Internet Of Things Data Analytics Research Articles

Integrating Wireless Remote Sensing and Sensors for Monitoring Pesticide Pollution in Surface and Groundwater.

Water constitutes an indispensable resource crucial for the sustenance of humanity, as it plays an integral role in various sectors such as agriculture, industrial processes, and domestic consumption. Even though water covers 71% of the global land surface, governments have been grappling with the challenge of ensuring the provision of safe water for domestic use. A contributing factor to this situation is the persistent contamination of available water sources rendering them unfit for human consumption. A common contaminant, pesticides are not frequently tested for despite their serious effects on biodiversity. Pesticide determination in water quality assessment is a challenging task because the procedures involved in the extraction and detection are complex. This reduces their popularity in many monitoring campaigns despite their harmful effects. If the existing methods of pesticide analysis are adapted by leveraging new technologies, then information concerning their presence in water ecosystems can be exposed. Furthermore, beyond the advantages conferred by the integration of wireless sensor networks (WSNs), the Internet of Things (IoT), Machine Learning (ML), and big data analytics, a notable outcome is the attainment of a heightened degree of granularity in the information of water ecosystems. This paper discusses methods of pesticide detection in water, emphasizing the possible use of electrochemical sensors, biosensors, and paper-based sensors in wireless sensing. It also explores the application of WSNs in water, the IoT, computing models, ML, and big data analytics, and their potential for integration as technologies useful for pesticide monitoring in water.

Smart Wet and Dry Waste Management System Using IoT

Abstract: Waste management is one of the important aspects when it comes to avoiding diseases caused by unsegregated waste. Many people have lesser knowledge about what is wet and dry waste. The escalating global waste generation hasled to a pressing need for innovative waste management solutions that are efficient, sustainable, and technologically advanced. This abstract introduces a "Smart Dry and Wet Waste Management System" that leverages the Internet of Things (IoT) to revolutionize waste management practices. Traditional waste management systems often lack real-time monitoring and optimization capabilities, resultingin inefficiencies, environmental concerns, and increased operational costs. The proposed system aims to address these challenges by integrating IoT technologies, sensor networks, and data analytics into waste collection and disposal processes.The main objective is to automate the segregation system thereby removing the human intervention. The hardware components such as Arduino, sensors, LCD are used to build the system. The integration of IoT in waste management contributes to a cleaner environment, reduced operational costs, and increased overall efficiency. By preventing overflows, minimizing unnecessary collection trips, and promoting recycling practices, the system aligns with sustainable waste management goals. Moreover, the collected data can be analyzed over time to identify longterm trends, enabling continuous optimization of waste management strategies..

Heterogeneous Internet of Things Big Data Analysis System Based on Mobile Edge Computing

The big data heterogeneous Internet of Things (IoT) requires mobile edge computing (MEC) to process some data, and the data analysis system of MEC often has the problem of excessive terminal energy consumption (ECS) or long delay. So this study designed an energy-saving optimization algorithm for the task offloading processing module in the big data heterogeneous IoT analysis system, and designed and conducted simulation experiments to verify the application performance of the algorithm. The experimental results show that the #04 scheme of the designed algorithm has the lowest terminal ECS under the same conditions. Choosing the #04 scheme to build the algorithm, comparative analysis shows that when the edge server (ES) computing rate is 10 cycles/s, the weighted sum values of terminal ECS for EOPU, MPCO, exhaustive search, and local computing methods are 23.6 J, 23.9 J, 28.5 J and 84.5 J, respectively. Moreover, the algorithm possesses a significantly higher percentage of remaining time under different conditions of total SMD devices and total subchannels compared to other methods. This indicates that the designed algorithm can markedly enhance the processing performance of the task offloading model of the big data heterogeneous IoT data analysis system, and can also effectively reduce terminal ECS and system latency. The research results can provide reference for improving the processing ability of heterogeneous IoT big data analysis systems. The contribution of this study to the academic field lies in providing a model that can effectively reduce the operational ECS and time consumption of heterogeneous IoT big data analysis systems containing mobile animal networking devices. Moreover, from an industrial perspective, the results of this study contribute to improving the efficiency of information exchange and processing in the field of IoT computing, thereby promoting the promotion of IoT technology.

