Romization: Early Deployment and Customization of Java Systems for Constrained Devices

2006 ◽  
pp. 57-76 ◽  
Author(s):  
Alexandre Courbot ◽  
Gilles Grimaud ◽  
Jean-Jacques Vandewalle
Keyword(s):  
Constrained Devices

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

A Novel Simplified AES Algorithm for Lightweight Real-Time Applications: Testing and Discussion

2020 ◽  
Vol 13 (3) ◽  
pp. 435-445 ◽  
Author(s):  
Malik Qasaimeh ◽  
Raad S. Al-Qassas ◽  
Fida Mohammad ◽  
Shadi Aljawarneh
Keyword(s):  
Processing Time ◽  
Block Size ◽  
Statistical Test ◽  
Mathematical Proofs ◽  
The Past ◽  
Aes Algorithm ◽  
Cryptographic Algorithm ◽  
Cryptographic Algorithms ◽  
Compare And Contrast ◽  
Constrained Devices

Background: Lightweight cryptographic algorithms have been the focus of many researchers in the past few years. This has been inspired by the potential developments of lightweight constrained devices and their applications. These algorithms are intended to overcome the limitations of traditional cryptographic algorithms in terms of exaction time, complex computation and energy requirements. Methods: This paper proposes LAES, a lightweight and simplified cryptographic algorithm for constricted environments. It operates on GF(24), with a block size of 64 bits and a key size of 80-bit. While this simplified AES algorithm is impressive in terms of processing time and randomness levels. The fundamental architecture of LAES is expounded using mathematical proofs to compare and contrast it with a variant lightweight algorithm, PRESENT, in terms of efficiency and randomness level. Results: Three metrics were used for evaluating LAES according to the NIST cryptographic applications statistical test suite. The testing indicated competitive processing time and randomness level of LAES compared to PRESENT. Conclusion: The study demonstrates that LAES achieves comparable results to PRESENT in terms of randomness levels and generally outperform PRESENT in terms of processing time.

scholarly journals Emotion Recognition on Edge Devices: Training and Deployment

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4496
Author(s):  
Vlad Pandelea ◽  
Edoardo Ragusa ◽  
Tommaso Apicella ◽  
Paolo Gastaldo ◽  
Erik Cambria
Keyword(s):  
Emotion Recognition ◽  
Language Processing ◽  
Computational Cost ◽  
Sequential Learning ◽  
High Quality ◽  
Fast Training ◽  
Online Sequential Learning ◽  
And Performance ◽  
Resource Constrained Devices ◽  
Constrained Devices

Emotion recognition, among other natural language processing tasks, has greatly benefited from the use of large transformer models. Deploying these models on resource-constrained devices, however, is a major challenge due to their computational cost. In this paper, we show that the combination of large transformers, as high-quality feature extractors, and simple hardware-friendly classifiers based on linear separators can achieve competitive performance while allowing real-time inference and fast training. Various solutions including batch and Online Sequential Learning are analyzed. Additionally, our experiments show that latency and performance can be further improved via dimensionality reduction and pre-training, respectively. The resulting system is implemented on two types of edge device, namely an edge accelerator and two smartphones.

scholarly journals The Cost of OSCORE and EDHOC for Constrained Devices

2021 ◽  
Author(s):  
Stefan Hristozov ◽  
Manuel Huber ◽  
Lei Xu ◽  
Jaro Fietz ◽  
Marco Liess ◽  
...  
Keyword(s):  
The Cost ◽  
Constrained Devices

scholarly journals Method for Dynamic Service Orchestration in Fog Computing

Electronics ◽  
2021 ◽  
Vol 10 (15) ◽  
pp. 1796
Author(s):  
Nerijus Morkevicius ◽  
Algimantas Venčkauskas ◽  
Nerijus Šatkauskas ◽  
Jevgenijus Toldinas
Keyword(s):  
Fog Computing ◽  
Optimization Method ◽  
Service Orchestration ◽  
Multi Objective ◽  
System A ◽  
Service Distribution ◽  
Judgment Matrix ◽  
Hierarchy Process ◽  
Application Specific ◽  
Constrained Devices

Fog computing is meant to deal with the problems which cloud computing cannot solve alone. As the fog is closer to a user, it can improve some very important QoS characteristics, such as a latency and availability. One of the challenges in the fog architecture is heterogeneous constrained devices and the dynamic nature of the end devices, which requires a dynamic service orchestration to provide an efficient service placement inside the fog nodes. An optimization method is needed to ensure the required level of QoS while requiring minimal resources from fog and end devices, thus ensuring the longest lifecycle of the whole IoT system. A two-stage multi-objective optimization method to find the best placement of services among available fog nodes is presented in this paper. A Pareto set of non-dominated possible service distributions is found using the integer multi-objective particle swarm optimization method. Then, the analytical hierarchy process is used to choose the best service distribution according to the application-specific judgment matrix. An illustrative scenario with experimental results is presented to demonstrate characteristics of the proposed method.

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