LeaD: Learn to Decode Vibration-based Communication for Intelligent Internet of Things

2021 ◽  
Vol 17 (3) ◽  
pp. 1-25
Author(s):  
Guangrong Zhao ◽  
Bowen Du ◽  
Yiran Shen ◽  
Zhenyu Lao ◽  
Lizhen Cui ◽  
...  
Keyword(s):  
Machine Learning ◽  
Transmission Rate ◽  
Low Cost ◽  
Communication Protocols ◽  
Computation Time ◽  
Classification Problem ◽  
Smart Devices ◽  
Smart Watch ◽  
Iot Devices ◽  
Short Messages

In this article, we propose, LeaD , a new vibration-based communication protocol to Lea rn the unique patterns of vibration to D ecode the short messages transmitted to smart IoT devices. Unlike the existing vibration-based communication protocols that decode the short messages symbol-wise, either in binary or multi-ary, the message recipient in LeaD receives vibration signals corresponding to bits-groups. Each group consists of multiple symbols sent in a burst and the receiver decodes the group of symbols as a whole via machine learning-based approach. The fundamental behind LeaD is different combinations of symbols (1 s or 0 s) in a group will produce unique and reproducible patterns of vibration. Therefore, decoding in vibration-based communication can be modeled as a pattern classification problem. We design and implement a number of different machine learning models as the core engine of the decoding algorithm of LeaD to learn and recognize the vibration patterns. Through the intensive evaluations on large amount of datasets collected, the Convolutional Neural Network (CNN)-based model achieves the highest accuracy of decoding (i.e., lowest error rate), which is up to 97% at relatively high bits rate of 40 bits/s. While its competing vibration-based communication protocols can only achieve transmission rate of 10 bits/s and 20 bits/s with similar decoding accuracy. Furthermore, we evaluate its performance under different challenging practical settings and the results show that LeaD with CNN engine is robust to poses, distances (within valid range), and types of devices, therefore, a CNN model can be generally trained beforehand and widely applicable for different IoT devices under different circumstances. Finally, we implement LeaD on both off-the-shelf smartphone and smart watch to measure the detailed resources consumption on smart devices. The computation time and energy consumption of its different components show that LeaD is lightweight and can run in situ on low-cost smart IoT devices, e.g., smartwatches, without accumulated delay and introduces only marginal system overhead.

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 Enhancing Security on IoT Devices via Machine Learning on Conditional Power Dissipation

Electronics ◽  
2020 ◽  
Vol 9 (11) ◽  
pp. 1799
Author(s):  
Dimitrios Myridakis ◽  
Stefanos Papafotikas ◽  
Konstantinos Kalovrektis ◽  
Athanasios Kakarountas
Keyword(s):  
Machine Learning ◽  
Power Dissipation ◽  
Low Cost ◽  
Rapid Development ◽  
Abnormal Behavior ◽  
Smart Devices ◽  
Side Channel ◽  
Sensitive Data ◽  
Wide Range ◽  
Iot Devices

The rapid development of connected devices and the sensitive data, which they produce, is a major challenge for manufacturers seeking to fully protect their devices from attack. Consumers expect their IoT devices and data to be adequately protected against a wide range of vulnerabilities and exploits. Successful attacks target IoT devices, cause security problems, and pose new challenges. Successful attacks from botnets residing on mastered IoT devices increase significantly in number and the severity of the damage they cause is similar to that of a war. The characteristics of attacks vary widely from attack to attack and from time to time. The warnings about the severity of the attacks indicate that there is a need for solutions to address the attacks from birth. In addition, there is a need to quarantine infected IoT devices, preventing the spread of the virus and thus the formation of the botnet. This work introduces the exploitation of side-channel attack techniques to protect the low-cost smart devices intuitively, and integrates a machine learning-based algorithm for Intrusion Detection, exploiting current supply characteristic dissipation. The results of this work showed successful detection of abnormal behavior of smart IoT devices.

