Dynamic Spectrum Access in Cognitive Radio Wireless Sensor Networks Using Different Spectrum Sensing Techniques

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1 Dynamic Spectrum Access in Cognitive Radio Wireless Sensor Networks Using Different Spectrum Sensing Techniques S. Anusha M. E., Research Scholar, Sona College of Technology, Salem , Tamil Nadu, India. Dr. V. Mohanraj Professor/IT, Sona College of Technology, Salem , Tamil Nadu, India. Abstract Cognitive Radio Wireless Sensor Networks are emerging technology which provides cognitive capabilities to Wireless Sensor Networks. WSN operates in unlicensed band. Same spectrum band is shared by other wireless devices like Wi-Fi and Bluetooth. Due to this, the unlicensed spectrum is overcrowded which degrades the performance of WSNs. To overcome this issue, Cognitive Radio is integrated with WSNs. Spectrum sensing techniques plays important role in Cognitive Sensor Networks. Cognitive radio has ability to know the unutilized spectrum in license and unlicensed band by spectrum sensing. With the help of cognitive radio, idle spectrum band is identified and allocated to sensor nodes. This approach reduces the interference and collisions in WSN which in turn avoids packet retransmissions ensure fair energy in WSN. This paper discuss about the Cognition in WSN and Cognitive Radio WSN s Architecture. Also, Reviews of different Spectrum Sensing techniques are explored for dynamic spectrum access. The pros and cons of each spectrum sensing techniques are discussed. Keywords: Cognitive Radio, Wireless Sensor Networks, Spectrum Sensing, Introduction Now a day, tremendous growth in applications of WSNs, which are operated in unlicensed band(ism) of 2. 4 GHz. Meanwhile same spectrum band is shared by other rapidly growing wireless devices with wireless applications like Wi- Fi and Bluetooth [1]. Spectrum scarcity is the main challenging in wireless communication technologies. To improve the spectrum utilization, cognitive radio networks are used [2]. The information about the licensed spectrum is known by the information exchange between Primary users (PUs) and Secondary Users (SUs) [3], which requires the cooperation between PUs and SUs and introduces extra signaling overhead. Another method of knowing spectrum information of licensed spectrum is SUs spectrum sensing and prediction [4]. This method does not need the information provided by PUs, but demanding accurate sensing algorithms. The capabilities of Cognitive Radio may provide many of the current wireless networks with opportunistic spectrum access in the deployment field. Hence improve overall spectrum utilization [5]. WSNs are traditionally assumed to employ fixed spectrum allocation and characterized by resource constraints of low end sensor nodes. In fact, a WSN comprised of sensor nodes equipped with Cognitive Radio which provide Dynamic Spectrum Access. In this paper introduction section describes about cognition in WSN, following sections describes about Cognitive Radio Wireless Sensor Networks (CR-WSN) and its architecture, and also describes the applications of CR-WSN. Following that different spectrum sensing techniques of CR-WSN are detailed in the paper. Figure 1: Wireless Sensor Networks Cognition in WSN Wireless Sensor Networks WSNs are group of sensors which monitors and measures the physical conditions like temperature, sound, pollution, humidity, wind speed, and pressure etc.. Collected data are organized at central location [6]. Communications in WSNs are event driven. When an event triggers, wireless sensor nodes try to access a channel and generates bursty traffic. Many sensitive and critical activities are monitored and observed by WSNs. It causes a long waiting time for sensitive data. WSNs consist of hundreds of WS nodes deployed in the sensor field. The distance between two neighboring WS nodes is generally limited to few meters. A sink node is responsible for collecting the data from the WS nodes in single or multiple-hop manner. Then the sink node sends the collected data to the users through gateway [7]. Often internet or any other communication channel is used. Figure 1 shows the scenario of conventional WSNs. 4044

2 Cognitive Radio Dynamic Spectrum Access is one of the main applications of Cognitive Radio Network.. A Cognitive Radio is an intelligent wireless communication system which has capable of obtaining information from its surrounding environment. It increases the communication channel reliability and access dynamically the unused resources, by adjusting its radio operating parameters. It leads to a more efficient utilization of the radio spectrum. A Cognitive Radio (CR) must be able to transmit and receive in different bands. It uses different coding and modulation schemes. CR must be based on the Software Defined Radio (SDR) philosophy. Spectrum sensing is one of the possible techniques used by a CR to locate the white spaces (WS). In spectrum sensing, received signal is processed to make a decision on the presence or not of a PU in a