Network Storage Research Articles

Research on energy coordinated control strategy for offshore wind power based on distributed consistency algorithm

Offshore wind power is clean and has a large capacity, making it an important component of the development of new power systems. However, the randomness of offshore wind power affects the stability of the power grid. A coordinated control strategy of offshore wind power and multi-source energy based on a distributed consistency algorithm is proposed for offshore wind farms’ energy coordination management and optimization. By constructing a source network storage load model and analyzing the output and load fluctuations of offshore wind power, energy storage, and power grid, the constraint equation and balance equation of source network load storage power are derived. An adaptive distributed consistency methodology is employed based on a proportional utilization micro growth function to preserve the stability of offshore wind power and the power grid. It can enhance the power distribution of offshore wind power, energy storage, and distribution networks. The outcomes of the simulation show that the suggested approach may quickly equalize power, decrease frequency variations of offshore wind power, and enhance the commercial viability of offshore wind power.

TinyML Algorithms for Big Data Management in Large-Scale IoT Systems

In the context of the Internet of Things (IoT), Tiny Machine Learning (TinyML) and Big Data, enhanced by Edge Artificial Intelligence, are essential for effectively managing the extensive data produced by numerous connected devices. Our study introduces a set of TinyML algorithms designed and developed to improve Big Data management in large-scale IoT systems. These algorithms, named TinyCleanEDF, EdgeClusterML, CompressEdgeML, CacheEdgeML, and TinyHybridSenseQ, operate together to enhance data processing, storage, and quality control in IoT networks, utilizing the capabilities of Edge AI. In particular, TinyCleanEDF applies federated learning for Edge-based data cleaning and anomaly detection. EdgeClusterML combines reinforcement learning with self-organizing maps for effective data clustering. CompressEdgeML uses neural networks for adaptive data compression. CacheEdgeML employs predictive analytics for smart data caching, and TinyHybridSenseQ concentrates on data quality evaluation and hybrid storage strategies. Our experimental evaluation of the proposed techniques includes executing all the algorithms in various numbers of Raspberry Pi devices ranging from one to ten. The experimental results are promising as we outperform similar methods across various evaluation metrics. Ultimately, we anticipate that the proposed algorithms offer a comprehensive and efficient approach to managing the complexities of IoT, Big Data, and Edge AI.

Optimal scheduling of electric-gas-heat system considering dual virtual energy storage of gas network management and heat network storage

Aiming at the problem of wind curtailment caused by the lack of system flexibility, an optimal scheduling strategy for improving the flexibility of the electricity-gas-heat interconnection system by the coordinated operation of the gas network management and storage characteristics and the heat storage characteristics of the heat network is proposed. Firstly, the influence of gas network storage and heat network storage on improving flexibility is analyzed respectively. Then, an electricity-gas-heat interconnected system scheduling model considering the dynamic characteristics of gas network management and t the delay characteristics of heat storage in heat network, which greatly improves the imbalance between supply and demand of flexibility in space and time brought by the anti-peak regulation characteristics of wind power to the system. Finally, the IEEE-24 nodes power system, Belgium’s 20-node natural gas network and 6-node thermal network are used, for example, analysis. The results show that the proposed scheduling scheme improves the flexibility of the system while its operating cost and wind curtailment cost are the lowest, which promotes the consumption of wind power.

Rigidity with Flexibility: Porous Triptycene Networks for Enhancing Methane Storage.

In the pursuit of advancing materials for methane storage, a critical consideration arises given the prominence of natural gas (NG) as a clean transportation fuel, which holds substantial potential for alleviating the strain on both energy resources and the environment in the forthcoming decade. In this context, a novel approach is undertaken, employing the rigid triptycene as a foundational building block. This strategy is coupled with the incorporation of dichloromethane and 1,3-dichloropropane, serving as rigid and flexible linkers, respectively. This combination not only enables cost-effective fabrication but also expedites the creation of two distinct triptycene-based hypercrosslinked polymers (HCPs), identified as PTN-70 and PTN-71. Surprisingly, despite PTN-71 manifesting an inferior Brunauer-Emmett-Teller (BET) surface area when compared to the rigidly linked PTN-70, it showcases remarkably enhanced methane adsorption capabilities, particularly under high-pressure conditions. At a temperature of 275 K and a pressure of 95 bars, PTN-71 demonstrates an impressive methane adsorption capacity of 329 cm3 g-1. This exceptional performance is attributed to the unique flexible network structure of PTN-71, which exhibits a pronounced swelling response when subjected to elevated pressure conditions, thus elucidating its superior methane adsorption characteristics. The development of these advanced materials not only signifies a significant stride in the realm of methane storage but also underscores the importance of tailoring the structural attributes of hypercrosslinked polymers for optimized gas adsorption performance.

