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Author(s):
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Fangfei Chen, Pennsylvania State University, USA ; Matthew Johnson, City University of New York, USA; Yosef Alayev, The Graduate Center, The City University of New York, USA; Amotz Bar-Noy, Brooklyn College & Graduate Center, CUNY, New York, USA; Tom La Porta, Penn State University, USA
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Abstract:
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We consider variations of a problem in which data must be delivered to mobile clients en-route, as they travel towards their destinations. The data can only be delivered to the mobile clients as they pass within range of wireless base stations. Example scenarios include the delivery of building maps to firefighters responding to \emph{multiple} alarms, and the in-transit ``illumination'' of simultaneous surface-to-air missiles. We cast this scenario as a parallel-machine scheduling problem with the little-studied property that jobs may have different release times and deadlines when assigned to different machines. We present new algorithms and also adapt existing algorithms, for both online and offline settings. We evaluate these algorithms on a variety of problem instance types, using both synthetic and real-world data, and including several geographical scenarios, and show that our algorithms produce schedules achieving near-optimal throughput.
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