A Novel Spectrum Sharing Scheme for Industrial ... - IEEE Xplore

IEEE ICC 2014 - Ad-hoc and Sensor Networking Symposium

A Novel Spectrum Sharing Scheme for Industrial Cognitive Radio Networks: From Collective Motion Perspective Feilong Lin, Cailian Chen, Li’an Li, Honghua Xu, Xinping Guan Department of Automation, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China Shanghai 200240, P.R. China Email: {bruce lin, cailianchen, li li an, xuhonghua, xpguan}@sjtu.edu.cn

Abstract—Spectrum sharing is a promising technique responsible for providing efficient and fair spectrum allocation. Considering the unevenness phenomenon of spectrum usage in industrial wireless networks, a novel spectrum sharing scheme, in the framework of industrial cognitive radio network (ICRN), is proposed in this paper via an autonomous switching technique to equalize the spectrum usage and increase the spectrum access possibility of new requests. The autonomous switching technique borrows the idea from the fact that specific collective motion can be achieved by local actions of individuals in many biological systems. The accessed nodes sense the limited spectrum range around their central frequency and then make the decision of channel switching autonomously. Several sensing report based rules are presented to facilitate the switching decision in order to equalize the channel usage among the sensing range of each node. It is demonstrated that by using these rules the spectrum usage becomes more even, and thus the spectrum utilization and fairness are both improved. Numerical examples are given to show the effectiveness of the proposed spectrum sharing scheme. Index Terms—Industrial cognitive radio network; spectrum sharing; collective motion; channel switching

I. I NTRODUCTION Being able to timely obtain the process data without failure is essential for plant automation. We can easily deploy wireless field instruments to temporarily or continuously monitor the status of equipment, process trends, locate assets and then control the process. Wireless communication in industrial automation has attracted increasing attention recently [1]. Typically, the communication is mostly based on standardized protocols such as Zigbee, ISA100.11a, WirelessHART or WISA in Industrial, Scientific and Medical (ISM) band. However, the limited available spectrum and the proliferation of wireless devices are becoming the vital contradiction in industrial wireless networks. On the other hand, spectrum access mechanisms exploited by those industrial wireless standards are nonflexible. In detail, Zigbee only simply uses CSMA/CA technique to select available channel during initialization of communication. ISA100.11a introduces the FHSS to enhance the communication robustness with a pseudo-random sequence generated by system manager. WirelessHART supports blacklisting of channels and clearing channel assessment according

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to the 802.15.4 PHY functions channel energy detection and link quality indication, but which is fulfilled by manual control. WISA adopts the frequency division duplex with each cell using different transmission frequency band [2]. All of the above spectrum access mechanisms are unable to provide timely response to spectrum dynamics and result in low spectrum utilization and low fairness. Spectrum utilization and corresponding fairness [3] are considered as vital performance criteria from the viewpoints of system frequency gain and individual QoS. Spectrum sharing is considered as a promising method to improve the spectrum efficiency and fairness of spectrum usage. It has attracted lots of attentions from academia and engineering. In general, there are two classes of spectrum sharing approaches. One class is based on centralized approaches [4], [5], where central entities are employed to collect spectrum information and schedule the spectrum allocation to fulfil the efficient spectrum using. However, for the large scale or hierarchical networks, the communication overhead for spectrum sharing increases dramatically, and it makes the centralized approaches inefficient to guarantee the two vital performance criteria. To tackle this challenge, the distributed spectrum sharing methods are then proposed. The work [6] presents a CSMA/CA based opportunistic spectrum sharing method. However, neither utilization nor fairness of spectrum usage is guaranteed in such a method. The works in [3], [7] present cooperative spectrum sharing methods, which take account of both system spectrum utilization and fairness across users. In these strategies, users need to implement a heuristic iteration and exchange spectrum usage information for multiple times, and the amount of interactive communications grows exponentially as nodes increasing [7]. Hence, how to design the convergence rate of the heuristic algorithm to tolerate the spectrum dynamics is a challenging problem. Another issue being worth to notice is that all the aforementioned methods depend on the reliable transmission of spectrum usage information. They are not suitable for industrial wireless networks since that industrial environment exists significant electromagnetic interference and multi-path propagation [8]. In this paper, we aim to propose a new autonomous spec-


