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		<title>Smart Scheduling Framework Cuts Task Interruptions in Vehicular Edge Computing by Nearly 43 Percent</title>
		<link>https://bioengineer.org/smart-scheduling-framework-cuts-task-interruptions-in-vehicular-edge-computing-by-nearly-43-percent/</link>
		
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		<pubDate>Sat, 17 Oct 2026 19:00:36 +0000</pubDate>
				<category><![CDATA[Technology]]></category>
		<category><![CDATA[action masking]]></category>
		<category><![CDATA[DDPG]]></category>
		<category><![CDATA[Deep Reinforcement Learning]]></category>
		<category><![CDATA[edge servers]]></category>
		<category><![CDATA[energy consumption]]></category>
		<category><![CDATA[Internet of Vehicles]]></category>
		<category><![CDATA[latency reduction]]></category>
		<category><![CDATA[Multi-objective optimization]]></category>
		<category><![CDATA[position prediction]]></category>
		<category><![CDATA[task interruption]]></category>
		<category><![CDATA[task offloading]]></category>
		<category><![CDATA[vehicular edge computing]]></category>
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					<description><![CDATA[A new DDPG-based scheduling framework called SPARCD uses vehicle position prediction, action masking, and state filling to cut task interruption rates by 42.7 percent and average latency by 31.5 percent in simulated urban vehicular edge computing scenarios.]]></description>
		
		
		
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