Building a large-scale object-based active storage platform for data analytics in the internet of things

In this paper, we propose a large-scale object-based storage platform, named Gem, for data analytics in the Internet of Things (IoT). In Gem, a region covered by an IoT network is partitioned into sub-regions, each of which can be identified by a unique ID and known to all participants, which is automatic and economical. Gem can preserve object locality using type and location sensitive hashing, as well as dynamically distribute objects among a server cluster to keep load balancing. All data from the IoT can be stored as objects with attributes, methods and policies in Object Store Devices (OSDs). For some applications such as data analytics, application-specific operations are executed by OSDs, and only the results are returned to clients, rather than data files are read by the clients. Thus, the platform Gem is able to greatly reduce the overhead of data analytics applications in the IoT.

This article delves into the many advantages of using data analytics and the Internet of Things (IoT) in logistics, with an emphasis on improved safety, optimized routing, and resource allocation. In optimized routing, IoT sensors embedded in vehicles, warehouses, and infrastructure collect real-time data on traffic conditions, weather patterns, and vehicle performance. Coupled with sophisticated data analytics algorithms, this information enables logistics companies to adjust routes dynamically, minimizing delays, fuel consumption, and environmental impact while maximizing efficiency and customer satisfaction. Safety enhancement is another critical aspect empowered by IoT and data analytics. By equipping vehicles with sensors that monitor driver behavior, road conditions, and surrounding environments, logistics companies can proactively identify and mitigate potential safety risks. Real-time analytics further enable predictive maintenance, ensuring vehicles are in optimal condition and reducing the likelihood of accidents. Resource planning is streamlined through IoT-enabled inventory management systems and predictive analytics. By continuously monitoring inventory levels, demand patterns, and supply chain dynamics, logistics companies can optimize warehousing space, reduce stockouts, and minimize carrying costs. Predictive analytics algorithms forecast future demand, enabling proactive decision-making and strategic resource allocation. Furthermore, the paper discusses the challenges and considerations associated with implementing IoT and data analytics solutions in logistics, including data privacy concerns, cybersecurity risks, and infrastructure requirements. This paper explores the symbiotic relationship between IoT and data analytics in logistics, focusing on their applications in three critical areas: optimized routing, safety enhancement, and resource planning. Safety enhancement is another crucial aspect where IoT and data analytics play a pivotal role. By deploying sensors and monitoring devices throughout the logistics network, organizations can gather comprehensive data on vehicle health, driver behavior, and environmental conditions. Analyzing this data in real-time enables proactive identification of safety hazards, facilitating timely interventions to prevent accidents and ensure compliance with regulatory standards. Resource planning in logistics involves the efficient allocation of assets such as vehicles, personnel, and storage facilities. IoT-enabled tracking devices provide granular visibility into the movement and utilization of these resources, while advanced analytics algorithms offer predictive insights into demand patterns and operational trends. By optimizing resource allocation based on data-driven forecasts, organizations can minimize costs, reduce wastage, and enhance overall operational agility.

Big data analytics helps in analyzing a huge set of data whereas IoT is about data, devices and connectivity. Internet of Things (IoT) involves connecting physical objects to the Internet to build smart systems and universal mobile accessibility advanced technologies like Intelligent Transportation System (ITS). IoT solutions are playing a major role in driving the global IoT in Intelligent Transportation System. Communication between vehicles using IoT will be a new era of communication that leads to ITS. IoT is a combination of storing and processing sensor data and computing using data analytics to achieve and assist in managing the Traffic system effectively. IoT based Intelligent transportation system (IoT-ITS) helps in automating railways, roadways, airways and marine which enhance customer experience about the way goods are transported, tracked and delivered. A case study on Intelligent Traffic Management System based on IoT and big data, which will be a part of, smart traffic solutions for smarter cities. The ITS-IoT system itself forms an eco-system comprising of sensor systems, monitoring system and display system. There are several techniques and algorithms involved in full functioning of IoT-ITS. The proposed case study will examine and explain a complete design and implementation of a typical IoT-ITS system for a smart city scenario set on typical Indian subcontinent. This case study will also explain about several hardware and software components associated with the system. How concepts like Multiple regression analysis, Multiple discriminant analysis and logistic regression, Cojoint analysis, Cluster analysis and other big data analytics techniques will merge with IoT and help to build IoT-ITS will also be emphasized. The case study will also display some big data analytics results and how the results are utilized in smart transportation systems.