trum sharing scheme to achieve better spectrum usage and users’ fairness, and reduce the information exchange. Based on the preceding analysis of spectrum sharing mechanisms, it is found that all the methods are proposed to facilitate the efficient spectrum access for new requests. Interestingly, it is possible to provide more accessing opportunities if the spectrum usage is evenly distributed among the whole available spectrum. It motivates us to design the spectrum sharing algorithm from the perspective of the spectrum usage distribution. Inspired by the collective behaviors appearing in the biological world such as the line forming of the flying gooses and vortices forming of the fish school [9], [10], which have common characteristics including self-organization and collision avoidance [11], we take insight to the spectrum sharing problem from the perspective of biological collective motion in this paper. In order to avoid the long delay and serious packet loss of CR users in the contention for few spectrum holes in specific overcrowded spectrum band, we change the normal accessing control based spectrum sharing approach into the concession based method. In this strategy, the accessed users take the concession by autonomously and locally switching their communication channels to other relatively spare spectrum bands, so that the new arrivals have more opportunities to access to the busy spectrum band without delay and packet loss. As each user only needs local spectrum sensing capability, it is easy to be implemented in the ICRN. Then, without any information exchange, the proposed autonomous spectrum sharing scheme can improve the spectrum utilization and spectrum accessing probability of new arrivals. The main contributions of this paper are two folds:

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First, inspired by the collective motion of biological systems, a new spectrum sharing scheme is proposed to promote the spectrum utilization and improve the fairness of spectrum usage. Then we design autonomous channel switching rules for accessed users, which is easy to be implemented without any extra communication overhead. Second, the proposed autonomous spectrum sharing approach can maximize the accessing probability of new arrivals. Detailed mathematical analysis is provided to demonstrate the improvement of the spectrum accessing probability. The lower bound of spectrum accessing probability is also given.

The rest of this paper is organized as follows. In section II, we introduce the architecture of ICRN and the network model, then give the problem formulation. In section III, we present the autonomous spectrum sharing rules, and give the theoretical analysis of its performance. In section IV, we consider four different spectrum sharing scenarios and present the comparison results by simulation. Conclusion is given in section V.

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II. A RCHITECTURE OF ICRN AND N ETWORK D ESCRIPTION A. Architecture of ICRN Fig.1(a) shows the 2050mm hot strip mill of Baosteel of China. The whole hot strip mill production line contains several sections from the functional and physical point of view, such as reversing rougher R1, reversing rougher R2, finishing mill, laminar cooling, down coiler and so on. Kinds of sensors are deployed to monitor different parameters of different mill processes. To fulfill the effective management of networks, we design the ICRN with three-layer architecture as shown in Fig.1(b). In this paper, we concern with the spectrum sharing problem within the field subnetwork which is comprised of a group of sensors and one access point (AP). Here, for each section of mill process, corresponding sensors form a field subnetwork to monitor key coefficients of process independently. In the field subnetwork shown in Fig.1(b), numerous sensors are deployed to collect process data and transmit to AP through wireless communication with limited spectrum in the ISM band. In order to save the energy, each sensor works in eventdriven manner. the sensors transmit data to AP only when event occurs, otherwise turn off the radio. In this paper, it is concerned with the spectrum sharing problems to improve the utilization and fairness of spectrum usage. B. Assumptions Assume that the given consecutive spectrum is divided into M non-overlapping orthogonal channels with equal bandwidth, denoted by Cm , m = 1, 2, ..., M in the order from lower frequency to higher frequency. All channels are assumed to have binary states, i.e., 0 for idle state and 1 for occupied state, respectively. The access point (AP) in this paper is powerful. Each AP is assumed to have maximal M interfaces and works all the time with wired power supply. The sensors transmit data with a single channel. Equipped with the cognitive radio, each sensor could sense three consecutive channels. Furthermore, we assume when a user switches its transmission channel, it tunes its central frequency to the new transmission channel correspondingly. For example, one sensor working on incumbent channel Cm can sense {Cm−1 , Cm , Cm+1 }. If it changes its communicating channel to Cm+1 , its sensing range is changed to {Cm , Cm+1 , Cm+2 }. We define three states of the sensors as sleep, accessing and accessed state. sleep user represents the sensor which has no data to send, so that its radio is turned to sleep state for saving the energy. A sensor awaiting and seeking idle channel is called accessing user and the sensor having accessed spectrum is called accessed user. In this paper, we concentrate on the uplink of the field subnetwork. Hence, how to quickly access to an idle channel is important for each sensor. We assume the data transmission is of burst type that each sensor collects the data and sends them out randomly at time domain with a random length of


