Fault-tolerant Monitoring Research Articles

Assessing and enhancing a Cloud-IoT monitoring service over federated testbeds

Monitoring resource availability along Cloud-IoT networks in a lightweight and fault-resilient manner is a challenging research problem due to scarce resource availability, infrastructure dynamics, and platform heterogeneity. In this article, we illustrate a thorough experimental assessment of a self-organising and fault-tolerant monitoring service, FogMon, especially targeting Cloud-IoT settings and capable of probing hardware resource, latency and bandwidth. The assessment is carried out over networks made up of 20 to 40 nodes across two testbeds within the Fed4Fire+ federation. As a result of the assessment, we agilely improved and refined FogMon into FogMon2, which settles at TRL5 and improves on monitoring accuracy and fault-resiliency. Experimental results show how FogMon2 can promptly and suitably handle different types of infrastructure failure, with an average relative error of 10% on measurements and limited footprint on hardware and network resources.

WITHDRAWN: Developing a software reliability evaluation model for wireless sensor networks based on analysis of software fault growth

Abstract Due to potential of various application in emergency and security monitoring fields, fault tolerance monitoring in wireless sensor networks (WSNs) software reliability became one of the most fundamental concern in computer field. In WSNs Previous literature had been proposed about how to improve network reliability. However the work done on establishing evaluating models of reliability for WSNs are insufficient yet. In this article, a model for wireless sensor networks (WSNs) is developed for the reliability evaluation. This network state based model can effectively do the evaluation of the probability of successful transmission of data in WSNs with the consideration of the work states of both the sensor node’s different modules. For WSNs we did the evacuation for reliability of in both tasks i.e., single, multi-task modes. At the same time, how different network topologies, affect the reliability of WSNs is also discussed respectively. To evaluate the reliability of WSNs the simulation attempts are shown in results that impacts network state on reliability and the effectiveness in different reliable mechanisms.

Edge Computing and Adaptive Fault-Tolerant Tracking Control Algorithm for Smart Buildings: A Case Study

The development and integration of technologies such as the Internet of Things (IoT) or edge computing devices is contributing to the formation of an increasingly digital, intelligent and connected world. As a result, there is a massive flow of data in different sectors of human activity. One example is intelligent buildings, where thousands of components, devices, systems and suppliers interact. In this context, failures in control and monitoring systems are frequent. To analyze this situation, this paper presents as a case study the problem of fault-tolerant robust adaptive monitoring control with state prediction performance for a class of IoT temperature systems subject to uncertainties of precision states and external disturbances. The authors propose a new control strategy based on consensus game theory and prediction of future precision states to reduce tracking error and improve algorithm efficiency. The authors present the development of a new algorithm that improves the functioning of monitoring and control of parcel networks. This has the purpose of increasing the energy efficiency of the same and ensure the effectiveness of our adaptive temperature control algorithm, compared to existing results. With the simulation presented in this research, it is possible to conclude that a new fault tolerant error tracking algorithm ensures robust monitoring of the reference model. It was shown that the predicted temperature signal is limited by a small range close to the collected temperature data. A case study result is provided to demonstrate the effectiveness of the proposed fault-tolerant adaptive monitoring control algorithm.

A lower bound on the number of opinions needed for fault-tolerant decentralized run-time monitoring

Runtime verification aims at extracting information from a running system, and using it to detect and possibly react to behaviors violating a given correctness property. Decentralized runtime verification involves a set of monitors observing the behavior of the underlying system. When the monitors themselves can fail, and communication among them is unreliable, it is unavoidable that the monitors may have different views of the system’s state and hence that they emit different opinions about its correctness at runtime. It is known that few correctness properties can be monitored in such a setting, when the set of opinions is the set {True, False}. In this paper, we initiate the investigation of decentralized fault-tolerant runtime monitoring under an arbitrary set of opinions. Specifically, we characterize the size of the opinion set required for monitoring a given correctness property in a decentralized manner. It turns out that the key factor impacting this size is the maximum number of times the monitored property can change its truth value over all executions of the monitors. Our lower bound is independent of the way the set of opinions returned by the monitors is globally interpreted, and it holds even when verifying a static system. Moreover, our lower bound is tight in the sense that we design a distributed protocol enabling any given set of monitors to verify any given correctness property on static systems, using as many different opinions as the one given by our lower bound.

Adaptive security monitoring for next-generation routers

In today’s Internet, modern routers rely on high-performance reliable general-purpose multi-core packet processing systems in order to support the flexibility and the plethora of protocol operations and applications. These processing systems are programmable and have replaced the traditional-fixed logic ASICs in the data path of such routers. Hence, lots of vulnerabilities and faults are introduced as the result of such programmability making the systems susceptible to attacks and failures. Particularly, it is a difficult task to detect whether a processing core behaves correctly, or it has a failure resulting from errors or attacks. In this paper, we address this problem by proposing a novel approach to verify the correct operation of the network processor. We propose a secure, fault-tolerant, and reliable monitoring subsystem which functions in parallel with the processing core of the router and aids in the detection of attacks changing the processing behavior of the processor. We prove experimentally that our system has the ability to detect the malicious activity and securely restore the router’s operation to a different, but functionally equivalent, state. We also show experimentally that our approach has a better efficiency when compared with other existing work.