The world generates an unfathomable amount of data every day. The speed with which the data gets generated, transmitted, ingested, and crunched is nothing but spectacular in the recent past. Expert thinkers and pundits across the globe are of the opinion that data is the transformation agent. Data is positioned as a strategic asset for any institution, innovator, and individual to grow and glow and is being reasoned as the new fuel for bringing in real and sustainable business transformation. It is a universally accepted fact that data can be methodically processed, mined, and analyzed to produce actionable insights. There are batch and real-time data processing methods to make sense of data heaps. Further on, there are integrated data analytics platforms in plenty, to extract hidden patterns, associations, and other useful insights out of data volumes. The data analytics ecosystem grows continuously considering the importance of data-driven insights and insights-driven decisions for businesses as well as people to be agile, adaptive and adroit in their dealings and deeds.Off late real-time data capture, storage, analytics, decision-making, and action are being insisted upon vehemently considering the evolving business dynamics. Any data or message has to be carefully captured, cleansed, and crunched immediately in order to be really beneficial for businesses and commoners. It is indisputable that the data value goes down sharply with time. Another facet is that agile and autonomous business systems are extremely event-driven. That is, any business event may trigger a suite of events across. Thus, all kinds of event data/messages have to be received and processed in real time in order to activate and automate one or more business operations. In short, for envisaging and realizing real-time services, applications and enterprises, real-time data analytics capability is very much indispensable. Enterprises are therefore keenly strategizing and setting up analytics infrastructure modules with all the clarity and alacrity to make sense out of both internal and external data in time. Such a futuristic and flexible capability helps business houses to be sagaciously steered in the right direction.

Recent advances in computing have lead to diverse applications in wide domains such as cyber-forensics, data science, business analytics, business intelligence, computer security, Web technology, and Big data analytics. Data analytics refer to broad term where many of the areas such as cyber-physical systems (CPS), Internet of Things (IoT), Big data, machine learning and data mining overlap among each other. However, there are subtle differences among these areas. Data analytics form as one of the key components of these wide areas in computing. For example, in IoT the data that are collected from various devices need to analyze for inferring the outcomes of it. Hence, data analytics need to be carried on it. There are various platforms available for data analytics. In this chapter, a brief overview of the term data analytics, different types of data, different types of analytics, and the analytical architecture is first discussed. It is later followed by the different phases that are involved in the lifecycle of the data analytics project and the interconnection of Big data and Hadoop ecosystem.

Recently, extensive research into healthcare services and their technological advancements is being carried out globally. The Internet of Things (IoT) has proven to be a predominantly potential candidate in these advancements. IoT supports infrastructure development in connecting a wide range of medical devices, sensors, and health practitioners in order to ensure the best medical treatment for patients in both cities and remote places. This has enabled the IoT to provide major support in medical application, giving rise to assisted healthcare facilities. The IoT ecosystem has a complicated architecture, with several components interacting and an evolving variety of applications for users. This is a comprehensive and interdependent system that involves real-time data analysis, interconnections of device, data transfers, and analysis. The IoT creates a connected ecosystem developed from several intelligent systems that integrates respondents with machines and allows for statistics decision making. A network of interconnected devices that perceive vital data related to medical conditions of patients in real time is referred to as the Internet of Medical Things (IoMT), a healthcare application of IoT technology. Sensors used for sensing real-time data related to monitoring temperature, blood pressure levels, heartbeat levels, specialized signal converters, and communication interfaces can be added to existing medical facilities to turn them into IoMT devices. IoMT can be used to create wearable devices, home-care and personal-care medical devices, and mobile healthcare apps, along with creating communication with medical specialists in a given area. The IoMT system’s challenges and ecosystem interface requirements are also a matter of concern considering the complexity of the systems. This study offers a comprehensive evaluation of the various domains of use of IoMT, as well as its future scope and challenges. Recent advances in sensor technology, networks, cloud storage and processing, mobility, and big data analytics have made it possible to create affordable smart medical devices and a connected healthcare environment.

