Most current sensor network research explores the use of extremely simple sensors on small devices called motes and focuses on overcoming the compute and power constraints of these devices. In contrast, our research explores the challenges of multimedia sensors and is motivated by the fact that multimedia devices, such as cameras, are rapidly becoming inexpensive, yet their use in a sensor network presents a number of unique challenges. For example, the data rates involved with multimedia sensors are orders of magnitude greater than those for sensor motes and this data cannot easily be processed by traditional sensor network techniques that focus on scalar data. In addition, the richness of the data generated by multimedia sensors makes them useful for a wide variety of applications. This paper presents an overview of IRISNET, a sensor network architecture that enables the creation of a planetary-scale infrastructure of multimedia sensors that can be shared by a large number of applications. To ensure the efficient collection of sensor readings, IRISNET enables the application-specific processing of sensor feeds on the significant computation resources that are typically attached to multimedia sensors. IRISNET enables the storage of sensor readings close to their source by providing a convenient and extensible distributed XML database infrastructure. Finally, IRISNET provides a number of multimedia processing primitives that enable the effective processing of sensor feeds in-network and at-sensor.<\/p>\n","protected":false},"excerpt":{"rendered":"
Most current sensor network research explores the use of extremely simple sensors on small devices called motes and focuses on overcoming the compute and power constraints of these devices. In contrast, our research explores the challenges of multimedia sensors and is motivated by the fact that multimedia devices, such as cameras, are rapidly becoming inexpensive, […]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","footnotes":""},"msr-content-type":[3],"msr-research-highlight":[],"research-area":[13547],"msr-publication-type":[193716],"msr-product-type":[],"msr-focus-area":[],"msr-platform":[],"msr-download-source":[],"msr-locale":[268875],"msr-post-option":[],"msr-field-of-study":[],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-155323","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-systems-and-networking","msr-locale-en_us"],"msr_publishername":"ACM","msr_edition":"MULTIMEDIA '05: Proceedings of the 13th annual ACM international conference on Multimedia","msr_affiliation":"","msr_published_date":"2005-01-01","msr_host":"","msr_duration":"","msr_version":"","msr_speaker":"","msr_other_contributors":"","msr_booktitle":"","msr_pages_string":"81\u201388","msr_chapter":"","msr_isbn":"1-59593-044-2","msr_journal":"","msr_volume":"","msr_number":"","msr_editors":"","msr_series":"","msr_issue":"","msr_organization":"","msr_how_published":"","msr_notes":"","msr_highlight_text":"","msr_release_tracker_id":"","msr_original_fields_of_study":"","msr_download_urls":"","msr_external_url":"","msr_secondary_video_url":"","msr_longbiography":"","msr_microsoftintellectualproperty":1,"msr_main_download":"209717","msr_publicationurl":"","msr_doi":"","msr_publication_uploader":[{"type":"file","title":"2005.Campbell.multimedia.pdf","viewUrl":"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2016\/02\/2005.Campbell.multimedia.pdf","id":209717,"label_id":0}],"msr_related_uploader":"","msr_attachments":[{"id":209717,"url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2016\/02\/2005.Campbell.multimedia.pdf"}],"msr-author-ordering":[{"type":"text","value":"Jason Campbell","user_id":0,"rest_url":false},{"type":"text","value":"Phillip B. 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