Changqing Luo

Cross-Layer Design for TCP Performance Improvement in Cognitive Radio Networks

In cognitive radio (CR) networks, the end-to-end transmission-control protocol (TCP) performance experienced by secondary users is a very important factor that evaluates the secondary user perceived quality of service (QoS). Most previous works in CR networks ignore the TCP performance. In this paper, we take a cross-layer design approach to jointly consider the spectrum sensing, access decision, physical-layer modulation and coding scheme, and data-link layer frame size in CR networks to maximize the TCP throughput in CR networks. The wireless channel and the primary network usage are modeled as a finite-state Markov process. Due to the miss detection and the estimation error experienced by secondary users, the system state cannot be directly observed. Consequently, we formulate the cross-layer TCP throughput optimization problem as a partially observable Markov decision process (POMDP). Simulation results show that the design parameters in CR networks have a significant impact on the TCP throughput, and the TCP throughput can be substantially improved if the low-layer parameters in CR networks are optimized jointly.

Green Communication in Energy Renewable Wireless Mesh Networks: Routing, Rate Control, and Power Allocation

The increasing demand for wireless services has led to a severe energy consumption problem with the rising of greenhouse gas emission. While the renewable energy can somehow alleviate this problem, the routing, flow rate, and power still have to be well investigated with the objective of minimizing energy consumption in multi-hop energy renewable wireless mesh networks (ER-WMNs). This paper formulates the problem of network-wide energy consumption minimization under the network throughput constraint as a mixed-integer nonlinear programming problem by jointly optimizing routing, rate control, and power allocation. Moreover, the min-max fairness model is applied to address the fairness issue because the uneven routing problem may incur the sharp reduction of network performance in multi-hop ER-WMNs. Due to the high computational complexity of the formulated mathematical programming problem, an energy-aware multi-path routing algorithm (EARA) is also proposed to deal with the joint control of routing, flow rate, and power allocation in practical multi-hop WMNs. To search the optimal routing, it applies a weighted Dijkstras shortest path algorithm, where the weight is defined as a function of the power consumption and residual energy of a node. Extensive simulation results are presented to show the performance of the proposed schemes and the effects of energy replenishment rate and network throughput on the network lifetime.

A Tensor-Based Approach for Big Data Representation and Dimensionality Reduction

Variety and veracity are two distinct characteristics of large-scale and heterogeneous data. It has been a great challenge to efficiently represent and process big data with a unified scheme. In this paper, a unified tensor model is proposed to represent the unstructured, semistructured, and structured data. With tensor extension operator, various types of data are represented as subtensors and then are merged to a unified tensor. In order to extract the core tensor which is small but contains valuable information, an incremental high order singular value decomposition (IHOSVD) method is presented. By recursively applying the incremental matrix decomposition algorithm, IHOSVD is able to update the orthogonal bases and compute the new core tensor. Analyzes in terms of time complexity, memory usage, and approximation accuracy of the proposed method are provided in this paper. A case study illustrates that approximate data reconstructed from the core set containing 18% elements can guarantee 93% accuracy in general. Theoretical analyzes and experimental results demonstrate that the proposed unified tensor model and IHOSVD method are efficient for big data representation and dimensionality reduction.

Distributed Relay Selection and Power Control in Cognitive Radio Networks with Cooperative Transmission

In this paper, we present a distributed relay selection and power allocation scheme concurrently considering the channel states of all related links and residual energy state of the relay nodes for cooperative transmission in cognitive radio (CR) networks. Specifically, we formulate the CR network with cooperative transmission as a restless bandit system, which has been widely applied in operations research and stochastic control. The channel state and residual energy state are presented by finite state Markov chains. With this stochastic optimization formulation, the optimal policy for relay selection and power allocation is indexable, meaning that the relay with the highest index should be selected. The proposed scheme can achieve the tradeoff between achievable rate and network lifetime. Simulation results are presented to illustrate the performance of the proposed scheme.

Energy-Efficient Distributed Relay and Power Control in Cognitive Radio Cooperative Communications

In cognitive radio cooperative communication (CR-CC) systems, the achievable data rate can be improved by increasing the transmission power. However, the increase in power consumption may cause the interference with primary users and reduce the network lifetime. Most previous work on CR-CC did not take into account the tradeoff between the achievable data rate and network lifetime. To fill this gap, this paper proposes an energy-efficient joint relay selection and power allocation scheme in which the state of a relay is characterized by the channel condition of all related links and its residual energy. The CR-CC system is formulated as a multi-armed restless bandit problem where the optimal policy is decided in a distributed way. The solution to the restless bandit formulation is obtained through a first-order relaxation method and a primal-dual priority-index heuristic, which can reduce dramatically the on-line computation and implementation complexity. According to the obtained index, each relay can determine whether to provide relaying or not and also can control the corresponding transmission power. Extensive simulation experiments are conducted to investigate the effectiveness of the proposed scheme. The results demonstrate that the power consumption is reduced significantly and the network lifetime is increased more than 40%.