Developing Decentralized Data Storage Network Using Blockchain Technology to Prevent Data Alteration

In the face of escalating global data exchange, the pronounced vulnerability oftraditional centralized storage networks to manipulation and attacks poses a pressing challenge. Digital service providers, entrusted with vast datasets, grapple with the formidable task of ensuring the security, integrity, and continuous availability of their stored information. This paper tackles these multifaceted issues by proposing a decentralized data storage network empowered by blockchain technology. This approach systematically mitigates the inherent susceptibilities of centralized systems, thereby providing heightened resilience against unauthorized alterations and malicious attacks that compromise digital information integrity. Moreover, the decentralized model holds significant promise for securing public data. By leveraging the transparency and immutability of blockchain ledgers, this approach not only safeguards against unauthorized access but also actively fosters transparency and accountability in data management. This makes it particularly well-suited for ensuring the security and integrity of public data, addressing concerns related to trust and reliability in the ever-evolving landscape of information exchange.

Efficient and Secure Data Storage for Future Networks: Review and Future Opportunities

Efficient and Secure Data Storage for Future Networks: Review and Future Opportunities

Enhanced Privacy Preservation and Data Storage in Blockchain-Based Electric Vehicle Network

Electric and autonomous vehicles (EV and AV) are spreading quickly on the road and producing extensive amounts of data that must be stored and retrieved securely. The ultimate goal of collecting these data is to feed machine learning models and make predictions to improve the traffic system, increase road safety, and save lives. Existing systems impose the problem of privacy leakage, storage space, and operating cost. They are causing panic among data owners and restricting them from sharing their data. This work presents a proposed solution that utilizes blockchain technology to tackle the aforementioned challenges. The proposed approach incorporates differential privacy and data compression methods to address privacy leaks and data storage space difficulties. Additionally, it aims to enhance the resistance of machine learning models against privacy attacks. By leveraging the features of blockchain, data stored on the blockchain becomes temper proof and protected against a single point of failure. Simulation results and numerical analysis show that the proposed methodology reduces data storage space requirements and improves privacy preservation.

Energy Management Approach to Battery Energy Storage in Unbalanced Distribution Networks

Energy Management Approach to Battery Energy Storage in Unbalanced Distribution Networks

Low Complexity and Mobility-aware Robust Radio, Storage, Computing, and Cost Management for Cellular Vehicular Networks

Low Complexity and Mobility-aware Robust Radio, Storage, Computing, and Cost Management for Cellular Vehicular Networks

Design and application of airport data storage under an information security optimization system

Abstract With the advancement of technology, the “partial construction” model is no longer able to meet the current information requirements of the aviation industry. Therefore, it is necessary to establish a complete “global informatization”, that is, an airport data center, which comprehensively includes multiple production management systems within the airport group, achieves information sharing, data analysis, and intelligent decision-making, thereby enabling the airport to achieve more accurate and reliable comprehensive economic and socially sustainable development. The current network storage technology often generates massive amounts of data when processing it, which leads to an increase in storage cycles and a decrease in storage performance. On this basis, the FPGA interface in FLASH chips was studied and analyzed in detail. On this basis, the FPGA receiving module is used to accurately receive and filter the massive data obtained, and convert it into high-quality FLASH format for daily use and maintenance. By analyzing the three-layer architecture, a large-scale network data storage solution based on the three-layer architecture is proposed. Finally, an implementation scheme for a new distributed storage system based on this model is presented, and simulation results are provided.

