Artificial intelligence in physiological characteristics recognition for internet of things authentication

Effective user authentication is key to ensuring equipment security,data privacy,and personalized services in Internet of Things(IoT)systems.However,conventional mode-based authentication methods(e.g.,passwords and smart cards)may be vulnerable to a broad range of attacks(e.g.,eavesdropping and side-channel attacks).Hence,there have been attempts to design biometric-based authentication solutions,which rely on physiological and behavioral characteristics.Behavioral characteristics need continuous monitoring and specific environmental settings,which can be challenging to implement in practice.However,we can also leverage Artificial Intelligence(AI)in the extraction and classification of physiological characteristics from IoT devices processing to facilitate authentication.Thus,we review the literature on the use of AI in physiological characteristics recognition pub-lished after 2015.We use the three-layer architecture of the IoT(i.e.,sensing layer,feature layer,and algorithm layer)to guide the discussion of existing approaches and their limitations.We also identify a number of future research opportunities,which will hopefully guide the design of next generation solutions.

Research on a Comprehensive Monitoring System for Tunnel Operation based on the Internet of Things and Artificial Intelligence Identification Technology

This article proposes a comprehensive monitoring system for tunnel operation to address the risks associated with tunnel operations.These risks include safety control risks,increased traffic flow,extreme weather events,and movement of tectonic plates.The proposed system is based on the Internet of Things and artificial intelligence identification technology.The monitoring system will cover various aspects of tunnel operations,such as the slope of the entrance,the structural safety of the cave body,toxic and harmful gases that may appear during operation,excessively high and low-temperature humidity,poor illumination,water leakage or road water accumulation caused by extreme weather,combustion and smoke caused by fires,and more.The system will enable comprehensive monitoring and early warning of fire protection systems,accident vehicles,and overheating vehicles.This will effectively improve safety during tunnel operation.

A Review of Hybrid Cyber Threats Modelling and Detection Using Artificial Intelligence in IIoT

The Industrial Internet of Things(IIoT)has brought numerous benefits,such as improved efficiency,smart analytics,and increased automation.However,it also exposes connected devices,users,applications,and data generated to cyber security threats that need to be addressed.This work investigates hybrid cyber threats(HCTs),which are now working on an entirely new level with the increasingly adopted IIoT.This work focuses on emerging methods to model,detect,and defend against hybrid cyber attacks using machine learning(ML)techniques.Specifically,a novel ML-based HCT modelling and analysis framework was proposed,in which L1 regularisation and Random Forest were used to cluster features and analyse the importance and impact of each feature in both individual threats and HCTs.A grey relation analysis-based model was employed to construct the correlation between IIoT components and different threats.

The role of artificial intelligence and IoT in prediction of earthquakes:Review

Earthquakes are classified as one of the most devastating natural disasters that can have catastrophic effects on the environment,lives,and properties.There has been an increasing interest in the prediction of earthquakes and in gaining a comprehensive understanding of the mechanisms that underlie their generation,yet earthquakes are the least predictable natural disaster.Satellite data,global positioning system,interferometry synthetic aperture radar(InSAR),and seismometers such as microelectromechanical system,seismometers,ocean bottom seismometers,and distributed acoustic sensing systems have all been used to predict earthquakes with a high degree of success.Despite advances in seismic wave recording,storage,and analysis,earthquake time,location,and magnitude prediction remain difficult.On the other hand,new developments in artificial intelligence(AI)and the Internet of Things(IoT)have shown promising potential to deliver more insights and predictions.Thus,this article reviewed the use of AI-driven Models and IoT-based technologies for the prediction of earthquakes,the limitations of current approaches,and open research issues.The review discusses earthquake prediction setbacks due to insufficient data,inconsistencies,diversity of earthquake precursor signals,and the earth’s geophysical composition.Finally,this study examines potential approaches or solutions that scientists can employ to address the challenges they face in earthquake prediction.The analysis is based on the successful application of AI and IoT in other fields.

A Review of Artificial Intelligence Applications in Contemporary Computer Network Technologies

Rapid advancement in science and technology has seen computer network technology being upgraded constantly, and computer technology, in particular, has been applied more and more extensively, which has brought convenience to people’s lives. The number of people using the internet around the globe has also increased significantly, exerting a profound influence on artificial intelligence. Further, the constant upgrading and development of artificial intelligence has led to the continuous innovation and improvement of computer technology. Countries around the world have also registered an increase in investment, paying more attention to artificial intelligence. Through an analysis of the current development situation and the existing applications of artificial intelligence, this paper explicates the role of artificial intelligence in the face of the unceasing expansion of computer network technology.

