QoS- and Security-Aware Dynamic Spectrum Management for Cyber ...

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Management for Cyber-Physical Surveillance System. Pengbo Si†, F. Richard Yu†‡ and ... of-service (QoS) and wireless link security, a novel dynamic spectrum ...
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QoS- and Security-Aware Dynamic Spectrum Management for Cyber-Physical Surveillance System Pengbo Si† , F. Richard Yu†‡ and Yanhua Zhang† †

College of Electronic Information and Control Engineering, Beijing University of Technology, Beijing, 100124 P.R. China ‡ Department of Systems and Computer Engineering, Carleton University, Ottawa, ON, Canada Email: [email protected], richard [email protected], [email protected]

Abstract—Cyber-physical system (CPS) has been widely used in various areas as the integration of computing and physical system. As a typical application of CPS, cyber-physical surveillance system (CPSS) allows real-time video monitoring for various fields such as smart transportation and warehouse management systems. For video streaming in CPSS, dynamic radio spectrum management is a key technology dealing with the current situation that the spectrum resource is almost used up. In this paper, taking into account the application layer qualityof-service (QoS) and wireless link security, a novel dynamic spectrum management scheme is proposed to minimize the system cost of CPSS. Video distortion is considered as the application layer QoS metric, and the system cost is defined as a combination of distortion and security cost. We use intra-refreshing rate in video coding to minimize the distortion. Furthermore, the problem is formulated as a restless bandit system, which uses current and historical information to optimize the action, with the objective of maximizing the total discounted system reward. We also describe the spectrum management operation processes. Extensive simulation results are presented to demonstrate the significant performance improvement of the proposed scheme compared with the existing one that ignores video distortion and subband security optimization.

I.

I NTRODUCTION

In recent years, the integration of computing and physical systems under monitoring and control draws a lot of attention. Cyber-physical system (CPS) is one of the most promising technologies in this research topic due to its wide applications in various areas such as transportation, power grids and robotic systems [1][2]. Besides, cyber-physical surveillance system (CPSS) becomes a typical and important application of CPS, in which large number of video cameras are adopted for real-time monitoring for surveillance and analyzing [3]. The surveillance system with video cameras is believed to be cost effective due to its real-time characteristic and large field of view, compared to other monitoring methods [4]. In such cyber-physical surveillance systems, the video cameras and other sensing and controlling devices are the physical world, while the wireless communication network connects the physical world and computing systems, since that in most cases, the devices need information transmission among them but are not installed at the same place [5]. In CPSS, video streaming to a centralized location is always required. And in order to reduce the infrastructure costs and enhance the flexibility, radio communication technology is widely adopted for video transmissions.

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However, radio spectrum, as one of the most important resources for wireless communications, is running out of use and becomes very valuable [6]. To utilize the spectrum resource with high efficiency, dynamic spectrum management is one of the solutions for cyber-physical surveillance systems, allowing optimal spectrum subband selection and coexistence with other wireless devices operating in the same spectrum bands [7]. Although dynamic spectrum management improves the security performance by using different subbands in different time slots, there is still a probability of being attacked by malicious devices, due to the vulnerability nature of wireless communications [8]. Thus security issue still merits efforts on it. Besides, in video surveillance systems, application layer quality of service (QoS), such as distortion, dramatically affects the performance of the video processing afterwards, and is dramatically affected by the noise and interference in the spectrum subband, thus it is another key feature to be considered while designing the system. A lot of work has been done on cyber-physical systems and their applications. In [2], the authors introduce real-time management methods for adaptively controlling the electric loads in CPS, with the objective of electric power utilization balancing to obtain an optimal power load. The work of [9] focuses on the databases in CPSS for transportation. An mutual operation multi-instance learning structure is proposed in the paper. Besides, for transportation surveillance architecture, a method based on tracking has been introduced, to cope with the problem of timely parts of lesser tracking concern. This method is based on the H.264 video compression scheme and improves the performance of quantization of spectrum coefficients [3]. For wireless surveillance CPS with video cameras of low capabilities in infrastructureless distributed wireless surveillance system with large number of nodes, an approach considering the overall optimization has been proposed in [4], which takes into consideration the device utilization upper bound to optimize the real-time operation of the system. Even when the CPU workloads change dramatically at runtime, according to the random sensing consequences, the system performance can be improved by jointly considering data fusion and feedback control. For radio resource management in CPS for QoS assurance, the authors in [10] introduce the cognitive radio technology into the system, to gather radio resource utilization information for radio resource management in self-governing nodes. Most existing work on cyber-physical and surveillance systems focuses on the system architecture, routing protocols, coding schemes and timeliness and reliability performances,