Survey on DDoS Attacks and Defense Mechanisms in Cloud and Fog Computing

2018 ◽  
Vol 10 (3) ◽  
pp. 61-83 ◽  
Author(s):  
Deepali Chaudhary ◽  
Kriti Bhushan ◽  
B.B. Gupta
Keyword(s):  
Cloud Computing ◽  
Defense Mechanisms ◽  
Low Cost ◽  
Fog Computing ◽  
Smart Devices ◽  
Cloud Environment ◽  
Security Issue ◽  
Process Data ◽  
Ddos Attack ◽  
Iot Devices

This article describes how cloud computing has emerged as a strong competitor against traditional IT platforms by offering low-cost and “pay-as-you-go” computing potential and on-demand provisioning of services. Governments, as well as organizations, have migrated their entire or most of the IT infrastructure to the cloud. With the emergence of IoT devices and big data, the amount of data forwarded to the cloud has increased to a huge extent. Therefore, the paradigm of cloud computing is no longer sufficient. Furthermore, with the growth of demand for IoT solutions in organizations, it has become essential to process data quickly, substantially and on-site. Hence, Fog computing is introduced to overcome these drawbacks of cloud computing by bringing intelligence to the edge of the network using smart devices. One major security issue related to the cloud is the DDoS attack. This article discusses in detail about the DDoS attack, cloud computing, fog computing, how DDoS affect cloud environment and how fog computing can be used in a cloud environment to solve a variety of problems.

A Tweakable Key Alternating Lightweight Cipher for Internet of Things

2020 ◽  
Vol 14 (4) ◽  
pp. 113-133
Author(s):  
Mary Shamala L. ◽  
Zayaraz G. ◽  
Vivekanandan K. ◽  
Vijayalakshmi V.
Keyword(s):  
Internet Of Things ◽  
Low Cost ◽  
Communication Protocols ◽  
Global Network ◽  
Limited Resources ◽  
Lightweight Cryptography ◽  
Cryptographic Algorithm ◽  
Wide Range ◽  
Iot Devices ◽  
Lightweight Cipher

Internet of things (IoT) is a global network of uniquely addressable interconnected things, based on standard communication protocols. As the number of devices connected to the IoT escalates, they are becoming a likely target for hackers. Also, the limited resources of IoT devices makes the security on top of the actual functionality of the device. Therefore, the cryptographic algorithm for such devices has to be devised as small as possible. To tackle the resource constrained nature of IoT devices, this article presents a lightweight cryptography algorithm based on a single permutation and iterated Even-Mansour construction. The proposed algorithm is implemented in low cost microcontrollers, thus making it suitable for a wide range of IoT nodes.

Survey on DDoS Attacks and Defense Mechanisms in Cloud and Fog Computing

2019 ◽  
pp. 1927-1951
Author(s):  
Deepali Chaudhary ◽  
Kriti Bhushan ◽  
B.B. Gupta
Keyword(s):  
Cloud Computing ◽  
Defense Mechanisms ◽  
Low Cost ◽  
Fog Computing ◽  
Smart Devices ◽  
Cloud Environment ◽  
Security Issue ◽  
Process Data ◽  
Ddos Attack ◽  
Iot Devices

This article describes how cloud computing has emerged as a strong competitor against traditional IT platforms by offering low-cost and “pay-as-you-go” computing potential and on-demand provisioning of services. Governments, as well as organizations, have migrated their entire or most of the IT infrastructure to the cloud. With the emergence of IoT devices and big data, the amount of data forwarded to the cloud has increased to a huge extent. Therefore, the paradigm of cloud computing is no longer sufficient. Furthermore, with the growth of demand for IoT solutions in organizations, it has become essential to process data quickly, substantially and on-site. Hence, Fog computing is introduced to overcome these drawbacks of cloud computing by bringing intelligence to the edge of the network using smart devices. One major security issue related to the cloud is the DDoS attack. This article discusses in detail about the DDoS attack, cloud computing, fog computing, how DDoS affect cloud environment and how fog computing can be used in a cloud environment to solve a variety of problems.

