Distributed Cognitive Radio Detection Using ...

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Distributed Cognitive Radio Detection Using Waspmote Sensor for Windows Based PC/Laptop M.N. Morshed, S. Khatun

L.M. Kamarudin, A. Zakaria, N.Azmi

Embedded Computing Research Cluster School of Computer and Communication Engineering Universiti Malaysia Perlis (UniMAP) Perlis, Malaysia [email protected], [email protected]

Centre of Excellence for Advanced Sensor Technology School of Computer and Communication Engineering Universiti Malaysia Perlis (UniMAP) Perlis, Malaysia [email protected] designing cognitive radio network [3]. Longer detection time improves the detection performance but decrease the overall system throughout. In a paper [4], detection throughput tradeoff has described to find out the optimal detection time that improves the performance of secondary user while at the same time providing maximum protection to the primary user. Hard decision fusion, such as k-out-of-N fusion technique to improve the sensing performance is cooperative detection [5]. There are various cooperative spectrum detection schemes to fuse the detecting information of the secondary users [6]-[8]. The schemes can be classified into hard decision based fusion, soft decision based fusion and data based fusion schemes according to the author of this work. In another work [9], a fusion center (FC) based distributed cooperative spectrum detection method have been proposed. The authors have considered a FC and some number of cognitive sensors to carry out detection in dedicated, periodic sensing slots. They propose a sleeping and censoring energy saving mechanism for their model. So far most of the proposed methods are based on computer simulations. No standard to build and test in real scenario. Considering this issue, we have developed a Waspmote based cognitive radio detection scheme for standalone computer or laptop which runs on windows based Operating System. Waspmote is a sensor device which works with different communication protocols (ZigBee, Bluetooth and GPRS) and frequencies (2.4GHz, 868MHz, 900MHz) [10]. For our study IEEE 802.11 b/g 2.4 GHz Wi-Fi frequency has been considered. We have used waspmote Wi-Fi module to detect the presence/absence of primary user. The most beneficial point of using this type of sensor is no special setup requirement is needed. It can be easily used with a PC or Laptop which are very common in our everyday working purpose. This method worked as distributed cooperative spectrum detection system, in which each secondary user will make its own decision about presence and absence of primary user and will try to transfer data between them.

Abstract— All over the world, the existing spectrum is already been saturated with ever increasing demand. Every day huge number of users is joined to the existing fixed band frequency but the bandwidth is not increasing. To solve this issue the cognitive radio is the best choice. Spectrum or white space detection is an essential and very important issue in cognitive radio communication. The aim of channel detection is to find out efficiently the presence or absence of primary or licensed frequency. In this paper, we have developed a Libelium Waspmote sensor based cognitive radio detection scheme which is very easy to use, energy and cost effective. The reason for selecting this type of sensor is it supports IEEE 802.11 b/g and the Tx/Rx antenna transmission and reception sensitivity can be changed by the software. It can scan the available channel resource within its range and can store the data files. Also the security of this type of sensor supports the highest level of wireless security that is Wireless Protected Access V2 or WPA2. It is Windows based which is very common worldwide. The application runs in background without interrupting the PC/Laptop users. It is able to find efficient spectrum hole (Ideal channel) for a secondary or cognitive user to use the primary or licensed band without interrupting the primary user. Index Terms—Cognitive Radio, Channel detection, Waspmote sensor, Wi-Fi.

