Controlled sampling using an energy harvesting sensor

Seyedi, Alireza

doi:10.1109/ita.2014.6804220

Cited by 1 publication

(2 citation statements)

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“…The data buffer constraints in (6)- (7) impose restrictions on the amount of bits per sample. The goal of the transmitter is to allocate its transmission power p i within each time slot and choose distortion level D i for each source, i = 1, ..., N, such that the causality, delay, and data buffer constraints are satisfied, while the sum distortion D at the destination is minimized.…”

Section: Distortion Minimization For a Battery-run Systemmentioning

confidence: 99%

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Source-Channel Coding Under Energy, Delay, and Buffer Constraints

Orhan¹,

Gündüz

Erkip³

2015

IEEE Trans. Wireless Commun.

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Source-channel coding for an energy limited wireless sensor node is investigated. The sensor node observes independent Gaussian source samples with variances changing over time slots and transmits to a destination over a flat fading channel. The fading is constant during each time slot. The compressed samples are stored in a finite size data buffer and need to be delivered in at most d time slots. The objective is to design optimal transmission policies, namely, optimal power and distortion allocation, over the time slots such that the average distortion at destination is minimized. In particular, optimal transmission policies with various energy constraints are studied. First, a battery operated system in which sensor node has a finite amount of energy at the beginning of transmission is investigated.Then, the impact of energy harvesting, energy cost of processing and sampling are considered. For each energy constraint, a convex optimization problem is formulated, and the properties of optimal transmission policies are identified. For the strict delay case, d = 1, 2D waterfilling interpretation is provided. Numerical results are presented to illustrate the structure of the optimal transmission policy, to analyze the effect of delay constraints, data buffer size, energy harvesting, processing and sampling costs.

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Section: Distortion Minimization For a Battery-run Systemmentioning

confidence: 99%

“…The problem of sensing and transmission for parallel Gaussian sources for a battery operated transmitter with processing and sensing costs is studied in [6]. In [7], maximization of the number of samples delivered with only the sampling cost is studied.…”

Section: Introductionmentioning

confidence: 99%