Smart healthcare framework for ambient assisted living using IoMT and big data analytics techniques

Pricing Models for the Internet of Things (IoT): Game Perspectives

In this article, the Internet of Medical Things (IoMT) framework based on Apache Spark big data processing technology is proposed for real‐time analysis of health data obtained from wireless body area networks (WBANs), which is one of the most important components of IoMT. The proposed framework consists of four layers: data source, data collection, data analytics and visualization. In addition, the proposed IoMT framework is presented with two different disease prediction scenarios, diabetes and heart disease. Diabetes and heart disease prediction processes are carried out using the random forest (RF), logistic regression (LR) and support vector machine (SVM) algorithms belonging to the Apache Spark machine learning library (MLlib). The analysis of health data generated in WBANs takes place in real‐time in the Apache Spark‐based data analytics layer. In this study, the performances of MLlib algorithms in the real‐time model developed for heart and diabetes disease are examined. The SVM algorithm with an accuracy rate of 93.33% for heart disease and the LR algorithm with an accuracy rate of 78.89% for diabetes are found to provide the best performances.

The Internet of Things (IoT) presents an extensive area for research, based on its growing importance in a multitude of different domains of everyday life, business and industry. In this context, different aspects of data analytics, e.g. algorithms or system architectures, as well as their scientific investigation play a pivotal role in the advancement of the IoT. Therefore, past research has presented a multitude of architectural approaches to enable data processing and analytics in various IoT domains, addressing different architectural challenges. In this paper, we identify and present an overview of these challenges as well as existing architectural proposals. Furthermore, we categorize found architectural proposals along various dimensions in order to highlight the evolution of research in this field and pinpoint architectural shortcomings. The results of this paper show that several challenges have been addressed by a large number of IoT system architectures for data analytics while others are either not relevant for certain domains or need further investigation. Finally, we offer points of reference for future research based on the findings of this paper.

In recent years, the amount of data increases continuously. With newly emerging paradigms, such as the Internet of Things, this trend will even intensify in the future. Extracting information and, consequently, knowledge from this large amount of data is challenging. To realize this, approved data analytics approaches and techniques have been applied for many years. However, those approaches are oftentimes very static, i.e., cannot be dynamically controlled. Furthermore, their implementation and modification requires deep technical knowledge only technical experts can provide, such as an IT department of a company. The special needs of the business users are oftentimes not fully considered. To cope with these issues, we introduce in this article a human-centered approach for interactive data processing and analytics. By doing so, we put the user in control of data analytics through dynamic interaction. This approach is based on requirements derived from typical case scenarios.

Internet of Things (IoT) and artificial intelligence (AI) technology are integrated under the umbrella title "Artificial Intelligence of Things" (AIoT). AIoT seeks to improve data management and analytics, increase human-machine interactions, and streamline IoT operations. The IoT is an interconnected system of computing devices, mechanical and digital machines, and items that may send data over a network without requiring human or computer-to-human interaction. Any device that can be assigned an Internet Protocol address and communicate data across a network is considered an IoT item. Examples include an implanted heart monitor or a car with built-in sensors that alert the driver when the tyre pressure is small. As AI enhances IoT through connectivity, signaling, and data exchange while IoT enhances AI through machine learning capabilities and enhanced decision-making processes, AIoT is a game-changer and mutually beneficial technology for both types of technology. The IoT may look up businesses and their services by adding value to the data they produce. Using AI, the IoT device may assess, learn from, and make judgments without the assistance of a human. The current chapter presents two new approaches towards the AIoT and IoT applications: AIoT-based water management system and IoT-based smart irrigation system which are very successful in smart agriculture system.

