IoT-based Applications Research Articles

Evaluation Method of Mesh Protocol over ESP32 and ESP8266

Internet of Things (IoT) is one of the newest matters in both industry and academia of the communication engineering world. On the other hand, wireless mesh networks, a network topology that has been debate for decades that haven’t been put into use in great scale, can make a transformation when it arises to the network in the IoT world nowadays. A Mesh IoT network is a local network architecture in which linked devices cooperate and route data using a specified protocol. Typically, IoT devices exchange sensor data by connecting to an IoT gateway. However, there are certain limitations if it involves to large number of sensors and the data that should be received is difficult to analyze. The aim of the work here is to implement a self-configuring mesh network in IoT sensor devices for better independent data collection quality. The research conducted in this paper is to build a mesh network using NodeMCU ESP 8266 and NodeMCU ESP 32 with two types of sensor, DHT 11 and DHT 22. Hence, the work here has evaluated on the delay performance metric in Line-of-Sight (LoS) and Non-Line-of-Sight (nLos) situation based on different network connectivity. The results give shorter delay time in LoS condition for all connected nodes as well as when any node fail to function in the mesh network compared to nLoS condition. The paper demonstrates that the IoT sensor devices composing the mesh network is a must to leverage the link communication performance for data collection in order to be used in IoT-based application such as fertigation system. It will certainly make a difference in the industry once being deployed on large scale in the IoT world and make the IoT more accessible to a wider audience.

Quasi-Mapping and Satisfying IoT Availability with a Penalty-Based Algorithm

The Internet of things and medical things (IoT) and (IoMT) technologies have been deployed to simplify humanity’s life, which the complexity of communications between their layers was increased by rising joining the applications to IoT and IoMT-based infrastructures. The issue is challenging for decision-making and the quality of service where some researchers addressed the reward-based methods to tackle the problems by employing reinforcement learning (RL) algorithms and deep neural networks (DNNs). Nevertheless, satisfying its availability remains a challenge for the quality of service due to the lack of imposing a penalty to the defective devices after detecting faults. This paper proposes a quasi-mapping method to transfer the roles of sensors and services onto a neural network’s nodes to satisfy IoT-based applications’ availability using a penalty-backwarding approach into the NN’s weights and prunes weak neurons and synaptic weights (SWs). We reward the sensors and fog services, and the connection weights between them when are covered the defective nodes’ output. Additionally, this work provides a decision-making approach to dedicate the suitable service to the requester using employing a threshold value in the NN’s output layer according to the application. By providing an intelligent algorithm, the study decides to provide a service based on its availability and updating initial information, including faulty devices and new joined components. The observations and results prove decision-making accuracy for different IoT-based applications by approximately 95.8–97% without imposing the cost. The study reduces energy consumption and delay by approximately 64.71% and 47.4% compared without using neural networks besides creating service availability. This idea affects deploying IoT infrastructures to decision-making about providing appropriate services in critical situations because of removing defective devices and joining new components by imposing penalties and rewards by the designer, respectively.

Internet of things-based automation design and organizational innovation of manufacturing enterprises

The Internet of Things (IoT) foresaw a transparent network between the real and virtual worlds. IoT offers a promising potential to develop strong market and industrial applications. The export-oriented economy of industrial manufacturers relies on product and service creativity to retain a competitive advantage. The IoT-based applications of innovative manufacturing companies (IOTs), where most companies are involved and engaged in many of the case IoT-based applications, is motivated by computing capacity, networking, cost reduction and electronic components. A common motive is the Internet of Things applications and the improvement of after-sales service activities. The suggested Linear Product Design Regression (LPDR) algorithm classifies business product innovation design based on consumer input to support business development. Businesses, companies and the technology industry: the inhibitors hinder the development of the four IT applications. IoT offers one of the latest innovations in manufacturing to allow us to meet key challenges. The product design and development engineering are engaged in utilizing the Internet of Things (IoT) to create comprehensive policies and policy creation for manufacturing companies.To support business development. Businesses, companies and the technology industry: the inhibitors hinder the development of the four IT applications. To allow us to meet key challenges and to create comprehensive policies and policy creation for manufacturing companies.

