Harris Corporation

Jerome Sonnenberg

David Bruce Chester

Joseph Bibb Cain

Marco Mattos Carvalho

Richard Ford

Bryan C Hehn

A communication network is defended using a distributed infrastructure that leverages coordination across disparate abstraction levels. At each node computing device comprising a communication network, a stored event list is used to detect at least one node event which occurs at a machine code level and is known to have the potential to interfere directly with the internal operation of the node computing device. The at least one node event is one which is exclusive of an event within a network communication domain. In response to detecting the at least one node event at one of the plurality of network nodes, an optimal network-level defensive action is automatically selectively determined by the network. The network level defensive action will involve a plurality of network nodes comprising the communication network.

Cross-layer correlation in secure cognitive network

Systems () and methods () for dynamically managing Secondary User Node ('SUN') access to a segment of a wireless spectrum licensed for use by Primary User Nodes ('PUNs'). The methods comprise: detecting physical data transfers by PUNs (-) at first licensed frequencies (f, f, f, f) during slot sample times of a first epoch (t1-t15); generating a report comprising sensed spectral data indicating (a) during which of the slot sample times each physical data transfer was detected by a respective SUN (-) and (b) at which of the first licensed frequencies each physical data transfer occurred; receiving a report broadcasted from a remote SUN at a non-licensed frequency during a respective slot report times (t16-t19) of the first epoch; and analyzing the sensed spectral data of the reports to determine a time for using a first licensed frequency without interfering with or only minimally interfering with use thereof by PUNs.

Cross-layer, cross-node cognitive network enhanced dynamic spectrum allocation

System and methods for providing a cognitive network (). The methods involve generating Initialization Parameters ('IPs') for a first Multi-Objective Optimization ('MOO') algorithm based on project requirements; determining a first Pareto Front (&#x201c;PF&#x201d;) for a first Protocol Stack Layer (&#x201c;PSL&#x201d;) of a protocol stack by solving the first MOO algorithm using IPS (); initialize or constrain a second MOO algorithm using the first PF (); determining a second PF for a second PSL succeeding the first PSL using the second MOO algorithm; analyzing the first and second PFs to develop Best Overall Network Solutions (&#x201c;BONSs&#x201d;); ranking the BONSs according to a pre-defined criteria; identifying a top ranked solution for BONSs that complies with current regulatory and project policies; computing configuration parameters for protocols of PSLs that enable implementation of the top ranked solution within the cognitive network; and dynamically re-configuring network resources of PSLs using the configuration parameters.

Sequential multi-layer cognitive network optimization

System () and methods () for providing a Cognitive Network ('CN'). The methods involve: partially solving Multi-Objective Optimization Algorithms ('MOOAs') for Protocol Stack Layers (&#x201c;PSLs&#x201d;) using initialization parameters generated based on project requirements (); and monitoring the convergence behaviors of MOOAs () to identify when solutions () thereof start to converge toward Pareto-Optimal solutions (). In response to said identification, a convergence of a solution trajectory for at least one MOOA is &#x201c;biased&#x201d; so that compatible non-dominated solutions are generated at PSLs. A Pareto Front () for each PSL is determined by generating remaining solutions for MOOAs. The Pareto Fronts are analyzed in aggregate to develop Best Overall Network Solutions (&#x201c;BONSs&#x201d;). BONSs are ranked according to a pre-defined criteria. A Top Ranked Solution (&#x201c;TRS&#x201d;) is identified for BONSs that complies with current regulatory/project policies. Configuration parameters are computed for PSL protocols that enable implementation of TRS within CN.

Parallel multi-layer cognitive network optimization

System and methods for providing a cognitive network (). Initialization parameters for distributed Multi-Objective Optimization ('MOO') algorithms are generated based on project requirements. A Pareto Front () is determined for each protocol stack layer by solving the distributed MOO algorithms using the initialization parameters. The Pareto Fronts are analyzed in aggregate to develop best overall network solutions. The best overall network solutions are then ranked according to a pre-defined criteria. A top ranked solution is identified for the best overall network solutions that comply with current regulatory and project/mission policies. Subsequently, operations are performed to compute configuration parameters for protocols of the protocol stack layers that enable implementation of the top ranked solution within the cognitive network. Network resources of the protocol stack layers are then configured in accordance with the configuration parameters.

Growing community of inventors

Melbourne, FL, United States of America

Jerome Sonnenberg