I. INTRODUCTION Cognitive Radio is a major part of research in communication industry from past few years. It is due to most of the frequency is being occupied by primary user and the resource is limited for this huge number of users [1], [2]. Cognitive radio is basically an organized system that a secondary user can use the primary licensed frequency when it is not occupied by the primary user, i.e., the primary user is idle. It is a promising technology to solve the frequency shortage issue and that’s why also a major issue in scientific research. Before the secondary user utilize a primary spectrum, it is necessary to ensure that the primary user is not occupying the channel. So the spectrum detection is the most important issue for

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II. SYSTEM MODEL The considered distributed channel detection model has N numbers of secondary user which are working individually in various locations. Each individual system contains one windows based PC or laptop along with a waspmote sensor. From the technical specification the lowest sensitivity of this device is -83 dBm [9]. So the threshold level of our experiment fixed to -83 dBm. If the sensor can detect any Wi-Fi signal, will consider as presence of primary user (H1) otherwise absence of primary user (H0). Fig. 1 shows the real scenario of our experiment. We consider the experimental system and the primary users both are fixed in their own specific area; The Wi-Fi APs are located in different location and different distance from the system. So the received signal strength (RSS) is different for different channels. The environment is also a major issue. It may often occur shadowing and multipath fading for wireless distance communication due to environment. For our experiment, we have used an open laboratory room only with some desks to reduce the channel fading and shadowing.

Fig. 2: Wi-Fi 2.4GHz channel overlapping [11]

I. EXPERIMENTAL OUTCOME Waspmote sensors are programmed with C programming language which is very basic and popular language to programmers. For our experiment, we have programmed the sensor device to be active. The device is now ready to detect primary user along with its corresponding channel number and MAC address of each primary user as shown in Fig 3.

Fig. 3: Output result of Waspmote based cognitive radio detection

From the Fig. 3 it can easily found that which channel is occupied by primary user at present in that specific area. The result from the above Fig. 3 shows that channels 01, 06 and channel 13 are occupied by primary user and rest of the channels from 01 to 14 is available for secondary user. As this is a distributed detection model, so there is no use of Fusion center. To measure the system performance we have used MATLAB software for graphical representation. First we have connect to a specific Access Point (AP) and keep the AP at some distance from the sensor node until the RSS comes around -50 dBm. Then we send 32 bytes of ping data packet to the AP and monitor the transition time and packet loss performance. The echo request "ping" is an ICMP message whose data is expected to be received back as an echo reply "pong". The host must respond to all echo requests with an echo reply containing the exact data received in the request message [11]. Ping is a simple packet data that contains IP header, ICMP header, ICMP payload and data transportation that used for network continuity checking. Then we change the distance for -55 dBm signal level and do the same task. The same procedure we have followed for -60 dBm, -65 dBm and up to -75 dBm. Below this level we measure for every RSS value up to -83 dBm because the packet loss may occurs anytime when the reception level is about to its threshold level; and the output has been recorded accordingly.

Fig. 1: Wapmote sensor based Cognitive Radio detection model.

We have considered 2.4 GHz IEEE 802.11b/g technology only as our primary user because the detection sensor is only capable to detect this range of frequency. They Wi-Fi system have 1 to 14 or 1 to 11 different channel depend on different country and region they are using. Each channel is separated by 22 MHz band to avoid interference, but the adjacent channel is still overlapped and interferes with each other. Fig. 2 shows the channel structure of a 2.4 GHz Wi-Fi [11]. The figure showing 14 different channel in Wi-Fi technology and only 4 nonoverlapping channel is can be used depends on which frequency is used by the primary user.


III. PERFORMANCE ANALYSIS

Packet Transfer Rate vs RSS 1

The performance of any kind of wireless communication system depends mostly on environments. As we have used an open laboratory hall room, the RSS change is quit smooth. Two parameters we have taken into consideration, one is Detection throughput vs. RSS and another one is packet transfer rate to RSS. Fig. 4 presents detection throughout of experimental sensor. Detection throughput refers to the ratio of successful detection to the attempt of detection. The acceptable level we have considered as the successful detection is more than 70%, i.e., the miss detection is less than 0.3. When the RSS is from -70 dBm to -78 dBm, there is no detection error i.e., the sensor always detects the signal. When the signal range comes below -78 dBm, the detection performance decrease but it is still acceptable. But when the signal comes below -80 dBm, the detection performance goes below the average acceptable level and the successful detection throughput reaches below 10% for -82 dBm and below 5% for -83 dBm.