Artificial Intelligence and Internet of Things Enabled Intelligent Framework for Active and Healthy Living

Obesity poses several challenges to healthcare and the well-being of individuals.It can be linked to several life-threatening diseases.Surgery is a viable option in some instances to reduce obesity-related risks and enable weight loss.State-of-the-art technologies have the potential for long-term benefits in post-surgery living.In this work,an Internet of Things(IoT)framework is proposed to effectively communicate the daily living data and exercise routine of surgery patients and patients with excessive weight.The proposed IoT framework aims to enable seamless communications from wearable sensors and body networks to the cloud to create an accurate profile of the patients.It also attempts to automate the data analysis and represent the facts about a patient.The IoT framework proposes a co-channel interference avoidance mechanism and the ability to communicate higher activity data with minimal impact on the bandwidth requirements of the system.The proposed IoT framework also benefits from machine learning based activity classification systems,with relatively high accuracy,which allow the communicated data to be translated into meaningful information.

Artificial Intelligence Based Threat Detection in Industrial Internet of Things Environment

Internet of Things(IoT)is one of the hottest research topics in recent years,thanks to its dynamic working mechanism that integrates physical and digital world into a single system.IoT technology,applied in industries,is termed as Industrial IoT(IIoT).IIoT has been found to be highly susceptible to attacks from adversaries,based on the difficulties observed in IIoT and its increased dependency upon internet and communication network.Intentional or accidental attacks on these approaches result in catastrophic effects like power outage,denial of vital health services,disruption to civil service,etc.,Thus,there is a need exists to develop a vibrant and powerful for identification and mitigation of security vulnerabilities in IIoT.In this view,the current study develops an AI-based Threat Detection and Classification model for IIoT,abbreviated as AITDC-IIoT model.The presented AITDC-IIoT model initially pre-processes the input data to transform it into a compatible format.In addition,WhaleOptimizationAlgorithm based Feature Selection(WOA-FS)is used to elect the subset of features.Moreover,Cockroach Swarm Optimization(CSO)is employed with Random Vector Functional Link network(RVFL)technique for threat classification.Finally,CSO algorithm is applied to appropriately adjust the parameters related to RVFL model.The performance of the proposed AITDC-IIoT model was validated under benchmark datasets.The experimental results established the supremacy of the proposed AITDC-IIoT model over recent approaches.

Improved Transportation Model with Internet of Things Using Artificial Intelligence Algorithm

In this paper,the application of transportation systems in realtime traffic conditions is evaluated with data handling representations.The proposed method is designed in such a way as to detect the number of loads that are present in a vehicle where functionality tasks are computed in the system.Compared to the existing approach,the design model in the proposed method is made by dividing the computing areas into several cluster regions,thereby reducing the complex monitoring system where control errors are minimized.Furthermore,a route management technique is combined with Artificial Intelligence(AI)algorithm to transmit the data to appropriate central servers.Therefore,the combined objective case studies are examined as minimization and maximization criteria,thus increasing the efficiency of the proposed method.Finally,four scenarios are chosen to investigate the projected design’s effectiveness.In all simulated metrics,the proposed approach provides better operational outcomes for an average percentage of 97,thereby reducing the amount of traffic in real-time conditions.

Using Artificial Intelligence in the Internet of Things

The Internet of Things （IoT） has received much attention over the past decade. With the rapid increase in the use of smart devices, we are now able to collect big data on a daily basis. The data we are gathering （and related problems） are becoming more complex and uncertain. Researchers have therefore turned to artificial intelligence （AI） to efficiently deal with the problems ereated by big data.

Special Issue on Using Artificial Intelligence in Internet of Things Guest Editors: Fuji Ren, Yu Gu

Interact of Things has received much attention over the past de cade. With the rapid increase in the use of smart devices, we are now able to collect big data on a daily basis. The data we are gather ing and related problems arc becoming more complex and uncer tain. Researchers have therefore turned to AI as an efficient way of dealing with the problems created by big data.

ZTE Communications Call for Papers Special Issue on Using Artificial Intelligence in Internet of Things

Internet of Things has received much attention over the past de- cade. With the rapid increase in the use of smart devices, we are now able to collect big data on a daily basis. The data we are gather- ing and related problems are becoming more complex and uncer- tain. Researchers have therefore turned to AI as an efficient way of dealing with the problems created by big data. This special issue of ZTE Communications will be dedicated to development, trends, challenges, and current practices in artificial intelligence for the Internet of Things. Position papers, technology overviews, and case studies are all welcome. Appropriate topics include but are not limited to：

Advanced Optimized Anomaly Detection System for IoT Cyberattacks Using Artificial Intelligence

