WSN-ANN: Parallel and distributed neurocomputing with wireless sensor networks

Serpen, Gürsel; Li, Jiakai; Liu, Linqian; Gao, Zhenning

doi:10.1109/ijcnn.2013.6706764

Cited by 12 publications

(3 citation statements)

References 19 publications

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“…In this method, the data delivery time between devices is minimized while considering two types of DNN layers and heterogeneous mobile devices which are used as computing resources. A study conducted in [34] proved that wireless sensor networks can be used as a hardware platform to implement parallel and distributed neurocomputing. This architecture is designed by mimicking the biological neural networks that exist in the brain.…”

Section: Related Workmentioning

confidence: 99%

Distributed CNN Inference on Resource-Constrained UAVs for Surveillance Systems: Design and Optimization

Jouhari,

Al-Ali,

Baccour

et al. 2021

Preprint

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Unmanned Aerial Vehicles (UAVs) have attracted great interest in the last few years owing to their ability to cover large areas and access difficult and hazardous target zones, which is not the case of traditional systems relying on direct observations obtained from fixed cameras and sensors. Furthermore, thanks to the advancements in computer vision and machine learning, UAVs are being adopted for a broad range of solutions and applications. However, Deep Neural Networks (DNNs) are progressing toward deeper and complex models that prevent them from being executed on-board. In this paper, we propose a DNN distribution methodology within UAVs to enable data classification in resource-constrained devices and avoid extra delays introduced by the server-based solutions due to data communication over air-to-ground links.The proposed method is formulated as an optimization problem that aims to minimize the latency between data collection and decision-making while considering the mobility model and the resource constraints of the UAVs as part of the air-to-air communication. We also introduce the mobility prediction to adapt our system to the dynamics of UAVs and the network variation. The simulation conducted to evaluate the performance and benchmark the proposed methods, namely Optimal UAV-based Layer Distribution (OULD) and OULD with Mobility Prediction (OULD-MP), were run in an HPC cluster. The obtained results show that our optimization solution outperforms the existing and heuristic-based approaches.

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Section: Related Workmentioning

confidence: 99%

Distributed CNN Inference on Resource-Constrained UAVs for Surveillance Systems: Design and Optimization

Jouhari,

Al-Ali,

Baccour

et al. 2021

Preprint

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show abstract

“…Gursel Serpen and colleagues mapped operation of an artificial neural network (ANN; see Figure 2c) to an ad hoc WSN. 7 Each sensor, apart from its own original monitoring task, hosts functionality of a neuron, whereas sensor-node communication is used for information exchange between the neurons. Although the proposed solution considers only one application, the overall approach can be extended for other analytics tasks.…”

Section: Bioinspired Analyticsmentioning

confidence: 99%

Precision Farming: Sensor Analytics

2015

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“…To realize the benefits of true parallelism and distribution offered by AI in a fully distributed computing system, a scalable hardware platform is required. The distributed nature of the IoT, where thousands of smart devices are available and can communicate with each other, offers such a platform [3].…”

Section: Introductionmentioning

confidence: 99%