Qiu Xue-song

A QoS-Aware Packet Scheduling Mechanism in Cognitive Radio Networks for Smart Grid Applications

Cognitive radio sensor network is applied to facilitate network monitoring and management, and achieves high spectrum efficiencies in smart grid. However, the conventional traffic scheduling mechanisms are hard to provide guaranteed quality of service for the secondary users. It is because that they ignore the influence of diverse transition requirements in heterogeneous traffic. Therefore, a novel QoS-aware packet scheduling mechanism is proposed to improve transmission quality for secondary users. In this mechanism, a QoS-based prioritization model is established to address data classification firstly. And then, channel quality and the effect of channel switch are integrated into priority-based packet scheduling mechanism. At last, the simulation is implemented with MATLAB and OPNET. The results show that the proposed scheduling mechanism improves the transmission quality of high-priority secondary users and increase the whole system utilization by 10%.

The contract net based task allocation algorithm for wireless sensor network

Since wireless sensor network has limited resources, its important to design its task allocation algorithm reasonably to reduce energy consumption. The contract net is simple and flexible so that it can meet the needs of the wireless sensor network. In this paper, we introduce the improved C-MEANS algorithm to cluster nodes to decrease the number of bidders, and at the same time, the LMS algorithm is adopted to predict the bid value of the nodes. The simulation results show that the energy consumption and traffic flow are reduced, and the bid value more accurately reflects the status of the node when allocated tasks, which increased the complete rate of network tasks.

Self-Organizing Energy-Saving Management Mechanism Based on Pilot Power Adjustment in Cellular Networks

Little literature concentrates on effective and self-organizing regional energy-saving schemes in cellular networks with dynamic traffic. In this paper, we propose a regional self-organizing energy-saving management mechanism through pilot power adjustment in cellular networks. The mechanism analyzes autonomic processes for saving energy firstly. Then, it proposes regional BS TP (Trigonal-Pair) compensation method to determine BS modes for saving energy. Next, it adopts intelligent coverage compensation algorithm to adjust pilot power of compensated BSs, so as to avoid coverage hole and ensure enough capacity. Integrated evaluation model is still proposed in the mechanism to validate efficiency of proposed algorithms. Simulations show that about 17% of regional energy consumption can be saved. In comparison to other ES methods, our ES mechanism can obtain better energy-saving outcomes and keep service quality and coverage above acceptable levels.

A Service-Oriented Alarm Correlation Method for IT Service Fault Localization

To improve the quality of IT services it is important to quickly and accurately detect and diagnose its faults. Because associations exist among entities, one fault in a single entity may cause a number of accompanying alarms beside the root alarm during fault propagation. Service-oriented alarm correlation analysis, that isolates the root causes or faults from numerous alarms, is an important method to ensure quality of service. This paper firstly provides an entity relationships model of IT infrastructure. Based on the fault propagation map depicted in the model, we then propose a service-oriented alarm correlation method. We have validated our efforts by developing a prototype and testing it in a real environment

Cell outage compensation based on CoMP and optimization of tilt

Abstract Cell outage compensation (COC) is a self-healing function of the self-organizing networks (SONs) concept for mobile wireless networks. In order to compensate the outage cell autonomously, this paper drives a solution for COC. This method is based on coordinated multiple points (CoMP) transmission and tilt adjusting. Firstly, the paper takes the coordinated pattern and tilt as the optimization objects. Then, it defines the rational objectives and the evaluation function of the situation and analysis the optimization model. Finally, based on the effective genetic algorithm (GA), the compensation mechanism is proposed. Simulation results show that this solution is better than the elder solutions in terms of coverage, interference, and throughput.