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without considering the application layer QoS and security problems simultaneously with dynamic spectrum utilization. In this paper, we propose a novel dynamic spectrum management scheme in cyber-physical surveillance system, taking into account the combined optimization of application layer QoS and security. The scheme is formulated as a restless bandit problem, which is based on the development of multiarmed bandit problem [11]. Restless bandit model, as a type of stochastic control systems, has been widely applied in many areas, including robot control, environment detection, financial investment and cognitive radio communications, among others [12][13][14]. The main contributions of this paper are as follows. •

We adopt QoS-centric design in the proposed scheme. Due to the importance of video distortion to surveillance systems, it is considered as the application layer QoS metric to be optimized in the system. Video distortion formulation based on the channel state information (CSI) is also presented in this paper.



Interference avoidance is considered in the proposed scheme to achieve optimized application layer QoS. The dynamic utilization of spectrum subband for transmission is partly based on the channel state information, and allows the system to share the spectrum band with other co-existing wireless systems.



For security issue, the proposed scheme minimizes the chance of being attacked by introducing the concepts of security level and security cost, both of which are functions of the attacking probability when choosing the subband for transmission.



The system optimization is based on the channel state estimation of the previous time slot and historic information. Considering that channel estimation in communication systems is not always up-to-date and accurate, this is more practical than assuming that the current channel estimation results are perfectly available.

The rest of the paper is organized as follows. In Section II, the QoS- and security-aware dynamic spectrum management for cyber-physical surveillance system is described and the system model is presented. Then the proposed scheme is formulated as a restless bandit problem in Section III. We provide the extensive simulation results in Section IV to demonstrate the performance improvement compared with existing scheme. Section V concludes this paper. II.

S YSTEM M ODEL

The cyber-physical surveillance system can be divided into physical component incorporating the video cameras/video image sensors and the monitoring/control/computing center, and the cyber component incorporating the wireless communication system. The objective of the system design discussed in this paper is to improve its QoS and security performance. A. System Description In the cyber-physical surveillance system considered in this paper, we assume that there are multiple video cameras or

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video image sensors equipped with wireless communication tranceivers, multiple controllers and a monitoring and computing center, all of which are connected by wireless links. Besides, in this paper, the cameras or sensors with tranceivers are called the terminals, and the monitoring and computing center is called the central point. The wireless communication channel is divided into subbands, some of which are selected as the transmission subbands for the communication among the terminals and the central point. The central point is capable of estimating the channel state information, and making the decision of subband utilization. We take into account the security problem by considering the possible peeping or attacking by malicious devices in some of the subbands. It is assumed that the malicious devices have limited capability and can not peep or attack all the subbands at the same time, instead, they choose to peep or attack a number of subbands based on their observation of the subband utilization. That is, malicious devices tend to peep or attack a subband that has been frequently used by the surveillance system with a larger probability, and vice versa. Consequently, in CPSS, the security of the system is based on the security of the subbands, which could be optimized by carefully design the subband utilization scheme. Besides, application layer QoS plays an important role in the performance of the whole surveillance system. For video streaming, distortion is the most important application layer QoS. Thus in this paper, we adopt distortion as one of the optimization objectives to manage the subband. The decision is based on the channel state information of each subband, which allows the system to share the spectrum band with other wireless systems. The other advantage of dynamic spectrum utilization is its inherent nature of frequency hopping, which significantly increases the difficulty of peeping or attacking by malicious devices. B. Physical Component Model We assume that there are Nt terminals in the system, collecting live videos and transmitting them to the central point. Recent advanced coding schemes such as H.264 and MPEG are adopted in the terminals. Rate control algorithm is utilized in these coding schemes to adaptively control the video encoder bit rate to improve the error performance [15]. The macroblocks (MBs)’ intra-refreshing is believed to be one of the key solutions for rate control and error correction. Previous frames of a macroblock may have been unsuccessfully received due to the instability of the propagation environment. Fortunately, the information from previous frames are not necessary to decode the current frames. Thanks to this characteristic, intra coded MBs forms an efficient error protection method, otherwise, with inter-coding instead of intra-coding, errors of previous frame may dramatically affect the current frame. In cyber-physical surveillance system, different channel subbands with different channel state provide different data rates to the video transmission. The authors in [15] have a deep discussion on the formulation on video distortion, considering the different and changing features of the video streams, the bit rate, the intra-refreshing rate ξ and the coding scheme. We adopt this rate-distortion model in this paper, assuming the video distortion to be a combination of source

