scholarly journals A Robust, Low-Cost and Secure Authentication Scheme for IoT Applications

Cryptography ◽  
2020 ◽  
Vol 4 (1) ◽  
pp. 8
Author(s):  
Md Jubayer al Mahmod ◽  
Ujjwal Guin
Keyword(s):  
Low Cost ◽  
Resource Limitation ◽  
Computational Power ◽  
Iot Applications ◽  
Up States ◽  
On Chip ◽  
Software Overhead ◽  
Private Network ◽  
Secure Authentication ◽  
The Internet Of Things

The edge devices connected to the Internet of Things (IoT) infrastructures are increasingly susceptible to piracy. These pirated edge devices pose a serious threat to security, as an adversary can get access to the private network through these non-authentic devices. It is necessary to authenticate an edge device over an unsecured channel to safeguard the network from being infiltrated through these fake devices. The implementation of security features demands extensive computational power and a large hardware/software overhead, both of which are difficult to satisfy because of inherent resource limitation in the IoT edge devices. This paper presents a low-cost authentication protocol for IoT edge devices that exploits power-up states of built-in SRAM for device fingerprint generations. Unclonable ID generated from the on-chip SRAM could be unreliable, and to circumvent this issue, we propose a novel ID matching scheme that alleviates the need for enhancing the reliability of the IDs generated from on-chip SRAMs. Security and different attack analysis show that the probability of impersonating an edge device by an adversary is insignificant. The protocol is implemented using a commercial microcontroller, which requires a small code overhead. However, no modification of device hardware is necessary.

scholarly journals Low-Power and Low-Cost Environmental IoT Electronic Nose Using Initial Action Period Measurements

Sensors ◽  
2019 ◽  
Vol 19 (14) ◽  
pp. 3183 ◽  
Author(s):  
Carlos J. García-Orellana ◽  
Miguel Macías-Macías ◽  
Horacio M. González-Velasco ◽  
Antonio García-Manso ◽  
Ramón Gallardo-Caballero
Keyword(s):  
Electronic Nose ◽  
Low Cost ◽  
Specific Model ◽  
Battery Life ◽  
Sleep State ◽  
Analog To Digital ◽  
Current Consumption ◽  
Initial Action ◽  
On Chip ◽  
The Internet Of Things

In this work, we present a complete hardware development and current consumption study of a portable electronic nose designed for the Internet-of-Things (IoT). Thanks to the technique of measuring in the initial action period, it can be reliably powered with a moderate-sized battery. The system is built around the well-known SoC (System on Chip) ESP8266EX, using low-cost electronics and standard sensors from Figaro’s TGS26xx series. This SoC, in addition to a powerful microcontroller, provides Wi-Fi connectivity, making it very suitable for IoT applications. The system also includes a precision analog-to-digital converter for the measurements and a charging module for the lithium battery. During its operation, the designed software takes measurements periodically, and keeps the microcontroller in deep-sleep state most of the time, storing several measurements before uploading them to the cloud. In the experiments and tests carried out, we have focused our work on the measurement and optimization of current consumption, with the aim of extending the battery life. The results show that taking measurements every 4 min and uploading data every five measurements, the battery of 750 mAh needs to be charged approximately once a month. Despite the fact that we have used a specific model of gas sensor, this methodology is quite generic and could be extended to other sensors with lower consumption, increasing very significantly the duration of the battery.

scholarly journals Wireless Positioning in IoT: A Look at Current and Future Trends

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2470 ◽  
Author(s):  
Pedro Silva ◽  
Ville Kaseva ◽  
Elena Lohan
Keyword(s):  
Asset Management ◽  
Large Scale ◽  
Low Cost ◽  
Battery Life ◽  
Power Devices ◽  
Smart Lighting ◽  
Iot Applications ◽  
Simulation Based ◽  
Wireless Positioning ◽  
The Internet Of Things

Connectivity solutions for the Internet of Things (IoT) aim to support the needs imposed by several applications or use cases across multiple sectors, such as logistics, agriculture, asset management, or smart lighting. Each of these applications has its own challenges to solve, such as dealing with large or massive networks, low and ultra-low latency requirements, long battery life requirements (i.e., more than ten years operation on battery), continuously monitoring of the location of certain nodes, security, and authentication. Hence, a part of picking a connectivity solution for a certain application depends on how well its features solve the specific needs of the end application. One key feature that we see as a need for future IoT networks is the ability to provide location-based information for large-scale IoT applications. The goal of this paper is to highlight the importance of positioning features for IoT applications and to provide means of comparing and evaluating different connectivity protocols in terms of their positioning capabilities. Our compact and unified analysis ends with several case studies, both simulation-based and measurement-based, which show that high positioning accuracy on low-cost low-power devices is feasible if one designs the system properly.

scholarly journals Compact Convolutional Neural Network Accelerator for IoT Endpoint SoC

Electronics ◽  
2019 ◽  
Vol 8 (5) ◽  
pp. 497 ◽  
Author(s):  
Fen Ge ◽  
Ning Wu ◽  
Hao Xiao ◽  
Yuanyuan Zhang ◽  
Fang Zhou
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Functional Modules ◽  
Computational Power ◽  
System A ◽  
Unit Design ◽  
Artificial Intelligence Algorithm ◽  
And Performance ◽  
On Chip ◽  
The Internet Of Things

