Internet Of Things Model Research Articles

Machine Learning algorithms for prediction of energy consumption and IoT modeling in complex networks

In this paper, the internal energy consumption on the Internet of Things (IoT) has been studied. The purpose of this paper is to predict the factors affecting energy consumption in buildings by considering machine learning algorithms such as k-nearest neighbors (KNN), Ada boost, random forest and neural network. These algorithms are implemented in the Orange tool. Also, the univariate regression algorithm is used to select the best feature. This algorithm determines the most important factors affecting energy consumption and their impact. Then, with the help of Gephi tool, these data are simulated in the IoT environment as a complex network. The simulated network in the Internet of things is also a small world network. This network shows the relationships between the features. The results of this paper show that the overall height, roof area, surface area and relative compaction have the greatest impact on the energy consumption of buildings. It can be seen that the predicted error percentages for these data for cooling loads and heating loads are 0.911 and 0.292, respectively. It should be noted that the best algorithm for cooling load and heating load is Ada boost algorithm.

Analysis of Privacy-Preserving Edge Computing and Internet of Things Models in Healthcare Domain.

The healthcare sector is rapidly being transformed to one that operates in new computing environments. With researchers increasingly committed to finding and expanding healthcare solutions to include the Internet of Things (IoT) and edge computing, there is a need to monitor more closely than ever the data being collected, shared, processed, and stored. The advent of cloud, IoT, and edge computing paradigms poses huge risks towards the privacy of data, especially, in the healthcare environment. However, there is a lack of comprehensive research focused on seeking efficient and effective solutions that ensure data privacy in the healthcare domain. The data being collected and processed by healthcare applications is sensitive, and its manipulation by malicious actors can have catastrophic repercussions. This paper discusses the current landscape of privacy-preservation solutions in IoT and edge healthcare applications. It describes the common techniques adopted by researchers to integrate privacy in their healthcare solutions. Furthermore, the paper discusses the limitations of these solutions in terms of their technical complexity, effectiveness, and sustainability. The paper closes with a summary and discussion of the challenges of safeguarding privacy in IoT and edge healthcare solutions which need to be resolved for future applications.

IOT-BASED SMART ENVIRONMENTS: STATE OF THE ART, SECURITY THREATS AND SOLUTIONS

Abstract. Smart environments provide many benefits to the users including comfort, convenience, energy efficiency, safety, automation, and service quality. The Internet of Things (IoT) has developed to become one of the widely used technologies in smart environments. Many security attacks and threats are generated by security flaws in IoT-based systems and devices, which may affect smart environments applications. As a result, security is one of the most important issues in any smart area or environment based on the IoT model. This paper presents an overview of smart environments based on IoT technology and highlights the main security issues and countermeasures in the four layers of smart environment IoT architecture. It also reviews some of the current solutions that ensure the security of information in smart environments applications.

Packet loss rate monitoring model of IoT based on differential evolution algorithm

In order to overcome the problems of high energy consumption and low execution efficiency of traditional Internet of things (IoT) packet loss rate monitoring model, a new packet loss rate monitoring model based on differential evolution algorithm is proposed. The similarity between each data point in the data space of the Internet of things is set as the data gravity. On the basis of the data gravity, combined with the law of gravity in the data space, the gravity of different data is calculated. At the same time, the size of the data gravity is compared, and the data are classified. Through the classification results, the packet loss rate monitoring model of the Internet of things is established. Differential evolution algorithm is used to solve the model to obtain the best monitoring scheme to ensure the security of network data transmission. The experimental results show that the proposed model can effectively reduce the data acquisition overhead and energy consumption, and improve the execution efficiency of the model. The maximum monitoring efficiency is 99.74%.

Review of Design Thinking Approach in Learning IoT Programming

Internet of Things (IoT) is one of the keys for the Fourth Industrial Revolution (IR4.0) which refers to the use of intelligently connected devices and systems by embedded sensors and actuators in machines and other physical objects. With the pace of rapid IoT adoption, the demand for skilled IoT developers rises in tandem. This paper review the integration of design thinking as a pedagogical in IoT programming education. Topics on challenges in developing IoT systems, models, or frameworks for learning IoT programming, design thinking, design thinking approach in IoT system prototype development and teaching strategy technique have been discussed.

