Sumit Kumar

Bhawani Sandeep, Surjeet Singh, Sumit Kumar

In this work, geometry optimization of mechanical truss using computer-aided finite element analysis is presented. The shape of the truss is a dominant factor in determining the capacity of load it can bear. At a given parameter space, our goal is to find the parameters of a hull that maximize the load-bearing capacity and also don't yield to the induced stress. We rely on finite element analysis, which is a computationally costly design analysis tool for design evaluation. For such expensive to-evaluate functions, we chose Bayesian optimization as our optimization framework which has empirically proven sample efficient than other simulation-based optimization methods. By utilizing Bayesian optimization algorithms, the truss design involves iteratively evaluating a set of candidate truss designs and updating a probabilistic model of the design space based on the results. The model is used to predict the performance of each candidate design, and the next candidate design is selected based on the prediction and an acquisition function that balances exploration and exploitation of the design space. Our result can be used as a baseline for future study on AI-based optimization in expensive engineering domains especially in finite element Analysis.

Sumit Kumar, Yiming Gu, Jerrick Hoang et al.

Behavior prediction of traffic actors is an essential component of any real-world self-driving system. Actors' long-term behaviors tend to be governed by their interactions with other actors or traffic elements (traffic lights, stop signs) in the scene. To capture this highly complex structure of interactions, we propose to use a hybrid graph whose nodes represent both the traffic actors as well as the static and dynamic traffic elements present in the scene. The different modes of temporal interaction (e.g., stopping and going) among actors and traffic elements are explicitly modeled by graph edges. This explicit reasoning about discrete interaction types not only helps in predicting future motion, but also enhances the interpretability of the model, which is important for safety-critical applications such as autonomous driving. We predict actors' trajectories and interaction types using a graph neural network, which is trained in a semi-supervised manner. We show that our proposed model, TrafficGraphNet, achieves state-of-the-art trajectory prediction accuracy while maintaining a high level of interpretability.

Adhirath Kabra, Sumit Kumar, Gaurav S. Kasbekar

Many Internet of Things (IoT) scenarios require communication to and data acquisition from multiple devices with similar functionalities. For such scenarios, group communication in the form of multicasting and broadcasting has proven to be effective. Group Key Management (GKM) involves the handling, revocation, updation and distribution of cryptographic keys to members of various groups. Classical GKM schemes perform inefficiently in dynamic IoT environments, which are those wherein nodes frequently leave or join a network or migrate from one group to another over time. Recently, the `GroupIt' scheme has been proposed for GKM in dynamic IoT environments. However, this scheme has several limitations such as vulnerability to collusion attacks, the use of computationally expensive asymmetric encryption and threats to the backward secrecy of the system. In this paper, we present a highly efficient and secure GKM protocol for dynamic IoT settings, which maintains forward and backward secrecy at all times. Our proposed protocol uses only symmetric encryption, and is completely resistant to collusion attacks. Also, our protocol is highly flexible and can handle several new scenarios in which device or user dynamics may take place, e.g., allowing a device group to join or leave the network or creation or dissolution of a user group, which are not handled by schemes proposed in prior literature. We evaluate the performance of the proposed protocol via extensive mathematical analysis and numerical computations, and show that it outperforms the GroupIt scheme in terms of the communication and computation costs incurred by users and devices.