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distortion, which is the quantization distortion caused by the video encoder in the transmitter to reduce the data rate, and the channel distortion, which is the distortion introduced by packet loss during transmission. An optimal intra-refreshing rate ξ is adopted in this paper, which can be obtained according to the model in [15]. C. Network and Communication Model All the Nt terminals are assumed to be equipped with wireless tranceivers with dynamic spectrum utilization capability, and the central point’s wireless communication device performs the spectrum sensing function to gain the channel state information estimation. Orthogonal frequency division multiplexing (OFDM) is assumed to be the physical layer modulation technology used for video streaming. An OFDM symbol can be divided into multiple subcarriers which are orthogonal, i.e., interference free, thus different terminals could use different subcarriers for data transmission. Due to the flexibility nature of OFDM, the total available spectrum band can be divided into subbands, each of which contains one or several OFDM subcarriers [16]. We assume the number of all available subbands to be Nb , and the set of all available subbands to be {bi }, where 1 ≤ i ≤ Nb , here Nb ≥ Nt . The noise and interference signal on the spectrum subband is considered to be distributed as Rayleigh distribution, from the aspect of the central point as the receiver, and the spectrum subband noise and interference power level can be modeled as a stochastic process with the Rayleigh distribution, of which the probability density function f (x) =

x −x22 e 2σ , σ2

(1)

and the cumulative distribution function −x2

F (x) = 1 − e 2σ2 .

(2)

Then the variance and mean of this Rayleigh p random variable can be expressed as (4 − π)σ 2 /2 and σ π/2 respectively. Furthermore, this spectrum subband state changing process is a stationary stochastic process, thus the distribution of a realization of the spectrum subband state over the time line is exactly the same as the distribution of the random subband state at a time point. For simplicity, we assume that the realization of the subband state at one time point is the mean of the the random subband state at next time point, i.e, when the rand subband state realization is Xk at time point tk , the probability density function and the cumulative distribution function of the spectrum subband state at time point tk+1 are −x2 k+1

f (xk+1 , Xk ) = and

√ xk+1 p e 2(Xk 2/π)2 (Xk 2/π)2

F (xk+1 , Xk ) = 1 − e

−x2 k+1 √ 2/π)2

2(Xk

,

(3)

(4)

respectively, where Xk is the realization of the subband state at time point tk . Due to the vulnerability nature of wireless communication, we also take into account the security of the system, by

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introducing the security cost ds (i)of subband bi NUSED (i) × PATTACK (i), (5) NTOTAL where NTOTAL is total number of time slots considered, and NUSED (i) is the number of time slots that subband bi is used during the recent NTOTAL time slots, PATTACK (i) is the probability of subband bi being attacked or peeped [8]. With this definition, 0 ≤ ds (i) ≤ 1, and ds (i) is the probability that subband bi is used in the current time slot while it is chosen to be peeped or attacked by the malicious devices. Larger ds (i) means that the probability of subband bi being attacked or peep is high, thus it is less secure. As mentioned above, the malicious devices peep or attack a number of subbands based on their observation of the subband utilization. For simplicity, we assume that NUSED (i) PATTACK (i) = . (6) NTOTAL 2 Thus ds (i) = PATTACK (i). ds (i) =

D. Optimization Objective The surveillance system is designed to improve the QoS and security, which can be interpreted as minimize video streaming distortion and maximizing security degree in this paper. We define the cost of subband bi to be ( a1 × Di + a2 × ds (i), ifsubbandbi isunderutilization, (7) Ci = a1 × D ′ + a2 , otherwise, where a1 and a2 are constant coefficients, a1 , a2 ≥ 0, D′ is the distortion measured when intra-refreshing rate ξ = 0.07, since that in practical video systems, ξ is always larger than 0.07, and D′ > D(ξ) for practical ξ. Thus when the subband is not used, its cost is always higher than when it is under utilization. Then the system optimization objective is to minimize the summation cost of all subbands along the time line. III.

