2015 · 425 citations · 15 references
BusinessComputer SystemsDistributed SystemsEnergy ManagementQueuing TheoryEnergy Management SystemsQueueing ProcessesQueueing SystemsQueueing TheoryStochastic Service SystemsSystem MonitoringEnergy-aware SchedulingQueueing Control PoliciesService Completion ProcessesStochastic Energy ManagementStochastic Energy Modeling
A source submits status updates to a service facility for delivery to a monitor, and each update consumes energy from a stochastic energy harvesting system. We evaluate updating policies for a source with a large battery using a status age timeliness metric. The source uses knowledge of the service facility state to avoid queue delays by sending a new update only after the previous one completes. An optimal policy is lazy, leaving the service facility idle after completion even when energy is available.
A source submits status updates to a service facility for delivery to a monitor. Each update requires energy and the source is powered by a stochastic energy harvesting system. With knowledge of the service facility state, the source avoids queue-induced delays by submitting a fresh update only after the service completion of a prior update. For a source with a large battery, we evaluate updating policies using a status age timeliness metric. We show that an optimal policy is lazy; following a service completion, the service facility is frequently left idle even though the server may have sufficient energy to submit an update.
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Wireless sensor networks for habitat monitoring
Alan Mainwaring, David Culler, Joseph Polastre et al. · 2002 · 4.2K citations
Computing, Computer Systems, Ecology +17
Real-time status: How often should one update?
Sanjit K. Kaul, Roy D. Yates, Marco Gruteser · 2012 · 2.4K citations