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C. Criteria of Spectrum Sharing In this paper, we focus on two aspects of the system performance. On one hand, we expect to improve the evenness of spectrum usage. Hence the utilization and fairness of spectrum usage are concerned. Because users are working in event-driven manner, we quantify utilization and fairness from the viewpoint of statistical analysis of each channel’s service time. With the model of the channel state, we let Cm (t) = 1 denote the channel m being used at slot t, and Cm (t) = 0 denote the channel m being idle. Referring to the definitions in [3] and our channel service model, we define the utilization function as the total service time of all channels Δ

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III. RULES OF AUTONOMOUS C HANNEL S WITCHING To alleviate the overcrowded phenomenon in specific few spectrum bands, we expect to design spectrum sharing rules to conduct accessed users’ channel switching and occupied channels to form a line motion which means occupied channels evenly or almost evenly lay out in the given spectrum range. A. Design of Channel Switching Rules Through sensing the states of neighboring channels, a user decides to continue using current channel or switch to neighboring channel at the next slot. In detail, for accessed user n using channel m, we borrow the conception of potential [10] to describe the relationship of neighboring users in frequency −→ l = 1 represent the potential formed by domain. Let Em the neighboring sensor using channel Cm−1 , if this neighbor −→ l exists. The potential Em propels sensor n to switch to channel −→ −→ l r Cm+1 . Em = 0 when Cm−1 is free. Similarly, Em = −1 represents the potential formed by the sensor on channel −→ r Cm+1 , and Em = 0 when Cm+1 is free. Here we assume the positive direction of switching is from low frequency channel −→ −→ −→ l r + Em to high frequency channel. Then, we define Em = Em to represent vectored potential of the sensor. To control the pace of channel switching and prevent users from continuous back and forth switching between neighboring channels, vm is introduced as follows. Define vm with binary state: 0 means the sensor n on channel m did not switch and 1 means the user is switched from other channel at the last time slot. In order to control the switching pace, when vm = 1, the sensor


n is not permitted to switch this time slot. In the following, the detail of the rules of autonomous channel switching is derived complying with the very simple principle of equalizing the distribution of channels utilization among the sensing range of the sensor n. Rules of autonomous channel switching: −→ 1) When Em = 0 or vm = 1, the sensor n will keep working on incumbent channel Cm and set vm to 0 at the next slot; −→ 2) When Em = 1 and vm = 0, the sensor n will switch to channel Cm+1 and set vm+1 to 1 at the next slot; −→ 3) When Em = −1 and vm = 0, the sensor n will switch to channel Cm−1 , and set vm−1 to 1 at the next slot; 4) When there are two sensors simultaneously satisfy the conditions to switch to the common channel, CSMA/CA is employed to avoid the collision; 5) The user using channel C1 or CM does not switch channel. Fig. 2 presents a simple example to illustrate the rules of autonomous channel switching. B. Performance Analysis From the viewpoint of maximizing the spectrum accessing probability for the accessing user, we give the definition of equilibrium of spectrum usage. Definition 1: (Equilibrium of spectrum usage) The Equilibrium of spectrum usage is defined as the state which satisfies any of the following two conditions: 1) for N ≤ M/2, there do not exist any two neighboring occupied channels, or 2) for N > M/2, there do not exist any two neighboring free channels, and both C1 and CM are occupied. With the definition of equilibrium of spectrum usage, the following relation between the equilibrium and the spectrum accessing probability (3) is given. Theorem 1: When the spectrum usage reaches the equilibrium, the spectrum accessing probability Pa for any accessing user satisfies N ≤ M/2, Pa = 1, (4) 2(M−N ) 3(M−N ) ≤ P ≤ min{ , 1}, N > M/2. a M−2 M−2 Proof: 1) For N ≤ M/2, the equilibrium of spectrum usage implies that there do not exist any two neighboring occupied channels. Thus Cm−1 Cm Cm+1 = 0, ∀ 2 ≤ m ≤ M − 1, which indicates that the accessing user can find at least one free channel in its sensing range with probability 1. 2) For N > M/2, the equilibrium of spectrum usage implies there do not exist any two neighboring free channels and {C1 , CM } are occupied. As N channels are occupied, there are M −N free channels. We analyze the worst and best cases for Pa : a) When there is one and only one occupied channel between any two consecutive channels, the spectrum accessing probability for new accessing user is minimal. From the ) definition of (3) we easy derive Pa = 2(M−N M−2 . b) When there are at lest one channel between any two consecutive channels,

