L. Gatani

Robust and Efficient Data Gathering for Wireless Sensor Networks

This paper describes a new strategy for data gathering in wireless sensor networks that takes into account the need for both energy saving, typical of such networks, and for a reasonable tradeoff between robustness and efficiency. The proposed algorithm implements an efficient strategy for retransmission of lost packets by discovering alternative routes and making clever use of multiple paths when necessary; in order to do that we build upon the general framework presented in recent works, that provided a formulation of duplicate and order insensitive aggregation functions, and by taking advantage of some intrinsic characteristics of the wireless sensor networks, we exploit implicit acknowledgment of reception and smart caching of the data. Assuming that, unlike in an ideal scenario, data originates from only a subset of all sensors, our approach provides a better usage of the resources and a minimization of the traffic in the network, and, as a consequence, of the overall consumed energy.

An efficient distributed algorithm for generating multicast distribution trees

Multicast transmission may use network resources more efficiently than multiple point-to-point messages; however, creating optimal multicast trees (Steiner Tree Problem in Networks) is prohibitively expensive. For this reason, heuristic methods are generally employed. Conventional centralized Steiner heuristics provide effective solutions, but they are unpractical for large networks, since they require complete knowledge of the network topology. This paper proposes a distributed algorithm for the heuristic solution of the Steiner Tree Problem. The algorithm allows the construction of effective distribution trees using a coordination protocol among the network nodes. The algorithm has been implemented and extensively tested both in simulation, and on experimental networks. Performance evaluation indicates that our algorithm performs as well as the centralized version, providing good levels of convergence time and communication complexity. Moreover, the proposed approach outperforms the state-of-the-art distributed algorithms for multicast tree setup.

Rule based reasoning for network management

This paper focuses on improving network management by the adoption of artificial intelligence techniques. We propose a distributed multi-agent architecture for network management, where a logical reasoner acts as a managing entity capable of directing, coordinating, and triggering monitoring and management actions in the proposed architecture. The logical inference system has been devised to enable automated isolation, diagnosis, and to repair network anomalies, thus enhancing the reliability, performance, and security of the network. The measurements of network events are captured by programmable sensors deployed on the network devices and are collected by the network management entity where they are merged with general domain knowledge, with a view to identifying the root causes of anomalies, and to decide on reparative actions. The relevant results inferred by the logical reasoner and the significant events occurred on the network are stored both in a global DB and in local distributed DBs, in order to enable successive analyses of network events. In order to illustrate the advantages and potential benefits deriving from the reasoning capabilities of our management system, the results of preliminaries experiments are analyzed.

A Logical Architecture for Active Network Management

This paper focuses on improving network management by exploiting the potential of “doing” of the Active Networks technology, together with the potential of “planning,” which is typical of the artificial intelligent systems. We propose a distributed multiagent architecture for Active Network management, which exploits the dynamic reasoning capabilities of the Situation Calculus in order to emulate the reactive behavior of a human expert to fault situations. The information related to network events is generated by programmable sensors deployed across the network. A logical entity collects this information, in order to merge it with general domain knowledge, with a view to identifying the root causes of faults, and to deciding on reparative actions. The logical inference system has been devised to carry out automated isolation, diagnosis, and even repair of network anomalies, thus enhancing the reliability, performance, and security of the network. Experimental results illustrate the Reasoner capability of correctly recognizing fault situations and undertaking management actions.

Monitoring wireless sensor networks through logical deductive processes

This paper proposes a distributed multi-agent architecture for wireless sensor networks management, which exploits the dynamic reasoning capabilities of the situation calculus in order to emulate the reactive behavior of a human expert to fault situations. The information related to network events is generated by tunable agents installed on the network nodes and is collected by a logical entity for network managing where it is merged with general domain knowledge, with the aim of identifying the root causes of faults, and deciding on reparative actions. The logical inference system has being devised to carry out automated isolation, diagnosis, and, whenever possible, repair of network anomalies, thus enhancing the reliability, performance, and security of the network. To illustrate the advantages and potential benefits deriving from the reasoning capabilities of our management system, we also discuss an application scenario concerning the need of effectively coping with congestion arising in critical parts of the network