IoT based Women Safety Smart System Development

Designing an effective system for women's safety is essential for reducing crimes against women and prosecuting the guilty. Women harassment at workplace, public transport etc., has been a rising concern all over the world. An early action and proper mechanism could reduce the number of crimes against women. The development of a Women Safety Smart System within the Internet of Things (IoT) domain represents a significant advancement in leveraging technology to address the pressing issue of women's safety. This paper outlines the design, implementation, and potential impact of such a system, which integrates IoT devices, sensors, and data analytics to create a proactive safety infrastructure. Key components of the system include IoT-enabled wearables, smart surveillance and monitoring systems, a dedicated mobile application, and predictive analytics capabilities. These components work synergistically to provide real-time monitoring, timely intervention, and community engagement in ensuring the safety and well-being of women. By harnessing IoT technology, this system offers a scalable and adaptable solution to mitigate risks, empower women, and foster societal change. The paper discusses the benefits, challenges, and future directions of Women Safety Smart Systems in the IoT domain, highlighting their potential to transform safety paradigms and promote inclusivity and equality in urban environments.

INTEGRATING IOT AND BIG DATA ANALYTICS FOR ENHANCED SUPPLY CHAIN PERFORMANCE IN INDUSTRIAL ENGINEERING SECTORS: A CROSS-MARKET STUDY

Integrating the Internet of Things (IoT) and big data analytics revolutionizes supply chain management across industrial engineering sectors, offering unprecedented opportunities for enhancing efficiency, responsiveness, and competitive advantage. This study employs a qualitative research design, leveraging expert interviews to explore the multifaceted impact of these technologies on supply chain performance. Findings underscore the critical importance of strategic alignment, leadership support, and a clear focus on business objectives for successful technology implementation. Enhanced real-time visibility, improved decision-making, and operational efficiency are identified as consistent benefits across sectors. However, the specific outcomes and applications vary according to industry-specific challenges and priorities. Despite the rich insights gained, the study acknowledges the limitations inherent in its qualitative approach. It suggests avenues for future research, including quantitative analyses and deeper dives into sector-specific implementations. This research contributes to a better understanding of how IoT and big data analytics can be effectively integrated into supply chains, providing a foundation for organizations seeking to navigate the complexities of digital transformation in an interconnected global marketplace.

Design and Implementation of a Fog Computing Architecture for IoT Data Analytics

The number of Internet of Things (IoT) devices has exponentially increased, creating an explosion of data that requires sophisticated processing and analysis techniques. When it comes to meeting the demands of long duration and narrow band of the things' Internet applications, traditional cloud computing solutions may encounter difficulties. To overcome these issues, fog computing has developed into a workable concept for extending nube services all the way to the system's edge. The development of a fog computing architecture for the analysis of data from the internet of things is the topic of this study. Our system's three primary parts are fog nodes, edge devices, and a central cloud server. Sensors and edge devices of the Internet of Things (IoT) are in charge of local preprocessing and data collection. In between network edge devices and the cloud server, fog nodes act as intermediaries. Their actions have reduced the volume of raw data sent to the cloud for processing and archiving. One cloud server manages all aspects of data analysis, storage, and archiving. In order to show how effective and efficient our architecture is, Our approach was supported by data gathered from a variety of Internet-connected devices, and by lowering the amount of data transferred to the cloud, we were able to considerably lower lag and the use of black band. The network's core fog nodes also offered the processing capacity required to carry out analysis relatively instantly. The advantages of the board devices, given that a large number of Internet of Things applications require real-time or almost real-time data processing, this architecture stands out because to its capacity to lower latency, save bandwidth, and improve system efficiency.

The Impact of Industrial Internet and the Digital Economy on the Management and Development of Manufacturing Information Systems Triggering Digitization as IoT and Artificial Intelligence

Technology adoption is crucial to organizational performance in the fast-changing digital world. Due to their potential to boost efficiency, productivity, and competitiveness, disruptive technologies including data analytics, the Internet of Things (IoT), and information system integration have received attention. This study examined how IoT adoption, data analytics, and information system integration affect organizational efficiency, productivity, and competitive advantage. The study also examined how cybersecurity, cloud infrastructure, and organizational culture mediate the technology-adoption-performance relationship. Quantitative research was conducted using Amos. A diverse sampling of enterprises provided data. Structural equation modelling (SEM) examined direct and indirect impacts by analyzing variable connections. IoT usage, data analytics, and information system integration improved organizational efficiency, productivity, and competitive edge. The study also showed that cybersecurity and cloud infrastructure mediate technology adoption and organizational results. Organizational culture moderated the effect of technology adoption on performance. This study added to the technology adoption literature by examining how IoT adoption, data analytics capabilities, and information system integration affect organizational efficiency, productivity, and competitive advantage. Cybersecurity, cloud infrastructure as mediators and organizational culture as a moderator helped us understand technology adoption and performance results.

