The Robert Bosch Centre honours excellent young faculty members of the Indian Institute of Science for groundbreaking research in areas of cyber-physical systems. Each faculty member is awarded 500,000 INR to support their work.
The following projects have been selected:
Efficient whole programming path tracing
Prof Murali Krishna Ramanathan (Assistant Professor, Department of Computer Science and Automation)
A whole program path (WPP) captures the run-time behaviour of a program in terms of the control flow trace. WPP is commonly used in identifying hot paths, detecting concurrency bugs, etc. The current approach to obtain WPP calculates Ball-Larus path identifiers and emits them before function calls, return sites and at loop backedges. This incurs a large time and space overhead. We propose an optimal approach which inserts minimum instrumentations into the program and retains the capability to reconstruct all possible paths in the program. We model the problem as finding the set of edges which resolves the conflicts between paths in the program. By using this model, we show that the problem is a variant of the hitting set problem whose solution is NPhard. Due to complexities involved in obtaining the optimal solution, we also propose an approach to obtain an approximate solution. The goal of our experimentation is to study the reduction in time and space overheads as compared to current approach by evaluating it using the DaCapo benchmark suite.
Nano-materials and nano-devices for electronic, optoelectronic and energy harvesting applications
Prof Kausik Majumdar (Assistant Professor, Department of Electrical Communication Engineering)
Light-matter interaction in the nanoscale has important implications from physics as well as an applications points of view. Two-dimensional monolayers of transition metal dichalcogenides (TMDs) exhibit excellent optical activity despite their sub-nanometer physical thickness, generating a lot of interest in two-dimensional optoelectronic devices. Inversion symmetry broken monolayer TMDs show valley-selective properties including circular dichroism and valley hall effect. In addition, the exciton binding energy is extremely high (0.3−0.7 eV) in these monolayers owing to their strong out of plane carrier confinement, large carrier effective mass, and small dielectric constant. We use the strong light-matter interaction in these materials to fabricate optoelectronic devices to be used for photodetection and solar energy harvesting applications. Various two-dimensional heterostructures are exploited for electronic and optoelectronic manipulation to achieve improved efficiency and performance.
Digi-Parto: Evidence-based guidance, management and early warning system for labor and delivery
Prof Manish Arora (Assistant Professor, Centre for Product Design and Manufacturing)
Prolonged or obstructed labour is a major contributor to maternal and new born morbidity and mortality. As per WHO data, every day, approximately 830 women die from preventable causes related to pregnancy and childbirth, and 99% of all maternal deaths occur in developing countries. WHO recommends the use of partograph (preprinted one page form on which labour observation are recorded) to follow labour and delivery. Though the purpose of partograph is to help health care providers record, interpret, analyze, and use data to make decisions for labour management, this purpose is often not fulfilled due to non-completion of requisite information and insufficient training of healthcare workers in resource limited settings.
This project aims to integrate WHO recommended partograph with affordable sensing technologies for monitoring labour progression to provide real time guidance to health-care workers. In this project, we will develop appropriate sensors for monitoring foetal heart rate and uterine contractions and integrate it together in a mobile/ tablet application to guide, manage and provide early warnings during labour and delivery.
Distributed abstractions, algorithms and platforms for deadline-driven IoT analysis
Prof Yogesh Simmhan (Assistant Professor, Department of Computational and Data Sciences)
The Internet of Things (IoT) is a distributed system – massive in its potential deployment and data acquisition scale, unique in the diversity of applications that it will run, and significant in it its impact on society. One of the characteristics of the IoT distributed system is the heterogeneity of the computing platforms and environments, be they embedded low-power devices, medium-scale gateway and mobile platforms, accelerated computing resources, or centralized cloud data centers. While the distributed data generation and control within IoT is well recognized, enabling seamless and coordinated distributed execution of analytics within this ecosystem is still an open problem. The state of the art prefers to have a centralized coordination on the cloud, as proposed by Amazon and Microsoft’s IoT fabrics, or at best distribute the execution across an edge device and the cloud rather than collaboratively leverage the hundreds if not more computing resources.
Addressing this wide gap requires a combination of: (1) programming models that users can use to define their data assimilation, processing and decision making tasks over realtime streams; (2) abstractions and policies to specify constraints and quality of service goals for the resources and composable applications; and (3) runtime platforms and scheduling algorithms to enact these distributed tasks across the heterogeneous computing platforms to meet the goals. These must encompass the diverse needs of IoT applications and the execution environment, such as resource limitations on constrained devices and the elasticity of on-demand resources; the ability to offer guarantees on time-bound completion of analytics; the ability to dynamically adapt rapidly to changing situations; energy awareness for low-power devices; and provenance for data assurance, quality and reusability.
We propose to address these novel and complex realtime data-processing needs of IoT applications through Big Data platforms over distributed computing resources. These problems will be motivated by and validated on emerging IoT applications seen in smart cities, such as water and power management, and mobile and edge platforms like Raspberry Pis and smart phones.