An LSTM-based distributed scheme for data transmission reduction of IoT systems

The growth of the number of connected devices in Internet of Things (IoT) systems causes a huge increase in network traffic. Thus, there is a significant demand for systems that can predict the measurements of the distributed IoT-based applications to mitigate the increasing network traffic. Existing methods utilized a distributed scheme, i.e., dual prediction schemes (DPS), to achieve this task. The idea of this scheme is based on deploying a predictive model on the data sources (i.e., sensors) and the fusion center in a distributive manner. The state-of-the-art results can be simply reached using predictive models, e.g., adaptive filters. Deep learning-based approaches are not well utilized to address this problem. In this context, we proposed a distributed scheme utilizing fog and edge computing technologies. The proposed DPS includes an LSTM predictive model to reduce the data transmission instances of connected IoT devices; it is called LSTM-DPS. In addition, we proposed an updating mechanism that updates the LSTM model according to a set of tracking parameters that observes the model behavior during the deployment due to the changes of data properties through time. The proposed updating mechanism guarantees that the deployed LSTM model is identical at the data source and fusion center. The LSTM-DPS is evaluated using two real datasets. The obtained results show that the LSTM-DPS outperforms state-of-the-art methods in terms of communication reduction ratios.

Smart Agriculture and Smart Farming using IoT Technology

It has become easier to access agriculture data in recent years as a result of a decline in digital breaches between agricultural producers and IoT technologies. These future technologies can be used to boost productivity by cultivating food more sustainably while also preserving the environment, thanks to improved water use and input and treatment optimization. The Internet of Things (IoT) enables the production of agricultural process-supporting systems. Referred to as remote monitoring systems, decision support tools, automated irrigation systems, frost protection systems, and fertilisation systems, respectively. Farmers and researchers must be provided with a detailed understanding of IoT applications in agriculture as a result of the knowledge described above. This study is about using Internet of Things (IoT) technologies and techniques to enhance agriculture. This article is meant to serve as an introduction to IoT-based applications in agriculture by identifying need for such tools and explaining how they support agriculture.

IoT-Based Services in Banking Industry Using a Business Continuity Management Approach

Background: The Internet of things (IoT) is a new technology that expanding rapidly and has led to the emergence of new services in the banking industry. Although IoT is an emerging technology in Iran, it is expected to become a paradigm in the future. IoT applications in banking have helped to provide high quality and fast responses to customers anytime, anywhere. Purpose: This research aims to identify and prioritize IoT applications in the Iranian banking industry based on business continuity indicators. Methods: First, based on the comprehensive literature review and practical experiences, IoT applications in the banking industry are identified. Then, each indicator's weight was calculated using the Grey Decision-making trial and evaluation laboratory (DEMATEL). Finally, IoT-based applications in the Iranian banking industry have prioritized using the multi-attribute utility theory (MAUT). Due to the unstable economic conditions for the financial sector, business continuity indicators are employed in this study. Also, the reason for using MAUT is to consider the utility of banking experts for performing IoT banking services. Findings: According to results, IoT-based services in the banking industry, including Transmit instant reports, smart ATMs, non-contact electronic payments, and electronic checks, are identified for Iranian banks. Then, These appropriate services are prioritized with the capabilities of Iranian banks.

Diffusion Bayesian Subband Adaptive Filters for Distributed Estimation Over Sensor Networks

Sensor networks are an indispensable part of the Internet of Things (IoT), where sensors perform data acquisition and information processing tasks to obtain the parameters of interest so that IoT-based monitoring, diagnosis and other systems respond quickly to the changing conditions, instantaneous faults, etc. Distributed estimation algorithms are usually employed to estimate the parameters of interest in these IoT-based applications. However, when sensor networks have highly correlated input signals and nonstationary behavior in which the parameters of interest are time-varying, conventional distributed estimation algorithms suffer from severely degraded learning performance due to the large eigenvalue spread in the covariance matrix of the input signals and the random perturbation of the parameters of interest. To address these problems, this paper proposes two diffusion Bayesian subband adaptive filter (DBSAF) algorithms from a Bayesian learning perspective. As the highly-correlated input signal is whitened in a multiband structure and an estimate of the uncertainty in the parameters of interest is obtained by performing Bayesian inference, the proposed DBSAF algorithms are able to achieve better learning performance in comparison with the competing diffusion algorithms. The transient and steady-state mean square error performance of the proposed DBSAF algorithms are analyzed, and are verified by numerical simulations. A lower bound on the time-varying step-size is derived to maintain the optimal steady-state performance in nonstationary scenarios. A new method for the estimation of the noise variance is also proposed. Numerical simulations demonstrate the excellent learning performance of the proposed algorithms in comparison with benchmark algorithms.

