Designing Internet Of Things Systems Research Articles

ViLanIoT: A visual language for improving Internet of Things systems representation

With the rapidly evolving and changing nature of Internet of Things (IoT) technologies, there is an absence of tools to support the IoT system design and development. For example, there is not a specialized tool for representing IoT systems. The common solution to this problem is to use general-purpose modeling tools, such as the Unified Modeling Language (UML) and its extensions, which have some disadvantages when representing IoT systems. This paper proposes a new visual language, called ViLanIoT, for making representations of IoT systems conceived as cyber–physical systems. We argue that diagrams obtained with ViLanIoT are more understandable and intuitive compared to improvised representations found in literature. To illustrate the use of ViLanIoT, we represent a smart campus system with visual elements according to our proposal. In addition, we also present a study of the application of ViLanIoT. In ViLanIoT, the inclusion of visual elements for each component reflecting semantic meaning improves the understandability, clarity, and simplicity of IoT system representations.

Secure IoT based smart system for monitoring health care for ambulatory and fetal

<p>The Internet of Things (IoT) along with Artificial Intelligence (AI) has now developed the most prevalent instruments in applications in the health care industry for widespread and intelligent automatic diagnostic systems. The maternal clinical information for ubiquitous and intelligent autonomous diagnostic systems, IoT with AI has now taken the lead as the predominant instrument in the healthcare industry. With respect to the incorporation of IoT sensors along with deep learning techniques, this article suggests the establishment of smart networks to monitor maternal and fetal signs in dangerous pregnancies. IoT sensors are utilized to gather clinical information about the mother, including her temperature, blood pressure levels, and saturation in oxygen level, heart rate, and heartbeat of the unborn child. This information is then stored in the cloud for tracking and forecasting. Additionally, a brand-new optimal Gated Recurrent Unit (GRU) is suggested for improved categorization and a forecast of the several emergencies affecting both pregnant women and unborn children. Additionally, to assist the data’s security and the design of IoT systems is based on a number of sensors that are interfaced with MICOT boards (Node MCU+MCP3008) and a cloud system. For the evaluation, about 500 pieces of data were gathered and utilized. With cloud-centric learning techniques like K-Nearest Neighbor (KNN), Random Forest (RF), Support Vector Machines (SVM), Convolution Neural Networks (CNN), and Extreme Learning Machines (ELM) thorough experimentation is conducted, and various parameters including accuracy, precision, recall, and sensitivity, along with F1-score are estimated. The evaluation found that the recommended classifier outperformed the competing learning strategies. The recommended framework is a practical and useful method for maternal and foetal surveillance that is powered by IoT and AI.</p>

Energy harvesting in self-sustainable IoT devices and applications based on cross-layer architecture design: A survey

The Internet of Things (IoT) is envisioned to become a driving force in the evolution of fifth-generation (5G) mobile networks, autonomous continuous monitoring and control platforms, and low-power consumption devices. Conventionally, these devices are battery-operated. The battery lifespan degrades faster in the case of continuous monitoring devices. Limited battery lifetime motivates us to investigate an eco-friendly solution to solve the issue of limited battery life and frequent replacement. Energy harvesting (EH) is a key-enabling technique that provides a viable solution to the challenge at hand. EH minimizes battery dependence by collecting energy from ambient sources. Although several studies have been conducted on EH-IoT networks, a tutorial on a possible mapping between the use cases/application to the IoT devices and the IoT devices to EH power source still needs to be included. To address this gap, we classify IoT devices based on their applications. Based on the different interfacing sections of layer architecture design of IoT device, this paper tries to fill the gap between IoT applications and the self-sustainable IoT system design. Next, each interfacing section is categorized into layer architecture of IoT device to study layer-specific power requirements (or demand) versus EH sources (or supply). We have considered various applications like agriculture, healthcare, industry, city, security, transportation, and water management. Further, we discuss different energy sources to harness ambient energy and the method to store the harvested energy. Then, the widely used standards and the various steps taken for the interoperability and compatibility of EH-IoT are outlined to understand the elements of the EH interface. Finally, we point out future research issues requiring specific attention for deploying self-sustainable IoT-based delay-limited applications.

