Xibin Zhao

Self-diagnosis for large scale wireless sensor networks

Existing approaches to diagnosing sensor networks are generally sink-based, which rely on actively pulling state information from all sensor nodes so as to conduct centralized analysis. However, the sink-based diagnosis tools incur huge communication overhead to the traffic sensitive sensor networks. Also, due to the unreliable wireless communications, sink often obtains incomplete and sometimes suspicious information, leading to highly inaccurate judgments. Even worse, we observe that it is always more difficult to obtain state information from the problematic or critical regions. To address the above issues, we present the concept of self-diagnosis, which encourages each single sensor to join the fault decision process. We design a series of novel fault detectors through which multiple nodes can cooperate with each other in a diagnosis task. The fault detectors encode the diagnosis process to state transitions. Each sensor can participate in the fault diagnosis by transiting the detectors current state to a new one based on local evidences and then pass the fault detector to other nodes. Having sufficient evidences, the fault detector achieves the Accept state and outputs the final diagnosis report. We examine the performance of our self-diagnosis tool called TinyD2 on a 100 nodes testbed.

Integrated Importance Measure of Component States Based on Loss of System Performance

This paper mainly focuses on the integrated importance measure (IIM) of component states based on loss of system performance. To describe the impact of each component state, we first introduce the performance function of the multi-state system. Then, we present the definition of IIM of component states. We demonstrate its corresponding physical meaning, and then analyze the relationships between IIM and Griffith importance, Wu importance, and Natvig importance. Secondly, we present the evaluation method of IIM for multi-state systems. Thirdly, the characteristics of IIM of component states are discussed. Finally, we demonstrate a numerical example, and an application to an offshore oil and gas production system for IIM to verify the proposed method. The results show that 1) the IIM of component states concerns not only the probability distributions and transition intensities of the states of the object component, but also the change in the system performance under the change of the state distribution of the object component; and 2) IIM can be used to identify the key state of a component that affects the system performance most.

Trust Based Routing for Misbehavior Detection in Ad Hoc Networks

Node misbehavior due to selfish or malicious intention could significantly degrade the performance of MANET because most existing routing protocols in MANET are aiming at finding most efficiency path. To deal with misbehavior in MANET, an incentive mechanism should be integrated into routing decision-making. In this paper firstly we review existing techniques for secure routing, and then propose to use trust vector model based routing protocols. Each node would evaluate its own trust vector parameters about neighbors through monitoring neighbors’ pattern of traffic in network. At the same time, trust dynamics is included in term of robustness. Then we integrated trust model into Dynamic Source Routing (DSR) and Ad-hoc On Demand Distance Vector (AODV) which are most typical routing protocols in MANET. We evaluate the performance of those trust routing protocols by compar ing the simulation results of with and without the proposed trust mechanism . The simulation results demonstrate that modified routing protocols can effectively detect malicious nodes and mitigate their attacks.

A novel fault diagnosis mechanism for wireless sensor networks

This paper considers a novel fault diagnosis mechanism for wireless sensor networks (WSNs). Without additional agents, the built-in and self-organized diagnosis mechanism can monitor each node in real time and identify faulty nodes. As the diagnosis is operated within a cluster of nodes, it can reduce power consumption and communication traffic. We present a modeling of the diagnosis algorithm for WSNs, with a probabilistic analysis of the local and global performances of our approach. Extensive experiments demonstrate the effectiveness of the proposed method.

Enhancing Grid security infrastructure to support mobile computing nodes

With the rapid development of the global information infrastructure, the use of virtual organization (VO) is gaining increasing importance as a model for studying business and organizational structures. The notion of VO is significant in that it could serve as a basic framework for implementing geographically distributed, cross-organizational application systems in a highly flexible manner. To further enhance the pervasiveness of VO, it is of great importance that participation of mobile computing nodes be supported. Thus, security is a critical issue due to the open nature of the wireless channels that provide connectivity to mobile devices. This paper discusses, from an application angle, the importance of supporting mobile devices in VO. It also discusses the design of security infrastructures that support mobile nodes in mission-specific applications. A simple grid security infrastructure that supports participation of mobile computing nodes is also proposed to illustrate the implementation feasibility of the infrastructure.

