Information Processing Techniques for Mobile Sensing and IoT at ScaleSpeaker: Prasant Kumar Misra – Bangalore, India
Topic(s): Applied Computing
The number of intelligent devices continues to grow exponentially, giving smart things the ability to sense and control over the hyper-connected Internet of Things (IoT) space. Certain applications, especially those centered around real-time monitoring and control of physical infrastructure in built environments, depend on systems with fixed devices; there are a different class of urban scale applications that rely on mobile systems (such as robots, unmanned aerial vehicles, and even commonplace moving objects such as cars or buses) for catering to dynamic settings. Such mobile objects would also remain connected to the Internet even while moving around.
Mobile IoT systems offer the benefit of deterministic (sample at the 'target') as well as opportunistic (sample 'on-the-go') sensing at large scale, thereby enabling applications that were previously beyond reach. However, they are constrained in terms of available energy resources required for sensing, processing, storage, and communication; and therefore, should be efficiently traded off for accuracy and/or latency. With the help of three case-studies viz. (i) RF sensing (GPS) with sensor nodes, (ii) acoustic sensing with drones, and (iii) visual sensing with mobile phones; this talk will present different information processing techniques (such as compressed sensing and sparse approximation; array signal processing; visual scene and location analytics) that piggyback on the platform mobility to improve the sensing performance, while adhering to the system limitations and application scope.
About this LectureNumber of Slides: 40
Duration: 120 minutes
Languages Available: English
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