Interactive System Modelling for the Internet of Things

The rapid growth in the number of Internet of Things (IoT) systems and their increasing use in safety-critical domains has led to the need for an evolution of development methods. Model-Driven Development (MDD) approaches have been used in software engineering to reduce development time and minimise errors in implemented systems. This paper introduces a novel Model-Driven Development (MDD) approach for Internet of Things (IoT) systems which considers interactivity from the perspective of both users and IoT components. A real-world example is presented to explain the motivation for the work and demonstrate the benefits and use of lightweight interactive system models adapted for the design and development of IoT systems.

User experience key performance indicators for industrial IoT systems: A multivocal literature review

In software systems, user experience (UX) is a quality that provides value to its intended users. Therefore, we consider it critical to manage UX and its link to business value quantitatively. Key performance indicators (KPIs) have been widely adopted for objectively measuring factors that influence business success, including measuring UX characteristics and their business contribution in the context of software systems in general. Yet, measuring UX in the context of complex Internet of Things (IoT) systems, especially Industrial IoT (IIoT) systems, remains a challenge.This paper presents a multivocal literature review (MLR) of the UX-KPIs proposed in the context of (I)IoT systems, along with factors influencing UX and challenges of quantifying UX. We investigate the status quo and derive recommendations for measuring the UX of (I)IoT systems.Our analysis of 64 publications shows that UX has so far received relatively little attention in the context of (I)IoT. Although we identified 605 KPIs, 78 influencing factors, and 119 challenges, most of the UX features discussed were defined in a broader context of software systems, i.e., they were less specific to IoT, but rather focused on universal system quality characteristics such as functional suitability, performance efficiency, usability, and reliability.Finally, no generally accepted approach for measuring UX in (I)IoT systems and how these contribute to the overall business value has been proposed yet. We also found little evidence that improving UX or achieving a good UX currently plays an explicit role in the design and development of IoT systems.

Security and Energy Efficiency: Breaking the Barriers of High Peak-to-Average Power Ratio and Disguised Jamming in NextG IoT System Design

As a popular modulation technique in wireless communications, orthogonal frequency-division multiplexing (OFDM) has two major disadvantages—one is its high peak-to-average power ratio (PAPR), which causes nonlinear distortion, lower power efficiency, and performance losses; the other is its fragility under hostile jamming attacks, where the authorized user asciitext’s signal is deliberately interfered by the adversary, leading to communication failures. In this article, first, we reintroduce the IFFT-relocated OFDM (IR-OFDM), which is essentially a single-carrier system with frequency-domain equalization. By relocating the inverse fast Fourier transform (IFFT) block in OFDM from the transmitter to receiver, IR-OFDM can completely liberate OFDM from the barriers of high PAPR while achieving the same spectral efficiency. Second, to combat hostile jamming, especially disguised jamming, where the jamming is highly correlated with the authorized signal, we propose a securely precoded IR-OFDM (SP-IR-OFDM). By integrating the advanced encryption standard (AES) into IR-OFDM transceiver design, we obtain a random (or dynamic) constellation. The shared secure randomness introduced by AES breaks the symmetry between the authorized signal and the jamming interference and, hence, ensures reliable performance of the system under disguised jamming. The efficiency and robustness of IR-OFDM and SP-IR-OFDM are demonstrated through simulation examples. It is shown that IR-OFDM can deliver comparable or better performances than OFDM under multipath propagation, and SP-IR-OFDM can achieve strong resistance under disguised jamming while enjoying low PAPR and relatively high spectral efficiency. Our result indicates that, potentially, SP-IR-OFDM can serve as a promising modulation candidate for next-generation secure and energy-efficient high-speed communications, especially for the resource-constrained Internet of Things (IoT) networks.

