IoT-based Devices Research Articles

Prioritizing the multi-criterial features based on comparative approaches for enhancing security of IoT devices

The Internet of Things (IoT) technologies continuously expand with time due to the advances in automated, connected device technology, mobility, and wide access to information. IoT is the collection of many linked devices due to their unique features, such as scalability, maintainability, fault tolerance, reliability, accuracy, and much more. With the growing number of hybrid devices in large organizations, security and privacy concerns are becoming more challenging. Security is essential for protecting the hardware, network aspects of devices, and information access from unauthorized entities. Most of the security methods and procedures provided by researchers are based on existing Internet security practices. The top-ranking authentication feature categories included the most compatible and common authentication feature for all types of IoT-based devices as an elementary security requirement for protection from unauthorized access. The future challenge is to address the incompatibility of the authentication feature with IoT devices based on appropriate technologies. AI and machine learning are also implementable in order to detect the vulnerability of IoT devices and inform the concerned operator or administration for protection. This research highlights the authentication feature of IoT devices from literature studies and evaluates the significant feature using COPRAS approach to assist organization in enhancing security of IoT devices.

HealthDote: A blockchain-based model for continuous health monitoring using interplanetary file system

The Healthcare industry demands increased privacy and security to protect confidential patient information and comply with regulations. Both these features can be incorporated into the existing systems using Blockchain technology. The only challenge faced here is the ease of users, but this can be quickly resolved by integrating the Internet of Things (IoT) and blockchain. IoT-based devices overcome limited computing capacity for personal intelligent health devices. Cloud-assisted IoT devices also require limited storage capacity for devices like wearable sensors. However, it must be considered that this system still has drawbacks, leading to its inefficiency. These problems include Data Privacy and Data sharing. This paper proposes an Interplanetary File System (IPFS) based solution to these problems. Here, the users continually upload the health data collected by IoT devices and add them to blockchain transactions that the other user nodes, such as physicians, pharmacists, insurance companies, hospital authorities, etc., can access. This system ensures the well-being of the users by monitoring the data gathered every 5 min and daily. It also facilitates the alarm feature in an emergency, making it reliable. The user receives daily notifications regarding his lifestyle, and the family members receive the notifications on his behalf if there are some chances of an emergency. The authorized doctors are also notified immediately in case an emergency is detected. Apart from this feature, the user can get consultations from doctors, prescriptions from the pharmacist, funds from insurance authorities, and hospital supplies, all through the transaction on the six blockchains of HealthDote using the cryptocurrency DoteCoins, which are designed specifically for this system.

A Self-Adaptive Trajectory Optimization Algorithm Using Fuzzy Logic for Mobile Edge Computing System Assisted by Unmanned Aerial Vehicle

The advancement of the Internet of Things (IoT) and the availability of wide cloud services have led to the horizon of edge computing paradigm which demands for processing the data at the edge of the network. The development of 5G technology has led to the increased usage of IoT-based devices and the generation of a large volume of data followed by increased data traffic, which is difficult to process by the mobile edge computing (MEC) platform. The latest inventions related to unmanned aerial vehicles (UAVs) helps to assist and replace the edge servers used for MEC. In the present work, the objective is to develop self-adaptive trajectory optimization algorithm (STO) which is a multi-objective optimization algorithm used to solve the vital objectives associated with the above scenario of a UAV-assisted MEC system. The objectives identified are minimizing the energy consumed by the MEC and minimizing the process emergency indicator, where the process emergency indicator implies the urgency level of a particular process. Finding the optimal values for these conflicting objectives will help to further efficiently apply UAV for MEC systems. A self-adaptive multi-objective differential evolution-based trajectory optimization algorithm (STO) is proposed, where a pool of trial vector generation strategies is extended. The strategies and the crossover rate associated with a differential evolution (DE) algorithm are self-adapted using fuzzy systems to improve the population diversity. The experimentation is planned to be conducted on hundreds of IoT device instances considered to be fixed on the ground level and to evaluate the performance of the proposed algorithm for a single unmanned aerial vehicle-assisted mobile edge computing system.