scholarly journals Using Pyspark Environment for Solving a Big Data Problem: Searching for Supersymmetric Particles

2020 ◽  
Vol 9 (7) ◽  
pp. 541-546
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Computation Time ◽  
Large Data ◽  
Classification Problem ◽  
High Energy ◽  
Accuracy Score ◽  
Supersymmetric Particles ◽  
Supersymmetry Theory ◽  
Big Data Classification

Supersymmetry theory predicts that every particle in the standard model has a superpartner particle with a different mass. The Classification Problem of Supersymmetric Particles in High-Energy represents a major challenge for physicists. This paper aims to resolve the Big data Classification Problem in the area of Supersymmetric Particles using the Apache Spark Environment with the "MLlib" library. This contribution attempts to explore the performance of Machine Learning methods in the context of large data such as a "Susy" dataset, collected from the UCI Machine Learning repository. In this work, the performance is measured using three metrics: Accuracy, Area Under Curve (AUC), and training Computation Time (CT). The results are promising and show that the Gradient Boosted Tree (GBT) classifier achieves a high accuracy score (79%). While the Logistic Regression (LR) algorithm realizes a well AUC score (86%).

scholarly journals Smart Devices Security Enhancement via Power Supply Monitoring

2020 ◽  
Vol 12 (3) ◽  
pp. 48
Author(s):  
Dimitrios Myridakis ◽  
Georgios Spathoulas ◽  
Athanasios Kakarountas ◽  
Dimitrios Schinianakis
Keyword(s):  
Intrusion Detection ◽  
Low Cost ◽  
Attack Detection ◽  
Smart Devices ◽  
Smart Device ◽  
Potential Threat ◽  
Public And Private ◽  
Continuous Growth ◽  
Supply Current ◽  
Iot Devices

The continuous growth of the number of Internet of Things (IoT) devices and their inclusion to public and private infrastructures has introduced new applciations to the market and our day-to-day life. At the same time, these devices create a potential threat to personal and public security. This may be easily understood either due to the sensitivity of the collected data, or by our dependability to the devices’ operation. Considering that most IoT devices are of low cost and are used for various tasks, such as monitoring people or controlling indoor environmental conditions, the security factor should be enhanced. This paper presents the exploitation of side-channel attack technique for protecting low-cost smart devices in an intuitive way. The work aims to extend the dataset provided to an Intrusion Detection Systems (IDS) in order to achieve a higher accuracy in anomaly detection. Thus, along with typical data provided to an IDS, such as network traffic, transmitted packets, CPU usage, etc., it is proposed to include information regarding the device’s physical state and behaviour such as its power consumption, the supply current, the emitted heat, etc. Awareness of the typical operation of a smart device in terms of operation and functionality may prove valuable, since any deviation may warn of an operational or functional anomaly. In this paper, the deviation (either increase or decrease) of the supply current is exploited for this reason. This work aimed to affect the intrusion detection process of IoT and proposes for consideration new inputs of interest with a collateral interest of study. In parallel, malfunction of the device is also detected, extending this work’s application to issues of reliability and maintainability. The results present 100% attack detection and this is the first time that a low-cost security solution suitable for every type of target devices is presented.

