Embedded IoT Devices Research Articles

An Intelligent Fingerprinting Technique for Low-Power Embedded IoT Devices

An Intelligent Fingerprinting Technique for Low-Power Embedded IoT Devices

A Novel Low-Cost Capacitance Sensor Solution for Real-Time Bubble Monitoring in Medical Infusion Devices

In the present day, IoT technology is widely applied in the field of medical devices to facilitate real-time monitoring and management by medical staff, thereby better-ensuring patient safety. In IoT intravenous infusion monitoring sensors, it is particularly important to ensure that air bubbles are not infused into the patient’s body. The most common method for bubble detection during intravenous infusions is the use of infrared or laser sensors, which can usually meet design requirements at a relatively low cost. Another method is the use of ultrasonic detection of bubbles, which achieves high accuracy but has not been widely promoted in the market due to higher costs. This proposed work introduces a new type of sensor that detects bubbles by monitoring changes in capacitance between two electrodes installed at the surface of the infusion pipe. If this sensor is deployed on the ESP32 platform, which is widely used in embedded IoT devices, it can achieve 35 μL bubble detection precision with an average power consumption of 5.18 mW and a mass production cost of $0.022. Although the precision of this sensor is significantly lower than the low-cost IR bubble sensor, it still satisfies the design requirement of the IV infusion IoT sensor.

An AI-Based Ventilation KPI Using Embedded IoT Devices

The air ventilation of enclosed premises has a direct impact on the occupants well-being. If not properly regulated, the air ventilation can originate a multitude of diseases and pathologies. The present study proposes a new KPI (ventilation KPI) adapted to Smart Cities. It is especially designed for academic environments (Smart Universities) in which community members spend a long time gathered in classrooms, seminars, laboratories, etc. The ventilation KPI (or KPIv) was designed to support decision-making and is based on the estimation of the number of occupants of an enclosed space and the accumulation of existing CO2. Two AI techniques are proposed to perform these estimations, specifically, two regressive neural networks. The resulting models, together with the KPI were implemented through the development of value-added services for the University of Alicantes Smart University platform. The network models were designed to be embedded within the built IoT device prototypes. These prototypes are small and inexpensive. They act as intelligent sensors and are connected via a low consumption and emission network (LoRa). The case study showed that it is possible to take advantage of the pre-existing services and resources of these platforms, and to validate the KPIv.

Enabling Space-Aware Service Discovery Model in Home Networks through a Compatible Extension to mDNS/DNS-SD

The advent of the smart home, fueled by the rapid emergence of tiny embedded IoT devices and sensors, brings the consideration of space and location information to the forefront of service discovery. In a smart home environment, spaces often have composite capabilities and complex structures. Since residents may have varying preferences based on their location within different spaces, traditional service discovery results may be suboptimal without considering the spatial context. This paper introduces a seamless approach to integrate space and location information with mDNS/DNS-SD, a widely utilized service discovery protocol in home networks. We provide a formal specification for this approach, develop a prototype, and perform a series of experiments to evaluate the efficacy and potential of the proposed method.

New Instances in IOT Technology

Abstract: Future of the Internet of Things with enabled Artificial Intelligence, block chain technology, Big data technology, cloud AWS - Azure endpoints with little coding or no coding to manager enamors powerful device management. Few of the application of Internet of Things like B2C sector of summers like smart homes, smart appliances and elderly care. In infrastructure area in internet of things like smart city, smart energy & utilities and environmental monitoring. In the Commerce area in the Internet of Things like Medical & healthcare, transportation and building accommodations. Coming to Industry and farming area in the Internet of Things like smart manufacturing and smart agricultures and more. Coming to IT Datacenters need to use Internet of Things and IoT enabled ship, airplanes and trains exceptional application going use in multiple manners. IoT application development services enables on all IoT devices / machines connect virtually with global wireless standard using fifth generation 5G mobile network (ultra low latency internet via embedded IoT devices). In Asia-Pacific zone, 5G adoption is going to hit 4 billion in 2025 as per Ericson mobility report.

