Intelligent Internet Of Things Research Articles

Robust Learning-Enabled Intelligence for the Internet of Things: A Survey From the Perspectives of Noisy Data and Adversarial Examples

The Internet of Things (IoT) has been widely adopted in a range of verticals, e.g., automation, health, energy, and manufacturing. Many of the applications in these sectors, such as self-driving cars and remote surgery, are critical and high stakes applications, calling for advanced machine learning (ML) models for data analytics. Essentially, the training and testing data that are collected by massive IoT devices may contain noise (e.g., abnormal data, incorrect labels, and incomplete information) and adversarial examples. This requires high robustness of ML models to make reliable decisions for IoT applications. The research of robust ML has received tremendous attention from both academia and industry in recent years. This article will investigate the state of the art and representative works of robust ML models that can enable high resilience and reliability of IoT intelligence. Two aspects of robustness will be focused on, i.e., when the training data of ML models contain noises and adversarial examples, which may typically happen in many real-world IoT scenarios. In addition, the reliability of both neural networks and reinforcement learning framework will be investigated. Both of these two ML paradigms have been widely used in handling data in IoT scenarios. The potential research challenges and open issues will be discussed to provide future research directions.

Application of Internet of Things Technology in Isolation Ward

Based on the existing information construction foundation of the isolation ward of the hospital, according to the relevant guidelines issued by the National Health Commission, the management of environmental isolation, disinfection, medical staff management and patient management are discussed, combining the application of Internet of things technology in hospital management, a series of new applications with distinctive features of Internet of Things (IoT) are built, and advanced technology and equipment such as Internet of Things are introduced. Realize the application scenario, implementation method and business mode of intelligent IoT in isolation ward, form an integrated data management center and monitoring system through data intelligent IoT, aggregation and operation, and realize the digital collection, processing, storage, transmission and analysis of medical information, equipment information, personnel information and management information, so as to realize medical closed-loop management, reduce the hidden danger of medical safety in isolated wards and improve the level of medical quality.

An intelligence optimization method based on crowd intelligence for IoT devices

Purpose The rapid development of 5G technology brings the expansion of the internet of things (IoT). A large number of devices in the IoT work independently, leading to difficulties in management. This study aims to optimize the member structure of the IoT so the members in it can work more efficiently. Design/methodology/approach In this paper, the authors consider from the perspective of crowd science, combining genetic algorithms and crowd intelligence together to optimize the total intelligence of the IoT. Computing, caching and communication capacity are used as the basis of the intelligence according to the related work, and the device correlation and distance factors are used to measure the improvement level of the intelligence. Finally, they use genetic algorithm to select a collaborative state for the IoT devices. Findings Experimental results demonstrate that the intelligence optimization method in this paper can improve the IoT intelligence level up to ten times than original level. Originality/value This paper is the first study that solves the problem of device collaboration in the IoT scenario based on the scientific background of crowd intelligence. The intelligence optimization method works well in the IoT scenario, and it also has potential in other scenarios of crowd network.

Assessing the Complexity of Intelligent Parks’ Internet of Things Big Data System

Today, intelligence in all walks of life is developing at an unexpectedly fast speed. The complexity of the Internet of Things (IoT) big data system of intelligent parks is analyzed to unify the information transmission of various industries, such as smart transportation, smart library, and smart medicine, thereby diminishing information islands. The traditional IoT systems are analyzed; on this basis, a relay node is added to the transmission path of the data information, and an intelligent park IoT big data system is constructed based on relay cooperation with a total of three hops. Finally, the IoT big data system is simulated and tested to verify its complexity. Results of energy efficiency analysis suggest that when the power dividing factor is 0.5, 0.1, and 0.9, the energy efficiency of the IoT big data system first increases and then decreases as α0 increases, where the maximum value appears when α0 is about 7 J. Results of outage probability analysis demonstrate that the system’s simulation result is basically the same as that of the theoretical result. Under the same environment, the more hop paths the system has, the more the number of relays is; moreover, the larger the fading index m, the better the system performance, and the lower the outage possibility. Results of transmission accuracy analysis reveal that the IoT big data system can provide a result that is the closest to the actual result when the successful data transmission probability is 100%, and the parameter λ values are between 0.01 and 0.05; in the meantime, the delay of successful data transmission is reduced gradually. In summary, the wireless relay cooperation transmission technology can reduce the outage probability and data transmission delay probability of the IoT big data system in the intelligent park by adding the multihop path, thereby improving the system performance. The above results can provide an experimental basis for exploring the complexity of IoT systems in intelligent parks.