Utility-Based Multi-Service Bandwidth Allocation in the 4G Heterogeneous Wireless Access Networks

Due to the heterogeneity of radio access technology and service in the 4G heterogeneous wireless access networks, it has been a great challenge on radio resource management. This paper considers a bandwidth allocation approach for multiple services in fourth generation (4G) heterogeneous wireless access networks where a mobile with multi-homing capability will be able to simultaneously connect to several wireless interfaces. In this scheme, a utility function is introduced to estimate the effect of network performance when the network provide bandwidth to a new arrival connection and the bandwidth offered by different wireless access networks is normalized by using their corresponding capacities. And then, based on the concept of network utility, a bandwidth allocation algorithm is proposed to allocate bandwidth to both CBR and VBR connections depending on utility fairness for same type of service not only within a wireless access network but also among different wireless access networks. Simulation results show that our bandwidth allocation algorithm is effective in allocating bandwidth for both CBR and VBR services while keeping connection blocking probability substantially low.

Cloud-Based Mobile Multimedia Recommendation System With User Behavior Information

Facing massive multimedia services and contents in the Internet, mobile users usually waste a lot of time to obtain their interests. Therefore, various context-aware recommendation systems have been proposed. Most of those proposed systems deploy a large number of context collectors at terminals and access networks. However, the context collecting and exchanging result in heavy network overhead, and the context processing consumes huge computation. In this paper, a cloud-based mobile multimedia recommendation system which can reduce network overhead and speed up the recommendation process is proposed. The users are classified into several groups according to their context types and values. With the accurate classification rules, the context details are not necessary to compute, and the huge network overhead is reduced. Moreover, user contexts, user relationships, and user profiles are collected from video-sharing websites to generate multimedia recommendation rules based on the Hadoop platform. When a new user request arrives, the rules will be extended and optimized to make real-time recommendation. The results show that the proposed approach can recommend desired services with high precision, high recall, and low response delay.

MobiFuzzyTrust: An Efficient Fuzzy Trust Inference Mechanism in Mobile Social Networks

Mobile social networks (MSNs) facilitate connections between mobile users and allow them to find other potential users who have similar interests through mobile devices, communicate with them, and benefit from their information. As MSNs are distributed public virtual social spaces, the available information may not be trustworthy to all. Therefore, mobile users are often at risk since they may not have any prior knowledge about others who are socially connected. To address this problem, trust inference plays a critical role for establishing social links between mobile users in MSNs. Taking into account the nonsemantical representation of trust between users of the existing trust models in social networks, this paper proposes a new fuzzy inference mechanism, namely MobiFuzzyTrust, for inferring trust semantically from one mobile user to another that may not be directly connected in the trust graph of MSNs. First, a mobile context including an intersection of prestige of users, location, time, and social context is constructed. Second, a mobile context aware trust model is devised to evaluate the trust value between two mobile users efficiently. Finally, the fuzzy linguistic technique is used to express the trust between two mobile users and enhance the humans understanding of trust. Real-world mobile dataset is adopted to evaluate the performance of the MobiFuzzyTrust inference mechanism. The experimental results demonstrate that MobiFuzzyTrust can efficiently infer trust with a high precision.

Optimal Channel Access for TCP Performance Improvement in Cognitive Radio Networks: A Cross-Layer Design Approach

In cognitive radio (CR) networks, the multichannel access problem is an important problem, which may directly affect user applications. However, most of previous work on this problem focuses on maximizing physical layer throughput, rather than the end-to-end transmission control protocol (TCP) performance. In this paper, we propose an optimal TCP throughput based channel access scheme in CR networks, and the TCP performance is improved from a cross-layer perspective. Specifically, we formulate the channel access process in CR network as a stochastic system. With the stochastic optimization formulation, the optimal channel access policy is indexable, meaning that the channels with highest indices should be selected to transmit TCP traffic. Simulation results show the TCP throughput can be improved substantially compared with the existing approach that maximizes physical layer throughput.

Optimal data fusion of collaborative spectrum sensing under attack in cognitive radio networks

Cognitive radio networks allow opportunistic spectrum access and can significantly improve spectral efficiency. To achieve higher sensing accuracy, cognitive radio systems often require cooperation among secondary users. One of the most important aspects in collaborative spectrum sensing is the data fusion algorithm which combines the sensing results from secondary users to produce the final channel status hypothesis. However, plenty of factors may affect the performance of certain data fusion rule, for example, the individual sensing nodes sensing accuracy, the number of involved nodes, and the like. If Spectrum Sensing Data Falsification (SSDF) attack exists, it will become more challenging to make proper data fusion. In this article, we first introduce framework, and then evaluate the data fusion rules in different scenarios through simulation examples. Finally, a Genetic Algorithm based optimal scheme is proposed to achieve better performance in all scenarios.