DeepTrack: Lightweight Deep Learning for Vehicle Trajectory Prediction in Highways

Vehicle trajectory prediction is essential for enabling safety-critical intelligent transportation systems (ITS) applications used in management and operations. While there have been some promising advances in the field, there is a need for modern deep learning algorithms that allow real-time trajectory prediction on embedded IoT devices. This article presents DeepTrack, a novel deep learning algorithm customized for real-time vehicle trajectory prediction and monitoring applications in arterial management, freeway management, traffic incident management, and work zone management for high-speed incoming traffic. In contrast to previous methods, the vehicle dynamics are encoded using Temporal Convolutional Networks (TCNs) to provide more robust time prediction with less computation. DeepTrack also uses depthwise convolution, which reduces the complexity of models compared to existing approaches in terms of model size and operations. Overall, our experimental results demonstrate that DeepTrack achieves comparable accuracy to state-of-the-art trajectory prediction models but with smaller model sizes and lower computational complexity, making it more suitable for real-world deployment.

Abnormal beat detection from unreconstructed compressed signals based on linear approximation in ECG signals suitable for embedded IoT devices

Abnormal beat detection from unreconstructed compressed signals based on linear approximation in ECG signals suitable for embedded IoT devices

Performance Analysis of Secure Elements for IoT

New protocol stacks provide wireless IPv6 connectivity down to low power embedded IoT devices. From a security point of view, this leads to high exposure of such IoT devices. Consequently, even though they are highly resource-constrained, these IoT devices need to fulfil similar security requirements as conventional computers. The challenge is to leverage well-known cybersecurity techniques for such devices without dramatically increasing power consumption (and therefore reducing battery lifetime) or the cost regarding memory sizes and required processor performance. Various semiconductor vendors have introduced dedicated hardware devices, so-called secure elements that address these cryptographic challenges. Secure elements provide tamper-resistant memory and hardware-accelerated cryptographic computation support. Moreover, they can be used for mutual authentication with peers, ensuring data integrity and confidentiality, and various other security-related use cases. Nevertheless, publicly available performance figures on energy consumption and execution times are scarce. This paper introduces the concept of secure elements and provides a measurement setup for selected individual cryptographic primitives and a Datagram Transport Layer Security (DTLS) handshake over secure Constrained Application Protocol (CoAPs) in a realistic use case. Consequently, the paper presents quantitative results for the performance of five secure elements. Based on these results, we discuss the characteristics of the individual secure elements and supply developers with the information needed to select a suitable secure element for a specific application.

Rootkit Detection on Embedded IoT Devices

IoT systems are subject to cyber attacks, including infecting embedded IoT devices with rootkits. Rootkits are malicious software that typically run with elevated privileges, which makes their detection challenging. In this paper, we address this challenge: we propose a rootkit detection approach for embedded IoT devices that takes advantage of a trusted execution environment (TEE), which is often supported on popular IoT platforms, such as ARM based embedded boards. The TEE provides an isolated environment for our rootkit detection algorithms, and prevents the rootkit from interfering with their execution even if the rootkit has root privileges on the untrusted part of the IoT device. Our rootkit detection algorithms identify modifications made by the rootkit to the code of the operating system kernel, to system programs, and to data influencing the control flow (e.g., hooking system calls), as well as inconsistencies created by the rootkit in certain kernel data structures (e.g., those responsible to handle process related information). We also propose algorithms to detect rootkit components in the persistent storage of the device. Besides describing our approach and algorithms in details, we also report on a prototype implementation and on the evaluation of our design and implementation, which is based on testing our prototype with rootkits that we developed for this purpose.

Wearable Internet of Cardiac Health (IoCH) Monitoring: The need of the Hour

In the recent years, the advancements in the wearable sensor technology has made it possible to apply sensor embedded IoT devices such as smart watch, smart glass, smart phone, and smart helmet to monitor the vital cardiac health parameters. The sensor embedded IoT devices collects the healthcare data in a continuous fashion, which are least useful if not stored, processed, and analyzed in a real-time. Moreover, mearly real-time processing of the healthcare data may not serve the purpose as the underlying data might be highly unstructured and messy. Therefore, Artificial Intelligent (AI) assisted analytical models are required to analyze the healthcare data for cardiac early warning prediction. In this paper, we provide a narrative mini review on the recent advancement in wearable technology is discussed. The paper describes the growin problem of Coronary Heart Diseases (CHDs) and the wearable devices that assist in the acquisition of healthcare data.

