Abstract: A method of designing an interplanetary communications network with an Artificial Neural Network (ANN) at each node. Object functions are generated that relate network characteristics and performance. Each object function is driven to an extreme (min or max) to provide an optimum network configuration. The optimum network configuration includes the number of nodes and the number of antennas for each node, node placement, specific node storage capacity and communications capacity for nodes and for links between nodes.

Abstract: Techniques are provided for simulating battlefield scenarios in an ad-hoc communications network comprising a plurality of nodes and a plurality of links between the nodes.

Abstract: A self healing ad hoc communications network and method of training for and healing the network. The network includes wireless devices or nodes that include a neural network element and the ad hoc network operates as a neural network. Some of the nodes are designated as healing nodes that are identified during network training and are strategically located in the network coverage area. Whenever one group of nodes loses connection with another a healing node may reposition itself to reconnect the two groups. Thus, the network can maintain connectivity without constraining node movement.

Abstract: A self healing ad hoc communications network and method of training for and healing the network. The network includes wireless devices or nodes that include a neural network element and the ad hoc network operates as a neural network. Some of the nodes are designated as healing nodes that are identified during network training and are strategically located in the network coverage area. Whenever one group of nodes loses connection with another a healing node may reposition itself to reconnect the two groups. Thus, the network can maintain connectivity without constraining node movement.

Abstract: A method of selecting a gateway node in a remote network and for handing over to the selected gateway node. Nodes in a remote energy aware network connect through a gateway node to a backbone network in an interplanetary communications network. Each node optimizes a stability function describing communications to neighboring nodes and to the backbone. Optimization is for maximum network stability and for efficient node energy consumption. Optimization identifies a handover time and nodes initiate handover sufficiently in advance of the identified handover time to complete at that time. Nodes continually monitor and update network characterization parameters to identify a next optimal handover time.

Abstract: A self healing ad hoc communications network and method of training for and healing the network. The network includes wireless devices or nodes that include a neural network element and the ad hoc network operates as a neural network. Some of the nodes are designated as healing nodes that are identified during network training and are strategically located in the network coverage area. Whenever one group of nodes loses connection with another a healing node may reposition itself to reconnect the two groups. Thus, the network can maintain connectivity without constraining node movement.

Abstract: A self managed ad hoc communications network and method of managing the network. The network includes wireless devices or nodes that include a neural network element and the ad hoc network operates as a neural network. One of the nodes is designated as a Network Management System (NMS) that provides overall network management. Clusters of nodes are organized around cluster leaders. Each cluster leader manages a cluster of nodes and communications between node clusters. Each cluster may also have other nodes identified as lower order cluster leaders.

Abstract: A method of managing an ad hoc communications network of wireless devices or nodes. The network is connected if all nodes can communicate with each other and otherwise partitioned. Partitions are identified by recursively applying a connectivity function to a connectivity matrix representative of the network. The number of times the connectivity function is recursively applied is determined by the network diameter. If the result of the recursive application is a unity matrix, the network is connected; otherwise it is disconnected. Also, if the network diameter exceeds a selected maximum length, the network may be voluntarily partitioned into connected sub-networks by recursively applying the connectivity function a lesser number of times to the connectivity matrix. The lesser number of times is determined by the selected maximum length or maximum allowable number of hops.

Abstract: A self managed ad hoc communications network nodes and node mobility management. Nodes include an Artificial Neural Network (ANN) that determines connection to other network nodes. The ANN may use free space propagation link life estimation, inverse modeling for partition prediction, Stochastic Approximation, and/or coarse estimation. The node includes storage storing network tables and matrices indicating network connectivity and connection to other nodes. Also, a wireless communications unit provides for wireless communicating with other nodes.

Abstract: Techniques are provided for simulating battlefield scenarios in an ad-hoc communications network comprising a plurality of nodes and a plurality of links between the nodes.

Abstract: Techniques are provided for simulating battlefield scenarios in an ad-hoc communications network comprising a plurality of nodes and a plurality of links between the nodes.

Abstract: A method of selecting a gateway node in a remote network and for handing over to the selected gateway node. Nodes in a remote energy aware network connect through a gateway node to a backbone network in an interplanetary communications network. Each node optimizes a stability function describing communications to neighboring nodes and to the backbone. Optimization is for maximum network stability and for efficient node energy consumption. Optimization identifies a handover time and nodes initiate handover sufficiently in advance of the identified handover time to complete at that time. Nodes continually monitor and update network characterization parameters to identify a next optimal handover time.

Abstract: A method of designing an interplanetary communications network with an Artificial Neural Network (ANN) at each node. Object functions are generated that relate network characteristics and performance. Each object function is driven to an extreme (min or max) to provide an optimum network configuration. The optimum network configuration includes the number of nodes and the number of antennas for each node, node placement, specific node storage capacity and communications capacity for nodes and for links between nodes.

Abstract: A self managed ad hoc communications network and method of managing the network. The network includes wireless devices or nodes that include a neural network element and the ad hoc network operates as a neural network. One of the nodes is designated as a Network Management System (NMS) that provides overall network management. Clusters of nodes are organized around cluster leaders. Each cluster leader manages a cluster of nodes and communications between node clusters. Each cluster may also have other nodes identified as lower order cluster leaders.

Abstract: A self managed ad hoc communications network nodes and node mobility management. Nodes include an Artificial Neural Network (ANN) that determines connection to other network nodes. The ANN may use free space propagation link life estimation, inverse modeling for partition prediction, Stochastic Approximation, and/or coarse estimation. The node includes storage storing network tables and matrices indicating network connectivity and connection to other nodes. Also, a wireless communications unit provides for wireless communicating with other nodes.

Abstract: A method of managing an ad hoc communications network of wireless devices or nodes. The network is connected if all nodes can communicate with each other and otherwise partitioned. Partitions are identified by recursively applying a connectivity function to a connectivity matrix representative of the network. The number of times the connectivity function is recursively applied is determined by the network diameter. If the result of the recursive application is a unity matrix, the network is connected; otherwise it is disconnected. Also, if the network diameter exceeds a selected maximum length, the network may be voluntarily partitioned into connected sub-networks by recursively applying the connectivity function a lesser number of times to the connectivity matrix. The lesser number of times is determined by the selected maximum length or maximum allowable number of hops.

Abstract: A self healing ad hoc communications network and method of training for and healing the network. The network includes wireless devices or nodes that include a neural network element and the ad hoc network operates as a neural network. Some of the nodes are designated as healing nodes that are identified during network training and are strategically located in the network coverage area. Whenever one group of nodes loses connection with another a healing node may reposition itself to reconnect the two groups. Thus, the network can maintain connectivity without constraining node movement.

Abstract: Techniques are provided for simulating battlefield scenarios in an ad-hoc communications network comprising a plurality of nodes and a plurality of links between the nodes.