As a classical artificial intelligence algorithm, the convolutional neural network (CNN) algorithm plays an important role in image recognition and classification and is gradually being applied in the Internet of Things (IoT) system. A compact CNN accelerator for the IoT endpoint System-on-Chip (SoC) is proposed in this paper to meet the needs of CNN computations. Based on analysis of the CNN structure, basic functional modules of CNN such as convolution circuit and pooling circuit with a low data bandwidth and a smaller area are designed, and an accelerator is constructed in the form of four acceleration chains. After the acceleration unit design is completed, the Cortex-M3 is used to construct a verification SoC and the designed verification platform is implemented on the FPGA to evaluate the resource consumption and performance analysis of the CNN accelerator. The CNN accelerator achieved a throughput of 6.54 GOPS (giga operations per second) by consuming 4901 LUTs without using any hardware multipliers. The comparison shows that the compact accelerator proposed in this paper makes the CNN computational power of the SoC based on the Cortex-M3 kernel two times higher than the quad-core Cortex-A7 SoC and 67% of the computational power of eight-core Cortex-A53 SoC.

scholarly journals Design and Manufacturing of a Disposable, Cyclo-Olefin Copolymer, Microfluidic Device for a Biosensor †

Sensors ◽  
2019 ◽  
Vol 19 (5) ◽  
pp. 1178 ◽  
Author(s):  
Jorge Prada ◽  
Christina Cordes ◽  
Carsten Harms ◽  
Walter Lang
Keyword(s):  
Microfluidic Device ◽  
Heating Temperature ◽  
Low Cost ◽  
Reaction Chamber ◽  
Microfluidic System ◽  
Heating Process ◽  
Design And Manufacturing ◽  
On Chip ◽  
Working Parameters ◽  
Auto Fluorescence

This contribution outlines the design and manufacturing of a microfluidic device implemented as a biosensor for retrieval and detection of bacteria RNA. The device is fully made of Cyclo-Olefin Copolymer (COC), which features low auto-fluorescence, biocompatibility and manufacturability by hot-embossing. The RNA retrieval was carried on after bacteria heat-lysis by an on-chip micro-heater, whose function was characterized at different working parameters. Carbon resistive temperature sensors were tested, characterized and printed on the biochip sealing film to monitor the heating process. Off-chip and on-chip processed RNA were hybridized with capture probes on the reaction chamber surface and identification was achieved by detection of fluorescence tags. The application of the mentioned techniques and materials proved to allow the development of low-cost, disposable albeit multi-functional microfluidic system, performing heating, temperature sensing and chemical reaction processes in the same device. By proving its effectiveness, this device contributes a reference to show the integration potential of fully thermoplastic devices in biosensor systems.

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.

scholarly journals Attack Categorisation for IoT Applications in Critical Infrastructures, a Survey

2021 ◽  
Vol 11 (16) ◽  
pp. 7228
Author(s):  
Edward Staddon ◽  
Valeria Loscri ◽  
Nathalie Mitton
Keyword(s):  
Critical Infrastructure ◽  
Attack Detection ◽  
Critical Infrastructures ◽  
Cyber Attack ◽  
Specific System ◽  
Iot Applications ◽  
War Zone ◽  
Clear Vision ◽  
The Internet Of Things ◽  
Do So

With the ever advancing expansion of the Internet of Things (IoT) into our everyday lives, the number of attack possibilities increases. Furthermore, with the incorporation of the IoT into Critical Infrastructure (CI) hardware and applications, the protection of not only the systems but the citizens themselves has become paramount. To do so, specialists must be able to gain a foothold in the ongoing cyber attack war-zone. By organising the various attacks against their systems, these specialists can not only gain a quick overview of what they might expect but also gain knowledge into the specifications of the attacks based on the categorisation method used. This paper presents a glimpse into the area of IoT Critical Infrastructure security as well as an overview and analysis of attack categorisation methodologies in the context of wireless IoT-based Critical Infrastructure applications. We believe this can be a guide to aid further researchers in their choice of adapted categorisation approaches. Indeed, adapting appropriated categorisation leads to a quicker attack detection, identification, and recovery. It is, thus, paramount to have a clear vision of the threat landscapes of a specific system.

Voice In Ear

2021 ◽  
Vol 5 (1) ◽  
pp. 1-25
Author(s):  
Yang Gao ◽  
Yincheng Jin ◽  
Jagmohan Chauhan ◽  
Seokmin Choi ◽  
Jiyang Li ◽  
...  
Keyword(s):  
User Authentication ◽  
Low Cost ◽  
The Body ◽  
Ear Canal ◽  
Replay Attacks ◽  
Increasing Demand ◽  
The Right ◽  
Sound Conduction ◽  
Enrollment Data ◽  
Secure Authentication

With the rapid growth of wearable computing and increasing demand for mobile authentication scenarios, voiceprint-based authentication has become one of the prevalent technologies and has already presented tremendous potentials to the public. However, it is vulnerable to voice spoofing attacks (e.g., replay attacks and synthetic voice attacks). To address this threat, we propose a new biometric authentication approach, named EarPrint, which aims to extend voiceprint and build a hidden and secure user authentication scheme on earphones. EarPrint builds on the speaking-induced body sound transmission from the throat to the ear canal, i.e., different users will have different body sound conduction patterns on both sides of ears. As the first exploratory study, extensive experiments on 23 subjects show the EarPrint is robust against ambient noises and body motions. EarPrint achieves an Equal Error Rate (EER) of 3.64% with 75 seconds enrollment data. We also evaluate the resilience of EarPrint against replay attacks. A major contribution of EarPrint is that it leverages two-level uniqueness, including the body sound conduction from the throat to the ear canal and the body asymmetry between the left and the right ears, taking advantage of earphones' paring form-factor. Compared with other mobile and wearable biometric modalities, EarPrint is a low-cost, accurate, and secure authentication solution for earphone users.

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