Dynamic neuromorphic architecture selection scheme for intelligent Internet of Things services

SummaryWith the development of Internet of Things (IoT)‐related technologies and artificial intelligence (AI) technologies, various IoT services are becoming more intelligent, and their use range is increasing and diversifying. IoT hardware and IoT software must support AI‐related functions to provide an intelligent IoT service. In general, IoT devices powered by batteries have limited computing performance when compared to general computing environments. Therefore, it is essential to provide AI‐related functions at low power in IoT devices to implement and offer various intelligent services. Neuromorphic computing devices or neuromorphic computing architectures can operate with low power energy consumption. If applied to IoT devices, AI‐related functions can be implemented in a resource‐constrained IoT device environment. The proposed neuromorphic architecture abstraction (NAA) model dynamically selects the proper neuromorphic architecture by comparing the parameter size of a given SNN model. It also considers the specifications and error probability of the available neuromorphic architecture. We also implement the proposed model in a real IoT computing environment and show that the proposed NAA model and dynamic selection scheme can reduce the execution time for training and inferencing. It reduces the training and inferencing time of a given model compared with the method of randomly specifying the neuromorphic architecture.

Intelligent circulation system modeling using bilateral matching theory under Internet of Things technology

The purpose is to promote the supply–demand market circulation of agricultural products and agricultural informationization. First, Artificial Intelligence (AI) and Internet of Things (IoT) applications in the matching circulation of agricultural products are analyzed theoretically. The IoT technology is then designed based on AI algorithms and used in a logistics company’s operating system that transports agricultural products. Compared with the traditional circulation mode of agricultural products, intelligent IoT (IIoT) can provide stable market supply, reduce time and circulation costs, and enhance information communication. Chinese farmers have the facilities to operate the IIoT platform. Advice and suggestions on implementing the platform are given. Applying IIoT to match agricultural supply and demand information can reduce the circulation cost, integrate the supply–demand information of various products, and timely and efficiently convey the supply–demand information, thereby promoting the informationization of agricultural market and rural economy development.

Multi-blockchain model ensures scalability and reliability based on intelligent Internet of Things

Multi-blockchain model ensures scalability and reliability based on intelligent Internet of Things

Design and study of a combining energy harvesting system based on thermoelectric and flapping triboelectric nanogenerator

ABSTRACT In this paper, anew hybrid energy converter is designed which consists of a Thermoelectric Generator (TEG) unit and aTriboelectric Nanogenerator (TENG) unit. The device can harvest solar energy and wind energy from the environment individually or simultaneously, and utilize them as the energy source of sensors and wireless transmission equipment in the system. When working individually, the TENG unit can achieve 244 V output voltage and 35 Aoutput current from wind energy of 12m/s. The output voltage of 4.27 V and the output current of 328 mA can be obtained from the solar thermoelectric unit at the hot end temperature of 110°C. According to the experimental data of capacitor charging by hybrid energy converter, compared with TEG unit and TENG unit individually, the charging voltage of the proposed device increases by 93.3% and 28.9%, respectively, and successfully drives Bluetooth module and realizes wireless data transmission. Compared with the traditional method, the proposed unit achieves not only hybrid energy harvesting but also better output performance. It is an important exploration for the development of self-powered sensors in the future intelligent Internet of things.

434 MHz Environmentally Safe Monitoring Schema for Vehicular Network by AI-ML-IoT Technologies

Connected vehicular tracking schema operated in environmentally safe radio frequency of 434 MHz, artificial intelligence, and machine learning and IoT technology (CVT-AIML-IoT) is cost effective and secured tracking or device monitoring system. The prime benefit of the proposed CVT-AIML-IoT system is that it utilizes cloud internet of things (IoT) technology and active radio frequency identification over global positioning system (GPS), which is prone to attackers due to self-defenseless network architecture. The sliding side of GPS is observed; when the GPS module is switched-off, it can be hidden without any authorization. Hence, an uninterrupted observing secured system like CVT-AIML-IoT is a promising solution with dynamic vehicular PIN generation by AI-ML concept. CVT-AIML-IoT grids the traceable area based on the topographical dependency. Detection range gateway coupled with IoT transceiver module captures data from each tracking zone to the cloud for monitoring over Web UI support and mapped with time stamp. Hence, CVT-AIML-IoT assures vehicular monitoring in a lucrative approach.

