Browsing by Author "Liu, Lanchao"
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Item Big Data Optimization for Modern Communication Networks(2014-12) Liu, Lanchao; Han, Zhu; Shih, Wei-Chuan; Ogmen, Haluk; Prasad, Saurabh; Pan, Miao; Hong, MingyiThe unprecedented big data in modern communication networks presents us opportunities and challenges. An efficient analytic method for the sheer volume of data is of significant importance for smart grid evolution, intelligent communication network management, efficient medical data management, personalized business model design and smart city development. Meanwhile, the huge volume of data makes it impractical to collect, store and processing in a centralized fashion. Moreover, the massive datasets are noisy, incomplete, heterogeneous, structured, prone to outliers, and vulnerable to cyber-attacks. Overall, we are facing a problem in which the classic resources of computation such as time, space, and energy, are intertwined in complex ways with the massive data sources, and new computational mathematical models as well as methodologies must be explored. With the rapid development of the modern communication networks comes the need of novel algorithms for large-scale data processing and optimization. In this thesis, we investigate the application of big data optimization methods for smart grid security and mobile data traffic management. Firstly, we review the parallel and distributed optimization algorithms based on an alternating direction method of multipliers for solving big data optimization problems. The mathematical backgrounds of the algorithms are given, and the implementations on large-scale computing facilities are also illustrated. Next, the applications of big data processing techniques for smart grid security are studied from two perspectives: how to exploit the inherent structure of the data, and how to deal with the huge size of the data sets. Explored problems are the sparse optimization approach for false data injection detection, and the distributed parallel approach for the security-constrained optimal power flow problem, respectively. Finally, we consider big data optimization methods for data traffic management in mobile cloud computing by two specific application cases: the mobile data offloading in a software defined network at the network edge, and the management of mobile cloud service request allocation and response routing. It is shown by numerical results that effective management and processing of ‘big data’ have the potential to significantly improve smart grid security as well as resource utilization and service quality of the mobile cloud computing.Item Offloading in Software Defined Network at Edge with Information Asymmetry: A Contract Theoretical Approach(Journal of Signal Processing Systems, 9/30/2015) Zhang, Yanru; Liu, Lanchao; Gu, Yunan; Niyato, Dusit; Pan, Miao; Han, ZhuThe proliferation of highly capable mobile devices such as smartphones and tablets has significantly increased the demand for wireless access. Software defined network (SDN) at edge is viewed as one promising technology to simplify the traffic offloading process for current wireless networks. In this paper, we investigate the incentive problem in SDN-at-edge of how to motivate a third party access points (APs) such as WiFi and smallcells to offload traffic for the central base stations (BSs). The APs will only admit the traffic from the BS under the precondition that their own traffic demand is satisfied. Under the information asymmetry that the APs know more about own traffic demands, the BS needs to distribute the payment in accordance with the APs’ idle capacity to maintain a compatible incentive. First, we apply a contract-theoretic approach to model and analyze the service trading between the BS and APs. Furthermore, other two incentive mechanisms: optimal discrimination contract and linear pricing contract are introduced to serve as the comparisons of the anti adverse selection contract. Finally, the simulation results show that the contract can effectively incentivize APs’ participation and offload the cellular network traffic. Furthermore, the anti adverse selection contract achieves the optimal outcome under the information asymmetry scenario.