IoT Blockchain Data Veracity with Data Loss Tolerance

Recent years have witnessed the advancement of the Internet of Things (IoT) and its emergence as a technology that could revolutionize many businesses. It helps considerably in creating data-driven business models with the insights it provides. IoT systems are deployed in data collection, monitor processes, provide insights and allow businesses to make data-driven productivity improvements. However, IoT systems are often experiencing data loss due to inevitable failures ranging from devices, networks, to the application layer, especially in scarce infrastructure resources environments. Data loss might be unrecoverable in many circumstances. As such, this research presents a blockchain based IoT model (framework) with the aim of circumventing data loss. We envisioned IoT blockchain technology in enhancing data veracity with data loss tolerance. That is, to have blockchain enhancing the IoT data veracity by leveraging on the features existed in its peer-to-peer network (P2P) and distributed ledger storage technology (DLT). Additionally, the edge computing of IoT blockchain technology is also conceptually workable; with intelligent small computing resources, it opens up a new era of bringing the intelligence of data collection, connectivity, computation and storage into the edge/device layer. A novel IoT blockchain strength monitoring system is also been studied to further enhance data veracity; this is achieved through a capacitance monitoring on the IoT blockchain system. The empirical results show that the proposed IoT blockchain with a strength monitoring model can alleviate data loss and thus enhance data veracity with data loss tolerance.

IAF: IoT Attack Framework and Unique Taxonomy

In the early 2000s, the Internet meant being able to connect different communication devices, whereas the focus in the last few years is on connecting “things” to the Internet. Although there is no distinct classification for these devices and things on the Internet, the Internet of Things (IoT) ecosystem primarily consists of a complex network of devices, sensors, and things. These “things” are controlled by humans and utilize the existing cloud infrastructure. These devices provide facilities and benefits to make our lives comfortable. IoT domains include smart homes, healthcare, manufacturing, smart wearables, smart cities, smart grids, industrial IoT, connected vehicles, and smart retail. Different IoT models involve human-to-IoT, IoT-to-IoT, IoT-to-traditional systems architectures. In most scenarios, the architecture ends up connecting to the unsecured Internet. This has thrown open several critical issues leading to cybersecurity attacks on IoT devices. IoT communications, protocols or the architecture were never been conceptualized to handle the new age cybersecurity attacks. IoT devices have limited compute, storage, network, or memory. In this research, the authors present a unique IoT attack framework named IAF focusing on the impact of IoT attacks on IoT applications and service levels. The authors also proposed an all-inclusive attack taxonomy classifying various attacks on IoT ecosystems.

A Novel Interference Avoidance Based on a Distributed Deep Learning Model for 5G-Enabled IoT.

The co-existence of fifth-generation (5G) and Internet-of-Things (IoT) has become inevitable in many applications since 5G networks have created steadier connections and operate more reliably, which is extremely important for IoT communication. During transmission, IoT devices (IoTDs) communicate with IoT Gateway (IoTG), whereas in 5G networks, cellular users equipment (CUE) may communicate with any destination (D) whether it is a base station (BS) or other CUE, which is known as device-to-device (D2D) communication. One of the challenges that face 5G and IoT is interference. Interference may exist at BSs, CUE receivers, and IoTGs due to the sharing of the same spectrum. This paper proposes an interference avoidance distributed deep learning model for IoT and device to any destination communication by learning from data generated by the Lagrange optimization technique to predict the optimum IoTD-D, CUE-IoTG, BS-IoTD and IoTG-CUE distances for uplink and downlink data communication, thus achieving higher overall system throughput and energy efficiency. The proposed model was compared to state-of-the-art regression benchmarks, which provided a huge improvement in terms of mean absolute error and root mean squared error. Both analytical and deep learning models reached the optimal throughput and energy efficiency while suppressing interference to any destination and IoTG.

Sports and Health Management Using Big Data Based on Voice Feature Processing and Internet of Things

With the support of big data and information technology, various sectors such as sports, health, and medical industry can realize the integration and readjustment of the existing resources, which improve the operation efficiency of the industry and tap its huge potential. With the advancement in big data analysis, voice features, and Internet of Things (IoT), personalized health management is becoming the development trend and breakthrough of sports and health industry. The application of big data will tap out the huge potential of the sports and health industry. In this paper, we have used the Mel-requency cepstrum coefficient as the speech feature processing method. When the linear frequency is transformed to the Mel frequency by Fourier transform, the calculation accuracy will decrease with the increase in the frequency, and the low-frequency signal will be retained to improve the anti-noise ability. With further study of the voice feature processing and IoT model of big data’s sports and health management, a vector addition regression was developed to compare the two real scoring features of the processing results that pave the way for further analysis and result evaluation. Through experimental verification, it is proved that the method in this paper can better learn the speech features. At the same time, with the introduction of noise reduction, the big data of speech recognition in sports health management has a stronger robustness and improves the overall system performance.

