scholarly journals High-level Programming and Symbolic Reasoning on IoT Resource Constrained Devices

2015 ◽  
Vol 1 (2) ◽  
pp. e6 ◽  
Author(s):  
Sal vatore Gaglio ◽  
Giuseppe Lo Re ◽  
Gloria Martorella ◽  
Daniele Peri
Keyword(s):  
Resource Constrained ◽  
Symbolic Reasoning ◽  
High Level ◽  
Resource Constrained Devices ◽  
Constrained Devices

scholarly journals Dumbo, Jumbo, and Delirium: Parallel Authenticated Encryption for the Lightweight Circus

2020 ◽  
pp. 5-30
Author(s):  
Tim Beyne ◽  
Yu Long Chen ◽  
Christoph Dobraunig ◽  
Bart Mennink
Keyword(s):  
The Internet ◽  
Authenticated Encryption ◽  
Small State ◽  
Resource Constrained ◽  
Direct Communication ◽  
Encryption Algorithms ◽  
High Level ◽  
State Size ◽  
Resource Constrained Devices ◽  
Constrained Devices

With the trend to connect more and more devices to the Internet, authenticated encryption has become a major backbone in securing the communication, not only between these devices and servers, but also the direct communication among these devices. Most authenticated encryption algorithms used in practice are developed to perform well on modern high-end devices, but are not necessarily suited for usage on resource-constrained devices. We present a lightweight authenticated encryption scheme, called Elephant. Elephant retains the advantages of GCM such as parallelism, but is tailored to the needs of resource-constrained devices. The two smallest instances of Elephant, Dumbo and Jumbo, are based on the 160-bit and 176-bit Spongent permutation, respectively, and are particularly suited for hardware; the largest instance of Elephant, Delirium, is based on 200-bit Keccak and is developed towards software use. All three instances are parallelizable, have a small state size while achieving a high level of security, and are constant time by design.

SIBSC: Separable Identity-Based Signcryption for Resource-Constrained Devices

Informatica ◽  
2017 ◽  
Vol 28 (1) ◽  
pp. 193-214 ◽  
Author(s):  
Tung-Tso Tsai ◽  
Sen-Shan Huang ◽  
Yuh-Min Tseng
Keyword(s):  
Resource Constrained ◽  
Identity Based ◽  
Resource Constrained Devices ◽  
Constrained Devices

RAP: A Software Framework of Developing Convolutional Neural Networks for Resource-constrained Devices Using Environmental Monitoring as a Case Study

2021 ◽  
Vol 5 (4) ◽  
pp. 1-28
Author(s):  
Chia-Heng Tu ◽  
Qihui Sun ◽  
Hsiao-Hsuan Chang
Keyword(s):  
Neural Networks ◽  
Environmental Monitoring ◽  
Convolutional Neural Networks ◽  
Model Building ◽  
Software Framework ◽  
Training Procedure ◽  
Monitoring Methods ◽  
Resource Constrained ◽  
Resource Constrained Devices ◽  
Constrained Devices

Monitoring environmental conditions is an important application of cyber-physical systems. Typically, the monitoring is to perceive surrounding environments with battery-powered, tiny devices deployed in the field. While deep learning-based methods, especially the convolutional neural networks (CNNs), are promising approaches to enriching the functionalities offered by the tiny devices, they demand more computation and memory resources, which makes these methods difficult to be adopted on such devices. In this article, we develop a software framework, RAP , that permits the construction of the CNN designs by aggregating the existing, lightweight CNN layers, which are able to fit in the limited memory (e.g., several KBs of SRAM) on the resource-constrained devices satisfying application-specific timing constrains. RAP leverages the Python-based neural network framework Chainer to build the CNNs by mounting the C/C++ implementations of the lightweight layers, trains the built CNN models as the ordinary model-training procedure in Chainer, and generates the C version codes of the trained models. The generated programs are compiled into target machine executables for the on-device inferences. With the vigorous development of lightweight CNNs, such as binarized neural networks with binary weights and activations, RAP facilitates the model building process for the resource-constrained devices by allowing them to alter, debug, and evaluate the CNN designs over the C/C++ implementation of the lightweight CNN layers. We have prototyped the RAP framework and built two environmental monitoring applications for protecting endangered species using image- and acoustic-based monitoring methods. Our results show that the built model consumes less than 0.5 KB of SRAM for buffering the runtime data required by the model inference while achieving up to 93% of accuracy for the acoustic monitoring with less than one second of inference time on the TI 16-bit microcontroller platform.

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