Internet Of Things Data Management Research Articles

CONVERGENCE OF INTERNET OF THINGS (IOT) AND EDGE COMPUTING: ENHANCING SCALABILITY AND EFFICIENCY THROUGH CLOUD INTEGRATION: A REVIEW

This paper investigates the convergence of edge computing, cloud computing, and the Internet of Things (IoT). Given the exponential growth of IoT devices, a robust infrastructure is necessary to manage the vast volumes of generated data. Cloud computing offers on-demand access to shared computing resources over the internet, but solely relying on it may pose security risks and connectivity issues. Edge computing mitigates these concerns by enabling faster processing closer to devices, reducing dependency on constant connectivity. The paper examines the advantages and challenges of both edge and cloud computing for IoT and explores how they can complement each other to create a resilient framework for IoT data management. Additionally, it assesses the current research landscape and proposes potential avenues for future development in this field.

Tiger Tally: A secure IoT data management approach based on redactable blockchain

Blockchain-based Internet of Things (IoT) data management is increasingly ubiquitous across smart cities, supply chains, e-health and other domains. The immutability of blockchain is crucial to securing these IoT data management systems. In actual application scenarios, redacting the on-chain IoT data is still desired or even legally required. Various redactable blockchain technologies have been suggested for breaking immutability and redacting on-chain data. However, conventional redactable blockchain technologies incur security and performance degradations like redact privileges abuse and key exposure, prior art merely reactively corrects rather than fundamentally forestalling such misuse. In this paper, we rethink the conflict between the immutability and redaction of blockchain-based IoT data systems and propose Tiger Tally as a secure redactable architecture to fundamentally forestall these vulnerabilities. Tiger Tally introduces a novel Targeted Policy-Based Chameleon Hash, along with tokenized redact privileges, to form integrated cryptography and access control mechanisms. We further propose a full lifecycle redactable blockchain framework with rigorous security proofs to instantiate Tiger Tally. Furthermore, to meet its practical needs, we introduce the proposed Tiger Tally to the comprehensive IoT data workflows. At last, proof-of-concept implementation and performance evaluation demonstrate that our Tiger Tally is practical for IoT data management systems and greatly reduces the time overhead caused by malicious modification by at least 73.0% or even 3.26 times at the cost of 5.4% incremental space overhead.

An Exhaustive and Methodical Analysis of Literature Pertaining To Technigues for Managing Big Data within the Context of the Internet of Things

The merging of big data and the Internet of Things (IoT) has brought both exceptional difficulties and possibilities in data management. This study offers a thorough and methodical examination of the current literature on large data management approaches inside the Internet of Things framework. The study encompasses a broad spectrum of inquiry, ranging from fundamental notions to sophisticated approaches. The Internet of Things (IoT) is a powerful force that seeks to improve user experience and lifestyle. It incorporates several essential technologies including human-machine and machine-to machine communications, networking technologies, and sensor technologies. Fundamental to the success of the Internet of Things is the effective management of data transmitted through these technologies. This article explores the issues and challenges associated with data management in the context of the Internet of Things and examines different aspects of data, including its sources, collection processes, processing methods, and transmission devices. The article identifies and discusses problems arising from the need to process huge amounts of heterogeneous data in different systems. In relation to these issues, the logical and physical aspects of data management and communications networks are discussed. Additionally, the article takes an in-depth look at the data models used in IoT and explores data management, cleansing, and indexing techniques that take into account the unique characteristics of IoT data. The final sections of the paper comprehensively discuss the benefits and limitations associated with data management in IoT.

Optimizing Medical IoT Disaster Management with Data Compression

In today's technological landscape, the convergence of the Internet of Things (IoT) with various industries showcases the march of progress. This coming together involves combining diverse data streams from different sources and transmitting processed data in real-time. This empowers stakeholders to make quick and informed decisions, especially in areas like smart cities, healthcare, and industrial automation, where efficiency gains are evident. However, with this convergence comes a challenge – the large amount of data generated by IoT devices. This data overload makes processing and transmitting information efficiently a significant hurdle, potentially undermining the benefits of this union. To tackle this issue, we introduce "Beyond Orion," a novel lossless compression method designed to optimize data compression in IoT systems. The algorithm employs advanced techniques such as Lempel Ziv-Welch and Huffman encoding, while also integrating strategies like pipelining, parallelism, and serialization for greater efficiency and lower resource usage. Through rigorous experimentation, we demonstrate the effectiveness of Beyond Orion. The results show impressive data reduction, with up to 99% across various datasets, and compression factors as high as 7694.13. Comparative tests highlight the algorithm's prowess, achieving savings of 72% and compression factor of 3.53. These findings have significant implications. They promise improved data handling, more effective decision-making, and optimized resource allocation across a range of IoT applications. By addressing the challenge of data volume, Beyond Orion emerges as a significant advancement in IoT data management, marking a substantial step towards realizing the full potential of IoT technology.