A noise-based privacy preserving model for Internet of Things

AbstractWith the ever-increasing number of devices, the Internet of Things facilitates the connection between the devices in the hyper-connected world. As the number of interconnected devices increases, sensitive data disclosure becomes an important issue that needs to be addressed. In order to prevent the disclosure of sensitive data, effective and feasible privacy preservation strategies are necessary. A noise-based privacy-preserving model has been proposed in this article. The components of the noise-based privacy-preserving model include Multilevel Noise Treatment for data collection; user preferences-based data classifier to classify sensitive and non-sensitive data; Noise Removal and Fuzzification Mechanism for data access and user-customized privacy preservation mechanism. Experiments have been conducted to evaluate the performance and feasibility of the proposed model. The results have been compared with existing approaches. The experimental results show an improvement in the proposed noise-based privacy-preserving model in terms of computational overhead. The comparative analysis indicates that the proposed model without the fuzzifier has around 52–77% less computational overhead than the Data access control scheme and 46–70% less computational overhead compared to the Dynamic Privacy Protection model. The proposed model with the fuzzifier has around 48–73% less computational overhead compared to the Data access control scheme and 31–63% less computational overhead compared to the Dynamic Privacy Protection model. Furthermore, the privacy analysis has been done with the relevant approaches. The results indicate that the proposed model can customize privacy as per the users’ preferences and at the same time takes less execution time which reduces the overhead on the resource constraint IoT devices.

Named Data Networking‐based communication model for Internet of Things using energy aware forwarding strategy and smart sleep mode

AbstractNamed data networking (NDN) has emerged as a promising communication paradigm, proposed to deal with the shortcomings of the traditional IP‐based model. NDN introduces new name‐based routing, receiver‐based service, caching, and self‐certifying contents features that obviously improve data delivery efficiency and reliability. Moreover, NDN offers lightweight forwarding rules that suits constrained devices. These features makes NDN as highly promising communication model for the Internet of Things (IoT). On the other hand, one of the widely adopted networking specification for IoT is the IEEE 802.15.4 standard. This latter proposes interesting energy saving functionalities. To take full advantages of the two technologies, we propose an NDN over IEEE 802.15.4 communication solution that meets the requirements of low‐data rate and low‐power‐consumption monitor and control IoT applications. The proposed solution includes two modules: a reliable energy‐aware forwarding strategy that selects the next hop forwarder based on its residual energy level and a sleep mode scheduling algorithm that schedules the sleep/wake‐up mode according to the role of the node in the forwarding and the path repair processes. Extensive simulations and analyses have been conducted to confirm the viability and effectiveness of our proposal in terms of energy consumption, network lifetime, delivery ratio, retrieval delay, and scalability.

A Review on future challenges and concerns associated with an Internet of Things based automatic health monitoring system

This research article surveys the most recent IoT healthcare system research articles as the integration of IoT models have been extended to healthcare systems, such as health monitoring, fitness routines, and other applications. Extensive research study has been conducted on Internet of Things (IoT) technology to enhance the monitoring efficiency. This research is aimed at investigating the Internet of Things [IoT] architecture with an emphasis on cloud-based applications. The most significant challenges in the Internet of Things [IoT] include different elements such as accuracy and energy consumption, wherein this research is focused on improving the performance of IoT-based medical equipment. In this research, data management techniques for the Internet of Things-based cloud healthcare system are also thoroughly investigated. The performance and limitations of the Internet of Things (IoT) health system are evaluated. The majority of studies are successful in detecting a wide range of markers and correctly predicting illness. The Internet of Things (IoT) health system is being developed as an effective solution to the health concerns of elderly population. The major drawbacks of current systems are their increased energy consumption, reduced availability of resources, and safety concerns resulting from the use of a large number of different pieces of equipment.

Structural equation modeling of internet of things (IoT) adoption for Indonesian Village-Owned Enterprises (BUMDes)

This study seeks to examine the antecedents of Internet of Things (IoT) intention and adoption of Indonesian village-owned enterprises (Badan Usaha Milik Desa, BUMDes) by applying the Technology Acceptance Model (TAM). The respondents comprised 237 managers and staff of BUMDes in Aceh, Indonesia. A questionnaire was designed to examine perceived usefulness of IoT on the product and service empowerment, perceived ease of use of IoT, and perceived credibility on IoT, intention to use the technology, and adoption of IoT to the community empowerment in the village setting. The data were analyzed using partial least squares structural equation modeling (PLS-SEM). The generated model of IoT showed three significant direct paths between perceived usefulness, perceived credibility on the IoT, and intention as well as between intention and adoption. The re-specified model of IoT adoption indicated two significant direct paths (perceived usefulness to intention and intention to adoption) and introduced three new paths (direct paths from perceived usefulness, perceived ease of use, and perceived credibility to adoption). The final model also established partial mediating effects of intention between Perceived Usefulness, Perceived Ease of Use, and IoT adoption. This study suggests the relevance of TAM to explain IoT adoption in Indonesian village-owned companies.