A subset of artificial intelligence (AI) is computational intelligence (CI). A human-like intelligence may be achieved in human-made robots by applying CI to develop intelligent systems. Compared to conventional methods, CI is more resilient, simpler, more efficient in producing tractable answers. When doing statistical computation (data analytics) in the fields of healthcare, social media research, industry, business, and pattern identification, recent technical advancements in CI have shown highly positive results. Big data is sometimes referred to as data that is “high volume, high diversity, and high velocity.” Big data is the enormous collection of information that businesses have gathered from a wide range of sources, including mobile applications, radio-wave reading and remote sensing equipment, wireless sensors, smartphones and other multimedia devices, remote sensing and location tracking equipment, and other similar sources. Data analytics is the process of applying analytical methods and code languages to large amounts of data in order to draw meaningful and appropriate conclusions from it. The Internet of things (IoT) is becoming widely used in almost all facets of contemporary life as a result of innovation in next-generation wireless technology, enabling people to work and live more intelligently. These fields cover a wide spectrum of commercial, military, and civil applications, including smart cities, smart homes, healthcare, the environment, and transportation. This chapter focuses on the various applications of the advancements in Computational Intelligence, big data technologies and Internet of things in various fields such as healthcare, agriculture, industries, business, energy sector, and retail business.

The convergence of Smart Cities and the Internet of Things (IoT) represents a revolutionary leap in urban development, reshaping the way cities function and interact with their inhabitants. This paper provides a concise overview of the landscape, focusing on emerging technologies and challenges within the realm of Smart Cities and IoT integration. Smart Cities leverage IoT technologies to enhance efficiency, sustainability, and the overall quality of urban life. This review explores the key components of this transformative shift, including sensor networks, data analytics, and advanced connectivity solutions. It examines how these technologies facilitate real-time data collection, enabling cities to make informed decisions for optimized resource management and improved citizen services. However, the path to realizing the full potential of Smart Cities and IoT is not without challenges. Privacy concerns arise as urban environments become more interconnected, raising questions about data security and individual rights. Additionally, the proliferation of IoT devices introduces potential vulnerabilities that demand robust cybersecurity measures. This study serves as a concise guide for policymakers, urban planners, and technology stakeholders, offering insights into both the promising innovations and the critical challenges associated with the integration of Smart Cities and IoT technologies. As cities globally navigate the complexities of this paradigm shift, understanding these dynamics is crucial for fostering sustainable, resilient, and technologically advanced urban environments.

The process of extracting and refining contaminants from drainage or wastewater so they can be recycled back into the water supply with the least amount of negative environmental impact is known as wastewater management. The current declining environmental condition necessitates the development of new strategies and tactics to guarantee safe and intelligent wastewater management systems in smart cities. The Internet of Things (IoT) and wireless sensor networks are potential technologies for treating wastewater. The comprehensive literature review develops a conceptual framework utilizing blockchain technology for an Internet of Things (IoT)- based wastewater management system in smart cities (IoT- WMS). Information is now being stored using blockchain technology to create an incentive scheme that promotes the reuse of waste water. In smart cities, tokens related to the quantity and quality of recovered wastewater are distributed to homes and businesses. However, this frequently promotes manipulating the data used to award these tokens in order to incorporate particular benefits. To determine whether there is evidence of altered IoT sensor data, anomaly detector techniques are employed. In order to facilitate the most efficient distribution of water depending on user usage at the plot holding level, this study suggests a water distribution and integrated management system based on the Internet of Things (IoT) and data analytics (DA). In addition to preventing water waste, the suggested approach would assist in gathering consumption statistics for macro-level research and town planning