Revenue-driven dynamic resource allocation for network virtualization

Network virtualization has emerged as a powerful and flexible platform for future network. A major challenge is virtual network embedding problem that deals with efficiently mapping of virtual nodes and virtual links onto substrate network resources. However, resource quantity requested by virtual network service is related to time and revenue, which is not concerned by existing researches, resulting in the inefficient utilization of substrate network resources and lower revenue. To address this problem, autonomic resource allocation model based on revenue and Revenue-Driven Dynamic Allocation (called RDDA) based on time interval classification (i.e., short-term, medium-term and long-term) are presented for first time. Simulation results show that RDDA improves the substrate network resource utilization rate and the revenue of Infrastructure Provider.

A model-driven conformance testing method for 3G network management north bound interface

Based on the Model-Driven Testing (MDT), this paper proposed a model-driven conformance testing method for 3G network management north bound interface in order to minimize the impact from specifications changes and technological innovations. The core thought of the method is to derive interface technology independent test model (PIT) from platform independent model (PIM) defined in analysis specifications and then according to mapping rules defined in design specifications transform PIT to interface technology specific test models (PSTs) which can be used in conformance testing of SUTs by proper test tools. In this method, the conformance testing can be performed with lower cost and in higher efficiency with reuses of the testing models.

Quality of experience evaluation of HTTP video streaming based on user interactive behaviors

Abstract User interactive behaviors play a dual role during the hypertext transfer protocol (HTTP) video service: reflection and influence. However, they are seldom taken into account in practices. To this end, this paper puts forward the user interactive behaviors, as subjective factors of quality of experience (QoE) from viewer level, to structure a comprehensive multilayer evaluation model based on classic network quality of service (QoS) and application QoS. First, dual roles of user behaviors are studied and the characteristics are extracted where the user experience is correlated with user interactive behaviors. Furthermore, we categorize QoE factors into three dimensions and build the metric system. Then we perform the subjective tests and investigate the relationships among network path quality, user behaviors, and QoE. Ultimately, we employ the back propagation neural network (BPNN) to validate our analysis and model. Through the simulation experiment of mathematical and BPNN, the dual effects of user interaction behaviors on the reflection and influence of QoE in the video stream are analyzed, and the QoE metric system and evaluation model are established.

A metric-correlation-based fault detection approach using clustering analysis in wireless sensor networks

Fault detection plays a crucial role in wireless sensor networks (WSNs). Many fault detection approaches requiring a priori knowledge of network faults have been proposed to distinguish faulty sensors by exploring spatial-temporal correlations among sensor readings. However, many faulty sensors that may not generate anomalous sensor readings, and potential failures with unknown types and symptoms remain undetected. In this paper, we propose a Metric-Correlation-Based Fault Detection (MCFD) approach using clustering analysis. It is motivated by the fact that the system metric correlations of most fault-free sensors usually show strong similarities, whereas different patterns of such correlations indicate potential failures. MCFD explores internal metric correlations inside sensors using correlation value views. An improved Neighbor-based Local Density Clustering Analysis (NLDCA) algorithm based on the Neighbor-based Local Density Factor (NLDF) is applied in spatial domain detection to cluster similar correlation value views together, thus potential faulty sensors with abnormal views not belonging to any cluster can be detected. Simulation results demonstrate that MCFD approach performs well in respects of higher detection accuracy and lower false positive rate even under high node failure ratios and dense distribution conditions.

MDA-based network management information model transformation from UML to Web Services

The definition of network management interface is generally divided into three phases, requirements, analysis and design. In analysis phase, UML is used as the modeling language to present technology independent models, and these models can be mapped into multiple technology specific models. With the development of Web Services applied in network management domain, it is required to define Web Services-based information models in design phase. This paper applied MDA approach to realize models transformation. As the main work, this paper proposed detailed mapping rules describing how to map the existing source UML models into the target Web Services-based models. An automatic transformation approach using XSLT was proposed to implement the mapping rules, and experiments were made for verification. The proposed mapping rules improved the defects in related work. Furthermore, the work of this paper could help resolve some issues in current models transformation work which is still being accomplished manually by standard developers.