Design, code generation and simulation of IoT environments with mobility devices by using model-driven development: SimulateIoT-Mobile

Systems based on the Internet of Things (IoT) are continuously growing in many areas such as smart cities, home environments, buildings, agriculture, industry, etc. Device mobility is one of the key aspects of these IoT systems, but managing it could be a challenge. Mobility exposes the IoT environment or Industrial IoT (IIoT) to situations such as packet loss, increased delay or jitter, dynamism in the network topology, new security threats, etc. In addition, there is no standard for mobility management for the most commonly used IoT protocols, such as MQTT or CoAP. Consequently, managing IoT mobility is a hard, error-prone and tedious task. However, increasing the abstraction level from which the IoT systems are designed helps to tackle the underlying technology complexity. In this regard, Model-driven development approaches can help to both reduce the IoT application time to market and tackle the technological complexity to develop IoT applications. In this paper, a Domain-Specific Language based on SimulateIoT is proposed for the design, code generation and simulation of IoT systems with mobility management for the MQTT protocol. The IoT systems generated integrate the sensors, actuators, fog nodes, cloud nodes and the architecture that supports mobility, which are deployed as microservices on Docker containers and composed suitability. Finally, two case studies focused on animal tracking and a Personal mobility device (PMD) based on bicycles IoT systems are presented to show the IoT solutions deployed.

Design and Evaluation of Wireless Power Monitoring IoT System for AC Appliances

The paper is aimed to develop a wireless alternating current (AC) power monitoring module which features the advantage of cost-effectiveness and sufficient reliability for the proposed AC power monitoring Internet of Things (IoT) system. The novel wireless AC power monitoring module consists of both ZMPT101B voltage sensor and ACS712-20 current sensor; a 16-bit analog-to-digital (ADC) ADS1114 with I2C interface and WeMos D1 Mini Wi-Fi module were integrated for monitoring refrigerator and air conditioner appliances with the ratings of single-phase 110/220 VAC, respectively. First, both analog readings of V/I sensors for AC appliances are converted into data streams in compliance with I2C (inter-integrated circuit) protocol, and are forwarded to a WeMos D1 Mini Wi-Fi module for the corresponding values of instantaneous electric power and energy, power factor (pf), and frequency well programmed in the built-in ESP8266EX IoT-based microcontroller unit (MCU) based on the well-known AC power fundamentals. All of the important AC power parameters are sent to the ThingSpeak IoT platform through Wi-Fi network. The visualization of voltage, current, electric power and energy, pf, and frequency is illustrated in the ThingSpeak IoT platform. Both close agreement and confidence of the proposed AC power monitoring IoT system for both refrigerator and air conditioner are evaluated with two CM3286-1 AC Power Meters. Taking the commercialized CM3286-1 instrument as reference, the values of mean absolute percentage error (MAPE) for above six electrical parameter readings are all less than 2%. The evaluation results illustrate sufficient closeness of agreement and confidence for the proposed wireless AC power monitoring IoT system for in situ monitoring AC appliances with single-phase 110/220 VAC ratings. Furthermore, the cost of the proposed AC power monitoring module is less than 100 USD, which makes the novel module more cost-effective than commercialized AC power meters which generally cost over 1000 USD. The novelties of the work are the following: (1) the introduction of ADS1114 provides I2C interface directly for Wi-Fi module to reduce the capital cost of the proposed wireless AC power monitoring module; (2) the sufficient confidence of the proposed AC power monitoring IoT system has been validated with closeness of agreement as compared to the commercialized CM3286-1 AC Power Meters. These make the assessment action of environmental, social, and governance (EGS) for stakeholders much more feasible with the advantages of cost-effectiveness and sufficient confidence.

Design and Implementation of an Intelligent IoT System based on Fuzzy Logic for Carbon Monoxide Detection

Carbon monoxide (CO) as been frequently identified as the origin of deadly domestic accidents. The heavy toll it makes every winter is often caused by using gas heaters. CO is difficult to detect without a detector because it has not particular odor or color. In this paper, we create and implement a prototype based on the Internet of Things (IoT) to combat this dangerous gas and save human lives. This prototype first allows the detection of carbon monoxide leaks and then the execution of the necessary reactions based on fuzzy logic. These responses entail turning on a ventilation system to ventilate the area where the leak occurs. The system also alerts users by making calls, emails, or notifications. In order to alert the relevant services when the gas level exceeds a predetermined threshold, this task is specifically carried out using an MQ2 sensor, calibrated to detect the level of gas present in the air, and a GMS sensor. Both of these sensors are connected to an Arduino development board.

