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

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 energ

A Logical Framework for Augmented Simulations of Wireless Sensor Networks

This paper describes a framework for practical and efficient monitoring of a wireless sensor network. The architecture proposed exploits the dynamic reasoning capabilities of the Situation Calculus in order to assess the sensor network behavior before actually deploying all the nodes. Designing a wireless sensor network for a specific application typically involves a preliminary phase of simulations that rely on specialized software, whose behavior does not necessarily reproduce what will be experienced by an actual network. On the other hand, delaying the test phase until deployment may not be advisable due to unreasonable costs. This paper suggests the adoption of a hybrid approach that involves coupling an actual wireless sensor network, composed of a minimal set of nodes, with a simulated one. We describe a framework that implements a logical monitoring entity able to analyze the network behavior by means of a superimposed communication control network. The system aims to enhance the simulation environment with a logical reasoning unit in order to extract higher level information about the network state, used to provide the network designer with guidance during the pre-deployment design phas

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