Understanding the Environment Through Wireless Sensor Networks

This paper presents a new cognitive architecture for extracting meaningful, high-level information from the environment, starting from the raw data collected by a Wireless Sensor Network. The proposed framework is capable of building rich internal representation of the sensed environment by means of intelligent data processing and correlation. Furthermore, our approach aims at integrating the connectionist, data-driven model with the symbolic one, that uses a high-level knowledge about the domain to drive the environment interpretation. To this aim, the framework exploits the notion of conceptual spaces, adopting a conceptual layer between the subsymbolic one, that processes sensory data, and the symbolic one, that describes the environment by means of a high level language; this intermediate layer plays the key role of anchoring the upper layer symbols. In order to highlight the characteristics of the proposed framework, we also describe a sample application, aiming at monitoring a forest through a Wireless Sensor Network, in order to timely detect the presence of fire.

A p2p architecture for multimedia content retrieval

The retrieval facilities of most Peer-to-Peer (P2P) systems are limited to queries based on unique identifiers or small sets of keywords. This approach can be highly labor-intensive and inconsistent. In this paper we investigate a scenario where a huge amount of multimedia resources are shared in a P2P network, by means of efficient content-based image and video retrieval functionalities. The challenge in such systems is to limit the number of sent messages, maximizing the usefulness of each peer contacted in the query process. We achieve this goal by the adoption of a novel algorithm for routing user queries. The proposed approach exploits compact representations of multimedia resources shared by each peer, in order to dynamically adapt the network topology to peer interests, on the basis of query interactions among users.

Notice of Violation of IEEE Publication Principles Reinforcement learning for P2P searching

For a peer-to-peer (P2P) system holding a massive amount of data, an efficient and scalable search for resource sharing is a key determinant to its practical usage. Unstructured P2P networks avoid the limitations of centralized systems and the drawbacks of a highly structured approach, because they impose few constraints on topology and data placement, and they support highly versatile search mechanisms. However their search algorithms are usually based on simple flooding schemes, showing severe inefficiencies. In this paper, to address this major limitation, we propose and evaluate the adoption of a local adaptive routing protocol. The routing algorithm adopts a simple reinforcement learning scheme (driven by query interactions among neighbors), in order to dynamically adapt the topology to peer interests. Preliminary evaluations show that the approach is able to dynamically group peer nodes in clusters containing peers with shared interests and organized into a small world network.

An efficient distributed algorithm for generating and updating multicast trees

As group applications are becoming widespread, efficient network utilization becomes a growing concern. Multicast transmission represents a necessary lower network service for the wide diffusion of new multimedia network applications. Multicast transmission may use network resources more efficiently than multiple point-to-point messages; however, creating optimal multicast trees (Steiner Tree Problem in networks) is prohibitively expensive. This paper proposes a distributed algorithm for the heuristic solution of the Steiner Tree Problem, allowing the construction of effective distribution trees using a coordination protocol among the network nodes. Furthermore, we propose a novel distributed technique for dynamically updating the multicast tree. The approach proposed has been implemented and extensively tested both in simulation, and on experimental networks. Performance evaluation indicates that the distributed algorithm performs as well as the centralized version, providing good levels of convergence time and communication complexity.

Notice of Violation of IEEE Publication Principles An adaptive routing protocol for ad hoc peer-to-peer networks

Ad hoc networks represent a key factor in the evolution of wireless communications. These networks typically consist of equal nodes that communicate without central control, interacting in a peer-to-peer way. In such a network, efficient and scalable data retrieval constitutes a challenging problem. Unstructured P2P networks avoid the limitations of centralized systems and the drawbacks of structured approaches, because they impose few constraints on topology and data placement, and support highly versatile search mechanisms. However their search algorithms are usually based on simple flooding schemes, showing severe inefficiencies. In order to address this major limitation, we evaluate the adoption of a local adaptive routing protocol, suitable for a self-organizing ad hoc environment. The routing algorithm uses a simple reinforcement learning scheme (driven by query interactions among peers), in order to adapt the topology to peer interests dynamically. In our simulation, this approach is able to group peer nodes dynamically in clusters containing peers with shared interests and organized into a small world network.