TEE-based protection of cryptographic keys on embedded IoT devices

TEE-based protection of cryptographic keys on embedded IoT devices

Internet of Things: Impact of IoT in Business Environment and Challenges in Secure Implementation

The Internet of Things as new paradigm is interconnection of computing ,physical and mechanical device together with people with the ability to transfer the data over network. The embedded devices may gather and swap over data with the support of network connectivity, sensors and electronics. The diversified deployment area of IoT are not limited to smart home application, health care industry, education industry and agriculture, etc. It also taking step ahead in developing new products or services in business. With the help of this emerging technology business will have the major impact by improved customer engagement, productivity enhancement, and better access to data, enhanced inventory tracking and security. Whereas the rapid growth rate of IoT network is getting attention of the cyber criminals. In recent advancement, different types of embedded IoT devices are connected together with wireless network and continuously access internet for communication. Cyber criminals are finding vulnerabilities on IoT devices and compromise them to launch massive attacks (e.g. DDoS, Spamming, MITM, RFID Skimming) to destroy the network. IoT devices having default authentication credentials are easy target. This paper highlights that how IoT may introduce the better opportunities in business and challenges of secure IoT connection and while communicating the IoT device. This research also highlights the security aspects of existing technologies and existing technologies and challenges with implementation.

A systematic review of the applications of multi-criteria decision-making methods in site selection problems

PurposeThe purpose of this paper is to perform a systematic review on the application of different multi-criteria decision-making (MCDM) methods in solving the site selection problem across multiple problem domains. The domains are energy generation, logistics, public services and retail facilities. This study aims to answer the following research questions: Which evaluating criteria were used for each site selection problem domain? Which MCDM methods were frequently applied in a particular site selection problem domain?Design/methodology/approachThe goals of the systematic review were to identify the evaluating criteria as well as the MCDM method used for each problem domain. A total of 81 recent papers (2014–2018) including 32 papers published in conference proceedings and 49 journal articles from various databases including IEEE Xplore, PubMed, Springer, Taylor and Francis as well as ScienceDirect were evaluated.FindingsThis study has shown that site selection for energy generation facilities is the most active site selection problem domain, and that the analytic hierarchy process (AHP) method is the most commonly used MCDM method for site selection. For energy generation, the criteria which were most used were geographical elements, land use, cost and environmental impact. For logistics, frequently used criteria were geographical elements and distance, while for public services population density, supply and demand, geographical layout and cost were the criteria most used. Criteria useful for retail facilities were the size (space) of the store, demographics of the site, the site characteristics and rental of the site (cost).Research limitations/implicationsThis study is limited to reviewing papers which were published in the years 2014–2018 only, and only covers the domains of energy generation, logistics, public services and retail facilities.Practical implicationsMCDM is a viable tool to be used for solving the site selection problem across the domains of energy generation, logistics, public services and retail facilities. The usage of MCDM continues to be relevant as a complement to machine learning, even as data originating from embedded IoT devices in built environments becomes increasingly Big Data like.Originality/valuePrevious systematic review studies for MDCM and built environments have either focused on studying the MCDM techniques itself, or have focused on the application of MCDM for site selection in a single problem domain. In this study, a critical review of MCDM techniques used for site selection as well as the critical criteria used during the MCDM process of site selection was performed on four different built environment domains.

Object detection mechanism based on deep learning algorithm using embedded IoT devices for smart home appliances control in CoT