Analysis of Smart Parking System Using IOT Environment

The typical parking experience has been transformed by smart parking systems that use the Internet of Things (IoT) environment to integrate technology to improve efficiency, convenience, and sustainability. In order to monitor and manage parking spaces in real-time, this unique technique makes use of IoT devices, such as sensors, cameras, and networking technologies. As a result of the system's reliable information on parking availability, drivers may find and book parking spaces in advance, which eases traffic and reduces aggravation. Additionally, parking systems with IoT capabilities optimize resource use, lowering carbon emissions and fostering sustainability. The adoption of IoT in parking systems is a crucial step towards building smarter, more connected cities that will enhance both drivers' and parking operators' experiences with parking. There are numerous crucial elements in the process for developing a smart parking system in an IoT context. First, sensors are placed in parking places to gather up-to-the-minute occupancy information. Then, using wireless communication protocols, this data is sent to a central server or cloud computing platform. After that, a data processing and analysis module interprets the gathered data using algorithms and machine learning techniques and presents parking availability information to users via a mobile application or other user interfaces. For effective management and monitoring of parking spaces, the system also includes automated payment methods and interacts with existing infrastructure. Taken as Alternative parameters is Park Smart, Street line, Park Whiz, ParkMobile, Spot Hero. Taken as evaluation parameters is Light Sensor, CCTV coins, SMS, Cost-effectiveness, Timestamp. This demonstrates the rank of the data set Park Smart is on 1st Rank, ParkMobile is on 2nd Rank, Park Whiz is on 3rd Rank, Street line is on 4th Rank and Spot Hero is on 5th Rank. To sum up, implementing a smart parking system employing IoT technology has shown to be a potential way to deal with the problems associated with urban parking. The system increases parking efficiency, lessens traffic congestion, and enhances user experience by utilising IoT sensors, data analytics, and real-time communication. The parking scene in smart cities has the potential to change dramatically, enhancing ease and sustainability.

Exploring the IoT Data Analytics Landscape: An Extensive Study

Abstract: Since the past decade, the data production and consumption has been increasing exponentially. Ever since the emergence of cheap computer chips, wireless networks, and technological advancements in the cloud, there has been a tremendous increase in the number of IoT devices being developed. The data collected by connected heterogeneous IoT devices are highly diverse and unstructured and need to be analyzed to gain valuable insights therefrom. Data analysis can effectively help Internet of Things (IoT) businesses to identify trends, enhance growth and productivity, and empower employees. To date, immense opportunities are being discovered by analyzing massive amounts of data generated by IoT devices. Through an indepth analysis of existing surveys, we explore IoT, data analytics, and their unification. In this survey, we investigated various applications with their extensive usage and significant challenges of IoT data analytics. By examining the plethora of advantages of data analysis in the IoT sector, one can conclude that field has numerous applications and the true potential of data analysis in IoT sector is yet to be realized.

IoT Data Management and Analytics: Challenges, Solutions & Trends

The Internet of Things (IoT) has revolutionized industries all over the world by introducing an era of unprecedented data generation and connection. Organizations must successfully manage, analyze, and derive meaningful insights from the massive amount of data that billions of devices routinely collect and transmit. This study examines the crucial facets of IoT data management and analytics, as well as the methodology, resources, and best practices that let businesses make the most of their IoT data. The fundamental properties of IoT data, such as their volume, velocity, diversity, and truthfulness, which provide particular obstacles in terms of storage, processing, and analysis, are first described in the study. The importance of data governance and security in the context of the Internet of Things is then discussed, with a focus on the necessity of effective data management techniques to guarantee data integrity, privacy, and compliance with legal requirements. The study incorporates case studies and real-world examples to show how IoT data management and analytics systems may be used in a variety of industries, including manufacturing, healthcare, smart cities, and agriculture. This study 's conclusion emphasizes the critical role that efficient IoT data management and analytics play in maximizing the potential of the Internet of Things. It is a helpful resource for businesses looking to use their IoT data for improved decision-making, efficiency, and competitiveness in the digital age by offering insights into best practices, cutting-edge technologies, and practical applications.