While emerging technologies such as the Internet of Things(IoT)have many benefits,they also pose considerable security challenges that require innovative solutions,including those based on artificial intelligence(AI),given that these techniques are increasingly being used by malicious actors to compromise IoT systems.Although an ample body of research focusing on conventional AI methods exists,there is a paucity of studies related to advanced statistical and optimization approaches aimed at enhancing security measures.To contribute to this nascent research stream,a novel AI-driven security system denoted as“AI2AI”is presented in this work.AI2AI employs AI techniques to enhance the performance and optimize security mechanisms within the IoT framework.We also introduce the Genetic Algorithm Anomaly Detection and Prevention Deep Neural Networks(GAADPSDNN)sys-tem that can be implemented to effectively identify,detect,and prevent cyberattacks targeting IoT devices.Notably,this system demonstrates adaptability to both federated and centralized learning environments,accommodating a wide array of IoT devices.Our evaluation of the GAADPSDNN system using the recently complied WUSTL-IIoT and Edge-IIoT datasets underscores its efficacy.Achieving an impressive overall accuracy of 98.18%on the Edge-IIoT dataset,the GAADPSDNN outperforms the standard deep neural network(DNN)classifier with 94.11%accuracy.Furthermore,with the proposed enhancements,the accuracy of the unoptimized random forest classifier(80.89%)is improved to 93.51%,while the overall accuracy(98.18%)surpasses the results(93.91%,94.67%,94.94%,and 94.96%)achieved when alternative systems based on diverse optimization techniques and the same dataset are employed.The proposed optimization techniques increase the effectiveness of the anomaly detection system by efficiently achieving high accuracy and reducing the computational load on IoT devices through the adaptive selection of active features.

Catalyzing Random Access at Physical Layer for Internet of Things:An Intelligence Enabled User Signature Code Acquisition Approach

Exploiting random access for the underlying connectivity provisioning has great potential to incorporate massive machine-type communication(MTC)devices in an Internet of Things(Io T)network.However,massive access attempts from versatile MTC devices may bring congestion to the IIo T network,thereby hindering service increasing of IIo T applications.In this paper,an intelligence enabled physical(PHY-)layer user signature code acquisition(USCA)algorithm is proposed to overcome the random access congestion problem with reduced signaling and control overhead.In the proposed scheme,the detector aims at approximating the optimal observation on both active user detection and user data reception by iteratively learning and predicting the convergence of the user signature codes that are in active.The crossentropy based low-complexity iterative updating rule is present to guarantee that the proposed USCA algorithm is computational feasible.A closed-form bit error rate(BER)performance analysis is carried out to show the efficiency of the proposed intelligence USCA algorithm.Simulation results confirm that the proposed USCA algorithm provides an inherent tradeoff between performance and complexity and allows the detector achieves an approximate optimal performance with a reasonable computational complexity.

Review of Artificial Intelligence with Retailing Sector

This research service provides an original perspective on how artificial intelligence(AI)is making its way into the retail sector.Retail has entered a new era where ECommerce and technology bellwethers like Alibaba,Amazon,Apple,Baidu,Facebook,Google,Microsoft,and Tencent have raised consumers’expectations.AI is enabling automated decision-making with accuracy and speed,based on data analytics,coupled with selflearning abilities.The retail sector has witnessed the dramatic evolution with the rapid digitalization of communication(i.e.Internet)and;smart phones and devices.Customer is no longer the same as they became more empowered by smart devices which has entirely prevailed their expectation,habits,style of shopping and investigating the shops.This article outlines the Significant innovation done in retails which helped them to evolve such as Artificial Intelligence(AI),Big data and Internet of Things(IoT),Chatbots,Robots.This article further also discusses the ideology of various author on how AI become more profitable and a close asset to customers and retailers.

Telemedicine and Smart Healthcare—The Role of Artificial Intelligence, 5G, Cloud Services, and Other Enabling Technologies

This paper discusses telemedicine and the employment of advanced mobile technologies in smart healthcare delivery. It covers the technological advances in connected smart healthcare, including the roles of artificial intelligence, machine learning, 5G and IoT platforms, and other enabling technologies. It also presents the challenges and potential risks that could arise from delivering connected smart healthcare services. Healthcare delivery is witnessing revolutions engineered by the developments in mobile connectivity and the plethora of platforms, applications, sensors, devices, and equipment that go along with it. Human society is evolving fast in response to these technological developments, which are also pushing the connectivity-providing sector to create and adopt new waves of network technologies. Consequently, new communications technologies have been introduced into the healthcare system and many novel applications have been developed to make it easier for sharing data in various forms and volumes within health-related services. These applications have also made it possible for telemedicine to be effectively adopted. This paper provides an overview of some of the recent developments within the space of mobile connectivity and telemedicine.