Leveraging context-awareness for Internet of Things ecosystem: Representation, organization, and management of context

Present-day devices are becoming increasingly smarter than their predecessors. From a simple passive light switch to an intelligent wristwatch, great strides have been made in networking smart devices, creating an autonomous ecosystem, the so-called Internet of Things. In an increasingly information-driven world, context-awareness supports the intended applications as well as their constituent devices, making them conscious of and adaptive to the specific scenario in real-time. Moreover, heterogeneous devices in the Internet of Things ecosystem peruse disparate data formats and semantics, giving rise to interoperability and information sharing challenges. Context modeling is a core feature that facilitates interoperability and information sharing between applications. Although generic context models exist, they do not consider pertinent dimensions of context to provide a generic vocabulary, and therefore, they cannot be extended to generalize situations commonly encountered in the Internet of Things environment. An extensible, generic modeling and representation of context is required to manage pertinent context dimensions in various ecosystems by being dynamically aware of the situation. This paper presents Context Model for Internet of Things, an extensible and generic ontology-based context modeling approach that provides relevant information at the right time. This work encompasses Context Ontology for Internet of Things, an ontology-based context organization approach, which provides an abstract and overarching vocabulary that fosters knowledge reusability and sharing. The proposed model has been implemented and evaluated with a use case to validate its adaptability, effectiveness, and viability. Our evaluation based on generality, effectiveness, and consistency shows that the proposed model can effectively represent, organize, and manage the context in different Internet of Things ecosystems.

Grouping and Sponsoring Centric Green Coverage Model for Internet of Things.

Recently, green computing has received significant attention for Internet of Things (IoT) environments due to the growing computing demands under tiny sensor enabled smart services. The related literature on green computing majorly focuses on a cover set approach that works efficiently for target coverage, but it is not applicable in case of area coverage. In this paper, we present a new variant of a cover set approach called a grouping and sponsoring aware IoT framework (GS-IoT) that is suitable for area coverage. We achieve non-overlapping coverage for an entire sensing region employing sectorial sensing. Non-overlapping coverage not only guarantees a sufficiently good coverage in case of large number of sensors deployed randomly, but also maximizes the life span of the whole network with appropriate scheduling of sensors. A deployment model for distribution of sensors is developed to ensure a minimum threshold density of sensors in the sensing region. In particular, a fast converging grouping (FCG) algorithm is developed to group sensors in order to ensure minimal overlapping. A sponsoring aware sectorial coverage (SSC) algorithm is developed to set off redundant sensors and to balance the overall network energy consumption. GS-IoT framework effectively combines both the algorithms for smart services. The simulation experimental results attest to the benefit of the proposed framework as compared to the state-of-the-art techniques in terms of various metrics for smart IoT environments including rate of overlapping, response time, coverage, active sensors, and life span of the overall network.

Convergence model of AI and IoT for virus disease control system.

Recently, virus diseases, such as SARS-CoV, MERS-CoV, and COVID-19, continue to emerge and pose a severe public health problem. These diseases threaten the lives of many people and cause serious social and economic losses. Recent developments in information technology (IT) and connectivity have led to the emergence of Internet of Things (IoT) and Artificial Intelligence (AI) applications in many industries. These industries, where IoT and AI together are making significant impacts, are the healthcare and the diagnosis department. In addition, by actively communicating with smart devices and various biometric sensors, it is expanding its application fields to telemedicine, healthcare, and disease prevention. Even though existing IoT and AI technologies can enhance disease detection, monitoring, and quarantine, their impact is very limited because they are not integrated or applied rapidly to the emergence of a sudden epidemic. Especially in the situation where infectious diseases are rapidly spreading, the conventional methods fail to prevent large-scale infections and block global spreads through prediction, resulting in great loss of lives. Therefore, in this paper, various sources of infection information with local limitations are collected through virus disease information collector, and AI analysis and severity matching are performed through AI broker. Finally, through the Integrated Disease Control Center, risk alerts are issued, proliferation block letters are sent, and post-response services are provided quickly. Suppose we further develop the proposed integrated virus disease control model. In that case, it will be possible to proactively detect and warn of risk factors in response to infectious diseases that are rapidly spreading worldwide and strengthen measures to prevent spreading of infection in no time.