licensed band. Spectrum Sensing has low infrastructure requirements. So CR must be able to detect signals at very low SNRs in a limited amount of time. It won t cause any harmful interference to the PUs. Cognitive Radio Network Architecture Cognitive Radio Network Architecture consists of primary networks and secondary networks. Figure 2 shows Cognitive Radio Network Architecture. Primary networks have right to access certain spectrum bands, e. g. common cellular systems and TV broadcast networks [8]. Users of these networks are called to as primary users. They have right to operate in licensed spectrum. Secondary networks have not license to operate in the spectrum band. They have opportunistic spectrum access. Users of these networks are called as secondary users. They have no right to access licensed bands currently used. To share licensed spectrum bands with other secondary or primary networks, additional functionalities are required. Spectrum sensing is used to share licensed spectrum bands with other secondary or primary networks. Functions of Cognitive Radio The functions of Cognitive Radio are as follows, i. Cognitive Radio continuously looks for the unused spectrum which is known as the spectrum hole or white space. It is shown in the Figure 3. This function of CR is refered as spectrum sensing. ii. Once the spectrum holes or white spaces are found, CR selects the available white space or channel. This function of CR is referred as spectrum management. iii. It allocates this channel to the secondary user as long as primary user does not need it. This function of CR is referred as spectrum sharing. iv. When a licensed user is detected, Cognitive radio vacates the channel This function of CR is referred as the spectrum mobility. Figure 2: Cognitive Radio Network Architecture Figure 3: Spectrum Hole Concept Spectrum Sensing Spectrum sensing is a key enabling technique for cognitive radio systems involving spectrum agility. To detect primary users, control protocol support is not available, each radio must sense the surrounding spectral environment to learn about licensed spectrum or interferers, from which it determines which frequency bands are used. Adopting Cognitive Radio in WSN Recently, cognitive techniques have been used in wireless networks to avoid the limitations of conventional WSNs. Cognitive radio (CR) is a candidate for the next generation of wireless communication system. The cognitive technique is the process of knowing through perception, planning, acting, and continuously updating with learning. CR overcomes the many challenges in current WSNs, if it is integrated with WSN. CR has the ability to know the unutilized spectrum in a license and unlicensed spectrum, and access the unused spectrum opportunistically. Wireless Sensors with CR mitigate the current issue spectrum inefficiency and increase the spectrum network efficiency. Cognitive Radio Wireless Sensor Networks (CR-WSN) CR-wireless sensor networks (CR-WSNs) are ad hoc network of distributed wireless sensors. They are equipped with cognitive radio capabilities. CR-WSNs are spatially distributed energy-constrained, self-configuring, self-aware 4045

3 WS nodes with cognitive capabilities. They have to transfer data packets and also to protect license users. CR-WSN is defined as a distributed network of wireless cognitive radio wireless sensor (CRWS) nodes that sense an event signal and send their readings dynamically over the available spectrum bands in a multi-hop manner. CR-WSN Node Architecture Cognitive radio sensor nodes form a architecture of CR-WSN. The information obtained from the environment is conveyed to the sink in multiple hops. The main function of the sensor nodes is to perform sensing on the environment. In addition to this sensing duty, CR-WSN nodes also perform sensing on the spectrum. Sensor nodes send their readings in an opportunistic manner to their next hop cognitive radio sensor nodes and to the sink, depending on the spectrum availability. The sink may be also equipped with cognitive radio capability. In addition to the event readings, sensors may exchange additional information with the sink including control data for group formation, spectrum allocation, and spectrum handoff. Figure 4: CR-WSN Node Architecture CR-WSN node hardware structure consists of sensing unit, processor unit, memory unit, power unit, and cognitive radio transceiver unit. Figure 4 shows the CR-WSN node architecture. Depending on the applications, CR-WSN nodes may have mobilization and localization units as well. The main difference between the hardware structure of conventional WSN nodes and CR-WSN nodes is the cognitive radio transceiver [9]. Cognitive radio enables the sensor nodes to adapt their communication parameters such as carrier frequency, transmission power, and modulation, dynamically. CR-WSN Topology According to the application requirements, CR-WSN may exhibit different network topologies as explored in the following. Ad Hoc CR-WSN: Sensor networks produce an ad hoc cognitive radio sensor