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In this paper, we consider a non-Bayesian sequential change detection basedon the Cumulative Sum (CUSUM) algorithm employed by an energy harvesting sensorwhere the distributions before and after the change are assumed to be known. Ina slotted discrete-time model, the sensor, exclusively powered by randomlyavailable harvested energy, obtains a sample and computes the log-likelihoodratio of the two distributions if it has enough energy to sense and process asample. If it does not have enough energy in a given slot, it waits until itharvests enough energy to perform the task in a future time slot. We deriveasymptotic expressions for the expected detection delay (when a change actuallyoccurs), and the asymptotic tail distribution of the run-length to a falsealarm (when a change never happens). We show that when the average harvestedenergy ($\bar H$) is greater than or equal to the energy required to sense andprocess a sample ($E_s$), standard existing asymptotic results for the CUSUMtest apply since the energy storage level at the sensor is greater than $E_s$after a sufficiently long time. However, when the $\bar H < E_s$, the energystorage level can be modelled by a positive Harris recurrent Markov chain witha unique stationary distribution. Using asymptotic results from Markov randomwalk theory and associated nonlinear Markov renewal theory, we establishasymptotic expressions for the expected detection delay and asymptoticexponentiality of the tail distribution of the run-length to a false alarm inthis non-trivial case. Numerical results are provided to support thetheoretical results.

Asymptotic Performance Analysis of Non-Bayesian Quickest Change Detection with an Energy Harvesting Sensor