Shaoyong Guo

PLC-oriented access point location planning algorithm in smart-grid communication networks

In this study, we investigate the optimal location of access points (APs) to connect end nodes with a service provider through power-line communication in smart-grid communication networks. APs are the gateways of power-distribution communication networks, connecting users to control centers. Hence, they are vital for the reliable, safe, and economical operation of a power system. This paper proposes a planning method for AP allocation that takes into consideration economics, reliability, network delay, and (n-1) resilience. First, an optimization model for the AP location is established, which minimizes the cost of installing APs, while satisfying the reliability, network delay, and (n-1) resilience constraints. Then, an improved genetic algorithm is proposed to solve the optimization problem. The simulation results indicate that the proposed planning method can deal with diverse network conditions satisfactorily. Furthermore, it can be applied effectively with high flexibility and scalability.

Traffic scheduling for wireless meter data collection in smart grid communication network

Due to the advantages of muti-hop communication, self-organizing, self-healing and reliability, wireless muti-hop mesh network becomes an ideal solution for smart grid meter data collection. However, wireless muti-hop meter data collection network faces limitation and challenge on communication performance of network caused by application layer data traffic and the network topology limitation. When a large number of data occur in emergence, some mesh nodes (smart meters) which are in pivotal location will face great communication pressure and probably lead to extremely data congestion. This paper utilizes a three-layer network to form the wireless muti-hop meter data collection communication network, next proposes a new random switching traffic scheduling algorithm based on meter data collection tree with the idea of load balancing, and then gives an optimization for random switching traffic scheduling. Simulation show that the new traffic scheduling mechanism can create a balanced meter data collection tree, significantly reduce the packet loss ratio of the burst data and release congestion of system.

WCDMA KPI framework definition methods and applications

With the development of the commercial applications of WCDMA in the whole world, the comprehensive and scientific measurement and assessment of network performance for WCDMA have become one of the urgent requirements for operators. KPI is always a significant assessment standard for network performance from the 2nd wireless communication. As the KPI/KQI assessment architecture is proposed by TMF and the End-to-End QoS/QoE has become one of the research focuses, KPI is therefore rapidly becoming a indispensable factor in the assessment architecture of WCDMA. Due to the lack of definition framework and methods for KPI, it is difficult for the existing network performance KPI to cover all importance aspects of network and to make fair evaluation of network performance. In response to these needs, this paper proposes a definition methodology composed of the definition criteria, the assessment framework, and the weight division principles for WCDMA KPI. Besides, it also illustrates the measurement and assessment processes of network performance KPI of WCDMA by the practical application experience of Chinese operators.

Distributed Fault Detection Based on Credibility and Cooperation for WSNs in Smart Grids

Due to the increasingly important role in monitoring and data collection that sensors play, accurate and timely fault detection is a key issue for wireless sensor networks (WSNs) in smart grids. This paper presents a novel distributed fault detection mechanism for WSNs based on credibility and cooperation. Firstly, a reasonable credibility model of a sensor is established to identify any suspicious status of the sensor according to its own temporal data correlation. Based on the credibility model, the suspicious sensor is then chosen to launch fault diagnosis requests. Secondly, the sending time of fault diagnosis request is discussed to avoid the transmission overhead brought about by unnecessary diagnosis requests and improve the efficiency of fault detection based on neighbor cooperation. The diagnosis reply of a neighbor sensor is analyzed according to its own status. Finally, to further improve the accuracy of fault detection, the diagnosis results of neighbors are divided into several classifications to judge the fault status of the sensors which launch the fault diagnosis requests. Simulation results show that this novel mechanism can achieve high fault detection ratio with a small number of fault diagnoses and low data congestion probability.

