Internet Of Things Data Storage Research Articles

A secure framework for effective workload resource management

An efficient and dynamic role-based access-control (RBAC) model is presented in this work which utilizes access-control for internet of things (IoT) nodes while minimizing storage and computational overhead. Also, for the identification of the malicious packets at the gateway server, a machine learning method has been presented. In addition, a framework for data management techniques in the IoT environment is designed to ensure efficient and secure storage, management, and processing of IoT data. The results have been evaluated by using the Montage and Cybershake workload in terms of energy consumption, processing time, detection accuracy and misclassification rate. The results show that the proposed secure framework for effective workload resource management (SFE-WRM) attains better performance in comparison to the reliable and energy‐efficient route selection (REERS) and FTA-WRM method. Also, by using the security method, the proposed method provides better security to the IoT nodes during the data aggregation and processing of the workload. The ultimate aim of this work is to provide a solution for the development of a secure and efficient IoT environment that can address critical security challenges and enable the widespread adoption of IoT devices and services.

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.

S-BDS: An Effective Blockchain-based Data Storage Scheme in Zero-Trust IoT

With the development of the Internet of Things (IoT) , a large-scale, heterogeneous, and dynamic distributed network has been formed among IoT devices. There is an extreme need to establish a trust mechanism between devices, and blockchain can provide a zero-trust security framework for IoT. However, the efficiency of the blockchain is far from meeting the application requirements of the IoT, which has become the biggest resistance to the application of the blockchain in the IoT. Therefore, this paper combines sharding to build an effective Blockchain-based IoT data storage scheme (S-BDS) . Sharding can solve the problem of blockchain capacity and scalability. While the blockchain provides data immutability and traceability for the IoT, it also brings huge demands for data credibility verification. The communication delay in the IoT system seriously affects the security of the system, while the Merkle proof of traditional blockchain occupies a lot of communication resources. This paper constructs Insertable Vector Commitment (IVC) in the bilinear group and replaces the Merkle tree with IVC to store IoT data in the blockchain. The construct has small-sized proof. It also has the ability to record the number of updates, which can prevent replay-attacks. Experiments show that each block processes 1,000 transactions, the proof size of a single data piece is 30% of the original scheme, and proofs from different shards can be aggregated. IVC can effectively reduce communication congestion and improve the stability and security of the IoT system.

SM2-Based Offline/Online Efficient Data Integrity Verification Scheme for Multiple Application Scenarios.

With the rapid development of cloud storage and cloud computing technology, users tend to store data in the cloud for more convenient services. In order to ensure the integrity of cloud data, scholars have proposed cloud data integrity verification schemes to protect users' data security. The storage environment of the Internet of Things, in terms of big data and medical big data, demonstrates a stronger demand for data integrity verification schemes, but at the same time, the comprehensive function of data integrity verification schemes is required to be higher. Existing data integrity verification schemes are mostly applied in the cloud storage environment but cannot successfully be applied to the environment of the Internet of Things in the context of big data storage and medical big data storage. To solve this problem when combined with the characteristics and requirements of Internet of Things data storage and medical data storage, we designed an SM2-based offline/online efficient data integrity verification scheme. The resulting scheme uses the SM4 block cryptography algorithm to protect the privacy of the data content and uses a dynamic hash table to realize the dynamic updating of data. Based on the SM2 signature algorithm, the scheme can also realize offline tag generation and batch audits, reducing the computational burden of users. In security proof and efficiency analysis, the scheme has proven to be safe and efficient and can be used in a variety of application scenarios.

Blockchain Data Scalability and Retrieval Scheme Based on On-Chain Storage Medium for Internet of Things Data

The combination of blockchain and internet of things (IoT) technology realizes reliable storage of IoT data. However, the data stored on the blockchain (on-chain) face the problem of poor scalability and inefficient retrieval. In this paper, the on-chain data scalability schemes based on transactions and smart contracts are first proposed. Subsequently, on the basis of the above on-chain data scalability scheme based on transactions, an on-chain data index based on skip lists is proposed to improve the retrieval efficiency. The experimental results show that both the on-chain data scalability schemes achieve on-chain data scalability while reducing storage overhead. Meanwhile, the on-chain data index based on skip lists has significantly improved dynamic range retrieval efficiency and reduced the time complexity of single data retrieval to O(log(n)).

