Diagnosis of static Topology MANETs in faulty environment

Mobile Ad-Hoc Networks (MANETs) are set of mobile nodes that communicates wirelessly without a centralized supporting system. Faulty nodes aect the reliable transmission of messages across the network. In this thesis we deal with the fault identication problem in static topology MANETs. A comparison based approach is used where a set of tasks is given to the nodes and outcomes are compared. Based on these comparisons the nodes are classied either as faulty or fault free. Our new diagnosis model is based on the spanning tree concept in which the testing of the nodes as well as the construction of the spanning tree takes place simultaneously. As a result of which the maintenance and the repairing overhead of the spanning tree is completely avoided thus reducing the number of messages exchanged. We have also developed a simulator which can be applied to a network with large number of nodes.We have carried out the simulation in-order to nd out the total number of messages exchanged and the total diagnosis time. On analysing the results we have seen that our model performs better than its previous counterparts. The correctness and complexity proofs are also being provided which also shows that our model performs better from a communication as well as latency viewpoint

Predictive intelligence to the edge through approximate collaborative context reasoning

We focus on Internet of Things (IoT) environments where a network of sensing and computing devices are responsible to locally process contextual data, reason and collaboratively infer the appearance of a specific phenomenon (event). Pushing processing and knowledge inference to the edge of the IoT network allows the complexity of the event reasoning process to be distributed into many manageable pieces and to be physically located at the source of the contextual information. This enables a huge amount of rich data streams to be processed in real time that would be prohibitively complex and costly to deliver on a traditional centralized Cloud system. We propose a lightweight, energy-efficient, distributed, adaptive, multiple-context perspective event reasoning model under uncertainty on each IoT device (sensor/actuator). Each device senses and processes context data and infers events based on different local context perspectives: (i) expert knowledge on event representation, (ii) outliers inference, and (iii) deviation from locally predicted context. Such novel approximate reasoning paradigm is achieved through a contextualized, collaborative belief-driven clustering process, where clusters of devices are formed according to their belief on the presence of events. Our distributed and federated intelligence model efficiently identifies any localized abnormality on the contextual data in light of event reasoning through aggregating local degrees of belief, updates, and adjusts its knowledge to contextual data outliers and novelty detection. We provide comprehensive experimental and comparison assessment of our model over real contextual data with other localized and centralized event detection models and show the benefits stemmed from its adoption by achieving up to three orders of magnitude less energy consumption and high quality of inference

Survivable algorithms and redundancy management in NASA's distributed computing systems

The design of survivable algorithms requires a solid foundation for executing them. While hardware techniques for fault-tolerant computing are relatively well understood, fault-tolerant operating systems, as well as fault-tolerant applications (survivable algorithms), are, by contrast, little understood, and much more work in this field is required. We outline some of our work that contributes to the foundation of ultrareliable operating systems and fault-tolerant algorithm design. We introduce our consensus-based framework for fault-tolerant system design. This is followed by a description of a hierarchical partitioning method for efficient consensus. A scheduler for redundancy management is introduced, and application-specific fault tolerance is described. We give an overview of our hybrid algorithm technique, which is an alternative to the formal approach given

A reliable and resource aware framework for data dissemination in wireless sensor networks

Distinctive from traditional wireless ad hoc networks, wireless sensor networks (WSN) comprise a large number of low-cost miniaturized nodes each acting autonomously and equipped with short-range wireless communication mechanism, limited memory, processing power, and a physical sensing capability. Since sensor networks are resource constrained in terms of power, bandwidth and computational capability, an optimal system design radically changes the performance of the sensor network. Here, a comprehensive information dissemination scheme for wireless sensor networks is performed. Two main research issues are considered: (1) a collaborative flow of information packet/s from the source to sink and (2) energy efficiency of the sensor nodes and the entire system. For the first issue, we designed and evaluated a reactive and on-demand routing paradigm for distributed sensing applications. We name this scheme as IDLF-Information Dissemination via Label ForwarDing IDLF incorporates point to point data transmission where the source initiates the routing scheme and disseminates the information toward the sink (destination) node. Prior to transmission of actual data packet/s, a data tunnel is formed followed by the source node issuing small label information to its neighbors locally. These labels are in turn disseminated in the network. By using small size labels, IDLF avoids generation of unnecessary network traffic and transmission of duplicate packets to nodes. To study the impact of node failures and to improve the reliability of the network, we developed another scheme which is an extension to IDLF. This new scheme, RM-IDLF - Reliable Multipath Information dissemination by Label Forwarding, employ an alternate disjoint path. This alternate path scheme (RM-IDLF) may have a higher path cost in terms of energy consumption, but is more reliable in terms of data packet delivery to sink than the single path scheme (IDLF). In the latter scheme, the protocol establishes multiple (alternate) disjoint path/s from source to destination with negligible control overhead to balance load due to heavy data traffic among intermediate nodes from source to the destination. Another point of interest in this framework is the study of trade-offs between the achieved routing reliability using multiple disjoint path routing and extra energy consumption due to the use of additional path/s. Also, the effect of the failed nodes on the network performance is evaluated within the sensor system; Performance of the label dissemination scheme is evaluated and compared with the classic flooding and SPIN. (Abstract shortened by UMI.)

Goal accomplishment tracking for automatic supervision of plan execution

It is common practice to break down plans into a series of goals or sub-goals in order to facilitate plan execution, thereby only burdening the individual agents responsible for their execution with small, easily achievable objectives at any one time, or providing a simple way of sharing these objectives amongst a group of these agents. Ensuring that plans are executed correctly is an essential part of any team management. To allow proper tracking of an agent's progress through a pre-planned set of goals, it is imperative to keep track of which of these goals have already been accomplished. This centralised approach is essential when the agent is part of a team of humans and/or robots, and goal accomplishment is not always being tracked at a low level. This paper presents a framework for an automated supervision system to keep track of changes in world states so as to chart progress through a pre-planned set of goals. An implementation of this framework on a mobile service robot is presented, and applied in an experiment which demonstrates its feasibility

Simplified Distributed Programming with Micro Objects

Developing large-scale distributed applications can be a daunting task. object-based environments have attempted to alleviate problems by providing distributed objects that look like local objects. We advocate that this approach has actually only made matters worse, as the developer needs to be aware of many intricate internal details in order to adequately handle partial failures. The result is an increase of application complexity. We present an alternative in which distribution transparency is lessened in favor of clearer semantics. In particular, we argue that a developer should always be offered the unambiguous semantics of local objects, and that distribution comes from copying those objects to where they are needed. We claim that it is often sufficient to provide only small, immutable objects, along with facilities to group objects into clusters.Comment: In Proceedings FOCLASA 2010, arXiv:1007.499

Requirements for Provenance on the Web

From where did this tweet originate? Was this quote from the New York Times modified? Daily, we rely on data from the Web but often it is difficult or impossible to determine where it came from or how it was produced. This lack of provenance is particularly evident when people and systems deal with Web information or with any environment where information comes from sources of varying quality. Provenance is not captured pervasively in information systems. There are major technical, social, and economic impediments that stand in the way of using provenance effectively. This paper synthesizes requirements for provenance on the Web for a number of dimensions focusing on three key aspects of provenance: the content of provenance, the management of provenance records, and the uses of provenance information. To illustrate these requirements, we use three synthesized scenarios that encompass provenance problems faced by Web users toda