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Audio/visual recognition and retrieval applications have recently garneredsignificant attention within Internet-of-Things (IoT) oriented services, giventhat video cameras and audio processing chipsets are now ubiquitous even inlow-end embedded systems. In the most typical scenario for such services, eachdevice extracts audio/visual features and compacts them into featuredescriptors, which comprise media queries. These queries are uploaded to aremote cloud computing service that performs content matching forclassification or retrieval applications. Two of the most crucial aspects forsuch services are: (i) controlling the device energy consumption when using theservice; (ii) reducing the billing cost incurred from the cloud infrastructureprovider. In this paper we derive analytic conditions for the optimal couplingbetween the device energy consumption and the incurred cloud infrastructurebilling. Our framework encapsulates: the energy consumption to produce andtransmit audio/visual queries, the billing rates of the cloud infrastructure,the number of devices concurrently connected to the same cloud server, {thequery volume constraint of each cluster of devices,} and the statistics of thequery data production volume per device. Our analytic results are validated viaa deployment with: (i) the device side comprising compact image descriptors(queries) computed on Beaglebone Linux embedded platforms and transmitted toAmazon Web Services (AWS) Simple Storage Service; (ii) the cloud side carryingout image similarity detection via AWS Elastic Compute Cloud (EC2) instances,with the AWS Auto Scaling being used to control the number of instancesaccording to the demand.

Media Query Processing For The Internet-of-Things: Coupling Of Device Energy Consumption And Cloud Infrastructure Billing