A Review on Fog Computing: Research Challenges and Future Directions

The traditional centralized cloud computing paradigm confronts several problems, including a lack of capacity, high latency, and network failure, due to the rapid expansion of IoT (Internet of Things) applications. Fog Computing brings cloud computing and IoT devices closer to addressing these issues. Instead of transferring IoT data to the cloud, the Fog allows local processing and storage of IoT data in IoT devices. Fog Computing offers services with a quicker response time and higher quality than the cloud. Therefore, Fog Computing may be considered the greatest option for enabling the IoT to provide efficient and secure services to many IoT users. In this paper, we define the term "Fog Computing," examine its architecture and list its features. We also talk about other related work and emphasize it.

Agricultural IoT Data Storage Optimization and Information Security Method Based on Blockchain

Given the issues of low efficiency of agricultural Internet of Things (IoT) data collection and data storage security, this study proposes a fast and reliable storage method for IoT data based on blockchain. Firstly, it performs RC5 encryption for data in the IoT sensor module. Secondly, it aggregates the same batch of collected data in the gateway into a transaction and reconstructs the Merkle ordered tree to verify the data integrity. Finally, it modifies the configuration rules of blockchain to improve the efficiency of blockchain data storage. Compared with experimental results for hash values of blockchain storage data and the stored data itself in the blockchain, the proposed method has significant advantages in data writing, and its efficiency in data reading was nearly 10 times higher than the other methods. At the same time, the method has the advantages of confidentiality, integrity, availability, controllability and non-repudiation of information security. The study can provide a solution for efficient collection and secure storage of agricultural IoT data, and it can provide technical support for realizing decentralized agricultural IoT data collection.

Detection and Prevention of DDoS Attacks on the IoT

The Internet of Things (IoT) system has been a hot topic in recent years. Its operation is a system that stores data in data storage and is completed by the exchange of network information about things. Therefore, the security of information between network transmissions is very important. In recent years, the most likely cause of information security problems has been a distributed denial of service (DDoS) attack. In this paper, we proposed an autonomous defense system that combines edge computing with a two-dimensional convolutional neural network (CNN) to recognize whether the data server in IoT suffers from DDoS attacks and identify the attack mode. The accuracy of trained two-dimensional CNN is up to 99.5% and 99.8% for packet traffic and packet features training, respectively. A field experiment’s results show that the data server in the proposed system can effectively distinguish the difference between the DDoS attacks and the normal transmission to reduce the impact of DDoS attacks on the IoT data storage while it is under attack.

Efficient Blockchain Scheme for IoT Data Storage and Manipulation in Smart City Environment

We describe an implementation of a data storage and manipulation system for the Internet of Things (IoT) networks based on blockchain and using a Practical Byzantine Fault Tolerance (PBFT)-like protocol to achieve consensus. In our proposed model, blockchain ledger consistency is ensured by leaders contending for the next available spot(s) in which the newly accepted blocks will be appended to the blockchain. The contention takes place on a shared overlay accessible to all leaders nodes. We use a multiple-entry PBFT to eliminate the need for a dedicated leader node and the delays involved in changing leaders in different rounds. Our model allowed the multiple leaders to work in parallel to propose blocks using dedicated overlay networks. The performance of the proposed scheme is analyzed through an analytical model that employs a discrete-time Markov chain and M/G/1 queuing model with exhaustive limited service. The results obtained from the model confirm the efficiency of the proposed model in a wide range of parameter values and show that the proposed model outperforms the multiple entry points of the PBFT protocol with single block insertion.

Empowering secure transmission for downlink of multiple access system relying non-orthogonal signal multiplexing

The growth of internet-of-things (IoT) inspired use cases in different run of the mill environments such as cities, industries, healthcare, agriculture, and transportation, has led to a greater desire for safer IoT data gathering and storage. However, securing IoT is challenging due to form-factor, complexity, energy, and connectivity limitations. Conventional coding-based security techniques are unsuitable for ultra-reliable low-latency and energy-efficient communication in IoT. Numerous research studies on physical layer security (PLS) techniques for fifth generation (5G) have emerged recently, but not all of the solutions can be used in IoT networks due to complexity limitations. Non-orthogonal multiple access (NOMA) is billed as a possible technology to solve connectivity and latency requirements in IoT. In this study, we exploit the power allocation characteristics of NOMA to enhance security in a downlink deviceto-device (D2D) decode and forward (DF) IoT network infiltrated by an eavesdropper. Our performance metric of choice is the secrecy outage probability (SOP). We formulate exact SOP results for different users. Simulation results demonstrate the positive impact of NOMA on SOP in a D2D IoT-NOMA network.