Chunk2vec: A novel resemblance detection scheme based on Sentence‐BERT for post‐deduplication delta compression in network transmission

AbstractDelta compression, as a complementary technique for data deduplication, has gained widespread attention in network storage systems. It can eliminate redundant data between non‐duplicate but similar chunks that cannot be identified by data deduplication. The network transmission overhead between servers and clients can be greatly reduced by using data deduplication and delta compression techniques. Resemblance detection is a technique that identifies similar chunks for post‐deduplication delta compression in network storage systems. The existing resemblance detection approaches fail to detect similar chunks with arbitrary similarity by setting a similarity threshold, which can be suboptimal. In this paper, the authors propose Chunk2vec, a resemblance detection scheme for delta compression that utilizes deep learning techniques and Approximate Nearest Neighbour Search technique to detect similar chunks with any given similarity range. Chunk2vec uses a deep neural network, Sentence‐BERT, to extract an approximate feature vector for each chunk while preserving its similarity with other chunks. The experimental results on five real‐world datasets indicate that Chunk2vec improves the accuracy of resemblance detection for delta compression and achieves higher compression ratio than the state‐of‐the‐art resemblance detection technique.

BFT-DSN: A Byzantine Fault Tolerant Decentralized Storage Network

BFT-DSN: A Byzantine Fault Tolerant Decentralized Storage Network

GRU-based digital twin framework for data allocation and storage in IoT-enabled smart home networks

In recent years, the Internet of Things (IoT) devices utilization with Information Communication Technology (ICT) has grown exponentially in various Smart City applications, including Smart Homes, Smart Enterprises, and others. The fusion of IoT, ICT, and Smart Home delivers interactive solutions to reduce costs and resource consumption, enhance performance, and engage people's needs more virtually and proactively. A smart home has numerous advantages with the integration of emerging advanced technologies. Big data, centralization, data and resource allocation, security, and privacy issues persist as challenges in IoT-enabled Smart Home Networks. To address these challenges, in this paper, we propose a GRU-based Digital Twin Framework for Data Allocation in IoT-enabled Smart Home Networks. Data and resource allocation of smart home applications are completed at the virtual twin layer using Gated Recurrent Unit (GRU)-based Digital Twin Networks. Low-priority data is stored and processed at the Macro-based Stations (MBSs), and high-priority data is transferred to the upper (Security) layer for authentication and validation. Blockchain-based distributed networks are utilized for Smart Home Data authentication at the security layer with a Proof of Authentication (PoAh) Consensus Algorithm; Data is stored at the cloud layer after validation. The validation results of the proposed framework demonstrate superior performance as the quantitative analysis with accuracy 0.9412, Root Mean Square Error (RMSE) 0.0588 for IoT-enable Smart Home compared to existing works as LSTM-based Digital Twin network and provide a secure environment in IoT-enabled Smart Home.

Designing a sustainable vaccine supply chain by considering demand substitution and value-added function during a pandemic outbreak

The severe social, economic, and health detriments caused by pandemics (such as COVID-19) lead governments toward exploring survivability challenges. In this regard, some countries produce national vaccines along with receiving global medical supplies, which necessitates the design of a sustainable vaccine supply chain and the evaluation of value-added gained by each vaccine application. Given each vaccine possesses specific efficacy and expiration time, this study addresses products’ brand-based efficacy and perishability derived from existing literature. Additionally, shortages are inevitable in a chaotic pandemic situation as a consequence of limitations in the procurement, distribution, and storage networks of vaccines. Therefore, we investigate the effect of demand substitution based on vaccine efficacy to reduce the unsatisfied demand and combat the prevalence of infection. Moreover, this study implements mobile vaccination centers as well as permanent centers (for instance hospitals and clinics) to relax the personal and geographical constraints of different segments of society and expedite the vaccination. To optimize the multi-objective mathematical model, the augmented ε-constraint method has been utilized, and a real case study is provided to assess the application of the proposed model. The numerical results and sensitivity analysis show the proper utilization of vaccination centers’ combination in our proposed model, also 60% of shortages are compensated by substitutable products, which leads to expeditious immunization.

An optimal allocation model for distributed energy storage in the distribution network based on probabilistic power flow

An increasing number of renewable energy resources (RES) are connected to the distribution network, including wind power and photovoltaic. The volatility and randomness of its power output have brought some problems, including that the voltage exceeds the upper limit when its output is large, and the local power consumption is small. The use of distributed energy storage in the distribution network can effectively alleviate those problems. This paper proposes a method of distributed energy storage capacity allocation for the distribution network, based on probabilistic power flow (PPF). This capacity allocation based on PPF can more accurately describe the impact of the randomness of RES power output. Its validity and correctness are verified based on the improved RBTS-bus 6 model.