A Suite of Design Quality Metrics for Internet of Things by Modelling Its Ecosystem as a Schema Graph

Internet of Things (IoT) infrastructure connects consumer electronics, household appliances, and other smart gadgets. For designing IoT systems and applications, different architectures and protocols are being used. The design quality of an IoT system and its services is assessed with quality of service (QoS) factors such as complexity, functional appropriateness, performance, efficiency, compatibility, maintainability, portability, and usability. The existing methods in the literature focus on measuring the quality of an IoT system during execution time; however, there is a need to define some standards that may be used to determine the quality of an IoT system from its design. This work models the IoT EcoSystem as a schema graph and presents a suite of metrics to determine the design quality of an IoT system and its services under the defined QoS factors. The presented metrics are mapped to the ISO-9126 QoS factors to ensure the standards and quality of IoT services during the design phase. The proposed metrics are validated with benchmarking on a case study smart healthcare IoT system. The results proved that the presented metrics are practical to quantify the quality of IoT design models under QoS factors, and the suite of metrics is able to compare differently designed IoT services.

Threat of Adversarial Attacks on DL-Based IoT Device Identification

With the rapid development of the information technology, the number of devices in the Internet of Things (IoT) is increasing explosively, which makes device identification a great challenge. Deep neural networks (DNNs) have been used for device identification in IoT due to their superior learning ability. However, DNNs are susceptible to adversarial attacks, which can greatly degrade the accuracy of deep learning (DL) models for device identification. The adversarial attack is one of the fundamental security concerns for DNNs, and it is of great importance to study the generation of adversarial examples and to examine the attack effects for the design of robust DNN-based device identification schemes. In this article, we examine the effects of nontargeted and targeted adversarial attacks on convolutional neural network (CNN)-based device identification and propose combined evaluation indicators of logits to enrich the evaluation criteria. Our experimental results demonstrate that the identification accuracy degrades with the increase of the perturbation level and iteration step size, and the proposed combined evaluation indicators are effective to show the individual device signal differences. The insights from this study will be useful for the design of robust DL-based IoT systems.

LabVIEW and Embedded System based New IoT Solution for Industrial applications

The Internet of Things (IoT) is becoming increasingly popular around the world. Efficiency in terms of time and cost is ensured in all sectors, thanks to the internet of things technology. A novel IoT system design was created in this study to add Internet of Things technology to a new perspective. All clients can connect to the server using this IoT platform, regardless of their hardware or software capabilities, even if they are not capable of industrial communication. The platform brings flexibility and ease of use to IoTapplications. Thanks to the platform's architecture, any device, whether inputs or outputs, can be easily added to an IoT network while it is running using the developed modules. The server software was created using the LabVIEW visual programming language and Raspberry Pi and MSP340F5529 embedded systems are used to develop hardware modules. To provide IoT data security, the TCP/IP protocol is used for data communication, and all data is encrypted using the AES (Advanced Encryption Standard) algorithm with a 128-bit key. A dynamic 1-N server-client IoT system has been implemented and tested with various analog, digital, and smart sensors, as well as smart devices. When the results are evaluated, it is found that industrial applications can be developed with the platform. Particularly in the case of transformation of industrial automation applications to IoT containing non-smart sensor and actuators, the developed platform can be preferred. In this case, the platform will bring opportunities to engineers in terms of cost and ease of development phase.

A novel hybrid meta‐heuristic concept for green communication in IoT networks: An intelligent clustering model

SummaryNowadays, there is an emerging need for applications based on the Internet of Things (IoT). The sensor nodes present in the IoT network produce data constantly, which directly influences the durability of the network. Therefore, two major challenges while designing IoT systems are network lifetime and energy consumption. Although the ability of IoT applications is huge, there are several limitations such as energy optimization, heterogeneity of devices storage, load balancing, privacy, and security that have to be addressed. These constraints have to be optimized for improving the efficiency of the networks. Hence, the main intention of this paper is to develop the intelligent‐based cluster head selection model for accomplishing green communication in IoT. The two famous algorithms like spotted hyena optimization (SHO) and sun flower optimization (SFO) are integrated to form sun flower‐spotted hyena optimization (SF‐SHO) by utilizing the hybrid meta‐heuristic concept for the optimal cluster head selection. The most significant parameters in IoT networks like delay, distance, energy, temperature, and load are considered for deriving a multi‐objective function to offer optimal clustering. The cluster head of the model is optimally tuned based on the hybrid SF‐SHO, to solve the multi‐objective problem, thus showing the enhanced green communication performance. The proposed model is analyzed and evaluated over different approaches in terms of energy‐specific factors, and the attained results confirm the efficiency of the developed method.