0.9

Packet Transfer Rate

0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 -83

-82

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-79 -78 RSS in dBm

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Fig. 5: Packet transfer rate to RSS

The limitation of this system, the sensor can only detect up to -83 dBm of signal strength. Below this level some primary user may exists and may interfere with the secondary users that are presently using primary users spectrum bands.

Plot of Detection Throughput vs RSS 1 0.9 0.8

Detection Throughput

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IV. CONCLUSION & FUTURE WORK

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In this paper we have developed a cognitive radio detection scheme based on Waspmote sensor and study its performance. This sensors performance is excellent within the range of operation. The distributed detection technique works fine within any kind of environment. In future there is a scope to combine all the distributed system of a specific network or working place together using cloud based fusion center, and also use of Artificial Intelligence (AI) to find out more efficient white spectrum available.

0.5 0.4 0.3 0.2 0.1 0 -86

-84

-82

-80 RSS in dBm

-78

-76

-74

Fig. 4: Detection Throughput performance to RSS

Fig. 5 shows the packet transfer performance for different RSS through the sensor. We have sent 100 packets each time and monitor the reply performance, from the ratio of successfully received packet to the sent packet is considered as packet transfer rate. From the figure it is seen that there is 100% successful packet transfer when the RSS up to -78 dBm. Below this level some packet has been lost and above average acceptable level up to -80 dBm. Below this level the packet loss has increased very quickly and reached up to 0% success rate at -83 dBm.

ACKNOWLEDGMENT This work is supported by Ministry of Higher Education, Malaysia, Grant LRGS/TD/2011/UKM/ICT/02/05/901200005. And also thankful to everyone who are directly or indirectly related to this work at Centre of Excellence for Advanced Sensor Technology (CEASTech), UniMAP, especially Dr. Latifah Munirah Kamarudin and Dr. Ammar Zakaria for their kind support.


REFERENCES [1] J. Mitola, III and G. Q. Maguire, Jr., “Cognitive radio: making software radios more personal,” IEEE Pers. Commun., vol. 6, no. 4, pp. 13–18, Aug. 1999. [2] S. Haykin, “Cognitive radio: brain-empowered wireless communications,” IEEE J. Sel. Areas Commun., vol. 23, no. 2, pp. 201–220, Feb. 2005. [3] Yucek T, Arslan H. “A survey of spectrum sensing algorithms for cognitive radio applications”, IEEE Commun Surv Tutor 2009; 11(1):116–30. [4] Y.-C. Liang, Y. Zeng, E. C. Y. Peh, and A. T. Hoang, “Sensingthroughput tradeoff for cognitive radio networks,” IEEE Trans. Wireless Commun., vol. 7, pp. 1326–1337, Apr. 2008. [5] Edward C. Y. Peh, Ying-Chang Liang and Yong Liang Guan, “Optimization of Cooperative Sensing in Cognitive Radio Networks: A Sensing-Throughput Tradeoff View”, IEEE ICC proceedings 2009. [6] Yucek T, Arslan H. “A survey of spectrum sensing algorithms for cognitive radio applications”, IEEE Commun Surv Tutor 2009; 11(1):116–30. [7] Letaief K B, Zhang W. “Cooperative communications for cognitive radio networks”, Proceedings of the IEEE, 2009, 97(5): 878–893 [8] Lunden J, Koivunen V, Huttunen A, Poor H V. “Collaborative Cyclostationary spectrum sensing for cognitive radio systems”, IEEE Transactions on Signal Processing, 2009, 57(11): 4182–4195 [9] Sina Maleki, Ashish Pandharipande and Geert Leus, “EnergyEfficient Distributed Spectrum Sensing for Cognitive Sensor Networks”, IEEE Sensors Journal, VOL. 11, NO. 3, March 2011. [10] www.libelium.com, Waspmote sensor Technical Datasheet. [11] www.wikepedia.org, Wi-Fi Channel structure.