Retracted: Soil monitoring and evaluation system using EDL‐ASQE: Enhanced deep learning model for IoT smart agriculture network

SummaryThe enormous growth of the Internet of Things (IoT) network provides abundant support to agriculture and development, which states the future scope of IoT‐based agriculture. In a recent scenario, agriculture IoT can be integrated with sensors, communication protocols, and microcontrollers for automated process executions to increase productivity. Moreover, deep learning effectiveness produces appropriate results and solves several real‐time issues related to agriculture‐based advancements. The proposed system presents the design of an IoT network communication system to estimate the soil conditions. Soil quality is an important factor in modernized agriculture, productivity enhancement, and hydrological cycles. By the soil quality analysis, the accurate prediction is very significant for sensible usage of resources. An enhanced deep learning model for IoT network‐based automated soil quality evaluation observes the complex soil features and meteorological factors with those concerns. Here, the real‐time samples are collected from the local area sensor network for analysis. The deep learning model is developed with big data fitting ability for soil quality prediction. The weight factors (W.F.) are derived for measuring the soil quality accurately. The proposed IoT network‐based agriculture structure allows a flexible approach to different types of crops and implementation in agricultural areas. Experimental results obtained in the laboratory and onsite confirmed the performance and reliability of the system. The result evaluations are carried out based on precision, accuracy, and processing time, and results show that the model achieves better results than compared models.

Internet of Things-Based Agricultural Mechanization Using Neural Network Extreme Learning on Rough Set

Recently, the basic functioning of monitoring in internet of things (IoT) is to apply the monitored data to the database for the regular analysis through mobile or computer platform. The purpose of this article is to highlight the application scope of IoT knowledge and to present the model of agricultural IoT for prediction by studying the influence of IoT technology towards modern agriculture. In order to explore the uncertain characteristics of the development of agricultural mechanization, the evaluation index system is simplified through the existing rough set theory. The neural network model is established with five random provinces and cities in 31 provinces and municipalities as test samples. By comparing the data of the neural network model established before and after the reduction, the results show that the index coefficient is reduced by about 60% based on the fixed information before and after the reduction. The simulation evaluation accuracy established by the artificial neural network model is 100%, which is consistent with the results of the original index system.

Trust Model for IoT Using Cluster Analysis: A Centralized Approach

The emergence of Internet-of-Things (IoT) resulted in building an environment of intelligent applications and services activated by a multitude of sensors. These sensor nodes are installed in a transparent and remote environment which makes devices vulnerable to security threats, especially when there are malicious node attacks in those networks. Malicious nodes can initiate multiple attacks, based on methods of coercion, retransmission, and discard. This paper suggests a centralized model of trust based on clusteringto address the security risks and challenges associated withIoT. Trust evaluation is a significant factor in planning and assessing defence solutions. Many theoretical works have been carried out to suggest trust evaluation and prediction methods. The main objective of this paper is to develop a centralized trust model for IoT. Initially, a cluster is formed based on the node location. Then for each group, the master node is selected based on their direct and indirect trust value, which includes Quality of Service (QoS) and Social trust properties. The master node is periodically updated using node data trust value and each node cluster sensed information using a regression model-based clustering. The intra-cluster and inter-cluster analysis is utilized to find the data trust value of the node, as well as outlier data. An extensive simulation is performed to analyze the performance of the proposed work. The result indicates that the projected model achievesimproved in terms of various operational metrics.

An Agent-Based Model of Task-Allocation and Resource-Sharing for Social Internet of Things

The things in the Internet of Things are becoming more and more socially aware. What social means for these things (more often termed as “social objects”) is predominately determined by how and when objects interact with each other. In this paper, an agent-based model for Social Internet of Things is proposed, which features the realization of various interaction modalities, along with possible network structures and mobility modes, thus providing a novel model to ask interesting “what-if” questions. The scenario used, which is the acquisition of shared resources in a common spatial and temporal world, demands agents to have ad-hoc communication and a willingness to cooperate with others. The model was simulated for all possible combinations of input parameters to study the implications of competitive vs. cooperative social behavior while agents try to acquire shared resources/services in a peer-to-peer fashion. However, the main focus of the paper was to analyze the impact of profile-based mobility, which has an underpinning on parameters of extent and scale of a mobility profile. The simulation results, in addition to others, reveal that there are substantial and systematic differences among different combinations of values for extent and scale.