networks, without any infrastructural element. Nodes send their sensing data to the sink node in multiple hops, in an adhoc manner. Spectrum sensing is performed by each node individually or collaboratively in a distributed way, in ad hoc CR-WSN. Spectrum allocation can also be based on decision of each sensor nodes. This topology imposes no communication overhead in terms of control data. Spectrum sensing results may be inaccurate, due to hidden terminal problem. It causes performance degradation in the primary user network. Clustered CR-WSN To exchange various control data, such as spectrum sensing results, spectrum allocation data, and neighbor discovery and maintenance information, a common channel is used. Most of the time, such common channel may not be possible throughout the entire network. Hence, cluster based network architecture is an effective method of dynamic spectrum management in CR-WSN. In this case, cluster-heads handled additional tasks such as the collection and dissemination of spectrum availability information. Heterogeneous and Hierarchical CR-WSN: In some cases, CR-WSN architecture incorporate special nodes equipped with renewable power sources such as the actor nodes in wireless sensor and actor networks[10]. These nodes have longer transmission ranges. They used as relay nodes much like the mesh network case. This forms a heterogeneous and multi-layer hierarchical topology, which consisting of ordinary CR-WSN nodes with high power relay nodes. Sensor and actor deployment increases communication overhead due to hierarchical coordination. The need for cognitive radio capability over the actor nodes need to be addressed. Mobile CRSN: When some or all of the architectural elements of a CR-WSN are mobile produces a more dynamic topology. For example, the sensor nodes and actors exist, the sink might be mobile depending on the application. Clearly, mobility increases the existing challenges on most of the aspects of CR-WSN. Hence the dynamic nature of the topology requires mobility-aware dynamic spectrum management solutions over resource-constrained CR-WSN nodes. Figure 5: Spectrum Sensing Techniques Applications of CR-WSN Wireless sensor networks already have a diverse range of application domains from smart home with embedded sensor to multimedia surveillance sensor networks. With the cognitive radio capability to sensor networks regime, CR- WSN might be the preferred solution for some specific 4046

4 application domains explained below, a. Indoor Sensing Applications b. Multimedia Applications c. Multi-class Heterogeneous Sensing Applications d. Real time Surveillance Applications Spectrum Sensing Techniques in CR-WSN Spectrum Sensing is very important function to find the unused spectrum in licensed band or unlicensed band. Sensing of unused spectrum can be based on signal processing techniques, interference based detection method or cooperative detection methods. The signal processing techniques are Matched filter, Energy detection and Cyclo stationary feature detection. Cooperative detection schemes include Centralized, Decentralized and hybrid spectrum sensing methods. Figure 5 shows the classification of spectrum sensing techniques. Signal Processing Techniques Energy Detection: Energy detection is the widely used spectrum sensing method Since the prior knowledge of licensed user signal is not required. In this method, received signal of spectrum is measured. If the measured signal is below the threshold value, the channel is considered as available. It has less computational and implementation complexity and also less delay to other methods [11]. Matched Filter: This method requires the accurate synchronization and the primary knowledge of primary user s features such as bandwidth, modulation type and operating frequency [12]. In demodulation, coherency must be achieved with primary user signal by performing timing, carrier synchronization and channel equalization. Cyclostationary Feature Detection: Transmitted signals have cyclo stationary features which are caused by periodicity or statistics of mean or auto correlation of the signal. Cyclo stationary detector requires excessive signal processing capabilities and its computationally very complex to implement. A cyclostationary detects the presence of a signal based on the periodicity of the transmission by using Spectral (SCF) instead the Power Spectrum Density (PSD) [13] Interference temperature: An Interference temperature level above the noise floor is determined. CR-WSN nodes calculate interference level at the primary user receiver. Then their power is adjusted such that their interference plus noise floor is greater than the interference temperature level. Co-operative Spectrum Sensing Signal processing techniques are used in individual nodes. By spectrum sensing, secondary nodes have access to different primary users and hence face problems like shadowing, multipath and receiver uncertainty. Information collected from each node can be combined in decision making to eliminate the above mentioned