Fault localization in service-based systems hosted in mobile ad hoc networks

Fault localization in general refers to a technique for identifying the likely root causes of failures observed in systems formed from components. Fault localization in systems deployed on mobile ad hoc networks (MANETs) is a particularly challenging task because those systems are subject to a wider variety and higher incidence of faults than those deployed in fixed networks, the resources available to track fault symptoms are severely limited, and many of the sources of faults in MANETs are by their nature transient. We present a suite of three methods, each responsible for part of the overall task of localizing the faults occurring in service-based systems hosted on MANETs. First, we describe a dependence discovery method, designed specifically for this environment, yielding dynamic snapshots of dependence relationships discovered through decentralized observations of service interactions. Next, we present a method for localizing the faults occurring in service-based systems hosted on MANETs. We employ both Bayesian and timing-based reasoning techniques to analyze the dependence data produced by the dependence discovery method in the context of a specific fault propagation model, deriving a ranked list of candidate fault locations. In the third method, we present an epidemic protocol designed for transferring the dependence and symptom data between nodes of MANET networks with low connectivity. The protocol creates network wide synchronization overlay and transfers the data over intermediate nodes in periodic synchronization cycles. We introduce a new tool for simulation of service-based systems hosted on MANETs and use the tool for evaluation of several operational aspects of the methods. Next, we present implementation of the methods in Java EE and use emulation environment to evaluate the methods. We present the results of an extensive set of experiments exploring a wide range of operational conditions to evaluate the accuracy and performance of our methods.Open Acces

Locating faults in MANET-hosted software systems

We present a method to locate faults in service-based software systems hosted on mobile ad hoc networks (MANETs). In such systems, computations are structured as interdependent services distributed across the network, collaborating to satisfy client requests. Faults, which may occur at either or both the service and network layers, propagate by cascading through some subset of the services, from their root causes back to the clients that initiate requests. Fault localization in this environment is especially challenging because the systems are typically subject to a wider variety and higher incidence of faults than those deployed in fixed networks, the resources available to collect and store analysis data are severely limited, and many of the sources of faults are by their nature transient. Our method makes use of service-dependence and fault data that are harvested in the network through decentralized, run-time observations of service interactions and fault symptoms. We have designed timing- and Bayesian-based reasoning techniques to analyze the data in the context of a specific fault propagation model. The analysis provides a ranked list of candidate fault locations. Through extensive simulations, we evaluate the performance of our method in terms of its accuracy in correctly ranking root causes under a wide range of operational conditions

Discovering service dependencies in mobile ad hoc networks

The combination of service-oriented applications, with their run-time service binding, and mobile ad hoc networks, with their transient communication topologies, brings a new level of complex dynamism to the structure and behavior of software systems. This complexity challenges our ability to understand the dependence relationships among system components when performing analyses such as fault localization and impact analysis. Current methods of dynamic dependence discovery, developed for use in xed networks, assume that dependencies change slowly. Moreover, they require relatively long monitoring periods as well as substantial memory and communication resources, which are impractical in the mobile ad hoc network environment. We describe a new method, designed speci cally for this environment, that allows the engineer to trade accuracy against cost, yielding dynamic snapshots of dependence relationships. Through extensive simulations, we evaluate the performance of our method in terms of the accuracy of the discovered dependencies, and draw insights on the selection of critical parameters under various operational conditions

Special issue on real‐time behavioral monitoring in IoT applications using big data analytics

Real-time social multimedia level threat monitoring is becoming harder, due to higher and rapidly increasing data induction. Data induction through electric smart devices is greater compared to information processing capacity. Nowadays, data becomes humongous even coming from the single source. Therefore, when data emanates from all heterogeneous sources distributed over the globe makes data magnitude harder to process up to a needed scale. Big data and Deep learning have become standard in providing well-known solutions built-up using algorithms and techniques in resolving data matching issues. Now, with the involvement of sensors and automation in generating data obscures everything, predicting results to overcome a current era of ever enhancing demands and getting real-time visualization brings the need of feature like human behavior mode extraction to overcome any future threats. Big data analytics can bring the opportunity of predicting any misfortune even before they happen. Map reduce feature of big data supports massive data oriented process execution using distributed processing. Real-time human feature identification and detection can occur through sensors and internet sources. A behavioral prediction can further classify the information collected for introducing enhanced security extents. Real-time sensor devices are producing 24/7-hour data for further processing recording each event. IoT-based sensors can support in behavioral analysis model of a human. Real-time human behavioral monitoring based on image processing and IoT using big data analytics

The Internet of Everything

In the era before IoT, the world wide web, internet, web 2.0 and social media made people’s lives comfortable by providing web services and enabling access personal data irrespective of their location. Further, to save time and improve efficiency, there is a need for machine to machine communication, automation, smart computing and ubiquitous access to personal devices. This need gave birth to the phenomenon of Internet of Things (IoT) and further to the concept of Internet of Everything (IoE)

The Internet of Everything

In the era before IoT, the world wide web, internet, web 2.0 and social media made people’s lives comfortable by providing web services and enabling access personal data irrespective of their location. Further, to save time and improve efficiency, there is a need for machine to machine communication, automation, smart computing and ubiquitous access to personal devices. This need gave birth to the phenomenon of Internet of Things (IoT) and further to the concept of Internet of Everything (IoE)