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the spectrum accessing probability for new accessing user 2M is ) 2M . When N ≤ at most for N > Pa = 3(M−N M−2 3 3 , 2(M−N ) 3(M−N ) Pa = 1. Hence, we have M−2 ≤ Pa ≤ min{ M−2 , 1}. Remark 1: Suppose that M = 12, N = 8, and spectrum usage has reached equilibrium. When {C1 , C2 , . . . , CM } = ) = 4/5. {1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1}, we get Pa = 2(M−N M−2 If {C1 , C2 , . . . , CM } = {1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1}, Pa = 3(M−N ) = 1. With any other occupation distribution of specM−2 ) ) trum usage equilibrium, we have 2(M−N ≤ Pa ≤ 3(M−N M−2 M−2 . Next, we introduce another important definition, which describes the potential from the system level. Definition 2: For accessed user n who uses Cm , define its absolute potential as ⎧ − → |E1r |, m = 1, C1 = 1, ⎪ ⎪ ⎪ −→ ⎨ −→ l r Δ |Em | + |Em |, 2 ≤ m ≤ M − 1, Cm = 1, (5) Em = −−→ ⎪ l ⎪ | E |, m = M, C = 1, M ⎪ M ⎩ 0, 1 ≤ m ≤ M, Cm = 0, and the absolute potential of the system as Esys =

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Under the rules of autonomous channel switching, Esys reveals an important property to be claimed by the following lemma. Lemma 1: Esys is monotonically non-increasing under the given rules of autonomous channel switching. Proof: Consider the subset comprised of five consecutive channels, {Cm−2 , Cm−1 , Cm , Cm+1 , Cm+2 |Cm = 1}. According to the rules of autonomous channel switching, we −→ −→ have Em = 1 or Em = −1 when the sensor n contents the −→ channel switching condition. First, assume Em = 1, which denotes that Cm−1 = 1, Cm+1 = 0, the channel switching will change the potential in two cases: 1) If Cm+2 = 0, both Em−1 and Em decrease by 1, hence Esys decreases by 2; or 2) If Cm+2 = 1, both Em−1 and Em decrease by 1, but both Em+1 and Em+2 increase by 1, hence Esys does not change. In this case, 2 units of the potential transfers from {Cm−1 , Cm } to −→ {Cm+1 , Cm+2 }. Accordingly, when Em = 1, the change of potential is decided by the state of Cm+2 .


−→ Analogically, for the situation that Em = −1, it means Cm−1 = 0 and Cm+1 = 1. Hence, Esys will decrease by 2 if Cm−2 = 0, and Esys does not change by potential transferring inside the subset if Cm−2 = 1. To analyze channel switching for sensor using C2 or CM−1 , we have the similar conclusion. From above, we conclude that for any sensor, the change of potential caused by channel switching is non-increasing. Therefore, Esys is monotonically non-increasing. The proof is then completed. Now, To illustrate how the sensors’ collective behavior to affect spectrum usage, we have another theorem. Theorem 2: Spectrum usage of system always approaches to equilibrium under the rules of autonomous channel switching within the maximal slots 2N − 3, N ≤ M/2, (7) Tmax = 2(M − N ) − 1, N > M/2, and when the system reaches equilibrium, we have 0, N ≤ M/2, Esys = 4N − 2M − 2, N > M/2.

According to the definition of Esys , it is easy to know that the maximal of Esys comes from the cases that all n users are layed out one by one at one spectrum end. The maximal max is Esys = 2N − 2. We have know that Esys will decrease until reach the equilibrium. Hence, we confirm that it takes the most steps to achieve equilibrium for the above cases. In such cases, if N ≤ M/2, it would take 2N − 3 slots to reach the equilibrium according to the rules; if N > M/2, it would take 2(M − N ) − 1 slots. When system reaches the equilibrium, according to definition of equilibrium of spectrum usage, there are no neighboring channels being occupied simultaneously for N ≤ M/2. It is easy to obtain that Esys = 0. For N > M/2, there exists no neighboring channels being free simultaneously and {C1 , CM } are being occupied, and we can obtain that Esys = 4N −2M −2. The proof of theorem is thus completed. IV. S IMULATION R ESULTS

