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The use cases of this chapter motivate the inclusion of visual and audio-visual sensor perception into the cognitive radio architecture (CRA). The diversity of user templates motivates further AML and uncertainty management technologies in the CRA. The iCR should save its user substantial work by performing increasingly complex home, of ce, and leisure tasks via trustable wireless services as directed by the user, but mostly autonomously. It should make life more interesting by autonomously looking up that ower in the garden of Hampton Court with interesting horticulture lineage. The chapter begins with a study of a near-term AACR assisting in maintaining personal safety in an emergency situation and concludes with a conceptual treatment of far-term iCR functions.
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Consider an AACR that monitors the owner s vital signs. Niche products already monitor and report blood pressure, heart rate, and respiration. Postulate a wardrobe in which the user wears the sensors and the wearable AACR itself autonomously monitors the readings for conformance to the user s historic norms and excursions. This AACR might use PROLOG rules to monitor these vital signs analogous to Kokar s rules for monitoring the adaptive equalizer. When bounds are out of range, the AACR takes
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Cognitive Radio Architecture: The Engineering Foundations of Radio XML By Joseph Mitola III Copyright 2006 John Wiley & Sons, Inc.
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appropriate action. Suppose now that an earthquake has left the owner injured in the rubble. The AACR detects the owner s elevated heart rate and labored breathing out of normal ranges, so it attempts to report the emergency but nds that the cellular network is down. The conventional emergency radio just keeps trying, draining the battery before help arrives. The iCR explores alternative behaviors. First, the iCR s audio and video sensory perception will register the sights and sounds of an earthquake. Suppose the user was at work in a midrise of ce building when the earthquake struck. Previously, the iCR had developed and reinforced many SKOs in the daily work routine. Suddenly, the parameters of the visual scene become chaotic as the iCR encounters its rst acoustic experience of a <Scream/>. In the CRA cognition cycles following the <Scream/> the iCR instantiates a <Personal-emergency/> UKO hypothesis for the chaotic audio-visual scene. The a priori UKO advises the iCR to use ASR to monitor police, re, and rescue radio channels for indications of a larger emergency. This UKO enables the iCR to reliably determine whether the user s vital signs are abnormal because of personal trauma like falling down stairs, or an event that includes other people, like an earthquake, re, or ood. This proactive iCR monitors the <RF-environment/> to discover changes in the <Information-landscape/> in its amateur radio, police, weather, sports, cellular, and WLAN bands. Although the cellular network is down police calls penetrate the rubble pile. The iCR s speech-to-text recognizer and <Histogram/> detects 501 as the <Topic/> which in the dictionary of local police terms downloaded weeks earlier means earthquake. Its <Speaker-ID/> algorithm detects two speakers on the police channels, triggering an RDH to instantiate UKOs for a <Police-cruiser/> and <Police-dispatcher/>. The cruiser is close by. Instantiating the <Earthquake/> UKO, the AACR powers down its own cell phone to conserve power. It then autonomously sends a formatted distress signal to the nearby police car on its VHF band, and the police acknowledge the message. It then advises the police AACR that it will sleep for 10 minutes to save power unless the vital signs turn for the worse. The message included the GPS coordinates of the building, the name of the user, company, oor, and suite number. It reports to the police AACR that the elevator s BlueTooth channel is reporting mechanical problems and heat. Police rescue the victim before the building is engulfed in ames. If I m in LA and worried about earthquakes, I want to be wearing this iCR. Implementing the Use Case: This iCR learned critical information about the user s situation by combining its native ability as a SDR with ASR and Zipf s Law <Topic/> spotting. Thus, autonomous reallocation of MIPS and radio resources enhances the user s likelihood of getting help quickly and thus of surviving the disaster. Technology challenges include reliably interpreting the chaotic scene. Social challenges include police acceptance of maritime safety protocols and standardization of message formats.
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