Concise extraction and characterization of the pore-throat network in unconventional hydrocarbon reservoirs: A new perspective

In this study, a new image-based method for the extraction and characterization of pore-throat network for unconventional hydrocarbon storage and exploitation is proposed. “Pore-throat solidity”, which is analogous to particle solidity, and a new method for automatic identification of pores and throats in tight sandstone oil reservoirs are introduced. Additionally, the “pore-throat combination” and “pure pore” are defined and distinguished by drawing the cumulative probability curve of the pore-throat solidity and by selecting an appropriate cutoff point. When the discrete grid set is recognized as a pore-throat combination, Legendre ellipse fitting and minimum Feret diameter are used. When the pore and throat grid sets are identified as pure pores, the pore diameter can be directly calculated. Using the new method, the analytical results for the physical parameters and pore radius agree well with most prior studies. The results comparing the maximum ball and the new model could also prove the accuracy of the latter's in micro and nano scales. The new model provides a more practical theoretical basis and a new calculation method for the rapid and accurate evaluation of the complex processes of oil migration.

Optimal planning of mobile energy storage in active distribution network

AbstractMobile energy storage (MES) has the flexibility to temporally and spatially shift energy, and the optimal configuration of MES shall significantly improve the active distribution network (ADN) operation economy and renewables consumption. In this study, an optimal planning model of MES is established for ADN with a goal of minimising the annual cost of a distribution system. Firstly, the annual cost of a distribution system is set up with consideration of the investment cost and operation cost of MES, wind and PV curtailment cost, network loss cost and the peak‐valley arbitrage income of MES. Then, the distributed photovoltaic and wind power access constraints, power conservation constraints of ADN, power generation constraint, system security constraint, energy coupling and displacement constraints of MES are further tailored to establish the MES planning model. Afterwards, the proposed model is solved by the second‐order cone relaxation combined with the large M algorithm. Finally, the simulation results of the modified IEEE 33‐bus distribution network validate the effectiveness of the proposed model.

Distributed Coordination of Charging Stations With Shared Energy Storage in a Distribution Network

Electric vehicle (EV) charging stations have experienced rapid growth, whose impacts on the power grid have become non-negligible. Though charging stations can install energy storage to reduce their impacts on the grid, the conventional “one charging station, one energy storage” method may be uneconomical due to the high upfront cost of energy storage. Shared energy storage can be a potential solution. However, effective management of charging stations with shared energy storage in a distribution network is challenging due to the complex coupling, competing interests, and information asymmetry between different agents. To address the aforementioned challenges, this paper first proposes an equilibrium model to characterize the interaction among charging stations, shared energy storage, and the distribution network. We prove that the equilibrium coincides with the centralized optimization result with trading prices equaling the value of dual variables at optimum. Then, to achieve the efficient equilibrium, a distributed coordination mechanism with a prediction and a correction step is developed to guide the behaviors of different agents with proof of convergence. Numerical experiments and comprehensive performance comparisons are conducted to validate the theoretical results and show the advantages of the proposed mechanism.

FileDAG: A Multi-Version Decentralized Storage Network Built on DAG-Based Blockchain

Decentralized Storage Networks (DSNs) can gather storage resources from mutually untrusted providers and form worldwide decentralized file systems. Compared to traditional storage networks, DSNs are built on top of blockchains, which can incentivize service providers and ensure strong security. However, existing DSNs face two major challenges. First, deduplication can only be achieved at the directory-level. Missing file-level deduplication leads to unavoidable extra storage and bandwidth cost. Second, current DSNs realize file indexing by storing extra metadata while blockchain ledgers are not fully exploited. To overcome these problems, we propose FileDAG, a DSN built on DAG-based blockchain to support file-level deduplication in storing multi-versioned files. When updating files, we adopt an increment generation method to calculate and store only the increments instead of the entire updated files. Besides, we introduce a two-layer DAG-based blockchain ledger, by which FileDAG can provide flexible and storage-saving file indexing by directly using the blockchain database without incurring extra storage overhead. We implement FileDAG and evaluate its performance with extensive experiments. The results demonstrate that FileDAG outperforms the state-of-the-art industrial DSNs considering storage cost and latency.

Optimal scheduling of energy storage in district heating networks using nonlinear programming

Optimal scheduling of energy storage in district heating networks using nonlinear programming