Task Allocation Performance Comparison for Low Power Devices

Cognitive agents are typically utilized in autonomous systems for automated decision making. These systems interact in real time with their environment and are generally heavily power constrained. Thus, there is a strong need for a real time agent running on a low power computing platform. An autonomous agent reasons like humans and enables enhanced agent-based decision-making. The agent examined is the Cognitively Enhanced Complex Event Processing (CECEP) architecture. Task allocation is one of most time consuming and power-hungry processes within the CECEP architecture. Task allocation is a combinatorial optimization problem in the field of operations research. In the CECEP architecture, the cognitive agent performs this task allocation. This paper examines the performance of two task allocation algorithms (the greedy algorithm and the auction algorithm) that have the potential to significantly reduce the power and energy required to perform this task. Thus, it may be possible to execute complex task allocation problems on custom, portable, low power devices.