Analysis of dimensionality reduction techniques on Internet of Things data using machine learning

Internet of Things (IoT), being a global infrastructure, aims to improve the quality of our lives. The potential applicability of IoT to almost every field of life, gives rise to an increased adoption of IoT solutions across the world. This increase in the number of IoT solutions leads to generation of large amount of data. Collecting, communicating and extracting useful information from this huge amount of data is a challenging task. Some effective methods are, therefore, required to handle and analyze IoT data, in order to extract some meaningful information. Dimension reduction is a method which retains only the meaningful features of the data and helps to have reduced data with only the necessary information. This study aims to identify the influence of dimension reduction on IoT data in terms of storage, and communication cost. it also analyses how dimensionality reduction of IoT data affects the performance of different classification algorithms applied to it. It has been found that dimension reduction helps to reduce the storage and communication costs of IoT data, while reducing the performance of classification algorithms applied to dimensionally reduced data. However, this reduction in performance is negligible as compared to storage and communication cost optimization.

A Study on Information Classification and Storage in Cloud Computing Data Centers Based on Group Collaborative Intelligent Clustering

Internet of things (IoT) and cloud computing are combined to form a cloud computing data center, and cloud computing provides virtualization, storage, computing, and other support services for IoT applications. Data is the foundation and core of cloud IoT platform applications, and massive multisource heterogeneous IoT data aggregation and storage have basic requirements such as real-time, security, and scalability. This paper focuses on the aggregation and storage methods of massive heterogeneous cloud IoT data, solving the multisource data aggregation problem caused by inconsistent protocols and the heterogeneous data storage problem caused by inconsistent data types. A heterogeneous network protocol adaptation and data aggregation method is proposed for the multisource data aggregation problem caused by protocol inconsistency. A protocol adaptation layer is set up in the IoT virtual gateway to achieve compatibility with multiple types of data aggregation protocols, ensuring adaptive access to different types of IoT nodes, and on this basis, data is transmitted to the cloud IoT platform through a unified interface, shielding the variability of IoT sensing devices. Given the problems of device forgery and malicious tampering in the data aggregation process, we propose a fast authentication and data storage method for IoT devices based on “API key” and implant the device authentication API key into the protocol adaptation layer of the virtual gateway to realize the source authentication of IoT nodes and ensure the authenticity of data.

ZT-BDS: A Secure Blockchain-based Zero-trust Data Storage Scheme in 6G Edge IoT

<p>With the rapid development of 6G communication technology, data security of the Internet of Things (IoT) has become a key challenge. This paper first analyzes the security issues and risks of IoT data storage in 6G, and then constructs a blockchain-based zero-trust data storage scheme (ZT-BDS) in 6G edge IoT to ensure data security. Under this framework, an improved scratch-off puzzle based on Proof of Recoverability (PoR) is firstly constructed to realize distributed IoT data storage, which can reduce resource consumption compared with other existing schemes. Secondly, the accumulator is used to replace the Merkle trees to store IoT data in the blockchain. Since the accumulator can provide not only membership proof, but also non-membership proof, the proposed blockchain-based data storage scheme is more secure. Thirdly, PoW is replaced by an improved PoR scheme as the consensus protocol. On the one hand, PoR can verify the integrity of data, which will further enhance the security of IoT data; on the other hand, the proposed PoR is composed of polynomial commitment, which can reduce bandwidth with the aid of the aggregation function of polynomial commitment. Experimental comparisons show that our scheme has better bandwidth and storage capacity.</p> <p> </p>

Load Balancing Cloud Storage Data Distribution Strategy of Internet of Things Terminal Nodes considering Access Cost.