IoT 엣지디바이스를 이용한 경량화 클라우드 컴퓨팅환경 구축 및 검증

This study validated a cloud computing environment using edge devices for Internet of Things (IoT) sensor data collection and communication. Lightweight cloud infrastructure enhanced resource efficiency, minimized latency, and optimized data storage and processing. Technical achievements encompass standardized data exchange, improved device scalability and flexibility through software technologies, real-time data updates via synchronization, and the creation of an API-based user interface for an intuitive dashboard. The study substantiates the efficient establishment and validation of a cloud computing environment using edge devices, providing practical solutions for IoT data management and processing.

Auditable Blockchain Rewriting in Permissioned Setting With Mandatory Revocability for IoT

The Internet of Things (IoT) connects everyday devices and generates real-time data that have greatly prompted business and life efficiency. The integration of IoT and blockchain has made IoT data management and storage more trustworthy. However, despite that the immutability property contributes a lot to the trustable reputation of blockchain-based IoT systems, from a data processing perspective, it is desired to achieve skillful and secure blockchain rewriting for scenarios such as device data sharing. Existing blockchain rewriting solutions usually rely on centralized modifiers where the rewriting power is difficult to control or withdraw. In this paper, we propose a new auditable redactable blockchain scheme named ACHR that supports self-management and mandatory revocation of the rewriting privilege. The scheme allows user devices to rewrite their blockchain transactions under strict auditing to ensure content security. To prevent centralization or rewriting power abuses, the revocation trapdoor can be computed compulsorily by an auditor when a redaction is published to the blockchain. We introduce a generic construction and an instantiation of the ACHR scheme for building the redactable blockchain and prove its security. We provide a prototype implementation to demonstrate that our scheme is effective and efficient compared to the traditional blockchain-IoT system with immutability.

FLIBD: A Federated Learning-Based IoT Big Data Management Approach for Privacy-Preserving over Apache Spark with FATE

In this study, we introduce FLIBD, a novel strategy for managing Internet of Things (IoT) Big Data, intricately designed to ensure privacy preservation across extensive system networks. By utilising Federated Learning (FL), Apache Spark, and Federated AI Technology Enabler (FATE), we skilfully investigated the complicated area of IoT data management while simultaneously reinforcing privacy across broad network configurations. Our FLIBD architecture was thoughtfully designed to safeguard data and model privacy through a synergistic integration of distributed model training and secure model consolidation. Notably, we delved into an in-depth examination of adversarial activities within federated learning contexts. The Federated Adversarial Attack for Multi-Task Learning (FAAMT) was thoroughly assessed, unmasking its proficiency in showcasing and exploiting vulnerabilities across various federated learning approaches. Moreover, we offer an incisive evaluation of numerous federated learning defence mechanisms, including Romoa and RFA, in the scope of the FAAMT. Utilising well-defined evaluation metrics and analytical processes, our study demonstrated a resilient framework suitable for managing IoT Big Data across widespread deployments, while concurrently presenting a solid contribution to the progression and discussion surrounding defensive methodologies within the federated learning and IoT areas.

N-Accesses: A Blockchain-Based Access Control Framework for Secure IoT Data Management.

With the rapid advancement of network communication and big data technologies, the Internet of Things (IoT) has permeated every facet of our lives. Meanwhile, the interconnected IoT devices have generated a substantial volume of data, which possess both economic and strategic value. However, owing to the inherently open nature of IoT environments and the limited capabilities and the distributed deployment of IoT devices, traditional access control methods fall short in addressing the challenges of secure IoT data management. On the one hand, the single point of failure issue is inevitable for the centralized access control schemes. On the other hand, most decentralized access control schemes still face problems such as token underutilization, the insecure distribution of user permissions, and inefficiency.This paper introduces a blockchain-based access control framework to address these challenges. Specifically, the proposed framework enables data owners to host their data and achieves user-defined lightweight data management. Additionally, through the strategic amalgamation of smart contracts and hash-chains, our access control scheme can limit the number of times (i.e., n-times access) a user can access the IoT data before the deadline. This also means that users can utilize their tokens multiple times (predefined by the data owner) within the deadline, thereby improving token utilization while ensuring strict access control. Furthermore, by leveraging the intrinsic characteristics of blockchain, our framework allows data owners to gain capabilities for auditing the access records of their data and verifying them. To empirically validate the effectiveness of our proposed framework and approach, we conducted extensive simulations, and the experimental results demonstrated the feasibility and efficiency of our solution.

