4 research outputs found
A SERVICE FRAMEWORK FOR REDUCING THE ATTACKS IN THE CLOUD ENVIRONMENT
We initiate a process for organize the sorts of stealthy attack, that display progressively rising intensity trend considered to cause finest financial cost to cloud customer, while enhancing job size in addition to service arrival rate that's forced while using techniques of recognition. Providers of cloud system give you services to buy the capacity of storage, offering the idea of indefinite resource convenience. Inside the technology of cloud furthermore degradation of partial service due to anxiety attack has effect on the cost and services information, and also on convenience that's perceived by user. The system will goal at utilizing cloud flexibility, forcing application to consume extra sources, affecting client more details on economic aspects compared to service convenience. Recommended attack pattern concentrates at exploiting cloud elasticity, forcing services to improve and consume additional sources, affecting customer on financial features compared to service openness. The qualities available by cloud provider, to make certain service level contracts negotiated by customer is maliciously utilized by means of recommended stealthy attack, that progressively exhausts sources that are provided by cloud provider. The procedure will execute stealthy attack designs that display progressively growing polymorphic conduct that avoid, otherwise delay techniques of earlier recommended
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λ¨Όμ , λμΉ΄κ°λ―Έ-m νμ΄λ© μ±λ νκ²½μ κ°μ§ μΌλ°©ν₯ μ€κ³ λ€νΈμν¬λ₯Ό μν νλ‘μ‘ν°λΈ(proactive) λ° λ¦¬μ‘ν°λΈ(reactive) λ°©μμ μμ μ νΈλμ‘μλΉ λμ λΆν¬ν¨μ κΈ°λ° μ€κ³κΈ° μ ν κΈ°λ²μ μ μνλ€. κ°κ°μ μ€κ³κΈ° μ ν κΈ°λ²μ μν΄ μ€κ³κΈ° μ ν νλ₯ μ μ λνμ¬ μ μλ κ° μ€κ³κΈ° μ ν κΈ°λ²λ€μ νκ· μ€κ³κΈ° 곡μ μ±μ λΆμνλ€. λν κ° μ ν κΈ°λ²μ λν λΆλ₯ νλ₯ μ μμμΌλ‘ μ λνκ³ , μ λν λΆλ₯ νλ₯ μ μ κ·Όμ ννμΌλ‘ λνλ΄μ΄ κ° κΈ°λ²λ€μ΄ μ»μ μ μλ λ€μ΄λ²μν° μ°¨μλ₯Ό λΆμνλ€. λͺ¨μμ€νμ ν΅ν΄ μ»μ΄μ§ νκ· μ€κ³κΈ° 곡μ μ±κ³Ό λΆλ₯ νλ₯ μ΄ μ λν νκ· μ€κ³κΈ° 곡μ μ± λ° λΆλ₯ νλ₯ κ°κ³Ό μΌμΉν¨μ νμΈνλ€. κ·Έλ¦¬κ³ μ μλ κΈ°λ²μ΄ μ€κ³κΈ°λ€ μ¬μ΄μ 곡μ μ±μ μλ²½νκ² λ³΄μ₯νκ³ λ€νΈμν¬ μλͺ
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λμ§Έ, λμΉ΄κ°λ―Έ-m νμ΄λ© μ±λ νκ²½μ κ°μ§ μλ°©ν₯ μ€κ³ λ€νΈμν¬λ₯Ό μν νλ‘μ‘ν°λΈ λ° λ¦¬μ‘ν°λΈ λ°©μμ μμ μ νΈλμ‘μλΉ λμ λΆν¬ν¨μ κΈ°λ° μ€κ³κΈ° μ ν κΈ°λ²μ μ μνλ€. μ μλ νλ‘μ‘ν°λΈ λ°©μμ μ€κ³κΈ° μ ν κΈ°λ²μ λν΄μλ μ νν μ€κ³κΈ° μ ν νλ₯ μ μ λλ₯Ό ν΅ν΄ νκ· μ€κ³κΈ° 곡μ μ±μ λΆμνλ€. μ μλ 리μ‘ν°λΈ λ°©μμ μ€κ³κΈ° μ ν κΈ°λ²μ λν΄μλ μ€κ³κΈ° μ ν νλ₯ μ μ λΆ λ° κ·Όμ¬ ννμ μ λνμ¬ νκ· μ€κ³κΈ° 곡μ μ±μ λΆμνλ€. λν κ° μ ν κΈ°λ²μ λν λΆλ₯ νλ₯ μ μμμΌλ‘ μ λνκ³ , μ λν λΆλ₯ νλ₯ μ μ κ·Όμ ννμΌλ‘ λνλ΄μ΄ κ° κΈ°λ²λ€μ΄ μ»μ μ μλ λ€μ΄λ²μν° μ°¨μλ₯Ό λΆμνλ€. λͺ¨μμ€νμ ν΅ν΄ μ»μ΄μ§ νκ· μ€κ³κΈ° 곡μ μ±κ³Ό λΆλ₯ νλ₯ μ΄ μ λν νκ· μ€κ³κΈ° 곡μ μ± λ° λΆλ₯ νλ₯ κ°κ³Ό μΌμΉν¨μ νμΈνλ€. κ·Έλ¦¬κ³ μ μλ κΈ°λ²μ΄ μ€κ³κΈ°λ€ μ¬μ΄μ 곡μ μ±μ μλ²½νκ² λ³΄μ₯νκ³ λ€νΈμν¬ μλͺ
