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Journal articles on the topic 'Distributed storage networks'

Author: Grafiati

Published: 4 June 2021

Last updated: 3 February 2022

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1

Chockler, Gregory, Seth Gilbert, Vincent Gramoli, Peter M. Musial, and Alex A. Shvartsman. "Reconfigurable distributed storage for dynamic networks." Journal of Parallel and Distributed Computing 69, no. 1 (January 2009): 100–116. http://dx.doi.org/10.1016/j.jpdc.2008.07.007.

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2

Georgiou, Constantinos, Evangelos Kranakis, Ricardo Marceí-n-Jiménez, Sergio Rajsbaum, and Jorge Urrutia. "Distributed Dynamic Storage in Wireless Networks." International Journal of Distributed Sensor Networks 1, no. 3-4 (July 1, 2005): 355–71. http://dx.doi.org/10.1080/15501320500330695.

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This paper assumes a set of identical wireless hosts, each one aware of its location. The network is described by a unit distance graph whose vertices are points on the plane two of which are connected if their distance is at most one. The goal of this paper is to design local distributed solutions that require a constant number of communication rounds, independently of the network size or diameter. This is achieved through a combination of distributed computing and computational complexity tools. Starting with a unit distance graph, the paper shows: 1. How to extract a triangulated planar spanner; 2. Several algorithms are proposed to construct spanning trees of the triangulation. Also, it is described how to construct three spanning trees of the Delaunay triangulation having pairwise empty intersection, with high probability. These algorithms are interesting in their own right, since trees are a popular structure used by many network algorithms; 3. A load balanced distributed storage strategy on top of the trees is presented, that spreads replicas of data stored in the hosts in a way that the difference between the number of replicas stored by any two hosts is small. Each of the algorithms presented is local, and hence so is the final distributed storage solution, obtained by composing all of them. This implies that the solution adapts very quickly, in constant time, to network topology changes. We present a thorough experimental evaluation of each of the algorithms supporting our claims.

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Tebbi, Ali, Terence H. Chan, and Chi Wan Sung. "Multi-Rack Distributed Data Storage Networks." IEEE Transactions on Information Theory 65, no. 10 (October 2019): 6072–88. http://dx.doi.org/10.1109/tit.2019.2927565.

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4

Dini, Gianluca, and Lanfranco Lopriore. "Distributed storage protection in wireless sensor networks." Journal of Systems Architecture 61, no. 5-6 (May 2015): 256–66. http://dx.doi.org/10.1016/j.sysarc.2015.03.002.

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5

Weimin, Gao, and Zhu Lingzhi. "Distributed Data Storage in Wireless Sensor Networks." International Journal of Database Theory and Application 8, no. 4 (August 30, 2015): 179–82. http://dx.doi.org/10.14257/ijdta.2015.8.4.18.

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6

Jepson, T. C. "The basics of reliable distributed storage networks." IT Professional 6, no. 3 (May 2004): 18–24. http://dx.doi.org/10.1109/mitp.2004.23.

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7

Iosifidis, George, Iordanis Koutsopoulos, and Georgios Smaragdakis. "Distributed Storage Control Algorithms for Dynamic Networks." IEEE/ACM Transactions on Networking 25, no. 3 (June 2017): 1359–72. http://dx.doi.org/10.1109/tnet.2016.2633370.

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8

Zhang, Song-juan, and Jian Yang. "Distributed Data Storage Strategy in Wireless Sensor Networks." International Journal of Online Engineering (iJOE) 12, no. 11 (November 24, 2016): 52. http://dx.doi.org/10.3991/ijoe.v12i11.6238.

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In order to solve the problem of large scalability and low energy efficiency in distributed data storage in wireless sensor networks, the author proposed a temporal-centric storage approach method. By using this method the sensing data are stored in some storage node indexed by a hash function parameterized with detecting time. These nodes act as rendezvous among sink and source nodes. Simulation results show that the proposed approach mitigates the hot-spot problem and can thus improve overall system performance substantially.

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9

Liu, Yi. "A Distributed Data Storage Method Based on Integrated Threshold." Advanced Materials Research 268-270 (July 2011): 595–600. http://dx.doi.org/10.4028/www.scientific.net/amr.268-270.595.