With the rapid development of Internet of Things (IoT) technology, IoT terminal nodes are facing many challenges in data storage, distribution, and data management. In particular, in the IoT terminal nodes considering access cost, the corresponding data distribution and storage are professional, complex, and miscellaneous. Based on the abovementioned current situation, this article innovatively proposes a complex sensor data placement algorithm based on the cloud storage distribution of IoT terminal nodes. Under this algorithm, the accurate division of IoT data I/O methods is realized through reasonable configuration. Through the adaptive sensing algorithm, while fully considering the access cost of the algorithm, the performance of the IoT data storage system is further optimized. In the corresponding terminal node load balancing problem, this article innovatively proposes the terminal node data sorting and distribution algorithm through the node data. The sorting and distribution algorithm realizes the precise segmentation of the IoT data to be processed, thereby realizing the improvement of data reading and processing speed. Based on the proposed algorithm, this article designs a load balancing cloud storage data distribution optimization system of IoT terminal nodes considering access cost and carries out experimental verification in a real environment. The experimental results show that the data pattern division accuracy corresponding to the proposed distribution strategy is improved to 97.13% and the corresponding data access efficiency is improved to 98.3%, compared with the traditional distribution strategy. Therefore, the data distribution strategy proposed in this article has obvious performance advantages and further promotion value.

Internet of Thing Based Confidential Healthcare Data Storage, Access Control and Monitoring Using Blockchain Technique

Internet of Things plays a significant role in multiple sectors like agriculture, manufacturing and healthcare for collecting information to automation. The collected information is in different diversity and consists of confidential and non-confidential information. Secure handling of confidential data is a crucial task in cloud computing like storage, access control and monitoring. The blockchain based storage technique provides immutable data storage, efficient access control and dynamic monitoring to confidential data. Thus, the secure internet of things data storage, access control and monitoring using blockchain technique is proposed in this work. The patients health information that are in different formats are pruned by a decision tree algorithm and it classifies the confidential data and non-confidential data by the fuzzy rule classification technique. Depending on data owner's willing, the fuzzy rule is framed and the confidential and non-confidential data collected by internet of things sensors are classified. To provide confidentiality to confidential data, Attribute Based Encryption is applied to confidential data and stored in an off-chain mode of blockchain instead of entire data encryption and storage. The non-confidential data is stored in a plaintext form in cloud storage. When compared to support vector machine, K-nearest neighbor and Naive Bayes classification techniques, the proposed fuzzy rule based confidential data identification produces greater than 96 % of accuracy based on data owner willing and confidential data storage takes lesser than 20 % of storage space and processing time in an entire data storage. Additionally, the blockchain performances like throughput, network scalability and latency is optimized through minimal block size and transactions. Thus, our experimental results show that the proposed blockchain based internet of things data storage, access control and monitoring technique provides better confidentiality and access control to confidential data than the conventional cloud storage technique with lesser processing time.

Energy‐efficient fog computing in Internet of Things based on Routing Protocol for Low‐Power and Lossy Network with Contiki

SummaryThe traditional centralized cloud computing (CC) model faces a range of problems with the exponential growth of the Internet of Things (IoT) applications, like high latency, reduced bandwidth, and network instability. Fog computing (FC) takes the cloud closer to IoT computers to overcome these problems. Rather than moving them to the cloud, the FC provides local IoT data processing and storage on IoT computers. This paper focuses on the Routing Protocol for Low‐Power and Lossy Network (RPL), a universally routing protocol for a static environment that is used for reducing energy consumption, delay, and packet loss with data aggregation at the border router using a fog simulation model in Contiki Cooja. The objective function (OF) choice has an impact on network topology, as each node selects a set of potential parents to send to the destination. However, no systematic analysis of the effects of OF behavior in the RPL environment has been undertaken. Here, three different OFs, Objective Function Zero (OF0), Advanced Objective Function Zero (AOF0), and Minimum Rank with Hysteresis Objective Function (MRHOF) for RPL in the static environment for different node numbers, have been compared. The findings demonstrate that altering all three OFs has a significant impact on RPL. The energy consumption is reduced in the case of the AOF0 in the fog node by 50.86%, which is less than the case of the OF0 and MRHOF function. Extensive simulations show that AOF0 outperforms the existing OFs.

Fair multi-owner search over encrypted data with forward and backward privacy in cloud-assisted Internet of Things

The trend towards the Internet of Things (IoT) technology promises the growth of home and organizational automation in the world. Due to the large amount of IoT data stored in the semi-trust cloud server, the IoT is vulnerable to cyberattacks. Therefore, the privacy violation of outsourced data is an obstacle to efficient IoT data storage. Searchable encryption is a promising solution for secure storage and selective data retrieval. However, achieving desired privacy, performance, and attributes such as dynamics, access control, scalability, and fair arbitration is still a challenge in searchable encryption schemes. In this paper, we propose a fair and dynamic multi-owner searchable encryption scheme wherein dynamics does not leak the sensitive information. In the proposed scheme, the verifiability and fair arbitration, respectively, prevent the malicious cloud servers and malicious users from having any motivation to disrupt the accuracy of the exchanged data. Generating a single trapdoor corresponding to the conjunctive keyword query, a client can search over all documents stored in the cloud, the owner of which has allowed the client to access. We also prove that our scheme is secure against the keyword guessing attack in the standard model. Finally, evaluating the performance of the proposed scheme on a real dataset, we demonstrate its efficiency as well.