Event Classification in Microblogs via Social Tracking

Social media websites have become important information sharing platforms. The rapid development of social media platforms has led to increasingly large-scale social media data, which has shown remarkable societal and marketing values. There are needs to extract important events in live social media streams. However, microblogs event classification is challenging due to two facts, i.e., the short/conversational nature and the incompatible meanings between the text and the corresponding image in social posts, and the rapidly evolving contents. In this article, we propose to conduct event classification via deep learning and social tracking. First, we introduce a Multi-modal Multi-instance Deep Network (M2DN) for microblogs classification, which is able to handle the weakly labeled microblogs data oriented from the incompatible meanings inside microblogs. Besides predicting each microblogs as predefined events, we propose to employ social tracking to extract social-related auxiliary information to enrich the testing samples. We extract a set of candidate-relevant microblogs in a short time window by using social connections, such as related users and geographical locations. All these selected microblogs and the testing data are formulated in a Markov Random Field model. The inference on the Markov Random Field is conducted to update the classification results of the testing microblogs. This method is evaluated on the Brand-Social-Net dataset for classification of 20 events. Experimental results and comparison with the state of the arts show that the proposed method can achieve better performance for the event classification task.

Multitask Oriented Virtual Resource Integration and Optimal Scheduling in Cloud Manufacturing

To deal with the problem of resource integration and optimal scheduling in cloud manufacturing, based on the analyzation of the existing literatures, multitask oriented virtual resource integration and optimal scheduling problem is presented from the perspective of global optimization based on the consideration of sharing and correlation among virtual resources. The correlation models of virtual resources in a task and among tasks are established. According to the correlation model and characteristics of resource sharing, the formulation in which resource time-sharing scheduling strategy is employed is put forward, and then the formulation is simplified to solve the problem easily. The genetic algorithm based on the real number matrix encoding is proposed. And crossover and mutation operation rules are designed for the real number matrix. Meanwhile, the evaluation function with the punishment mechanism and the selection strategy with pressure factor are adopted so as to approach the optimal solution more quickly. The experimental results show that the proposed model and method are feasible and effective both in situation of enough resources and limited resources in case of a large number of tasks.

Towards Accurate Object Localization with Smartphones

In this study, we explore the possibility of locating remote objects via cameras together with built-in inertial sensors of off-the-shelf smartphones. Our solution, CamLoc, enables a user taking two photos of an object using a smartphone at a fixed location and immediately knowing the location of the object in global coordinates, thus facilitating myriad location-based services. Such usage is user-friendly but error prone. We devise several techniques to mitigate the errors caused by cheap and noisy sensors, upgrading the positioning accuracy to an applicable level. We prototype CamLoc on Android OS, and evaluate its performance across different scenarios with various building densities. Experiment results show that our system achieves 89 percent and 72 percent physical location mapping accuracy in rural and downtown areas, respectively, which is competitive with existing solutions.

A Study on Joint Availability for k out of n and Consecutive k out of n Points and Intervals

Abstract The performance of maintenance systems can be described by many indexes such as availability, mean up-time and mean down-time and so forth. The availability is the most important measure among them. Availability has many types, including instantaneous, steady-state and average availability, etc. In this paper, the joint availabilities for k out of n intervals and consecutive k out of n intervals under the Markov assumption are presented in recursive equations. When n = k , the k out of n and consecutive k out of n systems reduce to a series system, and in this situation, the corresponding results have been given by Csenki [9] and Cui et al. [17] using different methods. In contrast, the major contributions of the paper are the extensions of the known results for series systems; that is, the situations when k < n have not been covered in the previous studies and will be presented in the present paper. Some numerical examples and discussions are given to illustrate the results obtained in the paper. Finally, the conclusions are summarized

Optimizing communication in mobile ad hoc network clustering

Mobile ad hoc networks (MANETs) are gaining popularity in recent years due to their flexibility, the proliferation of smart computing devices, and developments in wireless communications. Clustering is an important research problem for MANETs because it enables efficient utilization of resources, and must strike a delicate balance between battery energy, mobility, node degree, etc. In this paper, we consider the typical communication workload of every mobile node as well as the additional communication workload of clusterheads in MANET clustering. We propose an algorithm that optimizes communication workload, power consumption, clusterhead lifetime, and node degree. Experiment results show that our clustering approach produces effectively balanced clusters over a diverse set of random scenarios.