
Industrial Internet of Things and Advanced Techniques for Sensor Data Aggregation and Fusion
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Master the complexity of modern networks with this essential guide, which provides the state-of-the-art AI and machine learning techniques needed to execute seamless sensor data fusion and energy-efficient aggregation across Industrial IoT and smart city environments.
The use of artificial intelligence and machine learning techniques for data aggregation and fusion is becoming increasingly important, as these technologies can help extract important features and knowledge from data. Sensor data aggregation and fusion are essential components of IoT and Industrial IoT systems, as they enable the combination of data from multiple sources to provide a more comprehensive view of the system being monitored. This book is a comprehensive guide to the state-of-the-art techniques and methods used for sensor data aggregation and fusion in IoT and Industrial IoT environments, covering the fundamental principles of data aggregation and fusion, as well as the latest advancements and applications in the field. The book takes a practical approach to the subject matter, providing a deeper understanding of the challenges and opportunities associated with sensor data aggregation and fusion in IoT and Industrial IoT environments. It covers topics such as machine learning-based data aggregation, intelligent multi-sensor fusion, data aggregation and fusion in smart cities, and energy-efficient data aggregation and fusion. Written by leading experts in the field, the book will provide a comprehensive overview of the latest advancements in sensor data aggregation and fusion in IoT and Industrial IoT environments.
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1
Enhancing Privacy and Efficiency in IoT-Enabled Business Information Analytics and Blockchain-Based Contingency
P. Kumaresan* and R. Ramprashath
School of Computer Application, Karpagam College of Engineering, Coimbatore, Tamil Nadu, India
Abstract
In the article, a contingency model to build a sustainable business with the help of Internet of Things (IoT) features is proposed. It aims to identify a target audience, understand their needs, and assess the competitive landscape. IoT-enabled contingent leaders collaborating with partners can bring new opportunities and access to new markets and shared resources. Document all project requirements in detail and share them with your foreign client to confirm that you are on the same page. IoT sensors can provide real-time data to ensure accuracy and transparency. SHA-256 (secure hash algorithm 256-bit) acts as a cryptographic hash function that takes arbitrary data as input and produces a unique fixed-length (in this case, 256 bits) output called a hash. This hash is like a fingerprint of the data; any changes you make to the data result in a completely different hash. Each block in the proof-of-work blockchain contains a header containing various data, including a hash of the previous block. When a new block is mined, miners must find a specific hash value of the block header that meets certain difficulty requirements. SHA-256 is not the only hashing algorithm used in blockchain. Sharing the software requirements with the client helps to ensure that everyone involved in the project has a clear understanding of what is expected from the technology partner's solution. In this context, software requirements may be shared with the client to ensure that the technology partner's solution is integrated properly and meets the project's needs.
Keywords: SHA-256 (secure hash algorithm 256-bit), cryptographic hash function, blockchain, hashing algorithm, IoT-enabled, information analytics
1.1 Introduction
In our exploration of sustainable business development, we introduce a contingency model that serves as a strategic framework for building resilient enterprises. At its core, this model underscores the significance of key features in fostering sustainability, with a primary focus on identifying the target audience, understanding their needs, and conducting a thorough assessment of the competitive landscape [1, 2].
Rather than a generic approach, we advocate for a more tailored strategy that involves detailing aspects such as target markets, competitive advantages, marketing and sales plans, and operational requirements [3]. This nuanced perspective ensures a comprehensive understanding of the business environment, paving the way for more effective decision-making and sustainable growth. Integral to the success of the contingency model are contingent leaders who play a pivotal role in fostering collaboration with strategic partners. Such collaborations not only bring forth new opportunities but also provide access to untapped markets and shared resources, reinforcing the sustainability of the business [4, 5].
In the realm of technology, we delve into the significance of cryptographic hash functions, particularly focusing on SHA-256. Acting as a unique fingerprint for data, SHA-256 ensures data integrity and security in blockchain implementations. A recent article explores its role in proof-of-work blockchains, emphasizing the importance of finding specific hash values that meet predefined difficulty requirements during the mining process [6].