Ambient backscatter communication with mobile RF source for IoT-based applications

Ambient backscatter communication (AmBc) has been recently transpired as a promising communication methodology for battery-free internet of things (IoT)-based applications. This paper investigates an AmBc system with a mobile ambient radio-frequency (RF) source such as mobile wireless fidelity (Wi-Fi) access point. Mobility of the source causes the channels between source to tag and source to the reader to experience time-selective fading. This dynamic nature of channels is modeled using a first-order autoregressive (AR-1) process. The closed-form expressions for the bit-error-rate (BER) and thresholds are obtained for maximum-likelihood (ML) and energy detector (ED) with imperfect channel state information (CSI) at the reader. Further, the closed-form expressions for outage probability and channel capacity are also obtained. It is found that the overall system performance worsens with increase in mobility of the nodes. The results are validated using Monte-Carlo (MC) simulations.

An anonymous two-factor authentication protocol for IoT-based applications

Internet of things (IoT) allows people to establish a real-time connection with physical objects. To fulfill this lifelong ambition, the infrastructure of Wireless Sensor Networks (WSNs) plays a pivotal role. However, the public channel that is used by the sensors and users can imperil the security of their connection. Accordingly, many authentication protocol have been proposed to preserve the integrity and confidentiality of the transmitted messages. In this paper, we examine Wu et al.’s protocol as one of the most state-of-the-art protocols and prove that it is susceptible to sensor capture attack, user trace attack, and DoS attack, also it cannot provide forward secrecy. To mitigate these weaknesses, we propose our protocol and analyze that with both formal and informal methods to show that it is secure against various known attacks such as sensor and user trace, sensor capture, offline password guessing, and replay attacks. Finally, we evaluate our protocol in terms of security features and communication and computation costs. The results show that our protocol not only is more secure than other existing protocols but also reduces 62.1% of the computation cost and 30.76% of the communication cost in comparison with the costs of Wu et al.’s protocol.

Modeling of Occupancy-Based Energy Consumption in a Campus Building Using Embedded Devices and IoT Technology

Since occupancy affects energy consumption, it is common to model and simulate occupancy using simulation software. One drawback of simulation software is that it cannot provide data transmission information from the sensors, which is essential for real-time energy monitoring systems. This paper proposes an approach to integrating an occupancy model and a real-time monitoring system for real-time modeling. The integration was performed by implementing a model on embedded devices and employing an IoT-based real-time monitoring application. The experimental results showed that the proposed approach effectively configured and monitored the model using a smartphone. Moreover, the data generated by the model were stored in an IoT cloud server for monitoring and further analysis. The evaluation result showed that the model ran perfectly in real-time embedded devices. The assessment of the IoT data transmission performances yielded a maximum latency of 9.0348 s, jitter of 0.9829 s, inter-arrival time of 5.5085 s, and packet loss of 10.8%, which are adequate for real-time modeling of occupancy-based energy consumption.

Design of an Intelligent Controller of a Self-Tuned Quadcopter for IoT-Based Application on COVID Infected Area Sanitization

The COVID-19 outbreak has created a major global health crisis that has had an impact on the actions we perform. The only solution to the problem is getting sanitized and maintaining social distancing. Keeping all these in mind, an IoT-based application is an alternative to this problem. IoT-based location fixation and sanitization through a quadcopter is proposed. This work proposes the control design of a quad-copter UAV that helps to sanitize a COVID-19 containment zone based on latitude and longitude of the specific area. Stability is one of the important parameters in automatic control. The stable control of a quadcopter is an important issue for sanitization. There are two aspects of the design. First is the control of the movements of the quadcopter using an interval type 2 fuzzy-logic-based PID controller; second is the control of drop location of the quadcopter. The location data is fed to the telemetry of the quadcopter UAV through a COVID-19 drone Android application. To achieve balanced and stable performance of a quadcopter UAV with a center of gravity, variables are well managed. Both the controls used are based on interval type 2 fuzzy logic because of its ability to emulate the footprint of uncertainty and higher accuracy in nonlinear dynamic systems. Further, the work can be carried out for several applications in the field of medicine.

SODALITE@RT: Orchestrating Applications on Cloud-Edge Infrastructures

IoT-based applications need to be dynamically orchestrated on cloud-edge infrastructures for reasons such as performance, regulations, or cost. In this context, a crucial problem is facilitating the work of DevOps teams in deploying, monitoring, and managing such applications by providing necessary tools and platforms. The SODALITE@RT open-source framework aims at addressing this scenario. In this paper, we present the main features of the SODALITE@RT: modeling of cloud-edge resources and applications using open standards and infrastructural code, and automated deployment, monitoring, and management of the applications in the target infrastructures based on such models. The capabilities of the SODALITE@RT are demonstrated through a relevant case study.

