TIC-STH 2009
Cognitive Wireless Sensor Networks: Emerging Topics and Recent Challenges Amir Sepasi Zahmati, Sattar Hussain, Xavier Fernando
Ali Grami
Department of Electrical and Computer Engineering Ryerson University, Canada Email: {asepasi, s34hussa, fernando}@ee.ryerson.ca
Faculty of Engineering and Applied Science University of Ontario Institute of Technology, Canada Email:
[email protected]
Abstract—Adding cognition to the existing Wireless Sensor Networks (WSNs), or using numerous tiny sensors, similar to the idea presented in WSNs, in a Cognitive Radio Network (CRN) bring about many benefits. In this paper, we present an overview of Cognitive Wireless Sensor Networks (CWSNs), and discuss the emerging topics and recent challenges in the area. We discuss the main advantages, and suggest possible remedies to overcome the challenges. CWSNs enable current WSNs to overcome the scarcity problem of spectrum which is shared with many other successful systems such as Wi-Fi and Bluetooth. It has been shown that the coexistence of such networks can significantly degrade a WSN’s performance. In addition, cognitive technology could provide access not only to new spectrum, but also to spectrum with better propagation characteristics. Moreover, by the adaptive change of system parameters such as modulation type and constellation size, different data rates can be achieved which in turn can directly influence the power consumption and the network lifetime. Furthermore, sensor measurements obtained within the network can provide the needed diversity to cope with spectrum fading at the physical layer. Index Terms—Wireless Sensor Networks (WSN), Cognitive Radio, Spectrum Sensing.
I. I NTRODUCTION Rapid advances in processing capability, memory capacity, and radio technology have enabled the development of distributed networks with small, and inexpensive communication nodes. These nodes are capable of sensing, communicating, and can be deployed at a cost much lower compared to a traditional wired sensor system. Such systems are called wireless sensor networks (WSNs) [1-3]. WSNs are facing many challenges including the limited bandwidth assigned to them which is in general the industrial, scientific and medical (ISM) band. Bandwidth limitation is highlighted due to the ever increasing demand for spectrum usage by various wireless systems such as WiFi and Bluetooth. In general, spectrum scarcity is a major concern nowadays. Note that spectrum auction for third-generation (3G) mobile communications yielded USD 17 billion, USD 34 billion and USD 46 billion in US, England and Germany respectively [4]. To overcome the problem of spectrum scarcity in a WSN, a new concept of CWSN has been proposed in the literature [5-7]. Cognitive technology is a fundamentally new approach c 2009 IEEE 978-1-4244-3878-5/09/$25.00
978-1-4244-3878-5/09/$25.00 ©2009 IEEE
to the spectrum allocation and utilization concept. A Cognitive Radio (CR) is an intelligent wireless communication system that is aware of its surrounding environment, and adapts its internal parameters to achieve a reliable and efficient communication [9-10]. Through CR technology, unlicensed (secondary) users periodically monitor the spectrum for vacant channels, and use the channels even though they are originally owned by the licensed (primary) users. The first organization in the US which developed new technologies to allow multiple radio systems to share the spectrum through adaptive mechanisms in the form of the neXt Generation communications (XG) program was the Defense Advanced Research Projects Agency. The US Army has also been researching the socalled Adaptive Spectrum Exploitation for real-time spectrum management in the battlefield [4]. In this study, we present an overview of Cognitive Wireless Sensor Networks (CWSNs), and discuss the emerging topics and recent challenges in the area. First, in Section II we explore how a CWSN works, and study the specific characteristics of a CWSN. Then, we discuss CWSN’s advantages compared to the current WSNs in Section III. Section IV explains CWSNs design challenges, and presents possible remedies to overcome the challenges. Finally, in Section V, we conclude the paper. II. H OW A CWSN W ORKS ? Similar to the existing WSNs, a CWSN consists of many tiny and inexpensive sensors where each node operates on a limited battery energy. In a WSN, each node either sends and receives data or it is in idle state. However, in a CWSN, there would be another state called sensing state where the sensor nodes sense the spectrum to find spectrum opportunities or spectrum holes. Fig. 1 depicts different states for both networks. Among various tasks for each sensor node, the transmission and reception of data are the most energy consuming tasks. Spectrum sensing task in the CWSN sensing state, can be performed either by a distributed or centralized scheme. In a distributed scheme, each sensor competes with other sensors to access the available spectrum [5]. Thus, each sensor must have the ability to sense the whole channel, and determine an optimal scheme to maximize its benefits, such as the number of transmissions over time. However, due to the fact that CWSN
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Fig. 1.