Design and implementation of a hybrid IoT system of data collection and visualization

To carry out a scientific study on a given problem (medical, environmental, etc.), it is often necessary to have recourse to a reliable dataset. In Africa, access to data is generally a problem for the scientific community. Most of the existing datasets are detained by some tiers organization who often do not give access to it. To overcome these problems related to access to data, scientists generally use traditional methods (surveys, on-site readings, etc.) for their study. However, these methods are not only expensive, but also require a regular visit to the site for updates. In this article, we propose an autonomous solution for collecting and analyzing data based on the Internet of Things (IoT). The solution can be used to collect data of different kinds. Unlike traditional data collection methods, the proposed solution is much cheaper and allows real time data collection. To demonstrate its proper functioning, a practical case on the environmental data collection will be studied in this paper.

SimulateIoT-FIWARE: Domain Specific Language to Design, Code Generation and Execute IoT Simulation Environments on FIWARE

Systems based on the Internet of Things (IoT) are continuously growing in many areas such as smart cities, home environments, buildings, agriculture, industry, etc. This system integrates heterogeneous technologies into a complex architecture of interconnected devices capable of communicating, processing, analysing or storing data. There are several IoT platforms that offer several capabilities for the development of these systems. Some of these platforms are Google Cloud’s IoT Platform, Microsoft Azure IoT suite, ThingSpeak IoT Platform, Thingworx 8 IoT Platform or FIWARE. However, they are complex IoT platforms where each IoT solution has to be developed ad-hoc and implemented by developers by hand. Consequently, developing IoT solutions is a hard, error-prone and tedious task. Thus, increase the abstraction level from which the IoT systems are designed helps to tackle the underlying technology complexity. In this sense, model-driven development approaches can help to both reduce the IoT application time to market and tackle the technological complexity to develop IoT applications. In this paper, we propose a Domain-Specific Language based on SimulateIoT for the design, code generation and simulation of IoT systems which could be deployed on FIWARE infrastructure (an open-source IoT platform). This implies not only designing the IoT system for a high abstraction level and later on code generation, but also designing and deploying an additional simulation layer to simulate the system on the FIWARE infrastructure before final deployment. The FIWARE IoT environment generated includes the sensors, actuators, fog nodes, cloud nodes and analytical characteristics, which are deployed as microservices on Docker containers and composed suitability to obtain a service-oriented architecture. Finally, two case studies focused on a smart building and an agricultural IoT environment are presented to show the IoT solutions deployed using FIWARE.

The Challenges and Emerging Technologies for Low-Power Artificial Intelligence IoT Systems

The Internet of Things (IoT) is an interface with the physical world that usually operates in random-sparse-event (RSE) scenarios. This article discusses main challenges of IoT chips: power consumption, power supply, artificial intelligence (AI), small-signal acquisition, and evaluation criteria. To overcome these challenges, many works recently aimed at IoT system design have emerged. This work reviews the architecture and circuit innovations that have contributed to IoT developments. This paper does not cover security of IoT. Event-driven architectures and nonuniform sampling ADCs significantly reduce the long-term average power. Besides, embedding AI engines in IoT nodes (AIoT) is one critical trend. The computing-in-memory technique improves the energy efficiency of the AI engine. Asynchronous spike neural networks (ASNNs) AI engines show low power potential. In addition to data processing, small-signal acquisition is also critical. The charge-domain analog-front-end (AFE) techniques such as floating inverter-based amplifiers improve energy efficiency. In addition to the above low power and high energy efficiency technologies, energy harvesting can also enhance the lifetime of AIoT devices. This article discusses recent ambient RF and natural energy harvesting approaches and high-efficiency DC-DC with a wide load range. Finally, novel evaluation criteria are introduced to establish benchmark standards for AIoT chips.