Backup-resource based failure recovery approach in SDN data plane

Software Defined Networking (SDN) enables the underlying infrastructure to be abstracted from the network services and controlled by one or more controllers. If a link or a node fails, the switches that can detect the failure have to either inform controller to update flow tables or transform the data to pre-configured paths to recover the failure. However, existing failure recovery approaches mainly consider the recovery delay and packet loss, and ignore the storage resources consumption for backup paths in case of link or node failure. Moreover, the Ternary Content Addressable Memory (TCAM) that stores flow entries is expensive and limited with high-energy consumption. Thus in order to minimize the consumption of backup resources and meet the required failure recovery delay, a backup-resource based failure recovery approach is proposed. Two metrics are proposed to grade physical links, and three kinds of strategies for different graded links are provided, based on which the approach tries to use less flow entries to recover link failure and meets the required failure recovery delay, while guaranteeing the reliability of the network. Simulations show that backup-resource based approach can use as less flow entries as possible to ensure the performance of failure recovery and satisfy the required delay of important traffic at the same time. Moreover, the approach has good and steady performance in networks of different scales and connectivity.

An HMM-based performance diagnosis approach for Hadoop clusters

Hadoop has become a popular platform for the management of big data. To provide a healthy Hadoop platform for big data application, an HMM-based approach for performance diagnosis in Hadoop clusters is proposed. We use metrics which are collected under the normal situation to train HMM (Hidden Markov Model), then use this model to detect anomaly based on the probability, which is more accurate than other methods. Through evaluation in a controlled environment running Hadoop clusters, we find our approach can find out the real cause of performance problems in an average 84% precision and 83% recall, which is better than the method based on ARIMA and KNN (k-Nearest Neighbor).

Load balancing-based routing optimization mechanism for power communication networks

In power communication networks, it is a challenge to decrease the risk of different services efficiently to improve operation reliability. One of the important factor in reflecting communication risk is service route distribution. However, existing routing algorithms do not take into account the degree of importance of services, thereby leading to load unbalancing and increasing the risks of services and networks. A routing optimization mechanism based on load balancing for power communication networks is proposed to address the abovementioned problems. First, the mechanism constructs an evaluation model to evaluate the service and network risk degree using combination of devices, service load, and service characteristics. Second, service weights are determined with modified relative entropy TOPSIS method, and a balanced service routing determination algorithm is proposed. Results of simulations on practical network topology show that the mechanism can optimize the network risk degree and load balancing degree efficiently.

A random switching traffic scheduling algorithm in wireless smart grid communication network

One of the key technologies of smart grid is an efficient, reliable and secure two-way communication system for meter data collection. Because of the advantages of muti-hop communication, self-organizing, self-healing and reliability, wireless muti-hop communication technology becomes an ideal choice for smart grid meter data collection. However, forming wireless mesh network with advanced electricity devices (smart meters) which have the communication capabilities for meter data collection faces challenge on communication performance of network caused by application layer data traffic. When a large number of data occur in emergence, some smart meters (the last hop nodes) which are in pivotal location will face great communication pressure and probably lead to extremely data congestion. With the idea of load balancing, this paper proposes a new random switching traffic scheduling algorithm based on meter data collection tree. Simulation data show that the new algorithm can create a balanced meter data collection tree, significantly reduce the packet loss ratio of the burst data and release congestion of system.

Generalized Lagrange based Resource Negotiation Mechanism in MANETs

With the integration of mobile devices into ubiquitous environments, a wide range of services can be provided by exploiting the resources on heterogeneous devices, especially in MANETs. Because resources on mobile devices are shared out, it is essential to arrange highly-limited resources effectively and reasonably while considering utilities of multi-users and the network. To achieve this goal, abstract Buyer Agent and Seller Agent as well as the concept of resource market are introduced in the Resource Pricing Model. Then a novel Generalized Lagrange based Resource Negotiation Mechanism is proposed including the solution to the problem model using Generalized Lagrange Multiplier technique and the process of multi-round resource negotiation. At last, the experimental results demonstrate that the proposed mechanism is capable of optimizing the response time, maximizing the system utility and balancing the agent utility under the constraints of budget, deadline and energy resource.

Novel information modeling for management of heterogeneous device

The information modeling and management mechanism are the most important aspects of device management. By introducing the importance of device management, we analyze the limitation and deficiency of information modeling in device management. With the deepening research on existing information modeling methods, the paper proposes hybrid architecture and generic information modeling for heterogeneous device management. Firstly, the paper illustrates the heterogeneous device management architecture based on the converged device management protocol. Then, a generic information modeling (GIM) based on SID and CIM is proposed to meet the requirements of device management. And several integration and compression mechanisms are described to generate multi-device profiles. Finally, the ability of GIM is testified through its application in home network device management.