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Proof: Recalling the rules of autonomous channel switching and Lemma 1, we explore the insight of channel switching. Assume there exists three consecutive channels {Cm−1 , Cm , Cm+1 } with the phase {1, 1, 0} , and vm = 0. Then after the user switching from Cm to Cm+1 , the set {Cm−1 , Cm , Cm+1 } changes its phase from {1, 1, 0} to {1, 0, 1}, and the potential evolves in accordance with one of the follows cases: 1) if m = M − 1, Cm+1 is the channel with largest label, and Esys will decrease by 2; 2) if m < M − 1 and CM+2 = 0, the subset {Cm−1 , Cm , Cm+1 , Cm+2 } changes its phase from {1, 1, 0, 0} to {1, 0, 1, 0}, and Esys will decrease by 2; otherwise 3) if m < M − 1 and CM+2 = 1, the subset {Cm−1 , Cm , Cm+1 , Cm+2 } changes its phase from {1, 1, 0, 1} to {1, 0, 1, 1}. Esys will not change. However, a) if m + 2 = M , or m + 2 < M but Cm+3 = 1, the subset {Cm , Cm+1 , Cm+2 } changes to the phase {0, 1, 1} and the potential will be transferred to negative direction at the next slot; or b) if Cm+3 = 0, the subset {Cm+1 , Cm+2 , Cm+3 } changes to the phase {1, 1, 0}, then potential will be transferred to positive direction at the next slot. For the other case that the initial phase of the three consecutive channels {Cm−1 , Cm , Cm+1 } is {0, 1, 1} and vm = 0, we could get analogical conclusions. If the system has not reached the equilibrium of spectrum usage, according to Definition 1, we know the fact: for N ≤ M/2, there must coexist two or more consecutive occupied channels and two or more consecutive free channels; and for N > M/2, there must exists two or more consecutive free channels, or C1 = 0 or CM = 0. With above analysis it is easy to obtain that Esys will decrease by 2 at most in M slots if the system is not in equilibrium state. Thus, we guarantee that under rules of autonomous channel switching, the usage of spectrum converges to the equilibrium.

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A. Network Setup In our simulation, we assume there are 20 channels. Communication operation in this network is event driven, which is simulated by Poisson process with rate λ. Data length is in accordance with the exponential distribution with parameter μ. We further assume that data is out of date if the sensor could not access to any channel in Texpi slots, and Texpi is set to 10 in the simulations. We assume that accessing nodes sense the spectrum from low frequency to high in round-robin way. For comparison, we set two accessing strategies for accessing sensors. Each sensor is initialized with fixed three consecutive channels or randomly selected three consecutive channels from given spectrum range to sense free channel. The accessing strategies are as follows: keep waiting for free channel on the initialized channels or search free channel from next three channels at the next slot if the accessing users fails to access to current channels. We also consider two strategies for accessed users, determined by choosing the proposed channel switching rules or not. For simplicity, let ‘Aing’ and ‘Aed’ represent accessing user and accessed user, ‘S’ and ‘NS’ represent switch and non-switch, respectively. Hence, we have four scenarios based on different combinations of strategies: ‘Aing-NS/Aed-NS’, ‘Aing-S/Aed-NS’, ‘Aing-NS/Aed-S’ and ‘Aing-S/Aed-S’ respectively. B. Performance Evaluation First, we evaluate the performance of utilization and fairness of spectrum usage in the four spectrum sharing scenarios. As shown in Fig. 3(a) and Fig. 3(b), we set μ = 0.05 and let λ vary from 0.1 to 0.8, which denotes the sensors wake up more and more frequently as λ increasing. We can see the proposed autonomous channel switching rules prominently improves the network performance. Strategies ‘Aing-NS/Aed-S’ and ‘Aing-S/Aed-S’ outperform the strategies ‘Aing-NS/AedNS’ and ‘Aing-S/Aed-NS’ with respect to both of utilization and fairness of spectrum usage. Though ‘Aing-S/Aed-NS’ gains some improvement, it still keeps an obvious gap from


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Fig. 3. Comparison of utilization and fairness of channel using under the assumption: (a)∼(b) with M = 20, μ = 0.05, λ varies from 0.1 to 0.8; (c)∼(d) with M = 20, λ = 0.4, μ varies from 0.02 to 0.16.

