With the emergence of computationally intensive and delay sensitive applications, mobile edge computing(MEC) has become more and more popular. Simultaneously, MEC paradigm is faced with security challenges, the most harmful of which is DDoS attack. In this paper, we focus on the resource orchestration algorithm in MEC scenario to mitigate DDoS attack. Most of existing works on resource orchestration algorithm barely take into account DDoS attack. Moreover, they assume that MEC nodes are unselfish, while in practice MEC nodes are selfish and try to maximize their individual utility only, as they usually belong to different network operators. To solve such problems, we propose a price-based resource orchestration algorithm(PROA) using game theory and convex optimization, which aims at mitigating DDoS attack while maximizing the utility of each participant. Pricing resources to simulate market mechanisms, which is national to make rational decisions for all participants. Finally, we conduct experiment using Matlab and show that the proposed PROA can effectively mitigate DDoS attack on the attacked MEC node.

With the explosive growth of wireless multimedia applications over the wireless Internet in recent years, the demand for radio spectral resources has increased significantly. In order to meet the quality of service, delay, and large bandwidth requirements, various techniques such as source and channel coding, distributed streaming, multicast etc. have been considered. In this paper, we propose a technique for distributed multimedia transmission over the secondary user network, which makes use of opportunistic spectrum access with the help of cognitive radios. We use digital fountain codes to distribute the multimedia content over unused spectrum and also to compensate for the loss incurred due to primary user interference. Primary user traffic is modelled as a Poisson process. We develop the techniques to select appropriate channels and study the trade-offs between link reliability, spectral efficiency and coding overhead. Simulation results are presented for the secondary spectrum access model.

We study a buffer that implements the Random Early Detect/Discard (RED) mechanism to cope with congestion, and offers service differentiation by proposing a finite number of slopes at different prices for the RED probability. As a characteristic, the smaller the slope, the better the resulting QoS. Users are sensitive to their average throughput and to the price they pay. Since the study of the noncooperative game played is rendered difficult by the discrete nature of the strategy sets, and since it is not likely that users have a perfect knowledge of the game but only know their experienced utility, we introduce a decentralized learning algorithm to progressively reach a Nash equilibrium over time. We examine the effect of prices on the final game outcomes.

We explore the price dynamics in a competitive market consisting of spectrum agile network service providers and users. Here, multiple self interested spectrum providers operating with different technologies and costs compete for potential customers. Different buyers or consumers may evaluate the same seller differently depending on their applications, operating technologies and locations. Two different buyer populations, the quality-sensitive and the price-sensitive are investigated, and the resulting collective price dynamics are studied using a combination of analysis and simulations. Various scenarios are considered regarding the nature and accuracy of information available to the sellers. A myopically optimal strategy is studied when full information is available, while a stochastic learning based strategy is considered when the information is limited. Cooperating groups may be formed among the sellers which will in-turn influence the group profit for those participants. Free riding phenomenon is observed under certain circumstances

Spectrum is one of the most precious radio resources. With the increasing demand for wireless communication, efficiently using the spectrum resource has become an essential issue. With the Federal Communications Commission's (FCC) spectrum policy reform, secondary spectrum sharing has gained increasing interest. One of the policy reforms introduces the concept of an interference temperature - the total allowable interference in a spectral band. This means that secondary users can use different transmit powers as long as the sum of these power is less than the interference threshold. In this paper, we study two problems in secondary spectrum access with minimum signal to interference noise ratio (quality of service (QoS)) guarantee under an interference temperature constraint. First, when all the secondary links can be supported, a nonlinear optimization problem with the objective to maximize the total transmitting rate of the secondary users is formulated. The nonlinear optimization is solved efficiently using geometric programming techniques. The second problem we address is, when not all the secondary links can be supported with their QoS requirement, it is desirable to have the spectrum access opportunity proportional to the user priority if they belong to different priority classes. In this context, we formulate an operator problem which takes the priority issues into consideration. To solve this problem, first, we propose a centralized reduced complexity search algorithm to find the optimal solution. Then, in order to solve this problem distributively, we define a secondary spectrum sharing potential game. The Nash equilibria of this potential game are investigated. The efficiency of the Nash equilibria solutions are characterized. It is shown that distributed sequential play and an algorithm based on stochastic learning attain the equilibrium solutions. Finally, the performances are examined through simulations

