A method and packet filtering system that accelerates the packet classification problem is made. Using spectral analysis of rules and using real time spectral analysis of packets, it is possible to determine very quickly that a packet does not match any of the possible rules that have been defined. That is to say, using the packet filtering of the invention, there is no possibility of a false negative decision; when a packet is determined not to match any rule, the work of a packet classifier is complete. This method and system are also capable, with a high degree of accuracy, of determining that the packet has matched one of the defined rules, so that the packet may be then directed to a packet classifier to determine the specific rule it matches.

Packet classification acceleration using spectral analysis

Let be a sequence of independent and identically distributed random variables and let denote the sth largest order statistic of , . In this note, the limiting distributions of under power normalization are derived as the integer-valued random index follows the shifted negative binomial, shifted Poisson and shifted binomial distributions.

Limiting distributions of extreme order statistics under power normalization and random index

Wireless Sensor Networks (WSN) are dedicated networks used in applications where environmental information must be collected, such as temperature, humidity, level, flow, pressure, rain, radiation, among others. These kinds of networks are constrained regarding power, bandwidth, number of nodes per area unit, etc. It is desirable that they operate without supervision and can work steadily in time, because they are normally located in difficult or far places. Nonetheless, some of these metrics are conflicting with others, so if one improves, some of the others get worse. So, it is mandatory to know what is the best combination of metrics that in conjunction can fit an application the best. Literature reports works where $\neg$ optimization is used as a mathematical scheme to solve this problem, and two scenarios are provided: first, where a single objective function is proposed regarding one metric, and the other metrics are restricted via constraints, and second, where multi-objective optimization (MOOP) approaches are proposed, but without considering the whole set of significant metrics involved in WSN, so there is not a definitive solution that finds a real optimal set of metrics. System Dynamics (SD) is a computer-aided approach to design and analyze (mostly) social, economic and enterprise systems, that allows proposing a mathematical framework to analyze such complex systems, by using relationships of interdependence, mutual interaction, feedback and causality. This work aims to show a first dynamic hypothesis of a model that considers important metrics ofWSN, in order to find a set of equations that serve as objective functions in a MOOP context. By applying this methodology is possible to find some difficult relations between metrics, that are not clearly reported by previous work so far.

System dynamics baseline model for determining a multivariable objective function optimization in Wireless Sensor Networks

NETWORKING 2004 : networking technologies, services, and protocols; performance of computer and communication networks; mobile and wireless communications : third International IFIP-TC6 Networking Conference, Athens, Greece, May 9-14, 2004 : proceedings

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NETWORKING 2004 : networking technologies, services, and protocols; performance of computer and communication networks; mobile and wireless communications : third International IFIP-TC6 Networking Conference, Athens, Greece, May 9-14, 2004 : proceedings