issues and cooperative spectrum sensing method employs this technique. In this spectrum sensing scheme, the measurements of several secondary users are combined and examined together in order to determine the presence of the primary user [14]. Cooperative Spectrum Sensing is further classified into Centralized, Decentralized and Hybrid Spectrum Sensing techniques. Table 1 shows overview of spectrum sensing techniques. Table 1. Overview of Spectrum sensing Techniques Spectrum Advantages Disadvantages Sensing Techniques Energy Simpler method Performance of this Detection and low signal method depends on processing requirement variations of noise power level Matched Filter Optimal spectrum Requires a priori sensing method knowledge about the transmission of primary user, this increases cost and complexity Cyclo stationary Feature Detection Interference Temperature Very robust against variations of noise Recommended FCC Require the knowledge of carrier frequency and cyclic prefixes of primary user transmission by Requires the location of primary user for precise interference measurement Centralized Spectrum Sensing: Centralized cooperative spectrum sensing uses regular dependent management where a central unit collects sensing information from individual nodes. It identifies the available spectrum and allocates the idle spectrum to the secondary users [15]. Decentralized Spectrum Sensing: Decentralized spectrum sensing does not require a backbone infrastructure and final information is learnt from closest node. In this method, cognitive nodes share information among neighbors. But they make their own decisions about the unused spectrum which they use. Hybrid Spectrum Sensing: Hybrid spectrum sensing technique is used to obtain spectrum information with minimum sensing duration and low computational complexity. This method is balanced combination of the sensing approaches above. For eg. Energy detection is used on a broader band to have an idea about to know unused spectrum. Based on this information, more accurate sensing methods can be performed over selected potential channels. Conclusion CR-WSN overcomes the limitations of wireless sensor networks like collisions and packets retransmission. By spectrum sensing, unutilized spectrum of primary user is 4047

5 identified and allocated to wireless sensor networks by cognitive radio. In this paper different methods of spectrum sensing methods are studied. Advantages and disadvantages of spectrum sensing techniques are discussed. In this review, Co-operative Spectrum sensing method is most appropriate spectrum sensing technique for CR-WSN, in which shadowing and multipath fading effects are avoided. This method provides better sensing accuracy. [14] C. Song and Q. Zhang, 2009, Achieving cooperative spectrum sensing in wireless cognitive radio networks, SIGMOBILE Mob. Comput. Commun. Rev., Vol. 13, no. 2, pp [15] K. Haresh, 2013 Spectrum Sensing Techniques for Cognitive Radio Sensor Networks Master s Thesis, National Institute of Technology, Orissa, India. References [1] Santhosh Subedi, Saubhagya Das, N. Shekar V. Shet. V, 2014, Dynamic Spectrum Allocation in Wireless Sensor Networks, International Journal of Modern Engineering Research (IJMER), Vol. 4, Iss. 5, pp [2] S. Haykin, 2005, Cognitive radio: brain-empowered wireless communications, IEEE J. Sel. Areas Commun., Vol. 23, no. 2, pp [3] L. Duan, L. Geo, and J. Huang,, 2011, Contract based cooperative spectrum sharing in Proc. of IEEE DySPAN. [4] H. T. Cheg and W. Zhuang,, 2011, Simple channel sensing order in cognitive radio networks, IEEE J. Sel. Areas Commun., Vol. 29, no. 4, pp [5] Ozgur B. Akan, Osman B. Karli, Ozgur Ergul, 2009 Cognitive Radio Sensor Networks, IEEE Netw. Vol. 23, Iss. 4, pp [6] Neha Singh et al, 2012, Network Simulator NS , International Journal of Advanced Research in Computer Science and Software Engineering, Vol. 2, Iss. 5 pp [7] Gyanendra Prasad Joshi, Seung Yeob Nam and Sung Won Kim, 2013, Cognitive Radio Wireless Sensor Networks: Applications, Challenges and Research Trends, Sensors, pp [8] Ian F. Akyildiz, Won YeolLee, Mehmet C. Vuran, Shantidev Mohanty, 2006, NeXt generation/ dynamic spectrum access/ cognitive radio wireless networks: A survey, ELSEVIER Computer Networks, pp [9] I. F. Akyildiz W Su, Y. Sankarasubramaniam, E. Cayirci, 2002, A Survey on Sensor Networks, IEEE Communications Magazine, Vol. 40, No. 2, pp [10] I. F. Akyildiz, I. H. Kasimoglu, Wireless Sensor and Actor Networks: Research Challenges, Ad HOC Networks Journal (Elsevier), Vol. 2, No. 4, Oct2004, PP [11] H. Urkowitz, 1967, Energy detection of unknown deterministic signals, Proc. Of IEEE, pp [12] Rahul Tandra, 2005, Fundamental Limits of Detection in Low Signal to noise ratio Master s Thesis, University of California Berkeley, Spring. [13] P. D. Sulton, J. Lotze, K. E. Nolen and L. E. Dayle, 2007, cyclostationary signature detection in multipath Rayleigh fading environments, in Proc. IEEE Int Conf. Cognitive Radio Oriented Wireless Networks and Commun., Orlando, Florida, USA. 4048