Internet-of-Things Devices and Assistive Technologies for Health Care: Applications, Challenges, and Opportunities

Medical conditions and cases are growing at a rapid pace, where physical space is starting to be constrained. Hospitals and clinics no longer have the ability to accommodate large numbers of incoming patients. It is clear that the current state of the health industry needs to improve its valuable and limited resources. The evolution of the Internet of Things (IoT) devices along with assistive technologies can alleviate the problem in healthcare, by being a convenient and easy means of accessing healthcare services wirelessly. There is a plethora of IoT devices and potential applications that can take advantage of the unique characteristics that these technologies can offer. However, at the same time, these services pose novel challenges that need to be properly addressed. In this article, we review some popular categories of IoT-based applications for healthcare along with their devices. Then, we describe the challenges and discuss how research can properly address the open issues and improve the already existing implementations in healthcare. Further possible solutions are also discussed to show their potential in being viable solutions for future healthcare applications

The Amalgamation of Internet of Things and Recommender Systems

The Internet of Things (IoT) has upgraded the things and regular devices around to smart devices that can interact, share information and collaborate. With the plethora of IoT products, solutions, and workflows available online, the consumer finds it challenging to find the most suitable one. IoT, when integrated with the recommender system, can extend its features to the new space of innovation, usability, viability, and success. Various recommendation techniques can be employed to support users to get the best fit services, applications, sensors, products, and IoT workflows. This article will steer via the basic introduction of the IoT, its building blocks, hardware, software, applications, and its challenges. It analysed scientific articles indexed in the Scopus database with help of biblioshiny tool based on Rstudio software. The analysis is directed on 260 articles to figure out the trending amalgamation of Recommender System with the Internet of Things. Results point to fact that personalized recommendations for IoT-based applications are the most worked on topic by the authors.

LDA-IoT : a level dependent authentication for IoT paradigm

ABSTRACT The IoT-based services are getting a widespread expansion in all the directions and dimensions of this century. In most IoT-based applications, the sensor collects the data and communicates it to the end-user via gateway device or fog device over a precarious Internet channel. The attacker can use this open channel to capture the sensing device or the gateway device to collect the IoT data or control the IoT system. In this paper, we propose a novel approach of authentication for the IoT paradigm called as a Level Dependent Authentication (LDA). In the LDA protocol, we propose a security reliable and resource efficient key sharing mechanism in which users at level can communicate with the sensor at level if and only if the level of user in the organizational hierarchy is lower or equal to the level of sensor deployment. We provide a security analysis for the proposed LDA protocol using random oracle-based games & widely accepted AVISPA (Automated Validation of Internet Security Protocols and Applications) tools & BAN (Burrows–Abadi–Needham) logic. We also discuss a comparative analysis of the proposed protocol with other existing schemes based on communication cost, computation cost, and security index. We provide an implementation of the proposed scheme using MQTT (Message Queuing Telemetry Transport) protocol.

A Tuned classification approach for efficient heterogeneous fault diagnosis in IoT-enabled WSN applications

The advancement of the Internet of Things (IoT) technologies will play a significant role in the growth of smart cities and industrial applications. Wireless Sensor Network (WSN) is one of the emerging technology utilized for sensing and data transferring processes in IoT-based applications. However, heterogeneous faults like hardware, software, and time-based faults are the major determinants that affect the network stability of IoT based WSN (IWSN) model. In this paper, a novel Energy-Efficient Heterogeneous Fault Management scheme has been proposed to manage these heterogeneous faults in IWSN. Efficient heterogeneous fault detection in the proposed scheme can be achieved by using three novel diagnosis algorithms. The new Tuned Support Vector Machine classifier facilitates to classify the heterogeneous faults where the tuning parameters of the proposed classifier will be optimized through Hierarchy based Grasshopper Optimization Algorithm. Finally, the performance results evident that the diagnosis accuracy of the proposed scheme acquires 99% and the false alarm rate sustains below 1.5% during a higher fault probability rate. The diagnosis accuracy rate is enhanced up to 17% as compared with existing techniques.