SA102GENETIC AND CLINICAL ANALYSES OF PSYCHOSIS SPECTRUM IN A LARGE MULTI-ETHNIC YOUTH COHORT (N>8,000) REVEAL SIGNIFICANT LINK WITH ADHD

The power system at present is a complex network that accommodates several applications with diverse requirements. For the stable operation of this network the wide area synchrophasor measurement system (WASMS) has been instituted. The WASMS consists of several measuring devices called the phasor measurement units (PMUs), for collecting the information pertaining to the subtleties of the power grid, which is in the form of time-synchronized phasors measured at various spatial locations in the power system. The synchronization in the time domain is achieved by using the global positioning system timing signals. These synchronized phasors are communicated to the phasor data concentrator where the data is analyzed for detection and control of the power system abnormalities. Reliable performance of the WASMS is quintessential for effective monitoring and subsequent decision-making. Risk assessment of this system prior to its actual deployment would help the power system engineers in the design of fault-tolerant monitoring system. Risk quantifies the severity of failure, and uncertainties in the system parameters pose a major impediment for the accurate determination of the system’s risk. Therefore uncertainty analysis has to be included in the risk assessment framework for obtaining a better estimate. The aim of this book chapter is to present a Monte Carlo simulation (MCS)–based uncertainty analysis and risk assessment of the WASMS. MCS can model simultaneous component failures and is widely used for estimating the failure rates of complex systems. It can also model complex interconnections that are neither series nor parallel. MCS models are presented for the PMU and its communication system. With the uncertainty analysis included in the risk assessment, the WASMS is optimally designed such that the risk is minimized without compromising the system’s observability. Case study results of an Indian power system are presented to elucidate the proposed approach.

Cloud cover: monitoring large-scale clouds with Varanus

Cloud computing has quickly become the de facto means to deploy large scale systems in a robust and cost effective manner. Central to the maintenance of large scale systems is monitoring which allows for the detection of faults, errors and anomalies and the enacting of optimisation and corrective measures. Monitoring large scale systems is significant challenge requiring the low latency movement of large volumes of data and near real time analysis. This challenge is magnified by elasticity and other cloud properties which previous monitoring systems do not yet fully account for. In this paper we propose Varanus1 a cloud aware monitoring tool that provides robust, fault tolerant monitoring at scale. We describe in detail the mechanisms which enable Varanus to function effectively and explore the performance of Varanus through a detailed evaluation.

Developing Educational Smart Grid Laboratory for the UAEU

The goal of this paper is to present the first stage of developing an educational laboratory to teach smart grid design and implementations for both under graduate and graduate levels. Hampden 180 simulator that represents conventional generation, transmission, and distribution power system is integrated with intelligent electronic devices and tied together in an architecture that provides reliable and fault-tolerant protection, control and monitoring. The upgraded laboratory will demonstrate high level of functionality and satisfying the design and operation objectives of smart grid metering, protection, control, monitoring and communication. Part 2 of this paper will discuss and present the implementation stage.

A Comparison of Routing Protocol for WSNs: Redundancy Based Approach A Comparison of Routing Protocol for WSNs: Redundancy Based Approach

Wireless Sensor Networks (WSNs) with their dynamic applications gained a tremendous attention of researchers. Constant monitoring of critical situations attracted researchers to utilize WSNs at vast platforms. The main focus in WSNs is to enhance network localization as much as one could, for efficient and optimal utilization of resources. Different approaches based upon redundancy are proposed for optimum functionality. Localization is always related with redundancy of sensor nodes deployed at remote areas for constant and fault tolerant monitoring. In this work, we propose a comparison of classic flooding and the gossip protocol for homogenous networks which enhances stability and throughput quiet significantly.

Distributed, application-level monitoring for heterogeneous clouds using stream processing

As utility computing is widely deployed, organizations and researchers are turning to the next generation of cloud systems: federating public clouds, integrating private and public clouds, and merging resources at all levels (IaaS, PaaS, SaaS). Adaptive systems can help address the challenge of managing this heterogeneous collection of resources. While services and libraries exist for basic management tasks that enable implementing decisions made by the manager, monitoring is an open challenge. We define a set of requirements for aggregating monitoring data from a heterogeneous collections of resources, sufficient to support adaptive systems. We present and implement an architecture using stream processing to provide near-realtime, cross-boundary, distributed, scalable, fault-tolerant monitoring. A case study illustrates the value of collecting and aggregating metrics from disparate sources. A set of experiments shows the feasibility of our prototype with regard to latency, overhead, and cost effectiveness.

Q-LEACH: A New Routing Protocol for WSNs

Wireless Sensor Networks (WSNs) with their dynamic applications gained a tremendous attention of researchers. Con- stant monitoring of critical situations attracted researchers to utilize WSNs at vast platforms. The main focus in WSNs is to enhance network life-time as much as one could, for efficient and optimal utilization of resources. Different approaches based upon clustering are proposed for optimum functionality. Network life-time is always related with energy of sensor nodes deployed at remote areas for constant and fault tolerant monitoring. In this work, we propose Quadrature-LEACH (Q-LEACH) for homogenous networks which enhances stability period, network life-time and throughput quiet significantly.

