scholarly journals Enhanced Algorithm Implementation for Low Powered IoT Devices Using Authenticator to Improve Data Integrity

Webology ◽  
2021 ◽  
Vol 18 (Special Issue 04) ◽  
pp. 733-751
Author(s):  
D.M. Sheeba
Keyword(s):  
Data Security ◽  
Low Cost ◽  
Easy Access ◽  
Raspberry Pi ◽  
Network Availability ◽  
Services Delivery ◽  
Encryption And Decryption ◽  
Arduino Uno ◽  
Iot Devices ◽  
Algorithm Implementation

Internet of Things enables many industries to connect to end customers and provide seamless products and services delivery. Due to easy access to network, availability of devices, penetration of IoT services exponentially Growing. Meanwhile, Ensuring the Data Security and Integrity of devices connected to network is paramount. In this work, we bring the efficient way of implementing Secure Algorithm for low powered devices and enhancing the encryption and decryption process. In addition to the data security, to enhance node integrity with less power, Authenticator and intermediate network manager introduced which will acts as a firewall and manager of data flow. To demonstrate the approach, same is implemented using low cost Arduino Uno, Raspberry Pi boards. Arduino Uno used to demonstrate low powered encryption process using EDIA Algorithm and raspberry pi used as nodal manager to manage the integrity of nodes in a low-powered environment. Data Security and Integrity is ensured by the way of enhanced Algorithm and Integrity through BlockChain and results are provided and discussed. Finally result and future enhancement are explained.

scholarly journals Design and Evaluation of a New Machine Learning Framework for IoT and Embedded Devices

Electronics ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 600
Author(s):  
Gianluca Cornetta ◽  
Abdellah Touhafi
Keyword(s):  
Machine Learning ◽  
Data Analysis ◽  
High Performance ◽  
Low Cost ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Smart Devices ◽  
Raspberry Pi ◽  
Embedded Devices ◽  
Iot Devices

Low-cost, high-performance embedded devices are proliferating and a plethora of new platforms are available on the market. Some of them either have embedded GPUs or the possibility to be connected to external Machine Learning (ML) algorithm hardware accelerators. These enhanced hardware features enable new applications in which AI-powered smart objects can effectively and pervasively run in real-time distributed ML algorithms, shifting part of the raw data analysis and processing from cloud or edge to the device itself. In such context, Artificial Intelligence (AI) can be considered as the backbone of the next generation of Internet of the Things (IoT) devices, which will no longer merely be data collectors and forwarders, but really “smart” devices with built-in data wrangling and data analysis features that leverage lightweight machine learning algorithms to make autonomous decisions on the field. This work thoroughly reviews and analyses the most popular ML algorithms, with particular emphasis on those that are more suitable to run on resource-constrained embedded devices. In addition, several machine learning algorithms have been built on top of a custom multi-dimensional array library. The designed framework has been evaluated and its performance stressed on Raspberry Pi III- and IV-embedded computers.

scholarly journals DES encryption and decryption algorithm implementation based on FPGA

2020 ◽  
Vol 18 (2) ◽  
pp. 774
Author(s):  
Subhi R. M. Zeebaree
Keyword(s):  
Data Security ◽  
Hardware Implementation ◽  
Block Cipher ◽  
Encryption Algorithm ◽  
The Internet ◽  
Clock Frequency ◽  
Hardware Implementations ◽  
Encryption And Decryption ◽  
Encryption Decryption ◽  
Algorithm Implementation

Nowadays there is a lot of importance given to data security on the internet. The DES is one of the most preferred block cipher encryption/decryption procedures used at present. This paper presents a high throughput reconfigurable hardware implementation of DES Encryption algorithm. This achieved by using a new proposed implementation of the DES algorithm using pipelined concept.  The implementation of the proposed design is presented by using Spartan-3E (XC3S500E) family FPGAs and is one of the fastest hardware implementations with much greater security. At a clock frequency of 167.448MHz for encryption and 167.870MHz for decryption, it can encrypt or decrypt data blocks at a rate of 10688Mbps.

C3PO: C loud-based C onfidentiality-preserving C ontinuous Query P r o cessing

2022 ◽  
Vol 25 (1) ◽  
pp. 1-36
Author(s):  
Savvas Savvides ◽  
Seema Kumar ◽  
Julian James Stephen ◽  
Patrick Eugster
Keyword(s):  
Low Cost ◽  
Empirical Evaluation ◽  
Raspberry Pi ◽  
Sensitive Data ◽  
Cloud Resource ◽  
Small Device ◽  
Computationally Intensive ◽  
Iot Devices ◽  
Cloud Infrastructures ◽  
Memory Resources

With the advent of the Internet of things (IoT), billions of devices are expected to continuously collect and process sensitive data (e.g., location, personal health factors). Due to the limited computational capacity available on IoT devices, the current de facto model for building IoT applications is to send the gathered data to the cloud for computation. While building private cloud infrastructures for handling large amounts of data streams can be expensive, using low-cost public (untrusted) cloud infrastructures for processing continuous queries including sensitive data leads to strong concerns over data confidentiality. This article presents C3PO, a confidentiality-preserving, continuous query processing engine, that leverages the public cloud. The key idea is to intelligently utilize partially homomorphic and property-preserving encryption to perform as many computationally intensive operations as possible—without revealing plaintext—in the untrusted cloud. C3PO provides simple abstractions to the developer to hide the complexities of applying complex cryptographic primitives, reasoning about the performance of such primitives, deciding which computations can be executed in an untrusted tier, and optimizing cloud resource usage. An empirical evaluation with several benchmarks and case studies shows the feasibility of our approach. We consider different classes of IoT devices that differ in their computational and memory resources (from a Raspberry Pi 3 to a very small device with a Cortex-M3 microprocessor) and through the use of optimizations, we demonstrate the feasibility of using partially homomorphic and property-preserving encryption on IoT devices.