Different states in a WSN vs a CWSN
sensor nodes are mostly low-powered with limited capabilities, it may not be feasible to deploy the full functionalities of a distributed scheme in these networks. Thus, in many applications a centralized scheme is preferred [8]. In a centralized scheme, spectrum opportunities are detected by a single entity called network coordinator [7]. Fig. 2 compares a distributed sensing scheme with a centralized method. Moreover, the network coordinator broadcasts a channel switch command to indicate an alternate available channel. The alternate channel could be another licensed channel or an unlicensed channel in the ISM band. The broadcast message could be retransmitted by multiple nodes to reliably deliver the message. Typically, there exist two traffic load configurations in a CWSN: • Regular status report: each sensor sends regular status update to the coordinator. The information in such status updates depends on the particular application. • Control commands: control messages are sent by the coordinator. For example, in a heat control application, the coordinator sends commands to switch on/off the heaters. Apparently, there would be an overhead of message exchange in this scheme as the coordinator should send the results of spectrum sensing into a sensor whenever it receives transmission request. To make spectrum sensing task easier, primary users may transmit a pilot signal periodically on a spectrum band if that band is occupied by itself. By detecting the presence of such a pilot signal, secondary nodes can determine if that particular subcarrier is available or not. Similar to a CR network, a secondary user must be able to detect the presence of a primary signal stronger than the Primary Detection Threshold (PDT) within the Channel Detection Time (CDT). In addition, spectrum opportunity detection must be performed with a success probability greater than or equal to the Probability of Detection (PD) and a probability of false alarm lower than or equal to the maximum Probability of False Alarm (PFA). Furthermore, the secondary system has to vacate the channel within the Channel Move Time (CMT) once a primary signal is detected [5]. The values of the above parameters are set according to each specific application. For instance, in IEEE 802.22 standard [11], the primary signals
are TV broadcasting services and wireless microphones, and the adopted values are: CDT ≤ 2 sec, CMT = 2 sec, PDT= -107 dBm (for wireless microphones), PDT = -116 dBm (for TV broadcasting), PD = 90% and PFA = 10%. We consider the basic 802.15.4 standard, which has been used for a number of WSN applications, and present the cognitive features for its physical and MAC layers. The 802.15.4 standard [13] defines 16 channels, each of 2 MHz bandwidth, in the 2.4 GHz band. While the 2.4 GHz band is getting crowded, secondary operation in other spectrum bands would be inevitable. Reference [5] defines the physical layer of a CWSN, similar to the 2.4 GHz physical layer in 802.15.4, but shifted to a different center frequency. In this way, by using the same channel bandwidth and QPSK modulation and other similar physical layer parameters as in the 2.4 GHz mode, it is shown that CWSN could achieve the same rate with extended range, depending on the center frequency used [5]. Generally, a CWSN supports much lower data rate and much smaller transmit power compared to a WLAN [14]. As a result, sensing duration may not be an overhead issue, and most of the time, the sensors would be in the idle mode. However, for high throughput networks, the total sensing duration would be a critical issue. III. CWSN A DVANTAGES Adding cognition to a WSN provides many advantages. Sensor nodes in a CWSN can measure and provide accurate information at various locations within the network. Measurements made within the network provide the needed diversity to cope with multi-path fading. In addition, a CWSN could provide access not only to new spectrum (rather than the worldwide available 2.4 GHz band), but also to the spectrum with better propagation characteristics [5]. The path loss decreases as the operating frequency decreases. Therefore, if the transmission power of secondary user remains the same, its transmission range increases at lower frequencies [15]. Hence, a channel decision of lower frequency leads to the following advantages in a CWSN. • Higher transmission range. • Fewer sensor nodes required to cover an specific area. • Lower energy consumption. The higher communication range provides CWSNs with the smaller number of hops needed per route. Thus, the average end-to-end delays would be also smaller. Moreover, in future applications such as upcoming smart dust networks, opportunistic use of spectrum would be inevitable. The growing number of such short range nodes distributed in a region which use different radio technologies at unknown locations cause the spectral environment to be sharply variable, even over short distances and brief time periods. Hence, timelimited measurements carried out at a distance from the local area under study cannot characterize the local conditions [4]. Therefore, it is expected that CWSNs to be widely used in the future. The performance gains are obtained at the cost of a slight increase in the protocol complexity and network control overhead. Table I briefly compares WSNs and CWSNs.