Low‐Power Self‐Rectifying Memristive Artificial Neural Network for Near Internet‐of‐Things Sensor Computing

AbstractFrequent data transfers between Internet‐of‐Things (IoT) sensors and cloud servers consume energy and lead to latency—a bottleneck for ubiquitous computing. To reduce the need for such enormous data transfers, the combined function of IoT sensors and near‐sensor artificial neural networks can process data properly before they are transferred to cloud servers. Herein, energy‐efficient memristor crossbar arrays are demonstrated for image recognition tasks that are potentially adopted for IoT sensors. The adoption of the selector‐free memristor device with a self‐rectifying function allows for simple stacking of metal–dielectric–metal layer, thus significantly simplifying the fabrication process while achieving low‐current operation (<10 µA in microdevice). Area‐dependent resistive switching characteristics and the incorporation of interface effects reveal the role of the switching and rectifying phenomena in such devices. Finally, the Modified National Institute of Standards and Technology pattern recognition task is demonstrated with 32 × 32 memristor crossbar arrays combining a SPICE simulation. Therefore, it is expected that self‐rectifying memristor arrays can pave the way for the development of more intelligent IoT sensors.

Age of Processing: Age-Driven Status Sampling and Processing Offloading for Edge-Computing-Enabled Real-Time IoT Applications

The freshness of status information is of great importance for time-critical Internet-of-Things (IoT) applications. A metric measuring status freshness is the Age of Information (AoI), which captures the time elapsed from the status being generated at the source node (e.g., a sensor) to the latest status update. However, in intelligent IoT applications such as video surveillance, the status information is revealed after some computation-intensive and time-consuming data processing operations, which would affect the status freshness. In this article, we propose a novel metric, Age of Processing (AoP), to quantify such status freshness, which captures the time elapsed of the newest received processed status data since it is generated. Compared with AoI, AoP further takes the data processing time into account. Since an IoT device has limited computation and energy resources, the IoT device can choose to offload the data processing to the nearby edge server under constrained status sampling frequency. We aim to minimize the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">average</i> AoP in a long-term process by jointly optimizing the status sampling frequency and processing offloading policy. We first formulate this online problem as an infinite-horizon constrained Markov decision process (CMDP) with an average reward criterion. We then transform the CMDP problem into an unconstrained Markov decision process (MDP) by leveraging a Lagrangian method, and accordingly propose a Lagrangian transformation framework for the original CMDP problem. Furthermore, we integrate the framework with a perturbation-based refinement mechanism for achieving the optimal policy of the CMDP problem. Our investigation shows that to minimize the average AoP: 1) for processing offloading: the policy exploits good channel state to offload processing to the edge server and 2) for status sampling: the waiting time presents a threshold structure. Extensive numerical evaluations show that the proposed algorithm outperforms the benchmarks, with an average AoP reduction up to 30%.

Context-based Reasoning through Fuzzy Logic for Edge Intelligence

With the advent of edge computing, the Internet of Things (IoT) environment has the ability to process data locally. The complexity of the context reasoning process can be scattered across several edge nodes that are physically placed at the source of the qualitative information by moving the processing and knowledge inference to the edge of the IoT network. This facilitates the real-time processing of a large range of rich data sources that would be less complex and expensive compare to the traditional centralized cloud system. In this paper, we propose a novel approach to provide low-level intelligence for IoT applications through an IoT edge controller that is leveraging the Fuzzy Logic Controller along with edge computing. This low-level intelligence, together with cloud-based intelligence, forms the distributed IoT intelligence. The proposed controller allows distributed IoT gateway to manage input uncertainties; besides, by interacting with its environment, the learning system can enhance its performance over time, which leads to improving the reliability of the IoT gateway. Therefore, such a controller is able to offer different context-aware reasoning to alleviate the distributed IoT. A simulated smart home scenario has been done to prove the plausibility of the low-level intelligence concerning reducing latency and more accurate prediction through learning experiences at the edge.

