Joint sensor placement and power rating selection in energy harvesting wireless sensor networks

Bushnaq, Osama M.; Al-Naffouri, Tareq Y.; Chepuri, Sundeep Prabhakar; Leus, Geert

doi:10.23919/eusipco.2017.8081645

Cited by 5 publications

(8 citation statements)

References 14 publications

(19 reference statements)

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“…After quantization, the observation is encoded to be sent over N b channels with an average powerP l,k,b . Denoting the encoded signal as,x l,k,b = [x (1) l,k,bx…”

Section: B Digital Transmission Schemementioning

confidence: 99%

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Sensor placement and resource allocation for energy harvesting IoT networks

Bushnaq

Chaaban

Chepuri

et al. 2020

Digital Signal Processing

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The paper studies optimal sensor selection for source estimation in energy harvesting Internet of Things (IoT) networks. Specifically, the focus is on the selection of the sensor locations which minimizes the estimation error at a fusion center, and to optimally allocate power and bandwidth for each selected sensor subject to a prescribed spectral and energy budget. To do so, measurement accuracy, communication link quality, and the amount of energy harvested are all taken into account. The sensor selection is studied under both analog and digital transmission schemes from the selected sensors to the fusion center. In the digital transmission case, an information theoretic approach is used to model the transmission rate, observation quantization, and encoding. We numerically prove that with a sufficient system bandwidth, the digital system outperforms the analog system with a possibly different sensor selection.Two source models are studied in this paper: static source estimation for a vector of correlated sources and dynamic state estimation for a scalar source. The design problem of interest is a Boolean non convex optimization problem, which is solved by relaxing the Boolean constraints. We propose a randomized rounding algorithm which generalizes the existing algorithm. The proposed randomized rounding algorithm takes the joint sensor location, power and bandwidth selection into account to efficiently round the obtained relaxed solution.

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“…After quantization, the observation is encoded to be sent over N b channels with an average powerP l,k,b . Denoting the encoded signal as,x l,k,b = [x (1) l,k,bx…”

Section: B Digital Transmission Schemementioning

confidence: 99%

“…• We model a practical system which takes the EH, channel gain and measurement accuracy into account. Similar considerations were taken into account in [1,2,22,16], however, all of them assumed analog communication where sensors directly amplify and forward observations.…”

Section: Introductionmentioning

confidence: 99%

Sensor placement and resource allocation for energy harvesting IoT networks

Bushnaq

Chaaban

Chepuri

et al. 2020

Digital Signal Processing

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“…Second, in our optimization problem we want to select one sensor from a specific set, select another one from another set, etc., for a given number of such sets. This second obstacle is also considered in [33], but we are not aware of any similar studies. In [33], each sensor position is associated with a small pool of candidate sensors, corresponding to different sensor types.…”

Section: Introductionmentioning

confidence: 99%

“…This second obstacle is also considered in [33], but we are not aware of any similar studies. In [33], each sensor position is associated with a small pool of candidate sensors, corresponding to different sensor types. However, since the inverse covariance matrix in [33] is still a linear function of the selection variables, we cannot employ similar techniques to solve our problem.…”

Section: Introductionmentioning

confidence: 99%

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Coding Mask Design for Single Sensor Ultrasound Imaging

Meulen

Kruizinga

Bosch

et al. 2020

IEEE Trans. Comput. Imaging

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We study the design of a coding mask for pulse-echo ultrasound imaging. We are interested in the scenario of a single receiving transducer with an aberrating layer, or 'mask', in front of the transducer's receive surface, with a separate co-located transmit transducer. The mask encodes spatial measurements into a single output signal, containing more information about a reflector's position than a transducer without a mask. The amount of information in such measurements is dependent on the mask geometry, which we propose to optimize using an image reconstruction mean square error (MSE) criterion. We approximate the physics involved to define a linear measurement model, which we use to find an expression for the image error covariance matrix. By discretizing the mask surface and defining a discrete number of mask thickness levels per point on its surface, we show how finding the best mask can be posed as a variation of a sensor selection problem. We propose a convex relaxation in combination with randomized rounding, as well as a greedy optimization algorithm to solve this problem. We show empirically that both algorithms come close to the global optimum. Our simulations further show that the optimized masks have better a MSE than nearly all randomly shaped masks. We observe that an optimized mask amplifies echoes coming from within the region of interest (ROI), and strongly reduces the correlation between echoes of pixels within the ROI.

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Spatial compression in ultrasound imaging

Meulen

Kruizinga

Bosch

et al. 2017

2017 51st Asilomar Conference on Signals, Systems, and Computers

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High quality three dimensional ultrasound imaging is typically attained by increasing the amount of sensors, resulting in complex hardware. Compressing measurements before sensing addresses this problem, and could enable new clinical applications. We have developed an analogue compression technique, by positioning a plastic coding mask in front of the aperture, which distorts the ultrasound field by inducing varying local echo delays. This results in a compression of the spatial ultrasound field across the sensor surface, while retaining sufficient information for 3D imaging. Using only a single sensor, complementary measurements can be obtained by rotation of the sensor and the mask to increase the conditioning of the reconstruction problem. In this work, we study a method to optimize the shape of the coding mask. To this end, we define an approximate signal model that captures the ultrasound response of the mask, and use it to pose mask shape optimization as a sensor selection problem. We solve it by relaxing it to a convex problem, as well as by using a greedy selection method. Our simulation results show that these approaches are able to outperform the random design strategy, in particular when mask rotations are included in the problem.

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Joint sensor placement and power rating selection in energy harvesting wireless sensor networks

Cited by 5 publications

References 14 publications

Sensor placement and resource allocation for energy harvesting IoT networks

Sensor placement and resource allocation for energy harvesting IoT networks

Coding Mask Design for Single Sensor Ultrasound Imaging

Spatial compression in ultrasound imaging

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