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Based on the analysis and study of the data storage strategy in wireless sensor networks, this paper presents a distributed data storage method based on sleep scheduling to resolve the problems of network imbalance and storage hot spots problems.Finally, multi group analysis of simulate experiments results show that compared to other data storage method the distributed data storage method based on composite threshold have obviously advantages on the sides of overall energy consumption,data storage capacity,the number of failure node and data quality,thus have a significant effect on reducing energy consumption and extending network life cycle.

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10

Gong, Chen, and Xiaodong Wang. "On Partial Downloading for Wireless Distributed Storage Networks." IEEE Transactions on Signal Processing 60, no. 6 (June 2012): 3278–88. http://dx.doi.org/10.1109/tsp.2012.2189112.

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11

Sathiamoorthy, Maheswaran, Alexandros G. Dimakis, Bhaskar Krishnamachari, and Fan Bai. "Distributed Storage Codes Reduce Latency in Vehicular Networks." IEEE Transactions on Mobile Computing 13, no. 9 (September 2014): 2016–27. http://dx.doi.org/10.1109/tmc.2013.77.

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12

El Rouayheb, Salim, Sreechakra Goparaju, Han Mao Kiah, and Olgica Milenkovic. "Synchronization and Deduplication in Coded Distributed Storage Networks." IEEE/ACM Transactions on Networking 24, no. 5 (October 2016): 3056–69. http://dx.doi.org/10.1109/tnet.2015.2502274.

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13

Mukherjee, Anandarup, Pallav Kumar Deb, and Sudip Misra. "Timed Loops for Distributed Storage in Wireless Networks." IEEE Transactions on Parallel and Distributed Systems 33, no. 3 (March 1, 2022): 698–709. http://dx.doi.org/10.1109/tpds.2021.3100780.

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14

Shen, Yulong, Xiaowei Dang, Min Shu, Ning Xi, and Jianfeng Ma. "Two-Layer Storage Scheme and Repair Method of Failure Data in Wireless Sensor Networks." International Journal of Distributed Sensor Networks 8, no. 10 (October 1, 2012): 868943. http://dx.doi.org/10.1155/2012/868943.

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Distributed data storage is a key technology in the data collection in wireless sensor networks. The storage scheme based on network coding is applied to data collection in wireless sensor networks because of its high reliability and low overhead. However, it is an open problem to reduce data repair communication overhead caused by the failure of storage nodes. This paper focuses on this issue and presents a two-layer distributed data storage scheme. The lower-layer nodes store the encoded data blocks and the upper-layer nodes store the re-encoded blocks that are responsible for failure data recovery. Based on the two-layer data storage scheme, a data repair method is proposed to decrease the repair communication overhead with only sacrificing lower storage overhead. Compared with MSR, interference alignment-based scheme and group interference alignment scheme, the proposed method has lower repair communication overhead. We prove that the proposed method can reduce the repair communication overhead to [Formula: see text] times and it is suitable to resource-constrained distributed wireless sensor networks.

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15

Simco, Greg. "Internet 2 Distributed Storage Infrastructure." Internet and Higher Education 6, no. 1 (January 2003): 91–95. http://dx.doi.org/10.1016/s1096-7516(02)00168-9.

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16

Subramanian, Nalin, Chanjun Yang, and Wensheng Zhang. "Securing distributed data storage and retrieval in sensor networks." Pervasive and Mobile Computing 3, no. 6 (December 2007): 659–76. http://dx.doi.org/10.1016/j.pmcj.2007.06.002.

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17

Javed, Muhammad Umar, Mubariz Rehman, Nadeem Javaid, Abdulaziz Aldegheishem, Nabil Alrajeh, and Muhammad Tahir. "Blockchain-Based Secure Data Storage for Distributed Vehicular Networks." Applied Sciences 10, no. 6 (March 16, 2020): 2011. http://dx.doi.org/10.3390/app10062011.