Object detection and recognition is commonly used in diverse computer vision based applications and many algorithms are proposed in literature. However, application of object detection algorithms in real-time systems demand minimal computation time and comprehensive performance analysis needs to be conducted before deployment. This paper aims to conduct performance analysis of deep learning based algorithm i.e. single shot detector (SSD) in IoT based embedded devices for smart home appliances control. We have developed a smart home automation system using object detection algorithm based on model view controller (MVC) architecture deployed on Cloud of Things (CoT) i.e. Amazon Web Service (AWS) cloud for users to remotely monitor their homes. Message queuing telemetry transport (MQTT) protocol is used for communication with connected IoT devices. For load-balancing, we have proposed the concept of distributed broker to support high number of publishers and subscribers. For experimental analysis, we have connected a camera with Raspberry Pi for object detection based on deep learning algorithm (SSD) using OpenCV library in our proposed system. Experiments are conducted to evaluate the performance of object detection algorithm under varying environmental conditions by changing the light intensity level, distance of object from camera, and frame size of video. Results show that communication delay is very low (i.e. 0.2 s) as compared to processing delay in Raspberry Pi. Furthermore, changing environmental conditions have very low/insignificant impact on the processing delay of object detection algorithm i.e. average delay of 1.7 s (stdev. 0.18) and 1.8 s (stdev. 0.24) in bright and dark lighting levels, respectively. However, accuracy is deteriorated under low lighting intensity level and increased frame sizes i.e. from 95–100 to 80–85%. Selected embedded device, camera model and object detection algorithm limits the performance of object detection in real-time systems and shall be carefully selected to fulfill the requirement.

Integrative Acceleration of First-order Boolean Masking for Embedded IoT Devices

Integrative Acceleration of First-order Boolean Masking for Embedded IoT Devices

How to Improve the IoT Security Implementing IDS/IPS Tool using Raspberry Pi 3B+

This work shows a methodology of implementation and testing of the system is proposed and tested with a prototype; it is constructed with sensors and actuators that allow monitoring the behavior of the system in an environment under threats. We used an IDS / IPS as a protection tool for IoT systems, based on Raspberry Pi and Raspbian operating system. It is described in a block diagram the testing method used. We implemented the IDS/IPS Snort tool in an embedded platform Raspberry. It presents also the state of the art of cloud frameworks that have the same objective of protecting. The main contribution is the implemented testing method for Snort that can be used with security rules in other applications of embedded IoT devices.

Security Implications for Ultra-Low Power Configurable SoC FPAA Embedded Systems

We discuss the impact of physical computing techniques to classifying network security issues for ultra-low power networked IoT devices. Physical computing approaches enable at least a factor of 1000 improvement in computational energy efficiency empowering a new generation of local computational structures for embedded IoT devices. These techniques offer computational capability to address network security concerns. This paper begins the discussion of security opportunities for, and issues using, FPAA devices for small embedded IoT platforms. These FPAAs enable devices often utilized for low-power context aware computation. Embedded FPAA devices have both positive Security attributes, as well as potential vulnerabilities. FPAA devices can be part of the resulting secure computation, such as implementing unique functions. FPAA devices can be used investigate security of analog/mixed signal capabilities. The paper concludes with summarizing key improvements for secure ultra-low power embedded FPAA devices.

Deep Learning for the Internet of Things

How can the advantages of deep learning be brought to the emerging world of embedded IoT devices? The authors discuss several core challenges in embedded and mobile deep learning, as well as recent solutions demonstrating the feasibility of building IoT applications that are powered by effective, efficient, and reliable deep learning models.

Proxy Based Seamless Connection Management Scheme for Smart Transportation Service

Nowadays, complex IoT devices are developed that support smart transportation service, medical alarm service, and smart surveillance camera which require communication to the computation server. Most of IoT devices communicate to their servers on fixed network through wireless channel. In this paper, we are focused on smart transportation service that is one of bus, train, airplane or ship that takes embedded IoT devices and passengers using IoT devices. Embedded IoT devices are communicated with embedded access router (AR) on transportation by Bluetooth/Z-wave/NFC, and passengers' IoT devices are communicated with their own egg/AR on fixed network, or public AR on transportation. Transportation environment has a major problem that mobile node moves fast through multiple cells. Each IoT device in transportation keeps updating its current point of attachment to its home agent and corresponding node. To overcome this problem we consider feature of transportation. It has two major features. The one is mobile device has fast mobility by transportation. The other one is mobile device moves fixed route. It causes mobile device has intra mobility between several subnets of one agency when it moves into a subway or highway. Based on two features, we design proxy based architecture and extend router advertisement message to alert join in proxy domain. Then, we are classified the mobility into intra proxy mobility and global mobility. And we proposed proxy based mobility management scheme using prediction algorithm. By numerical analysis, we shows proposed scheme reduces signaling overheads and increase packet transfer rate than NEMO and proxy MIP.