QoS-Aware and Energy Data Management in Industrial IoT

Two crucial challenges in Industry 4.0 involve maintaining critical latency requirements for data access and ensuring efficient power consumption by field devices. Traditional centralized industrial networks that provide rudimentary data distribution capabilities may not be able to meet such stringent requirements. These requirements cannot be met later due to connection or node failures or extreme performance decadence. To address this problem, this paper focuses on resource-constrained networks of Internet of Things (IoT) systems, exploiting the presence of several more powerful nodes acting as distributed local data storage proxies for every IoT set. To increase the battery lifetime of the network, a number of nodes that are not included in data transmission or data storage are turned off. In this paper, we investigate the issue of maximizing network lifetime, and consider the restrictions on data access latency. For this purpose, data are cached distributively in proxy nodes, leading to a reduction in energy consumption and ultimately maximizing network lifetime. To address this problem, we introduce an energy-aware data management method (EDMM); with the goal of extending network lifetime, select IoT nodes are designated to save data distributively. Our proposed approach (1) makes sure that data access latency is underneath a specified threshold and (2) performs well with respect to network lifetime compared to an offline centralized heuristic algorithm.

Secure IoT data management and sharing architecture for information security using cryptographic technique

The massive amounts of data produced and gathered by smart devices through the internet support a wide range of applications, considerably improving our daily lives. Data sharing among smart devices must be safeguarded due to the sensitivity of the data involved in the transmission. The Internet of Things (IoT) environment must be protected from unauthorised access due to a variety of variables, including its attractiveness to cybercriminals, previous successful cyber-attacks, and consumers’ perceptions of security and reliability. Blockchain technology appears to be one promising technology that appears to address these security challenges extremely effectively. However, given the volume and rate at which smart devices generate data, Blockchain appears to be inefficient for storing it. The pace of data collection in the IoT context and the speed of transaction confirmation in the Blockchain network are the two key elements behind this. We connect the Blockchain and the Inter-Planetary File System (IPFS) in this study to permit data recording on a distributed storage and a mechanism to restrict access to recorded data to authorised organisations only. Over the Blockchain network, the access policy definition for safe data sharing and cryptographic hash content is stored. The real IoT-generated data, on the other hand, is collected via a distributed storage network, which improves availability and security. The proposed scheme’s analysis and performance evaluation show that it is secure and feasible. Furthermore, simulations are undertaken to assess the operating costs of smart contracts and to test the efficacy and viability of the suggested architecture.

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.

Blockchain integration with the internet of things for the employee performance management

As time went on, technological progress inevitably altered our daily routines. Many new technologies, such as the Internet of Things (IoT) and cryptocurrency, offer revolutionary possibilities. To put it simply, the blockchain is a distributed, public, and auditable database that can be used to record financial transactions. The IoT, or “Internet of Things,” is a system of interconnected electronic devices that can communicate with one another and be remotely monitored and handled. This paper reviews the most recent findings in the field of blockchain and Internet of Things with the goal of examining blockchain as a possible answer to secure IoT data management within supply networks. There is a dearth of literature in the early stages of both blockchain and IoT study because they are such novel topics. The study's findings suggest that in order to improve their leadership quality to intentionally impact employee performance, industry managers should pay attention to human resource management indicators like collaboration, involvement, actualization, perception, and teamwork. This is primarily because of the inherent limitations of IoT devices and the distributed ledger architecture of the blockchain technology. There is potential for IoT to provide many advantages if blockchain capabilities can be optimized for it.