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Fig. 2.
Distributed and centralized sensing schemes in a CWSN
IV. D ESIGN C HALLENGES AND P OSSIBLE R EMEDIES TO OVERCOME THE C HALLENGES In a CWSN, energy efficiency is a key design factor. Reference [6] investigates a resource allocation problem from energy efficiency standpoint. A fully distributed channel selection and power allocation scheme has been proposed to minimize the energy per bit over all sub carriers, subject to the required data rate and power constraints. Because the optimization problem is defined subject to two constraints, the solution space would include four regions. Fig. 3 depicts these regions. •
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Region (1): The first partition satisfies the data rate and power constraints. Hence, the optimal solution lies in this region. Region (2): The data rate and power allocation both exceed the upper bounds in this case. To get a feasible TABLE I CWSN S VS WSN S Parameter Higher transmission range Fewer sensor nodes required Lower energy consumption Less end-to-end delay More accuracy Less protocol complexity Less network control overhead
WSN
X X
CWSN X X X X X
•
•
solution, the allocated power on all sub-carriers needs to be decreased. Region (3): In this case, data rate does not reach the minimum constraints. Hence, the allocated power should be increased to achieve the data rate requirement under the maximal power bound. Region (4): In this case, the total energy consumption has already exceeded the maximal power bound, but the data rate requirement is still not met. Therefore, there is no feasible solution in this region.
Another challenge which causes an end-to-end delay in a CWSN, is the sensing duration time. During this interval all traffic is suspended, and spectrum sensing is performed. Particulary, in the cases where the coordinator is the only sensing-capable node and also the source of the application commands, primary user detection and channel allocation would directly impact the QoS. Reference [5] shows that this increases the average end-to-end delay. Another challenge which is raised by the multi-channel nature of CWSNs is the multi-dimensional nature of the resource allocation optimization problem in these networks [6]. In addition to finding the optimal subcarrier set and power allocation which is obtained individually for each sensor, another challenging problem would be to consider the cochannel interference when multiple new users decide to use the same frequency. Hence, a distributed power control scheme is needed in these cases to manage the co-channel interference.
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Lifetime maximization or energy efficiency at the physical layer is another design challenge. For instance, by adapting the modulation type and constellation size and channel coding rate, different data rates can be achieved which will directly influence the power consumption of each node, and in turn will affect the lifetime of the whole network. A possible solution to extend a CWSN’s lifetime is to perform spectrum sensing task by a subset of sensor nodes. The network coordinator could perform spectrum sensing within the network or a number of sensors could be employed and form a distributed scheme in a large network [4]. Due to the fact that spectrum sensing is a repetitive process, and it consumes extra energy from battery powered sensors, performing spectrum sensing in a small portion of the nodes saves the energy consumption of the whole network. Sensing frequency could be either fixed or variable. In a fixed scheme, sensing frequency is a predefined parameter which is scheduled at a known periodic interval within a given duration. On the contrary, in a variable scheme, sensing frequency adaptively varies according to the changes in the channel environment. Sensing duration is determined by the accuracy of the spectrum sensing algorithm and the required probability of detection, while the sensing frequency is set in order to satisfy the channel detection time parameter. Since sensing duration schedule could be set well ahead of time, a possible remedy to avoid overlap transmission within the interval is to broadcast the schedule by the network coordinator. Therefore, all nodes can adjust their transmissions according to the schedule. In addition, to prevent control commands lost, due to interference from primary users, some time-out mechanism could be implemented to deal with such situation. For example, nodes can use a timer to proactively check for the presence of the coordinator’s message [5]. Finally, in some applications, operational networks need to be mobile while the sensing modules does not need to be mobile. By separating
Fig. 3.