Abstract — We study the quality of service (QoS) issue in sec-ondary spectrum sharing subject to an interference temperature constraint. A non-linear optimization problem with the objective to maximize the total transmitting rate of the secondary users is formulated. The non-linear optimization is solved efficiently using geometric programming techniques. When not all the secondary links can be supported with their QoS requirement, a reduced complexity searching algorithm is introduced to find the optimal subset of links which contains the maximum number of links with both QoS and interference temperature constraints satisfied. We also defined a secondary spectrum sharing potential game. The Nash equilibria of this potential game are reached by distributed sequential play. The efficiency of the Nash equilibria solutions is characterized. Finally, the performances of both the reduced complexity algorithm and the sequential play are examined through simulations. I.

We study the problem of dynamic spectrum access by secondary users with minimum signal to interference noise ratio (quality of service (QoS)) and interference temperature constraints. A non-linear optimization problem with the objective to maximize the total transmitting rate of the secondary users is formulated. The non-linear optimization is solved efficiently using geometric programming techniques. When not all the secondary links can be supported with their QoS requirement, a reduced complexity search algorithm is introduced to find the optimal subset of allowable links. Secondary users may belong to different priority classes. Accessing opportunities should be proportional to priorities. Therefore, we defined a secondary spectrum sharing potential game which takes these priority classes into consideration. The Nash equilibria of this potential game are reached by distributed sequential play. The efficiency of the Nash equilibria solutions are characterized. Finally, the performances of both the reduced complexity algorithm and the sequential play are examined through simulations.

One of the reasons for the limitation of bandwidth in current generation wireless networks is the spectrum policy of the Federal Communications Commission (FCC). But, with the spectrum policy reform, open spectrum wireless networks, and spectrum agile radios are set to drive next general wireless networks. In this paper, we investigate continuous-time Markov models for dynamic spectrum access in open spectrum wireless networks. Both queueing and no queueing cases are considered. Analytical results are derived based on the Markov models. A random access protocol is proposed that is shown to achieve airtime fairness. A distributed version of this protocol that uses only local information is also proposed based on homo egualis anthropological model. Inequality aversion by the radio systems to achieve fairness is captured by this model. These protocols are then extended to spectrum agile radios. Extensive simulation results are presented to compare the performances of fixed versus agile radios.

Wireless multimedia services currently suffer due to limited bandwidth. One of the reasons for this limitation was the Federal Communications Commission's (FCC) spectrum regulation policy. But, with the advantage of open spectrum wireless networking the bandwidth constraint is minimized. In this paper, a Markov model is presented and used to analyze the unlicensed band access. Then a random access scheme is proposed which provisions the existing etiquettes with more efficiency and fairness. Later a homo egualis society model based access scheme is built to implement the random access scheme in a distributed fashion. Simulations are performed to demonstrate the accuracy of this Markov model and to show the efficiency and fairness of the proposed schemes.

Distributed power control is an important issue in wireless networks. Recently, due to the bursty nature of data communication, packet switching is used in cellular systems. In addition, majority of previous power control algorithm assume that the transmitter power level can take values in a continuous domain. However, recent trends such as the GSM standard and QUALCOMM's proposal to the IS-95 standard use a finite number of discretized power levels. These motivate the need to investigate solutions for distributed discrete power control for bursty transmission. Therefore, we propose a probabilistic power adaptation algorithm and analyze its theoretical properties along with the numerical behavior for bursty transmission. We approximate the discrete power control iterations by an equivalent ordinary differential equation (ODE) to prove that the proposed stochastic learning power control algorithm converges to a stable Nash equilibrium. Conditions when more than one stable Nash equilibrium may exist are also studied. Experimental results are presented for several cases and the impact of data burstiness on the proposed algorithm is also concerned.