Data Management in IoT: A Detailed Survey

This paper surveys the current state of data management in the Internet of Things (IoT). It begins by outlining the challenges and opportunities that data management in IoT presents. Firstly, data management deals with the technical challenges and solutions related to data management in IoT, including data acquisition, storage, and integration. The paper concludes with a set of recommendations for the development of effective data management strategies in the context of IoT. Secondly, the requirement of IoT for data management extends offline storage, query processing, and transaction management activities into online-offline communication and storage dual operations, and the idea of data management is broadened. This is accomplished by IPv6, as well as IoT-specific capabilities and protocols including CoAP, HTTP, and WebSocket. Users may track, monitor, and manage devices with Internet of Things (IoT) device management, ensuring that they operate effectively and securely after deployment. Finally, the paper discusses the various applications of IoT based on the concept of data management in IoT. Numerous more objects, including wearables, medical equipment, houses, cities, farms, industries, and workplaces, are being interacted with by billions of sensors. The IoT platforms assist in establishing and maintaining criteria to enhance and preserve data appropriately. The paper concludes with a set of recommendations for the development of effective data management strategies in the context of IoT. Smart gadgets automate processes so we may save time by controlling the environment. The most valuable data is protected by edge devices for data management, which also lowers bandwidth costs. These also offer excellent performance, data ownership, and cheap maintenance costs.

Smart City IoT Data Management with Proactive Middleware

With the increased emergence of cloud-based services, users are frequently perplexed as to which cloud service to use and whether it will be beneficial to them. The user must compare various services, which can be a time-consuming task if the user is unsure of what they might need for their application. This paper proposes a middleware solution for storing Internet of Things (IoT) data produced by various sensors, such as traffic, air quality, temperature, and so on, on multiple cloud service providers depending on the type of data. Standard cloud computing technologies become insufficient to handle the data as the volume of data generated by smart city devices grows. The middleware was created after a comparative study of various existing middleware. The middleware uses the concept of the federal cloud for the purpose of storing data. The middleware solution described in this paper makes it easier to distribute and classify IoT data to various cloud environments based on its type. The middleware was evaluated using a series of tests, which revealed its ability to properly manage smart city data across multiple cloud environments. Overall, this research contributes to the development of middleware solutions that can improve the management of IoT data in settings such as smart cities.

A comprehensive and systematic literature review on the big data management techniques in the internet of things

The Internet of Things (IoT) is a communication paradigm and a collection of heterogeneous interconnected devices. It produces large-scale distributed, and diverse data called big data. Big Data Management (BDM) in IoT is used for knowledge discovery and intelligent decision-making and is one of the most significant research challenges today. There are several mechanisms and technologies for BDM in IoT. This paper aims to study the important mechanisms in this area systematically. This paper studies articles published between 2016 and August 2022. Initially, 751 articles were identified, but a paper selection process reduced the number of articles to 110 significant studies. Four categories to study BDM mechanisms in IoT include BDM processes, BDM architectures/frameworks, quality attributes, and big data analytics types. Also, this paper represents a detailed comparison of the mechanisms in each category. Finally, the development challenges and open issues of BDM in IoT are discussed. As a result, predictive analysis and classification methods are used in many articles. On the other hand, some quality attributes such as confidentiality, accessibility, and sustainability are less considered. Also, none of the articles use key-value databases for data storage. This study can help researchers develop more effective BDM in IoT methods in a complex environment.

IoT-Chain and Monitoring-Chain Using Multilevel Blockchain for IoT Security.

In general, the Internet of Things (IoT) relies on centralized servers due to limited computing power and storage capacity. These server-based architectures have vulnerabilities such as DDoS attacks, single-point errors, and data forgery, and cannot guarantee stability and reliability. Blockchain technology can guarantee reliability and stability with a P2P network-based consensus algorithm and distributed ledger technology. However, it requires the high storage capacity of the existing blockchain and the computational power of the consensus algorithm. Therefore, blockchain nodes for IoT data management are maintained through an external cloud, an edge node. As a result, the vulnerability of the existing centralized structure cannot be guaranteed, and reliability cannot be guaranteed in the process of storing IoT data on the blockchain. In this paper, we propose a multi-level blockchain structure and consensus algorithm to solve the vulnerability. A multi-level blockchain operates on IoT devices, and there is an IoT chain layer that stores sensor data to ensure reliability. In addition, there is a hyperledger fabric-based monitoring chain layer that operates the access control for the metadata and data of the IoT chain to lighten the weight. We propose an export consensus method between the two blockchains, the Schnorr signature method, and a random-based lightweight consensus algorithm within the IoT-Chain. Experiments to measure the blockchain size, propagation time, consensus delay time, and transactions per second (TPS) were conducted using IoT. The blockchain did not exceed a certain size, and the delay time was reduced by 96% to 99% on average compared to the existing consensus algorithm. In the throughput tests, the maximum was 1701 TPS and the minimum was 1024 TPS.