Performance evaluation of the SRE and SBPG components of the IoT hardware platform security advisor framework

The applications of Internet of Things (IoT) and associated technologies have been spreading rapidly across a wide range of domains, including environmental monitoring, home automation, and supply chain, having a significant bearing on the social and economic well-being of humans as well as enhancing environmental sustainability. In recent years, however, there have been several data breaches and other security and privacy incidents involving IoT devices, which have attracted significant attention from the research community in both academia and industry. This has resulted in a surge of proposals put forward by many researchers, including IoT blockchain-based security solutions, IoT intrusion detection systems, IoT authentication systems, and IoT security analytics. While these proposals are aimed at addressing various IoT security and privacy-related issues, many of these solutions arguably seem not to focus on helping designers and developers with little or no security expertise in start-up companies to produce secure IoT systems. To this end, the IoT Hardware Platform Security Advisor (IoT-HarPSecA) framework was proposed to foster the design and implementation of secure IoT systems. In this paper, we present the performance and usability evaluation of the Security Requirements Elicitation (SRE) and Security Best Practice Guidelines (SBPG) component tools of IoT-HarPSecA, which are two of the three component tools of the security framework. Results show that the two components of the IoT-HarPSecA framework can facilitate the development of secure IoT systems and that the SRE and SBPG tools are easy to use.

IoT Big Data provenance scheme using blockchain on Hadoop ecosystem

The diversity and sheer increase in the number of connected Internet of Things (IoT) devices have brought significant concerns associated with storing and protecting a large volume of IoT data. Storage volume requirements and computational costs are continuously rising in the conventional cloud-centric IoT structures. Besides, dependencies of the centralized server solution impose significant trust issues and make it vulnerable to security risks. In this paper, a layer-based distributed data storage design and implementation of a blockchain-enabled large-scale IoT system are proposed. It has been developed to mitigate the above-mentioned challenges by using the Hyperledger Fabric (HLF) platform for distributed ledger solutions. The need for a centralized server and a third-party auditor was eliminated by leveraging HLF peers performing transaction verifications and records audits in a big data system with the help of blockchain technology. The HLF blockchain facilitates storing the lightweight verification tags on the blockchain ledger. In contrast, the actual metadata are stored in the off-chain big data system to reduce the communication overheads and enhance data integrity. Additionally, a prototype has been implemented on embedded hardware showing the feasibility of deploying the proposed solution in IoT edge computing and big data ecosystems. Finally, experiments have been conducted to evaluate the performance of the proposed scheme in terms of its throughput, latency, communication, and computation costs. The obtained results have indicated the feasibility of the proposed solution to retrieve and store the provenance of large-scale IoT data within the Big Data ecosystem using the HLF blockchain. The experimental results show the throughput of about 600 transactions, 500 ms average response time, about 2–3% of the CPU consumption at the peer process and approximately 10–20% at the client node. The minimum latency remained below 1 s however, there is an increase in the maximum latency when the sending rate reached around 200 transactions per second (TPS).

A Collaboration-centric Taxonomy of the Internet of Things: Implications for Awareness Support

The Internet of Things (IoT) has the potential to become a new collaboration modality. Building on this proposition, we developed a taxonomy to explore how the IoT can enable collaboration, and conversely, how current knowledge on collaboration systems can guide IoT system development and research. The taxonomy is grounded in IoT use cases and bridges the technical and human aspects by drawing on collaboration and computer network theories. Our preliminary analysis of a subset of the taxonomy suggested that providing awareness support to IoT system users is likely to be a fundamental challenge in IoT system design. Our subsequent conceptualization of the entire taxonomy—what we present in this paper—applies awareness theory to arrive at a novel, comprehensive, and structured understanding of the awareness needs of IoT system users. This understanding can be leveraged as a formative tool by system developers to identify requirements and by researchers to develop design guidance for effective collaboration in IoT systems.

Availability Modeling and Performance Improving of a Healthcare Internet of Things (IoT) System

Internet of Things (IoT) is improving human life in a more convenient and simpler way. One of the most promising IoT applications is healthcare. In this paper, an availability model of a healthcare IoT system is proposed which is composed of two groups of structures described by separate Markov state-space models. The two separate models are analyzed and combined to implement the whole IoT system modeling. The system balance equations are solved under a given scenario and some performance metrics of interest, such as probabilities of full service, degraded service, and the system unavailability, are derived. Detailed numerical evaluation of selected metrics is provided for further understanding and verification of the analytic results. An availability performance improving (API) method is also proposed for increasing the probability of system full service and decreasing the system unavailability. The proposed system modeling and performance improving method can serve as a useful reference for general IoT system design and evaluation.