Distributed computing in IoT: System-on-a-chip for smart cameras as an example

Chien, Shao-Yi; Chan, Wen S.; Tseng, Yu-Hsiang; Lee, Chia-Han; Somayazulu, V. Srinivasa; Chen, Yen-Kuang

doi:10.1109/aspdac.2015.7058993

Cited by 22 publications

(4 citation statements)

References 14 publications

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“…In video surveillance, a smart camera captures large volumes of data in the form of images or videos and requires efficient security techniques for data protection. Chien et al [28] used a special node for aggregation of data from video sensors in the IoT-based video surveillance and recommended lightweight algorithms and system-on-chip (SoC) approaches to further improve the computation power of sensors. Alsmirat et al [29] presented a framework for a secure surveillance system [30].…”

Section: State-of-the-artmentioning

confidence: 99%

Secure Smart Cameras by Aggregate-Signcryption with Decryption Fairness for Multi-Receiver IoT Applications

Ullah

Rinner

2019

Sensors

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Smart cameras are key sensors in Internet of Things (IoT) applications and often capture highly sensitive information. Therefore, security and privacy protection is a key concern. This paper introduces a lightweight security approach for smart camera IoT applications based on elliptic-curve (EC) signcryption that performs data signing and encryption in a single step. We deploy signcryption to efficiently protect sensitive data onboard the cameras and secure the data transfer from multiple cameras to multiple monitoring devices. Our multi-sender/multi-receiver approach provides integrity, authenticity, and confidentiality of data with decryption fairness for multiple receivers throughout the entire lifetime of the data. It further provides public verifiability and forward secrecy of data. Our certificateless multi-receiver aggregate-signcryption protection has been implemented for a smart camera IoT scenario, and the runtime and communication effort has been compared with single-sender/single-receiver and multi-sender/single-receiver setups.

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Section: State-of-the-artmentioning

confidence: 99%

Secure Smart Cameras by Aggregate-Signcryption with Decryption Fairness for Multi-Receiver IoT Applications

Ullah

Rinner

2019

Sensors

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“…While this could be solved by deploying more powerful edge devices, the cost of such a solution is prohibitive and may not be appropriate for the target application. For many applications the overall system's performance can be greatly improved by distributing more computation to other IoT edge devices [7]. An orthogonal solution is therefore the utilization of multiple cooperative edge devices to carry out the CNN inference task in a distributed and cooperative fashion.…”

Section: Introductionmentioning

confidence: 99%

DeeperThings: Fully Distributed CNN Inference on Resource-Constrained Edge Devices

Stahl

Hoffman

Mueller-Gritschneder

et al. 2021

Int J Parallel Prog

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Performing inference of Convolutional Neural Networks (CNNs) on Internet of Things (IoT) edge devices ensures both privacy of input data and possible run time reductions when compared to a cloud solution. As most edge devices are memory- and compute-constrained, they cannot store and execute complex CNNs. Partitioning and distributing layer information across multiple edge devices to reduce the amount of computation and data on each device presents a solution to this problem. In this article, we propose DeeperThings, an approach that supports a full distribution of CNN inference tasks by partitioning fully-connected as well as both feature- and weight-intensive convolutional layers. Additionally, we jointly optimize memory, computation and communication demands. This is achieved using techniques to combine both feature and weight partitioning with a communication-aware layer fusion method, enabling holistic optimization across layers. For a given number of edge devices, the schemes are applied jointly using Integer Linear Programming (ILP) formulations to minimize data exchanged between devices, to optimize run times and to find the entire model’s minimal memory footprint. Experimental results from a real-world hardware setup running four different CNN models confirm that the scheme is able to evenly balance the memory footprint between devices. For six devices on 100 Mbit/s connections the integration of layer fusion additionally leads to a reduction of communication demands by up to 28.8%. This results in run time speed-up of the inference task by up to 1.52x compared to layer partitioning without fusing.

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“…In particular, the latter drawback is strongest for multimedia data, for example, in applications that use video and image acquisition devices. For that reason, it is difficult to implement a centralized multimedia analysis system in the cloud [ 9 , 10 ].…”

Section: Introductionmentioning

confidence: 99%

Collaborative Working Architecture for IoT-Based Applications

Mora

Pont

Gil

et al. 2018

Sensors

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The new sensing applications need enhanced computing capabilities to handle the requirements of complex and huge data processing. The Internet of Things (IoT) concept brings processing and communication features to devices. In addition, the Cloud Computing paradigm provides resources and infrastructures for performing the computations and outsourcing the work from the IoT devices. This scenario opens new opportunities for designing advanced IoT-based applications, however, there is still much research to be done to properly gear all the systems for working together. This work proposes a collaborative model and an architecture to take advantage of the available computing resources. The resulting architecture involves a novel network design with different levels which combines sensing and processing capabilities based on the Mobile Cloud Computing (MCC) paradigm. An experiment is included to demonstrate that this approach can be used in diverse real applications. The results show the flexibility of the architecture to perform complex computational tasks of advanced applications.

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Distributed computing in IoT: System-on-a-chip for smart cameras as an example

Cited by 22 publications

References 14 publications

Secure Smart Cameras by Aggregate-Signcryption with Decryption Fairness for Multi-Receiver IoT Applications

Secure Smart Cameras by Aggregate-Signcryption with Decryption Fairness for Multi-Receiver IoT Applications

DeeperThings: Fully Distributed CNN Inference on Resource-Constrained Edge Devices

Collaborative Working Architecture for IoT-Based Applications

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