R ESTLESS BANDIT F ORMULATION

In this section, the dynamic spectrum subband management problem is formulated as a restless bandit system, whose indexibility feature reduces the optimization problem to simply selecting the subbands with the smallest indices. A. Time Slots and Actions During the system operation, the whole time period considered in the optimization is divided into time slots with equal length. We denote by the time slots as {t1 , t2 , . . . , tk , . . . , tK }. At the beginning of each time slot, the central point decides the spectrum subband utilization policy according to the current and historical subband channel state and security state information, to optimize the QoS and security performance of the system. We denote the action to subband bi at time slot tk to be ak (i) = {1, 0} = A , where A is the action space. ak (i) = 1 means that the subband is selected to be used for transmission, and ak (i) = 0 means that it is not selected in this time slot. In each time slot, the actions on the subbands always satisfy Nb X ak (i) = Nt . (8) i=1

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and

B. System State Space ski

We represent as the state of subband bi at time slot tk , ski ∈ S , where S is the set of all available subband states. ski is a two-dimensional discrete random variable composed of security state skSEC,i and subband channel state skCH,i , skSEC,i ∈ SSEC and skCH,i ∈ SCH , where SSEC and SCH are the sets of all available security states and subband channel states respectively. ski = {skSEC,i , skCH,i }NSEC ×NCH ,

(9)

0 PSEC (m, n)

=

  

m−1 NTOTAL , m−1 1 − NTOTAL ,

0,

ifn = m − 1, ifn = m, otherwise,

(13)

where 1 ≤ n, m, ≤ NSEC . We also model the discrete spectrum subband channel state as a NCH -state Markov chain, with the one-step state transition probability matrix PCH = {PCH (m, n)}, 1 ≤ m, n ≤ NCH , where

where NSEC and NCH are the size of SSEC and SCH respectively.

k PCH (m, n) = P (sk+1 CH,i = cn |sCH,i = cm ).

1) Security State: For subband bi , we use discrete security state skSEC,i to describe the possibility of being peeped or attacked by malicious devices. For simplicity, we use the probability of being peeped or attacked PATTACK (i) as the security state.

According to our discussion in Section 2.3, {PCH (m, n)} can be expressed as  F (ǫCH (1), cm ), ifn = 1,   F (ǫCH (n), cm ) − F (ǫCH (n − 1), cm ), PCH (m, n) = if2 ≤ n ≤ NCH − 1,   1 − F (ǫCH (NCH ), cm ), ifn = NCH . (15)

m−1 , (10) NTOTAL for all 1 ≤ m ≤ NTOTAL + 1. With this definition of security state, NSEC = NTOTAL + 1. Thus the security state of the subband can be formulated as a first order Markov chain and its one-step state transition probability will be discussed in Section 3.3. skSEC,i = em , ifPATTACK =

2) Subband Channel State: In this paper, similar to security state model, the channel state of spectrum subband bi is modeled as a discrete-time first order Markov chain {skCH,i }, where k denotes the time instant. We consider NCH discrete channel state levels to represent the different spectrum subband states detected by the central point’s channel estimation functions. We covert the continuous spectrum subband states discussed in Section 2.3 into discrete ones with thresholds ǫCH (m), 1 ≤ m ≤ NCH , i.e.,  c , ifxk (i) ≤ ǫCH (1),   1 cm , ifǫCH (m − 1) ≤ xk (i) ≤ ǫCH (m), k sCH,i = 2 ≤ m ≤ NCH − 1,   cNCH , ifxk (i) > ǫCH (NCH − 1), (11) where xk (i) is the spectrum subband noise and interference power level, which has been discussed in Section 2.3.