An intelligent internet of things-based secure healthcare framework using blockchain technology with an optimal deep learning model

Today, the internet of things (IoT) is becoming more common and finds applications in several domains, especially in the healthcare sector. Due to the rising demands of IoT, a massive quantity of sensing data gets generated from diverse sensing devices. Artificial intelligence (AI) techniques are vital for providing a scalable and precise analysis of data in real time. But the design and development of a useful big data analysis technique face a few challenges, like centralized architecture, security, and privacy, resource constraints, and the lack of adequate training data. On the other hand, the rising blockchain technology offers a decentralized architecture. It enables secure sharing of data and resources to the different nodes of the IoT network and is promoted for removing centralized control and resolving the problems of AI. This study develops an optimal deep-learning-based secure blockchain (ODLSB) enabled intelligent IoT and healthcare diagnosis model. The proposed model involves three major processes: secure transaction, hash value encryption, and medical diagnosis. The ODLSB technique comprises the orthogonal particle swarm optimization (OPSO) algorithm for the secret sharing of medical images. In addition, the hash value encryption process takes place using neighborhood indexing sequence (NIS) algorithm. At last, the optimal deep neural network (ODNN) is applied as a classification model to diagnose the diseases. The utilization of OPSO algorithm for secret sharing and optimal parameter tuning process shows the novelty of the work. We carried out detailed experiments to validate the outcome of the proposed method, and several aspects of the results are considered. At the time of the diagnosis process, the OPSO-DNN model has yielded superior results, with the highest sensitivity (92.75%), specificity (91.42%), and accuracy (93.68%).

Future IoT tools for COVID-19 contact tracing and prediction: A review of the state-of-the-science.

In 2020 the world is facing unprecedented challenges due to COVID‐19. To address these challenges, many digital tools are being explored and developed to contain the spread of the disease. With the lack of availability of vaccines, there is an urgent need to avert resurgence of infections by putting some measures, such as contact tracing, in place. While digital tools, such as phone applications are advantageous, they also pose challenges and have limitations (eg, wireless coverage could be an issue in some cases). On the other hand, wearable devices, when coupled with the Internet of Things (IoT), are expected to influence lifestyle and healthcare directly, and they may be useful for health monitoring during the global pandemic and beyond. In this work, we conduct a literature review of contact tracing methods and applications. Based on the literature review, we found limitations in gathering health data, such as insufficient network coverage. To address these shortcomings, we propose a novel intelligent tool that will be useful for contact tracing and prediction of COVID‐19 clusters. The solution comprises a phone application combined with a wearable device, infused with unique intelligent IoT features (complex data analysis and intelligent data visualization) embedded within the system to aid in COVID‐19 analysis. Contact tracing applications must establish data collection and data interpretation. Intelligent data interpretation can assist epidemiological scientists in anticipating clusters, and can enable them to take necessary action in improving public health management. Our proposed tool could also be used to curb disease incidence in future global health crises.

Virtualizing AI at the Distributed Edge towards Intelligent IoT Applications

Several Internet of Things (IoT) applications are booming which rely on advanced artificial intelligence (AI) and, in particular, machine learning (ML) algorithms to assist the users and make decisions on their behalf in a large variety of contexts, such as smart homes, smart cities, smart factories. Although the traditional approach is to deploy such compute-intensive algorithms into the centralized cloud, the recent proliferation of low-cost, AI-powered microcontrollers and consumer devices paves the way for having the intelligence pervasively spread along the cloud-to-things continuum. The take off of such a promising vision may be hurdled by the resource constraints of IoT devices and by the heterogeneity of (mostly proprietary) AI-embedded software and hardware platforms. In this paper, we propose a solution for the AI distributed deployment at the deep edge, which lays its foundation in the IoT virtualization concept. We design a virtualization layer hosted at the network edge that is in charge of the semantic description of AI-embedded IoT devices, and, hence, it can expose as well as augment their cognitive capabilities in order to feed intelligent IoT applications. The proposal has been mainly devised with the twofold aim of (i) relieving the pressure on constrained devices that are solicited by multiple parties interested in accessing their generated data and inference, and (ii) and targeting interoperability among AI-powered platforms. A Proof-of-Concept (PoC) is provided to showcase the viability and advantages of the proposed solution.

AIOT-Arch: Furthering Artificial Intelligence in Big Data IoT Applications

Applications for the Internet of Things (IoT) have evolved in excessive numbers, producing a vast amount of data needed for intelligent processing. The varying IoT infrastructures such as cloud and IoT application layer protocol limitations in the transmission/receiving of messages become the barriers in the implementation of intelligent IoT apps. In this paper, we review the importance of Big data, cloud computing and fog computing in IoT and the challenges of using machine learning in IoT. Finally, we discuss the general statistics of using artificial intelligence in IoT applications.