Convolutional Neural Network-Based Low-Powered Wearable Smart Device for Gait Abnormality Detection

Gait analysis is a powerful technique that detects and identifies foot disorders and walking irregularities, including pronation, supination, and unstable foot movements. Early detection can help prevent injuries, correct walking posture, and avoid the need for surgery or cortisone injections. Traditional gait analysis methods are expensive and only available in laboratory settings, but new wearable technologies such as AI and IoT-based devices, smart shoes, and insoles have the potential to make gait analysis more accessible, especially for people who cannot easily access specialized facilities. This research proposes a novel approach using IoT, edge computing, and tiny machine learning (TinyML) to predict gait patterns using a microcontroller-based device worn on a shoe. The device uses an inertial measurement unit (IMU) sensor and a TinyML model on an advanced RISC machines (ARM) chip to classify and predict abnormal gait patterns, providing a more accessible, cost-effective, and portable way to conduct gait analysis.

Scheduling Uses the Blynk 2.0 Automations Feature for Effective Ornamental Fish Feeding

Maintenance of ornamental fish can not be separated from the importance of regular feeding. Feeding which is still done manually will of course confiscate time and requires patience. However, the problem is when traveling that it doesn't Allows manual feeding. Matter this requires a system or tool that can provide fish feed automatically based on a schedule which is determined. One that can be used is with create Internet of Things (IoT)-based devices. There have been many developments in IoT-based devices gave birth to a variety of features that can be utilized in create automation tools in everyday life. Wrong one of which is a feature on the Blynk 2.0 application. on this application there is one feature that can be utilized in the process scheduling to perform one particular activity that very easy for users without having to be difficult to make a program commonly known as Automation feature. This feature will work continuously periodically according to a predetermined schedule. On research this will take advantage of these features in the manufacture of tools automatic fish feeding automation. scheduling feeding fish will utilize automation features at the same time can be monitored when the last feed was given. This study utilizes the NodeMCU ESP8266 as main controller and servo motor as driving tool feeder. The tool will be connected to the Blynk application via Wi-Fi network.

Assured and Provable Data Expuncturing in cloud using Ciphertext Policy–Attribute Based Encryption (CP-ABE)

Modern cloud computing strategies greatly reduce investment in infrastructure and data maintenance costs across startup companies and large organizations. This is mainly because of the emergence of the cloud-based IoT paradigm, which allows IoT-based devices to directly upload the data acquired from the environments to the remote cloud and allows the owner of those resources to manage their data through cloud-based applications. However, there are many challenges associated with this kind of data being outsourced, and the cloud server is entirely responsible for securing that data. Since these cloud servers cannot always be fully trusted, securing the deletion of unwanted sensitive data stored in the cloud to prevent data leaking is another challenging issue. As a result, most of the existing technology allows coarse-grained deletion of data on cloud servers. Consequently, cloud data security is jeopardized and new technological development in the cloud environment is hampered. This work implements a Cloud-based, Scalable, Secure unique data Deletion and Verification policy (CSSDV) that uses Ciphertext-Attribute-based encryption to enable fine-grained secure data deletion and data verification. We confirm the CSSDV scheme’s security under the standard model and demonstrate its accuracy and efficacy through theoretical analysis and extensive numerical experiment data. The experimental results for eleven attribute each with different sizes demonstrate the superiority of our algorithm in accessing and deleting the files in a secure and scalable manner.

Intelligent Internet of Things in Mammography Screening Using Multicenter Transformation Between Unified Capsules