FedMicro-IDA: A federated learning and microservices-based framework for IoT data analytics

The Internet of Things (IoT) has recently proliferated in both size and complexity. Using multi-source and heterogeneous IoT data aids in providing efficient data analytics for a variety of prevalent and crucial applications. To address the privacy and security concerns raised by analyzing IoT data locally or in the cloud, distributed data analytics techniques were proposed to collect and analyze data in edge or fog devices. In this context, federated learning has been recommended as an ideal distributed machine/deep learning-based technique for edge/fog computing environments. Additionally, the data analytics results are time-sensitive; they should be generated with minimal latency and high reliability. As a result, reusing efficient architectures validated through a high number of challenging test cases would be advantageous. The work proposed here presents a solution using a microservices-based architecture that allows an IoT application to be structured as a collection of fine-grained, loosely coupled, and reusable entities. The proposed solution uses the promising capabilities of federated learning to provide intelligent microservices that ensure efficient, flexible, and extensible data analytics. This solution aims to deliver cloud calculations to the edge to reduce latency and bandwidth congestion while protecting the privacy of exchanged data. The proposed approach was validated through an IoT-malware detection and classification use case. MaleVis, a publicly available dataset, was used in the experiments to analyze and validate the proposed approach. This dataset included more than 14,000 RGB-converted images, comprising 25 malware classes and one benign class. The results showed that our proposed approach outperformed existing state-of-the-art methods in terms of detection and classification performance, with a 99.24%

A Game Theory Study of Big Data Analytics in Internet of Things

With the rapid development of Internet of Things (IoT), big data analytics (BDA) has gradually stepped into the spotlight of IoT research. However, existing research mostly focused on improving the efficiency of data mining, the wishes and profits of the participants in BDA are largely ignored. In real systems, it is always supposed that the IoT data owners accept the big data analytics in IoT (BDA-IoT), the theoretical modeling of BDA-IoT is needed to be constructed urgently. In this paper, aiming to provide theoretical modeling of the practical application of BDA-IoT, we prove the feasibility of participants voluntarily participating in BDA-IoT for the first time. Subsequently, a non-cooperative game theory model with incentive and payment mechanisms is constructed, and the multi-parties interaction process in BDA-IoT is simulated. The overall benefits of all participants are then discussed, the rationality and feasibility of our study are proved. Simulation results show the feasibility of our model in improving the benefits of all stakeholders in a non-cooperative game, and the best choice for all participants is to accept BDA-IoT.

Smart Energy Meter Using IoT

The increasing demand for efficient energy management and the rapid growth of Internet of Things (IoT) technology have led to the development of smart energy metering systems. This abstract presents a conceptual design of a smart energy meter that leverages IoT capabilities for enhanced monitoring, control, and optimization of energy consumption in residential and commercial settings. The proposed smart energy meter integrates advanced metering infrastructure (AMI) with IoT sensors, connectivity, and data analytics to enable real-time energy monitoring, intelligent load management, and energy efficiency improvements. The meter collects data on energy consumption, voltage levels, power quality, and other relevant parameters, which are transmitted wirelessly to a central monitoring system. By utilizing IoT connectivity, the smart energy meter facilitates two-way communication, allowing consumers to access their energy usage data remotely via mobile applications or web portals. This empowers users to monitor their energy consumption patterns, set energy-saving goals, and receive personalized recommendations for optimizing their energy usage. Furthermore, the smart energy meter employs data analytics algorithms to analyze the collected energy data and generate actionable insights. These insights can be used to identify energy wastage, detect anomalies, and suggest energy-saving strategies. Additionally, the meter can automatically adjust energy loads based on peak demand periods, optimizing energy distribution and reducing overall energy costs. The proposed smart energy meter offers several benefits, including real-time monitoring, increased energy efficiency, cost savings, and reduced environmental impact. It enables consumers to make informed decisions about their energy usage, promotes sustainable practices, and contributes to the development of smarter and greener communities.

Sazgar IoT: A Device-Centric IoT Framework and Approximation Technique for Efficient and Scalable IoT Data Processing.