However, the technological landscape is diverse, and SHA-256 is just one of the many hashing algorithms in use in blockchain technology. This introduction sets the stage for understanding the broader context of cryptographic protocols and their role in ensuring the reliability of data in various blockchain frameworks [7].
Furthermore, we highlight the importance of transparent communication in project management. Detailed documentation of project requirements, especially in cross-border collaborations, is crucial. Sharing these requirements with foreign clients ensures alignment and a shared understanding of project expectations, laying the foundation for successful outcomes [8]. In this context, software requirements emerge as a critical aspect of project success. Sharing these requirements with clients not only facilitates seamless integration but also ensures that the technology partner's solution aligns with the specific needs of the project [9].
This introduction provides a glimpse into the multifaceted approach of the contingency model, combining strategic business considerations, collaborative leadership, and technological insights to establish a framework for sustainable business development.
1.2 Literature Survey
A recent paper explores the crucial management question surrounding the realization of anticipated economic benefits from information technology (IT) [10]. The study positions the issue as primarily measurement-related and introduces a novel process-oriented methodology designed for ex-post measurement, specifically for auditing IT impacts on the performance of a strategic business unit or profit center. This methodology involves a two-stage analysis of intermediate- and higher-level output variables, incorporating industry- and economy-wide exogenous variables to trace and measure IT contributions.
The evolving landscape of handling vast amounts of sensitive data distributed across mobile devices, wearables, and sensors is explored in [11]. Traditionally, single-system processing has been the norm, using complex models for valuable predictions. However, the rise of distributed machine learning techniques, notably federated and split learning, presents a paradigm shift in protecting user data and privacy without compromising performance.
In Pathak and Wainwright [12], the critical importance of measuring IT success is highlighted for organizational efficiency. Researchers and practitioners emphasize the need to understand the value of IT solutions. The resource-based view and contingency theory are commonly used to measure IT business value (ITBV). Numerous studies have explored the intricate relationship between IT and organizational performance, with ongoing research dedicated to understanding and measuring ITBV.
In Gupta and Raskar [13], the burgeoning field of business analytics, where data-driven insights play a pivotal role in decision-making, this study addresses a critical gap in understanding the mechanisms that enhance organizational decision-making effectiveness (DME). Rooted in the information processing view and contingency theory, the research model proposed in this paper establishes a comprehensive framework linking business analytics to organizational DME. The empirical validation of this model utilizes structural equation modeling and draws upon a substantial dataset of 740 responses from businesses in the United Kingdom.
1.3 Proposed System
To develop a partner management module within the system to streamline the onboarding process and facilitate efficient partner relationship management, implement robust security measures and ensure compliance with international data protection and privacy regulations.
Incorporate encryption, access controls, regular security audits, and vulnerability assessments into the proposed system to protect sensitive partner data. The business objectives of the project are to increase sales, improve customer service, and reduce costs.
The client may have specific usability or design preferences that are unique to their culture. SHA-256 acts as a cryptographic hash function, taking any data as input and generating a unique, fixed-length (256-bit in this case) output called a hash. It is helpful in securing the details in the blockchain.
Attributes definition
- Id: Unique identifier for each project
- Customer name: Name of the customer associated with the project
- Title: Title of the project
- Short description: Brief description of the project
- File type: Type of files associated with the project
- Developer: Name of the developer or development team working on the project
- Start date: The date when the project started
- Release date: Date when the project was released or is scheduled to be released
- Project completion: Percentage of project completion
- Project investment: Investment made in the project
- Status: Current status of the project (e.g., ongoing, completed, pending)
Figure 1.1 System architecture.
Figure 1.2 Datasets.
SHA-256 hashing
- SHA-256 is a cryptographic hash function that generates a fixed-size hash value (256 bits or 64 hexadecimal characters) from input data of any size. Figure 1.1 refers the system architecture. Figure 1.2 is sample data set.
- The hash function takes the input data and applies a series of mathematical and logical operations to produce the hash value.
- The output hash is unique to the input data, meaning even a small change in the input data will result in a significantly different hash value.
- SHA-256 is widely used for data integrity verification, password hashing, digital signatures, and various cryptographic applications due to its collision resistance and computational...
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