Intelligent Internet of Things and Advanced Machine Learning Techniques for COVID-19

INTRODUCTION: Coronavirus disease (COVID-19) has recently emerged around the world. The beginning of the disease was in the Chinese city of Wuhan and then it has been spread and became a global epidemic. An early diagnosis of COVID-19 disease is absolutely necessary to control the epidemic.OBJECTIVES: The aim of this paper is to present a review of the contribution of machine learning (ML) and IoT to confront the epidemic.METHODS: Diagnosis using real-time reverse transcriptase-polymerase chain reaction (RT-PCR) is a definite diagnosis, but this method takes time, while a diagnosis using a computed tomography (CT) scan is a faster approach to diagnosis. However, a large number of patients need a CT scan, which puts a lot of pressure on the radiologist so visual fatigue may lead to diagnostic errors so there is an urgent need for additional solutions. Artificial intelligence (AI) is an efficient tool to combat COVID-19 disease. Computer scientists have been developing many systems to handle this epidemic.RESULTS: It was found that ML is an efficient and powerful AI technology that can be used for trustworthy COVID-19 detecting and diagnosis from X-ray and CT images and it can be a potential method for diagnosis in the radiology department. In addition, ML can be used in segmentation, prediction purposes for COVID-19. Furthermore, ML can effectively support drug discovery procedure and can reduce clinical failures.CONCLUSION: IoT has a significant role in monitoring an individual's health and COVID-19 diagnosis. This paper also highlights the challenges of employing ML and intelligent IoT for fighting COVID-19.

Influencing Factors and Forecasting Statistics of Enterprise Market Sales Based on Big Data and Intelligent IoT

With the acceleration of economic development, enterprise management is facing more severe challenges. Big data analysis based on the intelligent Internet of Things (IoT) has a positive effect on the development of enterprise management and can make up for the shortcomings of enterprise management. In this paper, we develop a big data processing method based on intelligent IoT which can mine the factors that affect the company’s market sales from the collected data. Then, we propose a KNN classification algorithm based on overlapping k‐means clustering. This algorithm adds a training process to the traditional KNN algorithm, which can accurately classify data and greatly improve the efficiency of the classification algorithm. Numerical analysis results prove the effectiveness of the proposed algorithm.

Intelligent IoT‐Based Cross‐Border e‐Commerce Supply Chain Performance Optimization

Internet of Things (IoT) technology can benefit automated production, agriculture, intelligent autonomous driving, and many other industries by using billions of smart devices. As a good example, intelligent IoT and 5G are the main source of information acquisition and play an important role in the multiobjective optimization process of the supply chain. This paper develops an optimal management and coordination method to improve the performance of cross‐border e‐commerce supply chain by using IoT tracking technique and multiobjective decision‐making. The numerical results justify that our proposed scheme has high internal consistency, with the Cronbach’s alpha factor of each dimension of the optimized decision model all greater than 80%.

Recycled Clothing Classification System Using Intelligent IoT and Deep Learning with AlexNet.

Recently, Internet of Things (IoT) and artificial intelligence (AI), led by machine learning and deep learning, have emerged as key technologies of the Fourth Industrial Revolution (4IR). In particular, object recognition technology using deep learning is currently being used in various fields, and thanks to the strong performance and potential of deep learning, many research groups and Information Technology (IT) companies are currently investing heavily in deep learning. The textile industry involves a lot of human resources in all processes, such as raw material collection, dyeing, processing, and sewing, and the wastage of resources and energy and increase in environmental pollution are caused by the short-term waste of clothing produced during these processes. Environmental pollution can be reduced to a great extent through the use of recycled clothing. In Korea, the utilization rate of recycled clothing is increasing, the amount of used clothing is high with the annual consumption being at $56.2 billion, but it is not properly utilized because of the manual recycling clothing collection system. It has several problems such as a closed workplace environment, workers' health, rising labor costs, and low processing speed that make it difficult to apply the existing clothing recognition technology, classified by deformation and overlapping of clothing shapes, when transporting recycled clothing to the conveyor belt. In this study, I propose a recycled clothing classification system with IoT and AI using object recognition technology to the problems. The IoT device consists of Raspberry pi and a camera, and AI uses the transfer-learned AlexNet to classify different types of clothing. As a result of this study, it was confirmed that the types of recycled clothing using artificial intelligence could be predicted and accurate classification work could be performed instead of the experience and know-how of working workers in the clothing classification worksite, which is a closed space. This will lead to the innovative direction of the recycling clothing classification work that was performed by people in the existing working worker. In other words, it is expected that standardization of necessary processes, utilization of artificial intelligence, application of automation system, various cost reduction, and work efficiency improvement will be achieved.