Mammography screening is one of the important applications for the intelligent Internet of Things (IoT). Due to the efficient and personalized cyber-medicine system, early diagnosis can successfully reduce the breast cancer mortality rate by AI-driven healthcare. However, it is a huge challenge to extend the conventional single-center into the multicenter mammography screening, thus improving the effectiveness and robustness of intelligent IoT-based devices. To address this problem, we utilize multicenter mammograms by the modified capsule neural network and propose a novel framework called multicenter transformation between unified capsules (MLT-UniCaps) in this article. The proposed MLT-UniCaps is composed of Attentional Pose Embedding, Dynamic Source Capsule Traversal, and Adaptive Target Capsule Fusion to realize an intelligent remote assistant diagnosis. Attentional Pose Embedding extracts feature vectors via variations in position, orientation, scale, and lighting as the poses through an adversarial convolutional neural network with an attention-based layer. Based on the pose presentation, Dynamic Source Capsule Traversal deploys a dynamic routing mechanism between neurons to build a source cancer classifier for single-center mammography screening. Using the source cancer classifier, Adaptive Target Capsule Fusion integrates various centers of mammograms as the universal cancer detectors and optimizes heterogeneous distribution among them by the transformation-likelihood maximization. Owing to the three components, MLT-UniCaps effectively improves the results of single-center mammography screening and works in the multicenter breast cancer diagnosis. By comprehensive experiments on 58 965 samples, the proposed MLT-UniCaps obtains 90.1% of overall classification accuracy on single-center trials and 73.8% of overall F1 score on multicenter trials. All the experimental results illustrated that our MLT-UniCaps, an intelligent IoT-based clinical tool, inures the benefit of mammography screening.

Modeling and controlling IoT-based devices’ behavior with high-level Petri nets

In the last decade Internet of Things (IoT) emerged as a concept for intelligent, flexible, autonomous, and distributed system while IoT-based devices have highlighted for agent communication, data analysis, and decision-making tasks. Integrating high-level behavioral and low-level control has capital significance for their implementation and is still a challenge. In the field of high-level behavior, Petri nets are widely applied for modeling, simulating, and analyzing concurrent, discrete-event and knowledge-based systems, furthermore even models are used as basis for control algorithm, direct controlling devices’ behavioral with high-level Petri nets is little diffused. This paper presents a control architecture used to integrate models in high-level Petri nets with IoT-based devices. Operational tests were provided using CPN Tools and micro-controlled physical devices. A study with a mobile robot is shown and results indicate the reasonability and applicability of this proposed approach in IoT-based devices.

A Snort-based secure edge router for smart home

Cybercrimes are rising rapidly with the increasing use of the internet of things (IoT)-based gadgets at home. For instance, the Mirai-BotNet infected and compromised many IoT-based devices and routers, creating a zombie network of robots that can be controlled remotely. There is a need for a cost-effective, secure router for a smart home. This paper investigates and proposes a Snort-based secure edge router for smart home (SERfSH), which is resilient to many cyberattacks. SERfSH automatically generates Snort content rules by combining the extracted string, location information, header information, and sequential pattern. The experimental setup of SERfSH consists of a Raspberry Pi 4 model, an ESP32 microcontroller, six IoT devices, and a malicious actor machine. The proposed SERfSH is tested for 15 attacks, and the results show that 14 attacks were detected and 12 attacks were mitigated.

The Role of IoT in Woman’s Safety: A Systematic Literature Review

Women’s safety has been highlighted as one of the major concerns of any society where several women are dealing with various safety issues like harassment, rape, molestation, and domestic violence due to different social or cultural reasons. Internet of Things (IoT) is becoming a promising technology to support day-to-day concerns and provide support in handling various affairs. Many IoT-based devices have been introduced by the community to help women deal with their potential safety threats. This study presents a systematic literature review of research studies exhibiting the IoT devices for women’s safety, the main features these devices offer as well as the wearable, sensors used, and the machine learning algorithms used. The review is carried out by carefully examining and synthesizing the research articles published between 2016 to 2022 in well-reputed research venues. The results revealed that different types of sensors are used to capture the state of women undergoing safety issues where the pulse-rate, and pressure sensors are most commonly used sensors in these devices. In addition, the devices used different technology to transmit the alerts including global positing system (GPS), global system for mobile communication (GSM), and Raspberry pi. Furthermore, several machine learning algorithms such as logistic regression, hidden Markov, and decision trees are used to identify the potential under threat women and help prevent the undesirable situation for women beforehand. It was identified that despite producing notable research in the underlying domain the systems emphasizing auto-activation of alert generation with lesser human interaction and improved accuracies are required to be developed for effectively addressing the concern. In addition to reviewing the literature, this study suggests a taxonomy posing different techniques, features, wearables, and sensors used in IoT-based women safety devices. Furthermore, the gaps and challenges pertaining to the IoT devices and their usability for women’s safety have also been highlighted. In addition, this work proposes an architectural model that presents prominent components necessary to develop IoT-based women’s safety devices. Lastly, this study emphasizes the use of combinations of sensors to get multiple types of input data that could lead to determining the possibility of threat with better accuracies and precisions.