The security state skSEC,i is modeled as a NSEC -state Markov chain, with the one-step state transition probability matrix depending on the action at tk . Thus we have the state transition probability matrix while subband bi being selected in 1 the time slot P1SEC = {PSEC (m, n)}, and the state transition probability matrix while subband bi not being selected in the 0 time slot P0SEC = {PSEC (m, n)}, 1 ≤ m, n ≤ NSEC , where 1 k PSEC (m, n) = P (sk+1 SEC,i = en |sSEC,i = em , ak+1 (i) = 1), 0 k PSEC (m, n) = P (sk+1 SEC,i = en |sSEC,i = em , ak+1 (i) = 0).

Besides, we have 1 PSEC (m, n)

=

m−1 NTOTAL , m−1

  1−

,  NTOTAL 0,

Then the system state transition probability matrix P1STAT and P0STAT is a combination of P1SEC , P0SEC and PCH , whose 0 1 (em′ , en′ , cm , cn ) elements PSTAT (em′ , en′ , cm , cn ) and PSTAT are

1 PSTAT (em′ , en′ , cm , cn ) k+1 = P (si = {en′ , cn }|ski = {em′ , cm }, ak+1 (i) = 1), 1 = PSEC (m′ , n′ ) × PCH (m, n), 0 PSTAT (em′ , en′ , cm , cn ) = P (sk+1 = {en′ , cn }|ski = {em′ , cm }, ak+1 (i) = 0), i 0 = PSEC (m′ , n′ ) × PCH (m, n), (16)

due to the fact that the security state and channel state are independent random variables. D. System Reward and Policy As defined in Section 2.4, the objective of the system optimization is to minimize the subband cost Ci over all bi along the whole time line. Thus in the restless bandit formulation, we define the system reward R as R=

C. State Transition Probabilities

ifn = m + 1, ifn = m, otherwise,

(12)

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(14)

K X

k=1

Nb X −Ci (k)), β K−k (

(17)

i=1

where β is the discount factor, 0 ≤ β ≤ 1. R is also called the long-term total discounted reward. The restless bandit model has an indexability feature that could reduce the computational complexity significantly. For subband bi in time slot tk , the index is represented as δk (i). Then the optimal policy U is a set of optimal actions, of which the elements are the optimal actions for each subband in each time slot. According to the restless bandit approach, the optimization problem of the actions could be reduced to simply select the Nt subbands with smallest indices among the Nb ones for transmission by the Nt terminals. That is, the optimal action  1, ifδk (i)isoneofthesmallestNt ones, a∗k (i) = (18) 0, otherwise.

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And the policy U maximizes the long-term total discounted reward R, i.e., U =

{a∗k (i)}

= arg

max

ak (i)∈{1,2}

R.

4 Existing Scheme with σ = 0.05 Proposed Scheme with σ = 0.05

(19)

3.5

E. Solving the Restless Bandit Problem The restless bandit formulation in the proposed cyberphysical surveillance system allows Nt out of Nb spectrum subbands to be selected at time slot tk . The system reward Ri (k) = −Ci (k) is obtained by each terminal, with its state changing according to the transition probability matrix P0STAT and P1STAT . Based on the linear programming (LP) formulations of Markov decision chains (MDCs), a hierarchy of increasingly stronger LP relaxations is provided to solve the problem. The last relaxation is exact. To reduce the computational complexity, a heuristic algorithm for the restless bandits problem has been developed, utilizing the information contained in optimal primal and dual solutions to the first-order relaxation [12]. IV.