Data reduction in fog computing and internet of things: A systematic literature survey

Cloud computing, the most crucial computing and storage tool in IoT (Internet of Things), still meets various challenges. The remoteness of IoT end devices from cloud platforms may lead to significant issues for real-time applications such as disaster handling, healthcare application, etc. In order to address these issues, fog computing is a new platform with specialized features that perform essential IoT data management and real-time application management tasks. Data management in IoT via fog computing is critical for decreasing latency in real-time IoT applications and is needed to produce more professional knowledge and smart decisions. Reduction in the size of data sent to the cloud layer is a fundamental topic in data management on the fog computing platform. In this paper, to select and survey studies about data size reduction in fog computing, we applied the Systematic Literature Review (SLR) process to comprehend and classify the different topics and related approaches in this field. In addition, the studies presented in the edge computing field, which were close to our goal, were also investigated. The primary purpose of this study is to classify and analyze the fog data reduction (FDR) studies published between 2016 and 2022. The topics and related approaches of selected papers are presented in an approach-based taxonomy. The topics of provided taxonomy include data filtering, data compression, data aggregation, data prediction, data pattern recognition, and general facets and related approaches. Finally, open issues for FDR and relevant main challenges are presented in upcoming research.

Real-Time Anomaly Detection for an ADMM-Based Optimal Transmission Frequency Management System for IoT Devices.

In this paper, we investigate different scenarios of anomaly detection on decentralised Internet of Things (IoT) applications. Specifically, an anomaly detector is devised to detect different types of anomalies for an IoT data management system, based on the decentralised alternating direction method of multipliers (ADMM), which was proposed in our previous work. The anomaly detector only requires limited information from the IoT system, and can be operated using both a mathematical-rule-based approach and the deep learning approach proposed in the paper. Our experimental results show that detection based on mathematical approach is simple to implement, but it also comes with lower detection accuracy (). In contrast, the deep-learning-enabled approach can easily achieve a higher detection accuracy () in the real world working environment.

PbCP: A profit-based cache placement scheme for next-generation IoT-based ICN networks

The large number of connected devices generating a massive amount of data in the emerging technologies such as the Internet of Things (IoT), will surpass the capabilities of the current Internet infrastructure. Therefore, it is essential to design a new communication paradigm to meet the demands and requirements of new applications. Information-Centric Networking (ICN) is a new candidate for future Internet architecture that aims at addressing different challenges in the currently used host-centric Internet. In ICN, the data/content is searched, routed, located, and retrieved through the name of the content rather than the address of data source. ICN also enables in-network caching where the intermediate nodes can cache the content. In-network caching helps in improving the network scalability and reduces the content retrieval delay. To date, multiple caching schemes have been designed for IoT data management in ICN; however, most of them suffer from scalability or ignore constraints on the IoT devices. Moreover, the placement of content in a large-scale environment needs further improvement and optimization since it impacts overall network performance. To fill the gaps, in this paper, we combine various metrics that affect caching performance. We formulate the content placement as an optimization problem and then propose a profit-based caching scheme that harnesses the benefits of the Tabu local search algorithm. The simulation results and comparative analysis show that the proposed strategy outperforms the existing state-of-art schemes, notably in terms of memory resources utilization, data transmission time, content diversity ratio, and cache replacement operations.

Internet of Things data management: A systematic literature review, vision, and future trends

SummaryThe Internet of Things (IoT) is expected to connect devices with unique identifiers over a network to create an equilibrium system with high speeds and volumes of data while presenting an interoperability challenge. The IoT data management system is indispensable for attaining effective and efficient performance because IoT sensors generate and collect large amounts of data used to express large data sets. IoT data management has been analyzed from various perspectives in numerous studies. In this study, a Systematic Literature Review (SLR) method was used to investigate the various topics and key areas that have recently emerged in IoT data management. This study aims to classify and evaluate studies published between 2015 and 2021 in IoT data management. Therefore, the classification of studies includes five categories, data processing, data smartness application, data collection, data security, and data storage. Then, studies in each field are compared based on the proposed classification. Each study investigates novel findings, simulation/implementation, data set, application domain, experimental results, advantages, and disadvantages. In addition, the criteria for evaluating selected articles for each domain of IoT data management are examined. Big data accounts for the highest percentage of data processing fields in IoT data management, at 34%. In addition, fast data processing, distributed data, artificial intelligence data with 22%, and data uncertainty analysis account for 11% of the data processing field. Finally, studies highlight the challenges of IoT data management and its future directions.