MPdist-based missing data imputation for supporting big data analyses in IoT-based applications

Recent years have witnessed an enormous growth in the number of wireless IoT devices and thereby Internet of Things (IoT) is classified as one of the novel cutting edge technologies that has redesigned the traditional industry into a smart industry, given its applicability for a wide range of applications in providing data-driven decision-making. Meanwhile, the data with missing values is still emerging as one of the longstanding challenges in an IoT architecture due to the common-mode failures, potentially leading to both bias and loss of precision. In spite of the fact that numerous techniques have been developed for imputing missing values, the major issue in terms of imputation precision or computational complexity for large missing subsequences is still a matter under debate. To address this issue, this paper proposes a newly developed algorithm called MP-BMDI that ensures high imputation performance for supporting big data analyses in IoT-based applications, where the absence of large missing subsequences is fully required to offer unbiased results. In our approach, we initially seek a finite number of subsequences that are mostly similar to the subsequence before the missing values, then adjust the height of these following subsequences to optimal locations. Once the most proper subsequence for replacing is chosen among them based on the pattern score function PSF(r) introduced in this paper, the missing gap is completely filled by the corresponding subsequence. Numerical results are here presented to validate the merits of the proposed algorithm compared to the alternative benchmark approaches by leveraging sensor data collected from real-time environmental monitoring and deliver significant insights on the effectiveness of the proposed algorithm from various perspectives.

A Hybrid Fog Architecture: Improving the Efficiency in IoT-Based Smart Parking Systems

Background: The abundant use of personal vehicles has raised the challenge of parking the vehicle in crowded places such as shopping malls. To help the driver with efficient and troublefree parking, a smart and innovative parking assistance system is required. In addition to discussing the basics of smart parking, Internet of Things (IoT), Cloud computing, and Fog computing, this chapter proposes an IoT-based smart parking system for shopping malls. Methods: To process the IoT data, a hybrid Fog architecture is adopted in order to reduce the latency, where the Fog nodes are connected across the hierarchy. The advantages of this auxiliary connection are discussed critically by comparing with other Fog architectures (hierarchical and P2P). An algorithm is defined to support the proposed architecture and is implemented on two real- world use-cases having requirements of identifying the nearest free car parking slot. The implementation is simulated for a single mall scenario as well as for a campus with multiple malls with parking areas spread across them. Results: The simulation results have proved that our proposed architecture shows lower latency as compared to the traditional smart parking systems that use Cloud architecture. Conclusion: The hybrid Fog architecture minimizes communication latency significantly. Hence, the proposed architecture can suitably be applied for other IoT-based real-time applications.

The interplay between the Internet of things and supply chain management: challenges and opportunities based on a systematic literature review

PurposeThis study aims to identify and analyze how Internet of things (IoT) technology affects supply chain management (SCM) performance.Design/methodology/approachA systematic literature review was conducted (using Scopus, JSTOR, Emerald, ProQuest, Science Direct and Web of Science) covering a 20-year timeframe (2000–2020). Out of 2,572 papers identified, 171 peer-reviewed papers from the most important journals were selected. Content analysis was used following the Global Supply Chain Forum (GSCF) SCM framework.FindingsRegarding the GSCF SCM framework processes, most IoT-based studies have addressed improving order fulfilment, manufacturing flow management and demand management processes. However, no studies addressing the Supplier relationship management process were identified, suggesting that IoT-based applications are perceived to add more value in downstream than upstream SCM processes. The importance of using enabling technologies to realize the potential of value generation of IoT was also revealed. Findings suggest new research avenues related to product development and commercialization process, the supplier relationship management process, the returns management process, servitization strategies, new SCM models and new business models.Research limitations/implicationsThe review encompasses only academic papers from journals considered the most relevant (retrieved from specific databases), using the impact factor as the quality criterion.Practical implicationsThe findings can help business managers better understand the potential of IoT technology, such as the main applications identified in the literature and their impacts on SCM processes. Their importance in enabling technologies to leverage SCM performance is identified and the emerging SCM models/business models that IoT deployment can enable are highlighted.Originality/valueThis study contributes to filling a gap in the literature using a systematic literature review of how IoT technology affects SCM performance through content analysis, using an SCM framework to clarify which SCM processes are affected. Academic articles from the most important journals from 2000 to 2020 are identified.

Internet of Things Based Motorcycle Monitoring Application Development

A common problem that occurs very often in society is motorcycle theft. The rampant cases of motorcycle theft, which are even carried out in the parking lot of homes, need to find the right solution. One solution to handling motorcycle theft cases and securing motorbikes is by installing smart vehicle technology. The purpose of this research is to design a motorcycle safety system based on the Internet of Things. The design uses the Arduino Mega microcontroller, the ESP8266 wifi module, a fingerprint sensor, and the Blynk application on a smartphone. Hardware design with fingerprint and microcontroller integration then designing IoT-based applications with the ESP8266 and combining a security system with notifications to smartphones. Application this can help to turn on and off the engine of two-wheeled motorized vehicles remotely, can assist users in supervising or monitoring two-wheeled motorized vehicles.