estimator for Wireless Sensor Networks
[7] Miao Ma and Danny H. K. Tsan. Cross-Layer Throughput Optimization in Cognitive Radio Networks with SINR Constraints, Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology.
[8] T.charles Clancy,William A Arbaugh, Measuring Interference temperature, Department of computer Science, University of Maryland
[9] http://www.scribd.com/doc/23492277/85/Major- advantages-of-SINR-based-algorithmover-RSS-based
[10] Paul j kolodzy. Interference temperature: A metric for dynamic spectrum, March 2006, phD member, IEEE.
[11] http://
www.electronics.dit.ie/staff/amoloney/...2/dig-comms- ii-lecture-11-12.pdf
[12] http://www.csgnetwork.com/tvfreqtable.html
[13]. HJ Cheng, ZHSu R Guo, NX Xiong, WZ Guo, Service-oriented nodescheduling schemes with energy efficiency in wireless sensor networks.Int. J.
Distributed Sensor Netw.2014(2014)
[14]. H Zhang, Z Zhang, H Dai, R Yin, XM Chen, in IEEE GlobalTelecommunications Conference Distributed spectrum-aware clustering in
cognitive radio sensor networks, (2011), pp. 1–6
[15]. RM Eletreby, HM Elsayed, MM Khairy, inInternational Conference onCognitive Radio Oriented Wireless Networks and CommunicationsCogLEACH: a spectrum aware clustering protocol for cognitive radiosensor networks, (2014), pp. 179–184
IJSMER201724 380 | P a g e [16.] M Ozger, OB Akan, inProceedings IEEE
INFOCOM. Event-drivenspectrum-aware clustering in cognitive radio sensor networks, (2013),pp. 1483–
1491
[17] CF Li, M Ye, GH Chen, J Wu, inIEEE International Conference on Mobile Adhoc and Sensor Systems. An energy-efficient unequal clusteringmechanism for wireless sensor networks, (2005), pp. 604–612
[18]. AO Bicen, OB Akan, Reliability and congestion control in cognitive radiosensor networks. Ad Hoc Netw. J. (Elsevier).9(7), 1154–1164 (2011)
[19]. B Gulbahar, OB Akan.Information theoretical optimization gains in energyadaptive data gathering and relaying in cognitive radio sensor networks.IEEE Trans. Wireless Commun.11(5), 1788–1796 (2012)
[20]. GA Shah, VC Gungor, OB Akan, A cross-layer QoS-aware communicationframework in cognitive radio sensor networks for smart grid applications.IEEE Trans. Ind. Inf.9(3), 1477–1485 (2013)
[21]. GA Shah, OB Akan, Performance analysis of CSMA-based opportunisticmedium access protocol in cognitive radio sensor networks. Ad HocNetw. J.
(Elsevier).15 4–13 (2014)
[22]. GA Shah, F Alagoz, E Fadel, OB Akan, A spectrum-aware clustering forefficient multimedia routing incognitive radio sensor networks.
IEEETrans. Vehicular Technol.63(7), 3369–3380 (2014)
[23]. GA Shah, OB Akan, Cognitive adaptive medium access control in cognitiveradio sensor networks. IEEE Trans. vehicular Technol.64
(2), 757–767 (2015)
[24]. V Esmaeelzadeha, R Berangia, ES Hosseinia, OB Akanb,Stochastic backlogand delay bounds of
generic rate-based AIMD congestion control scheme incognitive radio sensor networks, (2015). Pervasive and Mobile Computing
[25]. W Heinzelman, A Chandrakasan, H Balakrishnan, An applicationspecificprotocol architecture for wireless microsensor networks. IEEE Trans.Wireless Commun.1
(4), 660–670 (2002)
[26]. GA Shah, OB Akan, inIEEE International Conference on CommunicationsSpectrum-aware cluster-based routing for cognitive radio sensornetworks, (2013), pp. 2885–2889