The Internet of Things (IoT) plays a fundamental role in monitoring applications; however, existing approaches relying on cloud and edge-based IoT data analysis encounter issues such as network delays and high costs, which can adversely impact time-sensitive applications. To address these challenges, this paper proposes an IoT framework called Sazgar IoT. Unlike existing solutions, Sazgar IoT leverages only IoT devices and IoT data analysis approximation techniques to meet the time-bounds of time-sensitive IoT applications. In this framework, the computing resources onboard the IoT devices are utilised to process the data analysis tasks of each time-sensitive IoT application. This eliminates the network delays associated with transferring large volumes of high-velocity IoT data to cloud or edge computers. To ensure that each task meets its application-specific time-bound and accuracy requirements, we employ approximation techniques for the data analysis tasks of time-sensitive IoT applications. These techniques take into account the available computing resources and optimise the processing accordingly. To evaluate the effectiveness of Sazgar IoT, experimental validation has been conducted. The results demonstrate that the framework successfully meets the time-bound and accuracy requirements of the COVID-19 citizen compliance monitoring application by effectively utilising the available IoT devices. The experimental validation further confirms that Sazgar IoT is an efficient and scalable solution for IoT data processing, addressing existing network delay issues for time-sensitive applications and significantly reducing the cost related to cloud and edge computing devices procurement, deployment, and maintenance.

Design and implementation of Internet of Things for emergency medical devices based on cloud-edge-device architecture

Internet of Things (IoT) technology plays an important role in smart healthcare. This paper discusses IoT solution for emergency medical devices in hospitals. Based on the cloud-edge-device architecture, different medical devices were connected; Streaming data were parsed, distributed, and computed at the edge nodes; Data were stored, analyzed and visualized in the cloud nodes. The IoT system has been working steadily for nearly 20 months since it run in the emergency department in January 2021. Through preliminary analysis with collected data, IoT performance testing and development of early warning model, the feasibility and reliability of the in-hospital emergency medical devices IoT was verified, which can collect data for a long time on a large scale and support the development and deployment of machine learning models. The paper ends with an outlook on medical device data exchange and wireless transmission in the IoT of emergency medical devices, the connection of emergency equipment inside and outside the hospital, and the next step of analyzing IoT data to develop emergency intelligent IoT applications.

A systematic review of IoT technologies and their constituents for smart and sustainable agriculture applications

Due to the world's rapid population expansion, the demand for food is anticipated to increase significantly during the coming decade. Traditional farming practices cannot meet the need for the food crop. Conventional farming methods use resources like land, water, herbicides, and fertilisers rather inefficiently. When it comes to making the most effective and sustainable use of resources to increase production, automation in agriculture is garnering a lot of interest. How people and machines operate on farms has been changed by integrating the Internet of Things (IoT) with numerous sensors, controllers, and communication protocols. A comprehensive literature review of the key technologies involved in smart and sustainable agriculture, viz. various sensors, controllers, communication standards, IoT based intelligent machinery, were compared and presented. These sensors were continuously producing a significant quantity of data on the agricultural field. These data were transmitted to the central control unit for analysis to meet the demands for water, fertiliser, pesticides, etc. The architecture and importance of data analytics in agriculture IoT, case studies of current agricultural automation utilising IoT, key challenges and open issues in agriculture IoT technology were discussed. The findings provide support for the selection of IoT technologies for specific applications.

At the Confluence of Artificial Intelligence and Edge Computing in IoT-Based Applications: A Review and New Perspectives.

Given its advantages in low latency, fast response, context-aware services, mobility, and privacy preservation, edge computing has emerged as the key support for intelligent applications and 5G/6G Internet of things (IoT) networks. This technology extends the cloud by providing intermediate services at the edge of the network and improving the quality of service for latency-sensitive applications. Many AI-based solutions with machine learning, deep learning, and swarm intelligence have exhibited the high potential to perform intelligent cognitive sensing, intelligent network management, big data analytics, and security enhancement for edge-based smart applications. Despite its many benefits, there are still concerns about the required capabilities of intelligent edge computing to deal with the computational complexity of machine learning techniques for big IoT data analytics. Resource constraints of edge computing, distributed computing, efficient orchestration, and synchronization of resources are all factors that require attention for quality of service improvement and cost-effective development of edge-based smart applications. In this context, this paper aims to explore the confluence of AI and edge in many application domains in order to leverage the potential of the existing research around these factors and identify new perspectives. The confluence of edge computing and AI improves the quality of user experience in emergency situations, such as in the Internet of vehicles, where critical inaccuracies or delays can lead to damage and accidents. These are the same factors that most studies have used to evaluate the success of an edge-based application. In this review, we first provide an in-depth analysis of the state of the art of AI in edge-based applications with a focus on eight application areas: smart agriculture, smart environment, smart grid, smart healthcare, smart industry, smart education, smart transportation, and security and privacy. Then, we present a qualitative comparison that emphasizes the main objective of the confluence, the roles and the use of artificial intelligence at the network edge, and the key enabling technologies for edge analytics. Then, open challenges, future research directions, and perspectives are identified and discussed. Finally, some conclusions are drawn.

