No abstract

The congested exosphere continues to contain more satellites and debris, raising the potential for destructive collisions. The Special Perturbations (SP) Tasker algorithm currently assigns the ground sensors tasks to track object locations. Accurate locations help avoid collisions. However, the SP Tasker ignores priority, which is the satellite's importance factor. This article introduces the Evolutionary Algorithm Tasker (EAT) to solve the Satellite Sensor Allocation Problem (SSAP), which is a hybrid Evolutionary Strategy and Genetic Algorithm concept including specific techniques to explore the solution space and exploit the best solutions found. This approach goes beyond the current method, which does not include priority and other methods from the literature that have been applied to small-scale simulations. The SSAP model implementation extends Multi-Objective Evolutionary Algorithms (MOEAs) from the literature while accounting for priorities. Multiple real-world factors are considered, including each sensor's field-of-view, the orbital opportunities to track a satellite, the capacity of the sensor, and the relative priority of the satellites. The single objective EAT is statistically compared to the SP Tasker algorithm. Simulations show that both the EAT and MOEA approaches effectively use priority in the core tasking algorithms to ensure that higher priority satellites are tracked.

This article proposes a robust mathematical method to strategically place trust nodes to compartmentalize a timecritical SCADA network. The trust nodes combine firewall and intrusion detection technology to provide communication network security for protection, control, and SCADA systems. The mathematical technique optimizes the placement of the trust nodes based on the timing requirements of existing systems and the number of trust nodes that are available in the system given constraints, which may arise due to budgetary limitations or the restrictions of existing utility hardware. The intent is to create a planning tool to allow utility system operators to determine the best locations to place trust nodes to increase system security given limited resources and/or hardware constraints. The operational requirements of the environment are translated into a mathematical model. Mixed integer linear programming is used to process this model in search of an optimal solution. Because the problem is provably NP-Hard, a heuristic is also given to quickly find good, but not optimal, solutions. Experiments show promise for the proposed techniques.