Browsing by Author "Chantem, Thidapat (Tam)"
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- A Fully Polynomial Time Approximation Scheme for Adaptive Variable Rate Task DemandWillcock, Aaron; Fisher, Nathan; Chantem, Thidapat (Tam) (ACM, 2024-11-06)The Adaptive Variable Rate (AVR) task model defines a task where job WCET and period are a function of engine speed. Motivated by a lack of tractable AVR task demand methods, this work uses predefined job sequences for the Bounded Precedence Constraint Knapsack Problem inherent in AVR task demand calculation instead of enumerating all considered speeds as in existing work. A new, exact approach is proposed and approximated, enabling the derivation of a Fully Polynomial Time Approximation Scheme that outperforms the state-of-the-art in runtime (7,800x improvement) and RAM use (99% reduction) with less than 8% demand overestimate.
- Software and Behavior Diversification for Swarm Robotics SystemsLi, Ao; Chang, Sinyin; Li, Guorui; Chang, Yuanhaur; Fisher, Nathan; Chantem, Thidapat (Tam) (ACM, 2023-11-26)Inspired by natural swarms, swarm robotics systems are used in safety-critical tasks due to their scalability, redundancy, and adaptability. However, their design exposes them to two primary vulnerabilities. First, their homogeneity makes them vulnerable to large-scale attacks. Second, logical flaws within swarm algorithms can be exploited, leading to mission failures or crashes. While existing studies can effectively identify these vulnerabilities using system testing and verification, they are often time-consuming and might require repetition following software updates. To this end, we propose a complementary, two-level diversification approach. The first level tackles system homogeneity through software diversification. The second level introduces algorithmic randomness to minimize the exploitability of logical flaws. By leveraging a social force model, we can ensure that the introduced randomized behaviors do not compromise safety. Our evaluations show that the performance overheads remain within acceptable limits, notably at 2% for missions characterized by self-organizing behaviors.