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Ubiquitous sensing devices frequently disseminate data among them. The use of a distributed event-based system that decouples publishers from subscribers arises as an ideal candidate to implement the dissemination process. In this paper, we present a network architecture that merges the network and overlay layers of typical structured event-based systems. Directional random walks are used for the construction of this merged layer. Our strategy avoids using a specific network protocol that provides point-to-point communication. This implies that the topology of the network is not maintained, so that nodes not involved in the system are able to save energy and computing resources. We evaluate the performance of the overlay layer using directional random walks and pure random walks for its construction. Our results show that directional random walks are more efficient because: (1) they use less nodes of the network for the establishment of the active path of the overlay layer and (2) they have a more reliable performance. Furthermore, as the number of nodes in the network increases, so do the number of nodes in the active path of the overlay layer for the same number of publishers and subscribers. Finally, we discard any correlation between the number of nodes that form the overlay layer and the maximum Euclidean distance traversed by the walkers.

This paper mainly investigates consensus problem with pull-based event-triggered feedback control. For each agent, the diffusion coupling feedbacks are based on the states of its in-neighbors at its latest triggering time and the next triggering time of this agent is determined by its in-neighbors' information as well. The general directed topologies, including irreducible and reducible cases, are investigated. The scenario of distributed continuous monitoring is considered firstly, namely each agent can observe its in-neighbors' continuous states. It is proved that if the network topology has a spanning tree, then the event-triggered coupling strategy can realize consensus for the multi-agent system. Then the results are extended to discontinuous monitoring, i.e., self-triggered control, where each agent computes its next triggering time in advance without having to observe the system's states continuously. The effectiveness of the theoretical results are illustrated by a numerical example finally.

Ubiquitous sensing devices frequently disseminate their data between them. The use of a distributed event-based system that decouples publishers of subscribers arises as an ideal candidate to implement the dissemination process. In this paper, we present a network architecture which merges the network and overlay layers of typical structured event-based systems. Directional Random Walks (DRWs) are used for the construction of this merged layer. Our first results show that DRWs are suitable to balance the load using a few nodes in the network to construct the dissemination path. As future work, we propose to study the properties of this new layer and to work on the design of Bloom filters to manage broker nodes.

Ubiquitous sensing devices frequently disseminate their data between them. The use of a distributed event-based system that decouples publishers of subscribers arises as an ideal candidate to implement the dissemination process. In this paper, we present a network architecture which merges the network and overlay layers of typical structured event-based systems. Directional Random Walks (DRWs) are used for the construction of this merged layer. Our first results show that DRWs are suitable to balance the load using a few nodes in the network to construct the dissemination path. As future work, we propose to study the properties of this new layer and to work on the design of Bloom filters to manage broker nodes.

Computations of distributed systems are extremely difficult to specify and verify using traditional techniques because the systems are inherently concurrent, asynchronous and nondeterministic. Furthermore, computing nodes in a distributed system may be highly independent, and the entire system may lack an accurate global clock. In this thesis, the author develops an event-based model to specify formally the behavior (the external view) and the structure (the internal view) of distributed systems. The specification technique has a rather wide range of applications. Examples from different classes of distributed systems, including communication systems, transaction-based systems and process control systems are demonstrated. Both control-related and data-related properties of distributed systems are specified using two fundamental relationships among events: the precedes relation, representing time order; and the enables relation, representing causality. No assumption about the existence of a global clock is made in the specifications. The correctness of a design can be proved before implementation by checking the consistency between the behavior specification and structure specification of a system. Moreover, since the specification technique defines the orthogonal properties of a system separately, each of them can then be verified independently. Thus, the proof technique avoids the exponential state-explosion problem found in state-machine specification techniques.

To produce a program guaranteed to satisfy a given specification one can synthesize it from a formal constructive proof that a computation satisfying that specification exists. This process is particularly effective if the specifications are written in a high-level language that makes it easy for designers to specify their goals. We consider a high-level specification language that results from adding knowledge to a fragment of Nuprl specifically tailored for specifying distributed protocols, called event theory. We then show how high-level knowledge-based programs can be synthesized from the knowledge-based specifications using a proof development system such as Nuprl. Methods of Halpern and Zuck then apply to convert these knowledge-based protocols to ordinary protocols. These methods can be expressed as heuristic transformation tactics in Nuprl.

Distributed fault diagnosis solutions are becoming necessary due to the complexity of modern engineering systems, and the advent of smart sensors and computing elements. This paper presents a novel event-based approach for distributed diagnosis of abrupt parametric faults in continuous systems, based on a qualitative abstraction of measurement deviations from the nominal behavior. We systematically derive dynamic fault signatures expressed as event-based fault models. We develop a distributed diagnoser design algorithm that uses these models for designing local event-based diagnosers based on global diagnosability analysis. The local diagnosers each generate globally correct diagnosis results locally, without a centralized coordinator, and by communicating a minimal number of measurements between themselves. The proposed approach is applied to a multi-tank system, and results demonstrate a marked improvement in scalability compared to a centralized approach.

Distributed fault diagnosis solutions are becoming necessary due to the complexity of modern engineering systems, and the advent of smart sensors and computing elements. This paper presents a novel event-based approach for distributed diagnosis of abrupt parametric faults in continuous systems, based on a qualitative abstraction of measurement deviations from the nominal behavior. We systematically derive dynamic fault signatures expressed as event-based fault models. We develop a distributed diagnoser design algorithm that uses these models for designing local event-based diagnosers based on global diagnosability analysis. The local diagnosers each generate globally correct diagnosis results locally, without a centralized coordinator, and by communicating a minimal number of measurements between themselves. The proposed approach is applied to a multi-tank system, and results demonstrate a marked improvement in scalability compared to a centralized approach.