Open Loop Speed Control Of Brushless DC Motor Using Low Cost Arduino UNO Processor

2016 ◽  
Vol 10 (1) ◽  
pp. 1
Author(s):  
Potnuru Devendra ◽  
Mary K. Alice ◽  
Ch. Sai Babu ◽  
◽  
◽  
...  
Keyword(s):  
Low Cost ◽  
Speed Control ◽  
Dc Motor ◽  
Open Loop ◽  
Brushless Dc Motor ◽  
Brushless Dc ◽  
Arduino Uno

Development of hardware and software system for access control and management

Ergodesign ◽  
2020 ◽  
Vol 2020 (1) ◽  
pp. 19-24
Author(s):  
Igor Pestov ◽  
Polina Shinkareva ◽  
Sofia Kosheleva ◽  
Maxim Burmistrov
Keyword(s):  
Access Control ◽  
High Mobility ◽  
Raspberry Pi ◽  
Software System ◽  
Hardware System ◽  
Software And Hardware ◽  
Arduino Uno ◽  
Control And Management ◽  
Hardware Platforms ◽  
Hardware And Software System

This article aims to develop a hardware-software system for access control and management based on the hardware platforms Arduino Uno and Raspberry Pi. The developed software and hardware system is designed to collect data and store them in the database. The presented complex can be carried and used anywhere, which explains its high mobility.

Bringing Security to The Smallest Embedded Systems

2017 ◽  
Author(s):  
JOSEPH YIU
Keyword(s):  
Internet Of Things ◽  
Embedded Systems ◽  
Low Cost ◽  
Security Requirements ◽  
The Internet ◽  
Security Measures ◽  
Significant Challenge ◽  
Iot Devices

The increasing need for security in microcontrollers Security has long been a significant challenge in microcontroller applications(MCUs). Traditionally, many microcontroller systems did not have strong security measures against remote attacks as most of them are not connected to the Internet, and many microcontrollers are deemed to be cheap and simple. With the growth of IoT (Internet of Things), security in low cost microcontrollers moved toward the spotlight and the security requirements of these IoT devices are now just as critical as high-end systems due to:

Medidor Inteligente de Energía Eléctrica utilizando la Tarjeta Electrónica Raspberry Pi

2018 ◽  
Vol 14 (1) ◽  
Author(s):  
L.F. Tipán ◽  
J.A. Rumipamba
Keyword(s):  
Raspberry Pi ◽  
El Sistema ◽  
Arduino Uno

El objeto de este documento es presentar un medidor de energia electrica inteligente con raspberry Pi y Arduino UNO, para visualizar el consumo electrico aproximado de un hogar tipo en tiempo real, mediante una aplicación Android y servidor web en la raspberry utilizando hojas de calculo en linea de google, porque aproximado debido a que no se hace un muestreo de voltaje sino que en base a parametros definidos se emplean valores establecidos. En la presente investigacion se muestra la arquitectura base y la metodologia empleada demostrando que los datos obtenidos con este sistema propuesto es muy parecido en comparacion con un sistema que se encuentra disponible en el mercado, especialmente europeo y norteamericano como lo es el sistema AEOTEC que utiliza protocolos z wave.

scholarly journals Virtualizing AI at the Distributed Edge Towards Intelligent IoT Applications

2021 ◽  
Vol 10 (1) ◽  
pp. 13
Author(s):  
Claudia Campolo ◽  
Giacomo Genovese ◽  
Antonio Iera ◽  
Antonella Molinaro
Keyword(s):  
Resource Constraints ◽  
Smart Cities ◽  
Traditional Approach ◽  
Low Cost ◽  
Iot Applications ◽  
Software And Hardware ◽  
Iot Devices ◽  
Consumer Devices ◽  
Hardware Platforms ◽  
Smart Factories

Several Internet of Things (IoT) applications are booming which rely on advanced artificial intelligence (AI) and, in particular, machine learning (ML) algorithms to assist the users and make decisions on their behalf in a large variety of contexts, such as smart homes, smart cities, smart factories. Although the traditional approach is to deploy such compute-intensive algorithms into the centralized cloud, the recent proliferation of low-cost, AI-powered microcontrollers and consumer devices paves the way for having the intelligence pervasively spread along the cloud-to-things continuum. The take off of such a promising vision may be hurdled by the resource constraints of IoT devices and by the heterogeneity of (mostly proprietary) AI-embedded software and hardware platforms. In this paper, we propose a solution for the AI distributed deployment at the deep edge, which lays its foundation in the IoT virtualization concept. We design a virtualization layer hosted at the network edge that is in charge of the semantic description of AI-embedded IoT devices, and, hence, it can expose as well as augment their cognitive capabilities in order to feed intelligent IoT applications. The proposal has been mainly devised with the twofold aim of (i) relieving the pressure on constrained devices that are solicited by multiple parties interested in accessing their generated data and inference, and (ii) and targeting interoperability among AI-powered platforms. A Proof-of-Concept (PoC) is provided to showcase the viability and advantages of the proposed solution.

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