SAREF4INMA: A SAREF extension for the industry and manufacturing domain

Semantic Web ◽  
2020 ◽  
Vol 11 (6) ◽  
pp. 911-926 ◽  
Author(s):  
Mike de Roode ◽  
Alba Fernández-Izquierdo ◽  
Laura Daniele ◽  
María Poveda-Villalón ◽  
Raúl García-Castro
Keyword(s):  
Communication Protocols ◽  
Use Cases ◽  
Smart Devices ◽  
Industrial Sectors ◽  
Iot Devices ◽  
Smart Industry ◽  
Semantic Layer

The IoT landscape is characterized by a fragmentation of standards, platforms and technologies, often scattered among different vertical domains. To prevent the market to continue to be fragmented and power-less, a protocol-independent semantic layer can serve as enabler of interoperability among the various smart devices from different manufacturers that co-exist in a specific industry domain, but also across different domains. To that end, the SAREF ontology was created in 2015 with the intention to interconnect data, enabling the communication between IoT devices that use different protocols and standards. A number of industrial sectors consequently expressed their interest to extend SAREF into their domains in order to fill the gaps of the semantics not yet covered by their communication protocols. Therefore, the SAREF4INMA ontology was recently created to extend SAREF for describing the Smart Industry & Manufacturing domain. SAREF4INMA is based on several standards and IoT initiatives, as well as on real use cases, and includes classes, properties and instances specifically created to cover the industry and manufacturing domain. This work describes the approach followed to develop this ontology, specifies its requirements and also includes a practical example of how to use it.

scholarly journals A Comprehensive Study of Anomaly Detection Schemes in IoT Networks Using Machine Learning Algorithms

Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8320
Author(s):  
Abebe Diro ◽  
Naveen Chilamkurti ◽  
Van-Doan Nguyen ◽  
Will Heyne
Keyword(s):  
Machine Learning ◽  
Anomaly Detection ◽  
Resource Constraints ◽  
Detection System ◽  
Machine Learning Algorithms ◽  
Smart Devices ◽  
Detection Systems ◽  
Massive Number ◽  
Iot Devices ◽  
Detection Schemes

The Internet of Things (IoT) consists of a massive number of smart devices capable of data collection, storage, processing, and communication. The adoption of the IoT has brought about tremendous innovation opportunities in industries, homes, the environment, and businesses. However, the inherent vulnerabilities of the IoT have sparked concerns for wide adoption and applications. Unlike traditional information technology (I.T.) systems, the IoT environment is challenging to secure due to resource constraints, heterogeneity, and distributed nature of the smart devices. This makes it impossible to apply host-based prevention mechanisms such as anti-malware and anti-virus. These challenges and the nature of IoT applications call for a monitoring system such as anomaly detection both at device and network levels beyond the organisational boundary. This suggests an anomaly detection system is strongly positioned to secure IoT devices better than any other security mechanism. In this paper, we aim to provide an in-depth review of existing works in developing anomaly detection solutions using machine learning for protecting an IoT system. We also indicate that blockchain-based anomaly detection systems can collaboratively learn effective machine learning models to detect anomalies.

scholarly journals Fostering a high efficient waveguide diplexer to improve 5G network frequency spectrum for IoT devices in cloud

2018 ◽  
Vol 7 (2.12) ◽  
pp. 228
Author(s):  
Gautami Alagarsamy ◽  
Dr J.Shanthini
Keyword(s):  
Frequency Spectrum ◽  
Low Cost ◽  
Smart Phone ◽  
Cloud Services ◽  
Smart Devices ◽  
Network Connection ◽  
High Efficient ◽  
5G Network ◽  
Iot Devices ◽  
The Impact

In recent days Internet of Things and 5G network connection are complement to each other. The 5G networks would surpass 4GLTE, 4G, 3G and the other networks we used. It has become a boon for the end users and corporate due to its architecture to handle the heavy data traffic of connected smart devices and large amount of smart phone users worldwide. 5G devices should support longer battery and available at low cost and consume less energy. Some smart phones have ability to charge wirelessly through inductive coupling between base and the phone. In advanced options for charging IoT devices Wireless deportation technology is integrated. To curb network congestion in denser areas and to operate at higher data rates in Ka band applications this research paper analyzes the impact of WR-28 Waveguide Diplexer to improve network frequency spectrum around 30GHz for IoT devices in cloud services. The design simulation and modeling are implemented by using Antenna Magus version 5.5 Software.  

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