the sensing and operational functions in such cases, unique advantages, especially to low power mobile applications would be provided. V. C ONCLUSION Cognitive Radio is a recently presented technology that can potentially improve the efficiency of spectrum utilization. In this paper, we presented Cognitive Wireless Sensor Networks (CWSNs), and discussed how they work. We further investigated the advantages brought about with adding cognition to the current WSNs. Finally, some of the main challenges and possible remedies to overcome them were studied. R EFERENCES [1] W. B. Heinzelman, A. Chandrakasan, and H. Balakrishanan, “An Application-Specific Protocol Architecture for Wireless Microsensor Networks,” IEEE Trans. Wireless Commun., vol. 1, no. 4, pp. 660-70, Oct. 2002. [2] A. S. Zahmati, B. Abolhassani, A. A. B. Shirazi, and A. S. Bakhtiari, “An Energy-Efficient Protocol with Static Clustering for Wireless Sensor Networks,” International Journal of Electronics, Circuits and Systems, vol. 1, no. 2, pp. 135 - 138, 2007. [3] N. M. Moghaddam, A. S. Zahmati, and B. Abolhassani, “Lifetime Enhancement in WSNs using Balanced Sensor Allocation to Cluster,” IEEE International Conference on Signal Processing and Communications (ICSPC 2007), pp. 101 - 104, Nov. 2007. [4] N. S. Shankar, C. Cordeiro, K. Challapali, “Spectrum Agile Radios: Utilization and Sensing Architectures,” First IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks, DySPAN 2005, pp. 160-169, Nov. 2005. [5] D. Cavalcanti, S. Das, W. Jianfeng, and K. Challapali, “Cognitive Radio based Wireless Sensor Networks,” Proceedings of 17th International Conference on Computer Communications and Networks, 2008. ICCCN ’08, pp. 1 - 6, Aug. 2008. [6] S. Gao, L. Qian, and D. R. Vaman, “Distributed Energy Efficient Spectrum Access in Wireless Cognitive Radio Sensor Networks,” Wireless Communications and Networking Conference 2008, WCNC ’08, pp. 1442 - 1447, Mar. 2008. [7] S. Gao, L. Qian, D. R. Vaman, and Q. Qu, “Energy Efficient Adaptive Modulation in Wireless Cognitive Radio Sensor Networks,” IEEE International Conference on Communications 2007, ICC ’07, pp. 3980 - 3986, Jun. 2007. [8] S. Byun, I. Balasingham, and X. Liang, “Dynamic Spectrum Allocation in Wireless Cognitive Sensor Networks: Improving Fairness and Energy Efficiency,” 68th IEEE Vehicular Technology Conference, VTC’08, pp. 1 - 5, Sept. 2008. [9] J. Mitola, “Cognitive Radio: An Integrated Agent Architecture for Software Defined Radio,” Ph.D. dissertation, Royal Inst. Technology, Stockholm, Sweden, 2000. [10] S. Haykin, “Cognitive Radio: Brain-Empowered Wireless Comm.,” IEEE J. Selected Areas in Comm., vol. 23, no. 2, pp. 201- 220, Feb. 2005. [11] IEEE 802.22 draft standard, “IEEE P802.22TM/D0.4 Draft Standard for Wireless Regional Area Networks,” http://www.ieee802.org/22/, November 2007. [12] C. Cordeiro, M. Ghosh, D. Cavalcanti, and K. Challapali, “Spectrum Sensing for Dynamic Spectrum Access of TV Bands,” CrownCom 2007, Orlando, Florida, August 2007. [13] IEEE 802.15.4 Standard, “Wireless medium access control (MAC) and physical layer (PHY) specifications for low-rate Wireless Personal Area Networks (LR-WPANs),” 2003 Edition. [14] A. Willig, “Recent and Emerging Topics in Wireless Industrial Communications: A Selection,” IEEE Transactions on Industrial Informatics, vol. 4, no. 2, pp. 102 - 124, Feb. May 2008. [15] I. F. Akyildiz, W. Lee, M. C. Vuran, and S. Mohanty, ”NeXt Generation/Dynamic Spectrum Access/Cognitive Radio Wireless Networks: A Survey,” Computer Networks, vol. 50, pp. 2127-2159, Sep. 2006.
Partitions of the constraint space
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