Privacy-Preservation Techniques for IoT Devices: A Systematic Mapping Study

The Internet of Things (IoT) is becoming a pervasive technology entangled in everyday life. The objective of IoT is to provide ubiquitous access to numerous devices and machines on service providers. However, IoT-based devices may expose a user to various privacy and security threats. Privacy-preservation techniques focus on securing any type of data transfer between different parties. We aim to deliver the current state of the art in terms of privacy-preservation techniques used for IoT devices that have been discussed in the literature. Therefore, we carried out a systematic identification of the privacy-preservation techniques for IoT devices that have been described in the cybersecurity domain. To do so, we followed rigorous guidelines to define our research protocol to increase the repeatability and reliability of our results. A set of ten research questions was created to drive the analysis of our study. This research work comprehensively analyzes and discusses the privacy-preservation techniques for IoT devices published in five different academic venues. We identified 260 studies, mostly published between 2017 and 2021, that were systematically selected from an initial set of 1394 papers. The most active research areas in privacy-preservation techniques for IoT devices discuss cryptography techniques to improve the authentication process to access IoT devices. The majority of authors presented privacy-preservation techniques for IoT that involved inventory privacy threats and discussed privacy interference attacks. We comprehensively analyze and discuss the trends, gaps, and possible future research directions of the privacy-preservation techniques used for IoT devices published in different academic venues.

Role of Internet of things in diabetes healthcare: Network infrastructure, taxonomy, challenges, and security model.

The Internet of things (IoT) is an emerging technology that enables ubiquitous devices to connect with the Internet. IoT technology has revolutionized the medical and healthcare industry by interconnecting smart devices and sensors. IoT-based devices and biosensors are ideal to detect diabetes disease by collecting the accurate value of glucose continuously. Diabetes is one of the well-known and major chronic diseases that has a worldwide social impact on community life. Blood glucose monitoring is a challenging task, and there is a need to propose a proper architecture of the noninvasive glucose sensing and monitoring mechanism, which could make diabetic people aware of self-management techniques. This survey presents a rigorous discussion of diabetes types and presents detection techniques based on IoT technology. In this research, an IoT-based healthcare network infrastructure has been proposed for monitoring diabetes disease based on big data analytics, cloud computing, and machine learning. The proposed infrastructure could handle the symptoms of diabetes, collect data, analyze it, and then transmit the results to the server for the next action. Besides, presented an inclusive survey on IoT-based diabetes monitoring applications, services, and proposed solutions. Furthermore, based on IoT technology the diabetes disease management taxonomy has also been presented. Finally, presented the attacks taxonomy as well as discussed challenges, and proposed a lightweight security model in order to secure the patient's health data.

Integrating IoT-Based Devices for Monitoring The Humidity and Temperature In the Boarding House Space

Boarding houses provide the service of renting out living quarters. A good boarding house maintains a pleasant temperature and humidity level. High humidity may damage the interior and the people who live in it; therefore, it must be monitored more closely. Simpang Kost is the boarding house the author used as an object of research on monitoring temperature and humidity using an application called Smart Life. The goal of this study is to use IoT-based sensors to monitor and automatically solve temperature and humidity concerns in various rooms. Smart temperature and humidity sensors are IoT-based. This device measures the temperature and humidity in the room. According to the collected data, three categories of rooms have issues with high humidity presentation levels, so the authors offer several solutions to address these issues. The dehumidifier functions as an actuator, starting when the humidity in the room rises and stopping when the humidity in the room reaches the standards. An exhaust fan is also linked to an IoT-based smart breaker and serves as an actuator. When the room temperature rises above a certain threshold, this tool will activate and turn off automatically.