μ μ¦κ°μν€λ©°, λ€μ΄λ²μν° μ°¨μκ° μ€κ³κΈ°μ μμ νμ΄λ© νλΌλ―Έν° m κ°μ λ°λΌ λ¬λΌμ§μ νμΈνλ€.Wireless relay technology is one of the most promising technologies for the future communication systems which provide coverage extension and better quality of service (QoS) such as higher data rate and lower outage probability with few excessive network loads. Due to its advantages, it has been adopted in wireless standards such as IEEE 802.16j and 3GPP LTE-Advanced.
In practice, since statistics of the channel between any two nodes vary depending on their locations, they are not identical which means that channels can experience different fading. When statistics of the channel are not identical, relay selection, which is one of the most useful techniques for wireless relay technology, can cause fairness
problem that particular relays are selected more frequently than other relays. Especially, this problem can cause reduction of lifetime in the network with multiple relays having limited battery power. In this network, it is needed to focus on selection fairness for relays as well as reliability at end-users.
In this dissertation, to focus on both selection fairness for relays and reliability at end-users, we propose novel relay selection schemes based on cumulative distribution functions (CDFs) of signal-to-noise ratios (SNRs) in wireless relay networks. The dissertation consists of two main results.
First, we propose the proactive and the reactive relay selection schemes based on CDFs of SNRs for one-way relay networks over Nakagami-m fading channels. If a relay
is selected before the source transmission, it is called as proactive relay selection. Otherwise, if a relay is selected after the source transmission, it is called as reactive relay selection. For both the proactive and the reactive relay selection schemes, we analyze average relay fairness by deriving relay selection probability. For the proactive
relay selection scheme, we obtain diversity order by deriving the integral and asymptotic expressions for outage probability. Also, for the reactive relay selection scheme, we obtain diversity order by deriving the exact closed-form and asymptotic expressions for outage probability. Numerical results show that the analytical results of the proposed schemes match the simulation results well. It is shown that the proposed schemes guarantee strict fairness among relays and extend network lifetime. Also, it is shown that diversity order depends on the number of relays and fading severity parameters.
