Nischal Murthy Piratla

CrowdUtility: know the crowd that works for you

Crowdsourcing platforms aim to leverage the collective intelligence of a largely distributed Internet workforce to solve a wide range of tasks. Crowd workers (unlike in a typical organization), exhibit varying work patterns, expertise, and performance - with little or no control that can be imposed on them. Requesters (e.g. enterprises) also exhibit diverse requirements in terms of the size, complexity and timings of the tasks, as well as SLAs (performance expectations). Clearly, the heterogeneity makes the choice of a platform suited for a given task difficult for the user. This paper highlights this problem and proposes CrowdUtility - a first-of-a-kind statistical machine learning approach, which models the dynamic behavioral characteristics of crowdsourcing platforms and uses them to recommend the best platform for the enterprise task(s). Initial results from real-world experiments suggest that the proposed system provides an attractive solution to this erstwhile unsolved problem

RoadEye: A System for Personalized Retrieval of Dynamic Road Conditions

Awareness of dynamically changing road conditions is crucial for a safe and quality driving experience, as well as, in augmenting trip planning. This work addresses the problem of keeping users informed in a timely and personalized manner about road conditions arising from both scheduled and ad hoc events. We propose Road Eye, a system for personalized retrieval of dynamic road conditions. The key contribution of Road Eye is the psi R-tree, which is a novel R-tree-based index augmented with linked lists for facilitating quick and personalized retrieval of user-queried road conditions. Our performance study indicates that the psi R-tree is indeed effective in retrieving dynamic road conditions with reduced query response times and disk I/Os.

RadioMap: fine-grained cellular coverage mapping in indoor environments

Poor signal quality results in frequent dropped calls, degradation of throughput, and high battery consumption. Coverage maps advertised by cellular operators are very abstract and provide coverage information for outdoor areas. In real-world, most of the cellular phone usage is reported indoors and therefore, call drops are more frequent in these environments. Aiming to provide seamless connectivity indoors, we develop a system RadioMap that uses sensing capabilities of a smartphone to create fine-grained cellular signal maps in indoor environments. These signal maps provide a low-cost and pervasive solution to the cellular operators for finding signal dead spots in indoor environments and accordingly, take rectifying measures such as installing signal boosters, etc.