Research on the Construction of Intelligent Fire Protection Virtual Simulation Teaching Platform Based on Internet of Things

Aiming at the teaching challenge of cultivating integrated talents of Internet of things worldwide, and combining with the social background of fire rescue and management problems, We propose a virtual simulation teaching platform for the Internet of Things under the background of intelligent fire protection application. Based on the overall structure of the Internet of Things, different fire scenarios that cannot be simulated in real life are constructed through virtual simulation technology. OMNet++ technology is adopted to carry out virtual deployment of fire nodes for the overall structure of the building, and experiments such as cluster routing simulation, communication transmission simulation and wireless sensor node data acquisition simulation are designed. Meanwhile, a 3D fire data model is established using big data to simulate the best fire extinguishing scheme and the best escape strategy. From aspects of the Internet of Things system design and development, sensor principle and application, Internet of Things communication technology and Internet of Things data storage and application, we have realized the efficiency, innovation and challenge of the Internet of Things teaching.The virtual simulation teaching platform we built has been deployed and put into practical teaching, which has received positive response from students and achieved excellent teaching effect.

A Blockchain-Based Framework for IoT Data Monetization Services

Abstract Within internet of things (IoT) research, there is a growing interest in leveraging the decentralization properties of blockchains, towards developing IoT authentication and authorization mechanisms that do not inherently require centralized third-party intermediaries. This paper presents a framework for sharing IoT data in a decentralized and private-by-design manner in exchange for monetary services. The framework is built on a tiered blockchain architecture, along with InterPlanetary File System for IoT data storage and transfer. The goal is to enable IoT data users to exercise fine-grained control on how much data they share with entities authenticated through blockchains. To highlight how the framework would be used in real-life scenarios, this paper presents two use cases, namely an IoT data marketplace and a decentralized connected vehicle insurance. These examples showcase how the proposed framework can be used for varying smart contract-based applications involving exchanges of IoT data and cryptocurrency. Following the discussion about the use cases, the paper outlines a detailed security analysis performed on the proposed framework, based on multiple attack scenarios. Finally, it presents and discusses extensive evaluations, in terms of various performance metrics obtained from a real-world implementation.

A Distributed framework for detecting DDoS attacks in smart contract‐based Blockchain‐IoT Systems by leveraging Fog computing

AbstractWith the advancement of blockchain technology, and the proliferation of Internet of things (IoT)‐driven devices, the blockchain‐IoT applications is changing the perception and working infrastructure of smart networks. Blockchain supports decentralized architecture and provides secure management, authentication, and access to IoT systems by deploying smart contracts provided by Ethereum. The growing demand and expansion of blockchain‐IoT systems is generating large volume of sensitive data. Moreover, distributed denial‐of‐service (DDoS) attacks are the most challenging threats to smart contracts in blockchain‐IoT systems. The 2016 decentralized autonomous organization and 2017 parity wallet attacks exposed the critical fault‐lines among Ethereum smart contracts. Currently, there is no security mechanism available for smart contracts after its deployment in blockchain‐IoT systems. In order to address these challenges, first we use two artificial intelligence techniques, random forest (RF) and XGBoost that gives full autonomy in decision making capabilities in the proposed security framework. Second, for data load balancing and distributed file storage of IoT data, interplanetary file system is suggested. Finally, we are the first to propose a distributed framework based on fog computing to detect DDoS attacks in smart contracts. The performance of the detection system is evaluated using actual IoT dataset, namely, BoT‐IoT. The proposed system is evaluated in terms of accuracy (AC), detection rate (DR), and false alarm rate (FAR). The results confirms the superiority of the proposed framework over some of the recent state‐of‐art techniques in detecting rare attacks. The proposed framework has achieved DR up to 99.99% using RF by using 10 features of BoT‐IoT dataset.