Second, we propose the proactive and the reactive relay selection schemes based on CDFs of SNRs for two-way relay networks over Nakagami-m fading channels. For
the proactive relay selection scheme, we analyze average relay fairness by deriving relay selection probability. Also, we analyze diversity order by deriving the integral and asymptotic expressions for outage probability. For the reactive relay selection scheme, we analyze average relay fairness by deriving the integral and asymptotic expressions for relay selection probability. Also, we obtain diversity order by deriving the asymptotic expression for outage probability. Numerical results show that the analytical results of the proposed schemes match the simulation results well. It is shown that the proposed schemes guarantee strict fairness among relays and extend network lifetime. Also, it is shown that diversity order depends on the number of relays and fading severity parameters.Contents
Abstract i
1 Introduction 1
1.1 Background and Related Work . . . . . . . . . . . . . . . . . . . . . 2
1.1.1 Diversity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.1.2 Wireless Relay Technology . . . . . . . . . . . . . . . . . . . . 3
1.2 Outline of Dissertation . . . . . . . . . . . . . . . . . . . . . . . . . . 7
1.3 Notations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
2 Relay Selection Based on CDFs of SNRs for One-Way Relay Networks
14
2.1 System Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
2.1.1 Proactive CDF-Based Relay Selection . . . . . . . . . . . . . 19
2.1.2 Reactive CDF-Based Relay Selection . . . . . . . . . . . . . . 20
2.2 Performance Analysis of Proactive CDF-Based Relay Selection . . . . 22
2.2.1 Average Relay Fairness Analysis . . . . . . . . . . . . . . . . . 22
2.2.2 Outage Probability Analysis . . . . . . . . . . . . . . . . . . . 27
2.3 Performance Analysis of Reactive CDF-Based Relay Selection . . . . 34
2.3.1 Average Relay Fairness Analysis . . . . . . . . . . . . . . . . . 34
2.3.2 Outage Probability Analysis . . . . . . . . . . . . . . . . . . . 36
2.4 Numerical Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
2.4.1 Average Relay Fairness . . . . . . . . . . . . . . . . . . . . . . 39
2.4.2 Network Lifetime . . . . . . . . . . . . . . . . . . . . . . . . . 48
2.4.3 Outage Probability . . . . . . . . . . . . . . . . . . . . . . . . 53
2.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65
3 Relay Selection Based on CDFs of SNRs for Two-Way Relay Networks
66
3.1 System Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67
3.1.1 Proactive CDF-based Relay Selection . . . . . . . . . . . . . . 68
3.1.2 Reactive CDF-based Relay Selection . . . . . . . . . . . . . . 72
3.2 Performance Analysis of Proactive CDF-Based Relay Selection . . . . 73
3.2.1 Average Relay Fairness Analysis . . . . . . . . . . . . . . . . . 73
3.2.2 Outage Probability Analysis . . . . . . . . . . . . . . . . . . . 74
3.3 Performance Analysis of Reactive CDF-Based Relay Selection . . . . 82
3.3.1 Average Relay Fairness Anlaysis . . . . . . . . . . . . . . . . . 82
3.3.2 Outage Probability Analysis . . . . . . . . . . . . . . . . . . . 86
3.4 Numerical Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88
3.4.1 Average Relay Fairness . . . . . . . . . . . . . . . . . . . . . . 89
3.4.2 Network Lifetime . . . . . . . . . . . . . . . . . . . . . . . . . 100
3.4.3 Outage Probability . . . . . . . . . . . . . . . . . . . . . . . . 105
3.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115
4 Conclusion 116
4.1 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116
4.2 Possible Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . 118
4.2.1 Device-to-Device (D2D) Communications . . . . . . . . . . . 118
4.2.2 Low Power Body Sensor Networks . . . . . . . . . . . . . . . 120
4.3 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121
Bibliography 122
Korean Abstract 139Docto
On the Exploitation of CDF Based Wireless Scheduling
AbstractβChannel-aware scheduling strategies- such as the CDF Scheduler (CS) algorithm for the CDMA/HDR systems-provide an effective mechanism for utilizing the channel data rate for improving throughput performance in wireless data networks by exploiting channel fluctuations. A highly desired property of such a scheduling strategy is that its algorithm will be stable, in the sense that no user has incentive βcheating β the algorithm in order to increase his/her channel share (on the account of others). We present a scheme by which coordination allows a group of users to gain permanent increase in both their time slot share and in their throughput, on the expense of others, by misreporting their rates. We show that for large populations consisting of regular and coordinated users in equal numbers, the ratio of allocated time slots between a coordinated user and a regular one converges to e β 1 β 1.7. Our scheme targets the very fundamental principle of CS (as opposed to just attacking implementation aspects), which bases its scheduling decisions on the Cumulative Distribution Function (CDF) of the channel rates reported by users. Our scheme works both for the continuous channel spectrum and the discrete channel spectrum versions of the problem. I