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Business Intelligence System Relaxing Internet Solution Developer Direct
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By Jamal Bzai Jamal Bzai Scilit Preprints.org Google Scholar 1, Furqan Alam Furqan Alam Scilit Preprints.org Google Scholar 2, *, Arwa Dhafer Arwa Dhafer Scilit Preprints.org Google Scholar 3, Miroslav Bojović Miroslav Bojović Scrit Preprints.org. , Saleh M. Altovaijri Saleh M.
Received: 19 July 2022 / Revised: 11 August 2022 / Accepted: 18 August 2022
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Machine learning (ML) enables the Internet of Things (IoT) to extract hidden insights from a treasure trove of sensitive data and make knowledge and data patterns truly ubiquitous without the obvious need to search for them. Without ML, IoT cannot withstand the future demands of enterprises, governments and individual users. The main purpose of IoT is to sense what is happening around us and allow us to automate decision-making through intelligent methods that mimic the decisions made by humans. In this paper, we discuss the literature on ML-enabled IoT by categorizing it from three perspectives: data, application, and industry. We detail a dozen leading approaches and applications by reviewing nearly 300 published sources on how ML and IoT can work together to make our environment smarter. We also cover emerging IoT trends, such as the Internet of Things (IoB), pandemic management, connected autonomous vehicles, edge and fog computing, and lightweight deep learning. In addition, we have divided the challenges to IoT into four categories: technological, individual, business, and societal. This article explores the opportunities and challenges of IoT to help our society become more prosperous and sustainable.
Internet of Things (IoT) Outliers Data Computing Feature Selection Machine Learning Smart Cities Smart Homes Edge and Fog Computing Lightweight Deep Learning Internet of Things (IoB)
The Internet of Things (IoT) will become one of the most important technological developments of our time, if only we can realize its full potential. The IoT is “the global infrastructure enabled by existing and evolving information and communication technologies based on the interconnection of (physical and virtual) objects using advanced services.” The IoT was named one of the top six civilian technologies impacting America’s power in a 2008 report by the National Intelligence Council (NIC). IoT is the enabler of ubiquitous computing as envisioned by Mark Weiser. IoT is no longer a technical term as described in [1], but a reality that connects the physical world with the digital world, changing how we perceive our surroundings. Currently, IoT is being implemented in parts due to the lack of technology and other limitations in the global arena.
The IoT industry has attracted IT giants such as Microsoft, Cisco, Google, Amazon, Apple and Samsung to invest in IoT-enabled hardware and software. According to market research analysis company Statista and Transforma Insights, the number of IoT-connected objects may reach 25-30 billion by 2030 due to the widespread distribution of various IoT devices [2, 3].
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The main goal of this growing number and projects is to make intelligent decisions by making valuable decisions about existing locations in the operational environment. This is achieved by obtaining the context we need and analyzing past, present and future data. This data may help us make better decisions about ourselves and our environment. This huge, diverse growth in the IoT landscape generates $1.5 trillion in annual revenue. The IoT landscape is shown in Figure 1. Although Europe is at the forefront of early adoption of IoT, South Korea ranks first in the world ranking of connected things, while the United States lags far behind [4]. Figure 1 also illustrates the application areas of IoT: smart home, alarm system, smart shopping, smart device, smart city, smart road, healthcare, fire protection system, threat detection system, tracking and monitoring. .
One of the key features of IoT is to provide the technological infrastructure to sense different activities and events happening around us. IoT is expected to generate huge amounts of data. This data is created by a variety of vendors that create data as a service. For smart cities and communities to reach their full potential, sharing and collaborating on data and information will be key to providing them with sustainable, ubiquitous applications and services. The integration of various data and formats is important in the context of data quality improvement and decision-making. Data Fusion “Theories, techniques, and tools used to combine sensor data or data generated from sensory data into a common representation.” Real-time synthesis and analysis of big data (volume, velocity, diversity, and authenticity) obtained from IoT’s sensor networks enables accurate and reliable decision-making. However, managing a ubiquitous environment will be a major challenge for IoT. Also, various sensors and intelligent algorithms play a critical role in solving the above challenges.
The goal of IoT is to understand what people want and how people think, predict desirable and undesirable events, and learn to manage certain situations. All of this requires IoT to make sense of the data produced by millions of objects. This insight can be achieved using machine learning algorithms (MPs). Machine learning (ML) plays a very important role in IoT models. IoT is ubiquitous in nature, meaning use anywhere is one of its main goals [5]. ML plays a key role in this by mining the data produced by thousands and millions of connected devices. ML will be useful for IoT devices, IoT will only be truly ubiquitous [6]. Embedded intelligence (EI) is at the core and plays an important role in achieving the goals of IoT. EI is the integration of products and intelligence to achieve better automation, efficiency, productivity, and connectivity [7, 8]. Whether in the physical or virtual world, intelligence is acquired through learning.
ML’s tendency to find patterns may be the underpinnings of human-like intelligence. Further generalization of these patterns to more valuable insights and trends provides a better understanding of the world around us. The actual goal of ML in IoT is to enable full automation through reinforcement learning that facilitates intelligent performance through intelligent objects [9]. ML enables IoT-enabled systems to make human-like decisions after training them on data and improve their understanding of our surroundings. The impact of information vision on human vision systems is enormous, and systems can better understand data and insights. Data visualization brings several advantages to users, such as: (1) better knowledge of the data without further analysis and (2) using cognitive skills that allow humans to better understand the data. IoT will replace several existing systems that are expensive to replace and maintain with low-cost sensor-based ML systems. For example, severe weather has killed around 20,000 people in developing countries. Most weather monitoring is done by radar-based weather monitoring systems (WMS). However, radar WMSs are expensive and unavailable in many parts of the world. An IoT system with ML capabilities, consisting of a network of inexpensive sensors that study lighting and cloud patterns for weather forecasting, has been successfully deployed in economically backward countries such as Guinea and Haiti [11].
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IoT not only affects how we see technology, but also how technology can advance and make our world more prosperous [12, 13]. Every day, every aspect of our life is made easier and easier by IoT. ML is smart and pervasive in IoT. Because the IoT consists of a variety of devices, network technologies, protocols, data types, applications, and users, the word “different” can be an apt term. Such is the heterogeneous nature of IoT
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