Fault-Tolerant Event Monitoring in Wireless Sensor Networks

Event monitoring is an important issue in wireless sensor networks,however,the limits of sensor networks and the uncertainty existed in sensed data propose challenges for it.This paper analyzes the uncertainty existed in sensor networks and presents fault-tolerant event monitoring mechanism.We first introduce the fault-tolerant event detection and monitoring nodes choosing algorithms,and then estimate the event appearing area under the condition of static sensor nodes and moving sensor nodes.Finally,we evaluate our algorithms via a serial of simulations.The experimental results show that the proposed algorithms can obtain optimal performance in event monitoring.

Using cooperative mobile agents to monitor distributed and dynamic environments

Monitoring the changes in data values obtained from the environment (e.g., locations of moving objects) is a primary concern in many fields, as for example in the pervasive computing environment. The monitoring task is challenging from a double perspective. First and foremost, the environment can be highly dynamic in terms of the rate of data changes. Second, the monitored data are often not available from a single computer/device but are distributed; moreover, the set of data providers can change along the course of time. Therefore, obtaining a global snapshot of the environment and keeping it up-to-date is not easy, especially if the conditions (e.g., network delays) change. In this article, a decentralized, loose, and fault-tolerant monitoring approach based on the use of mobile agents is described. Mobile agents allow easy tracking of the involved computers, carrying the monitoring tasks to wherever they are needed. A deadline-based mechanism is used to coordinate the cooperative agents, which strive to perform their continuous tasks in time while considering data as recent as possible, constantly adapting themselves to new environmental conditions (changing communication and processing delays). This approach has been successfully used in a real environment and experiments were carried out to prove its feasibility and benefits.

Trends in wireless sensor networks for manufacturing

Wireless communications in general, Wireless Sensor Networks and Radio-Frequency Identification (RFID) constitute a family of technologies for delivering an increased spatial density of sensors, local computation at the sensor-node and the potential for distributed and fault-tolerant monitoring. This paper focuses on wireless sensor nodes and networking. Nodes can be mounted on various parts of machinery and plant to promote early fault detection and analysis. Their small size and autonomy enables their placement in locations that are usually difficult to access. In addition, it is also possible, with minimal changes to the machine configuration, to deploy sensors on the machinery after it has been installed. The sensor nodes not only monitor their own output but also collaborate with neighbouring nodes to determine the health of the overall machines and provide early warnings of potential failure. The paper discusses the uses of these networks and the issues that must be addressed for successful implementations.

A TEST RIG FOR EXPERIMENTATION ON FAULT TOLERANT CONTROL AND CONDITION MONITORING ALGORITHMS IN FLUID POWER SYSTEMS: FROM DESIGN THROUGH IMPLEMENTATION

This paper reports the relevant aspects of a novel experimental test facility for research on fault tolerant control design and condition monitoring of fluid power systems recently constructed at the University of Manitoba. Common faults and their effects on the operation of valve controlled hydraulic actuators, such as those found in aerospace applications, are summarized first. The manuscript then presents the main features of the test rig and the modifications made to simulate various faults. The design also incorporates techniques to easily simulate load conditions from viscous forces to complex dynamic loads. The significance of the test rig in the development of advanced condition monitoring and fault tolerant control strategies is also illustrated by the presentation of the results of some recent research work conducted using the test facility. By sharing their experience with other investigators in this active area of fluid power research, the authors hope to establish a national benchmark test facility for the objective evaluation of state-of-the-art monitoring and control strategies for fluid power systems.

Development of a Fault-tolerant Intelligent Monitoring and Control System in Machining

The dynamic characteristics of industrial processes frequently cause an abnormal situation which is undesirable in terms of the productivity and the safety of workers. The goal of fault-tolerance is to continue performing certain activities even after the failure of some system cononents. A fault-tolerant intelligent monitoring and control system which is robust under disturbances is proposed in this paper. Specifically, the fault-tolerant monitoring scheme proposed consists of two process models and the inference module to preserve such a robustness. The results of turning experiments demonstrate the effectiveness of the fault-tolerant scheme in the presence of built-up edge.

An Automated Fault Diagnosis For Manufacturing Process Monitoring And Control

A fault-tolerant monitoring system for manufacturing process control is defined with sensor fusion for process-feature preparation and neural networks for process-feature analysis. Considering the complexity of operation mechanisms and the variability of process parameters in typical manufacturing environments, the author develops an automated fault-diagnosis method for the fault-tolerant monitoring system itself, called adaptive feature scaling, which is a modified version of the input feature scaling algorithm. In conjunction with clustering-type neural networks, adaptive feature scaling performs not only feature evaluation for self-diagnosis of process features, but also feature adjustment for improvement of the monitoring performance. In experimental evaluation, adaptive feature scaling proves superior to conventional approaches from the implementation point of view.