Information Processing Techniques for Mobile Sensing and IoT at Scale

Abstract

The number of intelligent devices continues to grow exponentially, enabling smart things to sense, compute, and interact within an increasingly hyper-connected Internet of Things (IoT) ecosystem. While many IoT applications (particularly those involving real-time monitoring and control of built infrastructure) are built around fixed sensing systems, an emerging class of urban-scale applications relies on mobile platforms such as robots, unmanned aerial vehicles, and every day moving entities like cars and buses operating in dynamic environments. These mobile IoT systems offer unique advantages, enabling both deterministic sensing (sampling at specific target locations) and opportunistic sensing (sampling on-the-go) at scale. This dual capability opens new application possibilities that were previously difficult to realize. However, such systems are inherently constrained by limited energy resources, which must be efficiently allocated across sensing, processing, storage, and communication tasks. This introduces fundamental trade-offs between accuracy, latency, and energy efficiency. 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 leverage platform mobility as a strategic advantage to enhance sensing performance within real-world system/application constraints and scope.

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