Enhancing Internet of Things Intrusion Detection Using Artificial Intelligence

Escalating cyber security threats and the increased use of Internet of Things(IoT)devices require utilisation of the latest technologies available to supply adequate protection.The aim of Intrusion Detection Systems(IDS)is to prevent malicious attacks that corrupt operations and interrupt data flow,which might have significant impact on critical industries and infrastructure.This research examines existing IDS,based on Artificial Intelligence(AI)for IoT devices,methods,and techniques.The contribution of this study consists of identification of the most effective IDS systems in terms of accuracy,precision,recall and F1-score;this research also considers training time.Results demonstrate that Graph Neural Networks(GNN)have several benefits over other traditional AI frameworks through their ability to achieve in excess of 99%accuracy in a relatively short training time,while also capable of learning from network traffic the inherent characteristics of different cyber-attacks.These findings identify the GNN(a Deep Learning AI method)as the most efficient IDS system.The novelty of this research lies also in the linking between high yielding AI-based IDS algorithms and the AI-based learning approach for data privacy protection.This research recommends Federated Learning(FL)as the AI training model,which increases data privacy protection and reduces network data flow,resulting in a more secure and efficient IDS solution.

Multidiscipline Applications of Triboelectric Nanogenerators for the Intelligent Era of Internet of Things

In the era of 5G and the Internet of things(IoTs),vari-ous human-computer interaction systems based on the integration of triboelectric nanogenerators(TENGs)and IoTs technologies dem-onstrate the feasibility of sustainable and self-powered functional systems.The rapid development of intelligent applications of IoTs based on TENGs mainly relies on supplying the harvested mechanical energy from surroundings and implementing active sensing,which have greatly changed the way of human production and daily life.This review mainly introduced the TENG applications in multidisci-pline scenarios of IoTs,including smart agriculture,smart industry,smart city,emergency monitoring,and machine learning-assisted artificial intelligence applications.The challenges and future research directions of TENG toward IoTs have also been proposed.The exten-sive developments and applications of TENG will push forward the IoTs into an energy autonomy fashion.

I^2oT: Advanced Direction of the Internet of Things

The Internet of Things （IoT） is still in its infancy because of the limited capability of its embedded processor. In the meantime, re- search on artificial intelligence （AI） has made plenty of progress. The application of AI to loT will significantly increase the capa- bilities of IoT, and this will benefit both economic and social development. In this paper, the elementary concepts and key tech- nologies of AI are explained, and the model and principle of intelligent IoT, denoted I^2oT, resulting from the integration of AI and loT are discussed. I^2oT will be the most promising version of IoT. Finally, recommendations for further study and standardization of I2oT are made.

Time to forge ahead:The Internet of Things for healthcare

Situated at the intersection of technology and medicine,the Internet of Things(IoT)holds the promise of addressing some of healthcare's most pressing challenges,from medical error,to chronic drug shortages,to overburdened hospital systems,to dealing with the COVID-19 pandemic.However,despite considerable recent technological advances,the pace of successful implementation of promising IoT healthcare initiatives has been slow.To inspire more productive collaboration,we present here a simple—but surprisingly underrated—problemoriented approach to developing healthcare technologies.To further assist in this effort,we reviewed the various commercial,regulatory,social/cultural,and technological factors in the development of the IoT.We propose that fog computing—a technological paradigm wherein the burden of computing is shifted from a centralized cloud server closer to the data source—offers the greatest promise for building a robust and scalable healthcare IoT ecosystem.To this end,we explore the key enabling technologies that underpin the fog architecture,from the sensing layer all the way up to the cloud.It is our hope that ongoing advances in sensing,communications,cryptography,storage,machine learning,and artificial intelligence will be leveraged in meaningful ways to generate unprecedented medical intelligence and thus drive improvements in the health of many people.

An Interpretable Artificial Intelligence Based Smart Agriculture System

With increasing world population the demand of food production has increased exponentially.Internet of Things(IoT)based smart agriculture system can play a vital role in optimising crop yield by managing crop requirements in real-time.Interpretability can be an important factor to make such systems trusted and easily adopted by farmers.In this paper,we propose a novel artificial intelligence-based agriculture system that uses IoT data to monitor the environment and alerts farmers to take the required actions for maintaining ideal conditions for crop production.The strength of the proposed system is in its interpretability which makes it easy for farmers to understand,trust and use it.The use of fuzzy logic makes the system customisable in terms of types/number of sensors,type of crop,and adaptable for any soil types and weather conditions.The proposed system can identify anomalous data due to security breaches or hardware malfunction using machine learning algorithms.To ensure the viability of the system we have conducted thorough research related to agricultural factors such as soil type,soil moisture,soil temperature,plant life cycle,irrigation requirement and water application timing for Maize as our target crop.The experimental results show that our proposed system is interpretable,can detect anomalous data,and triggers actions accurately based on crop requirements.