Biblio

Embry-Riddle Aeronautical University (ERAU) is working with the Air Force Research Lab (AFRL) to develop a distributed multi-layer autonomous UAS planning and control technology for gathering intelligence in Anti-Access Area Denial (A2/AD) environments populated by intelligent adaptive adversaries. These resilient autonomous systems are able to navigate through hostile environments while performing Intelligence, Surveillance, and Reconnaissance (ISR) tasks, and minimizing the loss of assets. Our approach incorporates artificial life concepts, with a high-level architecture divided into three biologically inspired layers: cyber-physical, reactive, and deliberative. Each layer has a dynamic level of influence over the behavior of the agent. Algorithms within the layers act on a filtered view of reality, abstracted in the layer immediately below. Each layer takes input from the layer below, provides output to the layer above, and provides direction to the layer below. Fast-reactive control systems in lower layers ensure a stable environment supporting cognitive function on higher layers. The cyber-physical layer represents the central nervous system of the individual, consisting of elements of the vehicle that cannot be changed such as sensors, power plant, and physical configuration. On the reactive layer, the system uses an artificial life paradigm, where each agent interacts with the environment using a set of simple rules regarding wants and needs. Information is communicated explicitly via message passing and implicitly via observation and recognition of behavior. In the deliberative layer, individual agents look outward to the group, deliberating on efficient resource management and cooperation with other agents. Strategies at all layers are developed using machine learning techniques such as Genetic Algorithm (GA) or NN applied to system training that takes place prior to the mission.

Wireless Sensor Network has a wide range of applications including environmental monitoring and data gathering in hostile environments. This kind of network is easily leaned to different external and internal attacks because of its open nature. Sink node is a receiving and collection point that gathers data from the sensor nodes present in the network. Thus, it forms bridge between sensors and the user. A complete sensor network can be made useless if this sink node is attacked. To ensure continuous usage, it is very important to preserve the location privacy of sink nodes. A very good approach for securing location privacy of sink node is proposed in this paper. The proposed scheme tries to modify the traditional Blast technique by adding shortest path algorithm and an efficient clustering mechanism in the network and tries to minimize the energy consumption and packet delay.