Using neural networks to predict hourly energy consumptions in office and industrial buildings as a function of weather data

Office and industrial premises are among the most energy consuming type of buildings. Compared to residential buildings, they are characterized by more regular occupation patterns and stricter control of building systems. Under these conditions, it is expected that energy consumptions may be more easily predictable and may be significantly influenced by outdoor conditions more than by individual preferences. This may result in availability of straightforward predictions of energy use (at daily or hourly basis) which may contribute to trade energy at lower costs, make a better use of renewable energies, while balancing energy saving and occupants’ comfort. An essential contribution to the ability to easily and accurately predict energy consumptions, is given by the ever-increasing number of smart and IoT-based devices that collect data inside and outside buildings and consequently make them available for processing. Taking advantage of such data, it is worth investigating if advanced artificial intelligence methods (like neural networks and machine learning) are capable of yielding predictions of energy consumptions and, ideally, indoor conditions. For the purpose of the present paper, the dataset (including both input and output parameters) was obtained through simulation (using the popular EnergyPlus tool), including one office and one industrial reference building, and using three different climatic datasets. Finally, artificial neural networks were trained assuming daily and hourly energy consumptions (subdivided by category) as the target variable, showing that in most of the cases very accurate predictions could be obtained.

A Survey on IoT-Enabled Smart Grids: Emerging, Applications, Challenges, and Outlook

Swift population growth and rising demand for energy in the 21st century have resulted in considerable efforts to make the electrical grid more intelligent and responsive to accommodate consumers’ needs better while enhancing the reliability and efficiency of modern power systems. Internet of Things (IoT) has appeared as one of the enabling technologies for smart energy grids by delivering abundant cutting-edge solutions in various domains, including critical infrastructures. As IoT-enabled devices continue to flourish, one of the major challenges is security issues, since IoT devices are connected through the Internet, thus making the smart grids vulnerable to a diverse range of cyberattacks. Given the possible cascading consequences of shutting down a power system, a cyberattack on a smart grid would have disastrous implications for the stability of all grid-connected infrastructures. Most of the gadgets in our homes, workplaces, hospitals, and on trains require electricity to run. Therefore, the entire grid is subject to cyberattacks when a single device is hacked. Such attacks on power supplies may bring entire cities to a standstill, resulting in massive economic losses. As a result, security is an important element to address before the large-scale deployment of IoT-based devices in energy systems. In this report, first, we review the architecture and infrastructure of IoT-enabled smart grids; then, we focus on major challenges and security issues regarding their implementation. Lastly, as the main outcome of this study, we highlight the advanced solutions and technologies that can help IoT-enabled smart grids be more resilient and secure in overcoming existing cyber and physical attacks. In this regard, in the future, the broad implementation of cutting-edge secure and data transmission systems based on blockchain techniques is necessary to safeguard the entire electrical grid against cyber-physical adversaries.

Applying Adaptive Security Techniques for Risk Analysis of Internet of Things (IoT)-Based Smart Agriculture

In modern times, the Internet of Things (IoT) is having a major impact on agriculture. Risk and security parameters are always linked when researching, developing, implementing and deploying IoT-based devices. It is a myth that security does not play a major role in IoT applications in agriculture. Data accuracy and availability is a high-priority requirement for farmers who help achieve high yields. A secure IoT network requires a situational approach, often referred to as dynamic security. An advanced security approach to improving IoT security is adaptive security, a cybersecurity-based approach. The lack of security in a smart farming environment is a very important factor for agricultural growth. In this study, we introduce IoT together with adaptive security operations and integrate it into a smart farming environment. We propose an evaluation framework that can be applied to diverse smart farming environments. Several scenarios of an agricultural environment with smart devices and sensors are described for execution. Storylines with real-time environments are then derived from these scenarios to extend and incorporate adaptive security frameworks and scenarios in IoT-based agriculture.