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This work was partially supported by Ministry of Science and Technology of China under National Basic Research Project 2010CB731803, by NSF of China under the grants 61221003, 61290322, 61174127, 61273181 and 60934003, by Program for New Century Excellent Talents in University, Ministry of Education of China, and by Science and Technology Commission of Shanghai Municipal, China under the grant 13QA1401900.

R EFERENCES

[1] V. C. Gungor and G. P. Hancke, “Industrial wireless sensor networks: Challenges, design principles, and technical approaches,” IEEE Trans. on Industrial Electronics, vol. 56, no. 10, pp. 4258–4265, 2009. [2] D. Christin, P. S. Mogre, and M. Hollick, “Survey on wireless sensor network technologies for industrial automation: the security and quality of service perspectives,” Future Internet, vol. 2, no. 2, pp. 96–125, 2010. [3] C. Peng, H. Zheng, and B. Y. Zhao, “Utilization and fairness in spectrum assignment for opportunistic spectrum access,” Mobile Networks and Applications, vol. 11, no. 4, pp. 555–576, 2006. [4] C. Raman, R. D. Yates, and N. B. Mandayam, “Scheduling variable rate links via a spectrum server,” in in Proc. of The First IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks (DySPAN 2005), Maryland, USA, Nov.8-11, 2005, pp. 110–118. [5] W. C. Cheung, T. Q. Quek, and M. Kountouris, “Throughput optimization, spectrum allocation, and access control in two-tier femtocell networks,” IEEE J. Sel. Areas Commun, vol. 30, no. 3, pp. 561–574, 2012. [6] S. D. Jones, N. Merheb, and I.-J. Wang, “An experiment for sensingbased opportunistic spectrum access in csma/ca networks,” in in Proc. of The First IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks (DySPAN 2005), Maryland, USA, Nov.8-11, 2005, pp. 593–596. [7] L. Cao and H. Zheng, “Distributed rule-regulated spectrum sharing,” IEEE J. Sel. Areas Commun, vol. 26, no. 1, pp. 130–145, 2008. ¨ [8] J. Chilo, J. Ferrer-Coll, P. Angskog et al., “Challenges and conditions for wireless machine-to-machine communications in industrial environments,” IEEE Communications Magazine, vol. 51, no. 6, pp. 187–192, 2013. [9] A. Czirók and T. Vicsek, “Collective behavior of interacting selfpropelled particles,” Physica A: Statistical Mechanics and its Applications, vol. 281, no. 1-4, pp. 17–29, 2000. [10] T. Vicsek and A. Zafeiris, “Collective motion,” Physics Reports, vol. 517, no. 3-4, pp. 71–140, 2012. [11] J. Buhl, D. Sumpter, I. Couzin, J. Hale, E. Despland, E. Miller, and S. Simpson, “From disorder to order in marching locusts,” Science, vol. 312, no. 5778, pp. 1402–1406, 2006.

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In this paper, a novel spectrum sharing scheme for ICRNs is proposed from the perspective of collective motion, and the autonomous channel switching rules are developed. Mathematical analysis is provided to show that the proposed rules are effective to improve the spectrum accessing probability. Through simulations of four different scenarios, we demonstrate that the rules improve the utilization and fairness of spectrum usage and decrease the mean of waiting time of accessing and mean of failed accessing users.


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(b) Failed accesses with respect to λ (d) Failed accesses with respect to μ Fig. 4. Comparison of mean of waiting time and mean of failing accesses of accessing users under the assumption: (a)∼(b) with M = 20, μ = 0.05, λ varies from 0.1 to 0.8; (c)∼(d) with M = 20, λ = 0.4, μ varies from 0.02 to 0.16.

the two strategies with autonomous channel switching rules. Simulation result in Fig. 3(c) and Fig. 3(d) are conducted with λ = 0.4 and μ varying from 0.02 to 0.16 so that the performance of strategies can be evaluate for decreasing average data flow length to be sent decreases. When average data flow length is lager, strategies ‘Aing-NS/Aed-S’ and ‘Aing-S/Aed-S’ obviously outperform other two. Moreover, the mean of waiting time and mean of failed accessing users are evaluated for the four scenarios. In Fig. 4(a) and Fig. 4(b), as λ increases, the mean of waiting time

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