Offloading in Software Defined Network at Edge with Information Asymmetry: A Contract Theoretical Approach

The 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.Comment: 10 pages, 9 figure

Big Data Optimization for Modern Communication Networks

The 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.Electrical and Computer Engineering, Department o

Scalable traffic management for mobile cloud services in 5G networks

Mobile cloud computing has been introduced to improve the performance of mobile application clients by offloading data processing and storage to cloud. By deploying the service on several cloud-enabled data centers, the service provider can optimally locate service instances on the cloud to provide qualified services at a reasonable cost. However, a centralized approach for both request allocation and response routing does not scale efficiently due to a large number of mobile clients involved in the mobile service traffic management. Moreover, the random and unpredictable wireless network performance (e.g., delay) complicates the problem further. In this paper, we present a stochastic distributed optimization framework for mobile cloud traffic management in 5G networks. The framework takes the impact of random wireless network characteristics into account. Utilizing the alternating direction method of multipliers, the optimization problem is decomposed into independent subproblems, which are solved in a parallel fashion on distributed agents and coordinated through dual variables. The convergence issue under the stochastic setting is addressed, and the numerical tests validate the effectiveness of the proposed algorithm

Spectrum Sensing and Primary User Localization in Cognitive Radio Networks via Sparsity

The theory of compressive sensing (CS) has been employed to detect available spectrum resource in cognitive radio (CR) networks recently. Capitalizing on the spectrum resource underutilization and spatial sparsity of primary user (PU) locations, CS enables the identification of the unused spectrum bands and PU locations at a low sampling rate. Although CS has been studied in the cooperative spectrum sensing mechanism in which CR nodes work collaboratively to accomplish the spectrum sensing and PU localization task, many important issues remain unsettled. Does the designed compressive spectrum sensing mechanism satisfy the Restricted Isometry Property, which guarantees a successful recovery of the original sparse signal? Can the spectrum sensing results help the localization of PUs? What are the characteristics of localization errors? To answer those questions, we try to justify the applicability of the CS theory to the compressive spectrum sensing framework in this paper, and propose a design of PU localization utilizing the spectrum usage information. The localization error is analyzed by the Cramér-Rao lower bound, which can be exploited to improve the localization performance. Detail analysis and simulations are presented to support the claims and demonstrate the efficacy and efficiency of the proposed mechanism

Some details of experiments

The file includes 1. Synthesis of P(PDI-DTT) 2. Schematic of gold-layer sticking technique 3. More photographs of nanotube

Data from: Convenient fabrication of conjugated polymer semiconductor nanotubes and their application in organic electronics

Organic heterojunction is indispensable in organic electronic devices, such as organic solar cells (OSCs), organic light-emitting diodes (OLEDs) and so on. Fabrication of core-shell nanostructure provides feasible and novel way to prepare organic heterojunction, which is beneficial for miniaturization and integration of organic electronic devices. Fabrication of nanotubes which constitute the core-shell structure in large quantity is the key for the realization of application. In this work, a simple and convenient method to prepare nanotubes utilizing conjugated copolymer of perylene diimide and dithienothiophene (P(PDI-DTT)) was demonstrated. The relationship between preparation condition (solvent atmosphere, solution concentration and pore diameter of templates) and morphology of nanostructure was studied systematically. P(PDI-DTT) nanotubes could be fabricated in regular shape and large quantity by preparing the solution with appropriate concentration and placing Anodic Aluminum Oxide (AAO) template with nanopore diameter of 200 nm in the solvent atmosphere. The tubular structure was confirmed by scanning electron microscope (SEM). P(PDI-DTT) nanotubes exhibited electron mobility of 0.02 cm2V-1s–1 in field effect transistors under ambient condition. Light emitting nanostructures were successfully fabricated by incorporating tetraphenyl ethylene (TPE) into polymer nanotubes

Data from: Convenient fabrication of conjugated polymer semiconductor nanotubes and their application in organic electronics

Organic heterojunction is indispensable in organic electronic devices, such as organic solar cells (OSCs), organic light-emitting diodes (OLEDs) and so on. Fabrication of core-shell nanostructure provides feasible and novel way to prepare organic heterojunction, which is beneficial for miniaturization and integration of organic electronic devices. Fabrication of nanotubes which constitute the core-shell structure in large quantity is the key for the realization of application. In this work, a simple and convenient method to prepare nanotubes utilizing conjugated copolymer of perylene diimide and dithienothiophene (P(PDI-DTT)) was demonstrated. The relationship between preparation condition (solvent atmosphere, solution concentration and pore diameter of templates) and morphology of nanostructure was studied systematically. P(PDI-DTT) nanotubes could be fabricated in regular shape and large quantity by preparing the solution with appropriate concentration and placing Anodic Aluminum Oxide (AAO) template with nanopore diameter of 200 nm in the solvent atmosphere. The tubular structure was confirmed by scanning electron microscope (SEM). P(PDI-DTT) nanotubes exhibited electron mobility of 0.02 cm2V-1s–1 in field effect transistors under ambient condition. Light emitting nanostructures were successfully fabricated by incorporating tetraphenyl ethylene (TPE) into polymer nanotubes

Data from: Convenient fabrication of conjugated polymer semiconductor nanotubes and their application in organic electronics

Organic heterojunction is indispensable in organic electronic devices, such as organic solar cells (OSCs), organic light-emitting diodes (OLEDs) and so on. Fabrication of core-shell nanostructure provides feasible and novel way to prepare organic heterojunction, which is beneficial for miniaturization and integration of organic electronic devices. Fabrication of nanotubes which constitute the core-shell structure in large quantity is the key for the realization of application. In this work, a simple and convenient method to prepare nanotubes utilizing conjugated copolymer of perylene diimide and dithienothiophene (P(PDI-DTT)) was demonstrated. The relationship between preparation condition (solvent atmosphere, solution concentration and pore diameter of templates) and morphology of nanostructure was studied systematically. P(PDI-DTT) nanotubes could be fabricated in regular shape and large quantity by preparing the solution with appropriate concentration and placing Anodic Aluminum Oxide (AAO) template with nanopore diameter of 200 nm in the solvent atmosphere. The tubular structure was confirmed by scanning electron microscope (SEM). P(PDI-DTT) nanotubes exhibited electron mobility of 0.02 cm2V-1s–1 in field effect transistors under ambient condition. Light emitting nanostructures were successfully fabricated by incorporating tetraphenyl ethylene (TPE) into polymer nanotubes