S IMULATION R ESULTS

AND

D ISCUSSIONS

In this section, extensive simulation results are presented to demonstrate the performance improvement of the proposed scheme. In the simulation, we assume that the proposed cyberphysical surveillance system uses orthogonal frequency division multiplexing (OFDM) as the physical layer modulation technology, with the modulation scheme 64QAM and the guard interval 1/16 [17]. All subbands between the terminals and the central point are experiencing independent identically distributed Rayleigh fading without spatial correlation. Two values of Rayleigh parameter σ defined in Eq. (1), 0.05 and 0.07, are considered in most simulations. We also adopt that Nt = 5, Nb = 10, a1 = a2 = 0.5, β = 0.9 and NTOTAL = 5 if there is no further explanation. For simplicity, we assume three channel states, i.e., NCH = 3. Besides, all the terminals are assumed to adopt the H.264 video coding scheme, with the data rate 1024 Kbps, macroblock size 16 × 16 and block size 4 × 4. In the distortion model, we adopt the following parameters: the energy loss ratio of the encoder filter Ω1 = 0.001, the constant depending on the motion randomness of the video data Ω2 = 1.0 and the average value of the frame difference E[Fd (y, y − 1)] = 100. Dynamically utilizing the subbands is also assumed to be one of the capabilities of the terminals. The central point is assumed to be equipped with the classical pilotsymbol-assisted (PSA) channel estimator for all subbands, as described in [18]. We compare the system cost of our proposed scheme with the existing one that ignores the optimization of subband management and intra-refreshing rate. Since that the subbands that are not selected for utilization have the constant cost, we simply ignore this part in the following simulation. A. Dynamic State and Actions

System Cost

3

2.5

2

1.5

1

Fig. 1.

0

100

200 300 Time Slot tk

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500

Dynamic system cost comparison along the time line.

NTOTAL = 3 in this subsection, thus the number of security state NSEC = NTOTAL + 1 = 4. In Fig. 1, the total system cost combining distortion and security cost is presented. We can observe from the figure that the proposed scheme significantly improves the system performance, due to the consideration of the current and historical channel state and security state, and taking video distortion as the application layer QoS metric in the system optimization. B. System Cost with Different Terminal and Subband Numbers In this subsection, we compare the system cost of our proposed scheme with the existing one, taking into account the different terminal number Nt and subband number Nb . The system cost in the simulation is the total cost of all subbands under utilization averaged over the time slots. In Fig. 2, as the terminal number Nt varying from 2 to 8, the proposed scheme always outperforms the existing one. Since that Nt is also the number of subbands under utilization, and the system cost in the figure is the summation value of all the Nt subbands, the system cost increases as Nt increases. Besides, larger Nt indicates larger probability of a subband being utilized, thus the security cost for one subband ds (i) = NUSED (i) NTOTAL ×PATTACK (i) also goes up, which leads to the rising of total system cost. Furthermore, the system cost with different number of available subbands Nb is compared in Fig. 3. As Nb increases, the system cost is reduced due to the lower security ds (i) = NUSED (i) NTOTAL × PATTACK (i). And we can draw the same conclusion from the figure that our proposed scheme significantly improves the performance compared to the existing one. V.

The action decision procedure in the central point is based on the current and historical system state information including channel state and security state. For simplicity, we assume

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C ONCLUSIONS

Cyber-physical surveillance system that provides real-time video monitoring is becoming a popular application of CPS. In this paper, we have proposed a QoS- and security-aware

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9 Existing Scheme with σ = 0.07 Existing Scheme with σ = 0.05 Proposed Scheme with σ = 0.07 Proposed Scheme with σ = 0.05

8 Average System Cost per Time Slot

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6 5

[4]

4 3 2

[5]

1 0

Fig. 2.

[6] 2

3

4 5 6 The Number of Terminals Nt

7

8

[7]

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[8]

[9]

5 Existing Scheme with σ = 0.07 Existing Scheme with σ = 0.05 Proposed Scheme with σ = 0.07 Proposed Scheme with σ = 0.05

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4.5

[10]

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[11] 3

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2.5 2

[13] 1.5 1

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8

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b

Fig. 3. System cost comparison with different number of available subbands Nb .

[15]

[16]

dynamic spectrum management scheme for CPSS, taking into account the application layer QoS, video distortion, and subband security state, the probability of being attacked or peeped. To minimize the system cost, we formulated the problem as a restless bandit system, with the indexibility characteristic that can be used to reduce the on-line computation complexity for real-time systems. The restless bandit formulation makes use of the current and historical system state information and optimizes the actions in each time slot to achieve the minimized system cost by minimizing the video distortion and the chance of being attacked or peeped in the transmission subband. Extensive simulation results demonstrated that the system cost can be significantly reduced under different circumstances compared with the existing one that ignores the intra-refreshing and subband security optimization.

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