Blockchain-Based Federated Learning for Securing Internet of Things: A Comprehensive Survey

The Internet of Things (IoT) ecosystem connects physical devices to the internet, offering significant advantages in agility, responsiveness, and potential environmental benefits. The number and variety of IoT devices are sharply increasing, and as they do, they generate significant data sources. Deep learning (DL) algorithms are increasingly integrated into IoT applications to learn and infer patterns and make intelligent decisions. However, current IoT paradigms rely on centralized storage and computing to operate the DL algorithms. This key central component can potentially cause issues in scalability, security threats, and privacy breaches. Federated learning (FL) has emerged as a new paradigm for DL algorithms to preserve data privacy. Although FL helps reduce privacy leakage by avoiding transferring client data, it still has many challenges related to models’ vulnerabilities and attacks. With the emergence of blockchain and smart contracts, the utilization of these technologies has the potential to safeguard FL across IoT ecosystems. This study aims to review blockchain-based FL methods for securing IoT systems holistically. It presents the current state of research in blockchain, how it can be applied to FL approaches, current IoT security issues, and responses to outline the need to use emerging approaches toward the security and privacy of IoT ecosystems. It also focuses on IoT data analytics from a security perspective and the open research questions. It also provides a thorough literature review of blockchain-based FL approaches for IoT applications. Finally, the challenges and risks associated with integrating blockchain and FL in IoT are discussed to be considered in future works.

Role of Disruptive Technology in Efficiency of 21st Century Organisation

Disruptive technology is playing a bigger and bigger part in how effectively 21st-century enterprises operate in India. The country is undergoing a digital revolution as a result of the rapid use of new technologies including artificial intelligence (AI), cloud computing, and the Internet of Things (IoT). Traditional industries including banking, healthcare, and agriculture are being significantly transformed by disruptive technologies. With the government's goal for a cashless economy and the launch of UPI, digital payments have become widely used in India (Unified Payments Interface). For both enterprises and people, this has increased the efficiency and convenience of financial transactions. Telemedicine has become a revolutionary technology in the healthcare industry, enabling doctors to conduct distant consultations and enhancing access to healthcare for people living in rural locations. In a similar vein, precision agriculture has increased crop yields and decreased waste thanks to IoT sensors and data analytics. Organizations that use these technologies are expected to gain a competitive advantage, boost operational effectiveness, and provide better customer service as India continues to embrace disruptive technology.

A Density-Center-Based Automatic Clustering Algorithm for IoT Data Analysis

With the rapid development of Internet of Things (IoT), much data has been produced, and new requirements have been posed for data mining. Clustering plays an essential role in discovering the underlying patterns of IoT data. It is widely used in health prognoses, pattern recognition, information retrieval, and computer vision. Density clustering is crucial to find arbitrary-shaped clusters and noise points without knowing the number of clusters in advance. However, its efficiency and applicability are reduced sharply when there exists mutual interference among parameters. In this article, a new algorithm called density-center-based automatic clustering (DAC) is proposed. First, this work presents a nonparametric density computing method. Second, it proposes to use an adaptive neighborhood whose radius is automatically calculated based on all the points in a data set. Finally, it selects appropriate density centers from a decision graph, which merge their surrounding points into the same groups. Experiments are conducted to show that DAC has higher accuracy than six classic and updated algorithms. Its effectiveness is shown via data from photovoltaic power and oil extraction systems. As an outstanding feature that its compared peers lack, it can determine parameters automatically. Thus this work greatly advances the state-of-the-art of clustering algorithms in the field of IoT data analysis.