MobiTrust: Trust-Aware Pricing Scheme for Edge-Based Mobile Sensor-Cloud for Vehicular IoT

In this work, we propose MobiTrust, a game theoretic pricing scheme, to address the problem of ensuring trust in the edge-enabled mobile sensor-cloud (MSC) architecture for vehicular Internet-of-Things (IoT). In the edge-enabled MSC, a sensor-cloud service provider (SCSP) utilises multiple sensor-equipped vehicular IoT-based devices belonging to different owners and multiple edge devices deployed by different edge infrastructure providers (IPs) to provision Sensors-as-a-Service (Se-aaS). Since these entities are revenue-driven and they depend on each other for successful service provisioning, there is a need to establish trust among them to ensure the sustainability of MSC. Existing works on sensor-cloud address the issue of ensuring trust among selfish device-owners. However, these works do not consider the presence of IPs and assume that the IoT devices are stationary in nature, which renders them unsuitable for MSC. Therefore, in this work, we aim to address this drawback of the existing literature by proposing MobiTrust, which is comprised of two components. First, we propose a mobility-aware trust model designed based on the Transferable Belief Model, which utilizes different types of evidence collected by the devices to estimate their trust values. Second, we propose a trust-based pricing and resource allocation scheme based on the Stackelberg game that aims to maximise the profits and satisfaction of the various stakeholders. We analyze the proposed scheme theoretically and examine the existence of equilibrium in the proposed game. Results of extensive simulations show that MobiTrust increases profits for the stakeholders and QoS by 9% and 11%, respectively, compared to benchmarks.

Digital Technology Implementation in Battery-Management Systems for Sustainable Energy Storage: Review, Challenges, and Recommendations

Energy storage systems (ESS) are among the fastest-growing electrical power system due to the changing worldwide geography for electrical distribution and use. Traditionally, methods that are implemented to monitor, detect and optimize battery modules have limitations such as difficulty in balancing charging speed and battery capacity usage. A battery-management system overcomes these traditional challenges and enhances the performance of managing battery modules. The integration of advancements and new technologies enables the provision of real-time monitoring with an inclination towards Industry 4.0. In the previous literature, it has been identified that limited studies have presented their reviews by combining the literature on different digital technologies for battery-management systems. With motivation from the above aspects, the study discussed here aims to provide a review of the significance of digital technologies like wireless sensor networks (WSN), the Internet of Things (IoT), artificial intelligence (AI), cloud computing, edge computing, blockchain, and digital twin and machine learning (ML) in the enhancement of battery-management systems. Finally, this article suggests significant recommendations such as edge computing with AI model-based devices, customized IoT-based devices, hybrid AI models and ML-based computing, digital twins for battery modeling, and blockchain for real-time data sharing.

The Effectiveness of IoT Based Wearable Devices and Potential Cybersecurity Risks: A Systematic Literature Review from the Last Decade

Wearable technology has enormous promise, particularly for data collection for cutting-edge health research, and its popularity has soared in recent years. This study aims to provide IoT-based wearable devices' effectiveness and potential cybersecurity threats to these innovative technologies. Using the PRISMA-2020 (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology, we conducted a scoping study to understand better the application of inexpensive, consumer-grade wearables for health research from a population health viewpoint. Scopus databases yielded a total of 43 articles. Our findings show that academics and research participants are tremendously interested in this technology, but they are also wary about using wearables. These devices are still vulnerable to cybersecurity assaults such as data privacy and security in healthcare. However, blockchain technology is a potential alternative for integrating with IoT-based wearables to ensure data privacy. Furthermore, according to the findings, wearables have an increasingly diverse range of applications, including COVID-19 prediction, fertility tracking, heat-related sickness, pharmacological effects, and psychological therapies; they also include underrepresented populations, such as those with rare diseases. In low-resource environments, there is a lack of research on wearable devices.

Energy-Harvesting for IoT-based Wireless Nodes: A Progress Study

Energy-harvesting technology is a promising renewable energy source for many applications through deriving energy from ambient environments. In IoT-based devices, this plays a significant role in providing sustainable energy and overcomes the need for battery maintenance and replacement, leading to increased efficiency, reliability, and operation time. This paper focuses on energy- harvesting in the context of IoT-based wireless nodes, its current progress, and future expectations. The main components, energy dissipations, and powering sources of IoT-based nodes are, first, identified. Then, the various ambient energy-harvesting sources and general architecture of the IoT-based energy-harvesting node are presented and concisely discussed. Finally, a progress discussion on the current and future trends of energy-harvesting technology for IoT devices is provided from different aspects, while shedding light on the key challenges facing the growth of energy harvesting for IoT-based systems. Overall, the paper provides a contemporary study that helps researchers who are working in this area and aiming to participate in its developments.