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In this paper, a blockchain-based secure data sharing mechanism is proposed for Vehicular Networks (VNs). Edge service providers are introduced along with ordinary nodes to efficiently manage service provisioning. The edge service providers are placed in the neighborhood of the ordinary nodes to ensure smooth communication between them. The huge amount of data generated by smart vehicles is stored in a distributed file storage system, known as Interplanetary File System (IPFS). It is used to tackle the issues related to data storage in centralized architectures, such as data tampering, lack of privacy, vulnerability to hackers, etc. Monetary incentives are given to edge vehicle nodes to motivate them for accurate and timely service provisioning to ordinary nodes. In response, ordinary nodes give reviews to the edge nodes against the services provided by them, which are further stored in a blockchain to ensure integrity, security and transparency. Smart contracts are used to automate the system processes without the inclusion of an intermediate party and to check the reviews given to the edge nodes. To optimize gas consumption and to enhance the system performance, a Proof of Authority (PoA) consensus mechanism is used to validate the transactions. Moreover, a caching system is introduced at the edge nodes to store frequently used services. Furthermore, both security and privacy are enhanced in the proposed system by incorporating a symmetric key cryptographic mechanism. A trust management mechanism is also proposed in this work to calculate the nodes’ reputation values based upon their trust values. These values determine the authenticity of the nodes involved in the network. Eventually, it is concluded from the simulation results that the proposed system is efficient for VNs.

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18

Thrampoulidis, Christos, Subhonmesh Bose, and Babak Hassibi. "Optimal Placement of Distributed Energy Storage in Power Networks." IEEE Transactions on Automatic Control 61, no. 2 (February 2016): 416–29. http://dx.doi.org/10.1109/tac.2015.2437527.

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19

Martalò, M., M. Amoretti, M. Picone, and G. Ferrari. "Sporadic decentralized resource maintenance for P2P distributed storage networks." Journal of Parallel and Distributed Computing 74, no. 2 (February 2014): 2029–38. http://dx.doi.org/10.1016/j.jpdc.2013.11.001.

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20

Sun, Frank X., John Cosmas, Muhammad Ali Farmer, and Abdul Waheed. "Distributed Storage Manager System for Synchronized and Scalable AV Services across Networks." International Journal of Digital Multimedia Broadcasting 2011 (2011): 1–8. http://dx.doi.org/10.1155/2011/687071.

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This paper provides an innovative solution, namely, the distributed storage manager that opens a new path for highly interactive and personalized services. The distributed storage manager provides an enhancement to the MHP storage management functionality acting as a value added middleware distributed across the network. The distributed storage manager system provides multiple protocol support for initializing and downloading both streamed and file-based content and provides optimum control mechanisms to organize the storing and retrieval of content that are remained accessible to other multiple heterogeneous devices.

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21

Singh, Pushpendra, Nand Kishor Meena, Jin Yang, Shree Krishna Bishnoi, Eduardo Vega-Fuentes, and Chengwei Lou. "Modified Dragonfly Optimisation for Distributed Energy Mix in Distribution Networks." Energies 14, no. 18 (September 10, 2021): 5690. http://dx.doi.org/10.3390/en14185690.

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This article presents a two-stage optimization model aiming to determine optimal energy mix in distribution networks, i.e., battery energy storage, fuel cell, and wind turbines. It aims to alleviate the impact of high renewable penetration on the systems. To solve the proposed complex optimization model, a standard variant of the dragonfly algorithm (DA) has been improved and then applied to find the optimal mix of distributed energy resources. The suggested improvements are validated before their application. A heuristic approach has also been introduced to solve the second stage problem that determines the optimal power dispatch of battery energy storage as per the size suggested by the first stage. The proposed framework was implemented on a benchmark 33-bus and a practical Indian 108-bus distribution network over different test cases. The proposed model for energy mix and modified DA technique has significantly enhanced the operational performance of the network in terms of average annual energy loss reduction, node voltage profiles, and demand fluctuation caused by renewables.

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22

Ramabhadran, Sriram, and Joseph Pasquale. "Durability of replicated distributed storage systems." ACM SIGMETRICS Performance Evaluation Review 36, no. 1 (June 12, 2008): 447–48. http://dx.doi.org/10.1145/1384529.1375514.

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23

Kal’ner, V. D. "Distributed Networks "Blockchain": Environmental Risks and Threats." Ecology and Industry of Russia 22, no. 12 (December 4, 2018): 52–55. http://dx.doi.org/10.18412/1816-0395-2018-12-52-55.

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The possibilities of using distributed "blockchain" networks not only in financial transactions of crypto-currencies, but in a wide range of information messages and for storing confidential information from unidentified third parties are discussed. The positive economic and social possibility of using distributed encrypted networks on the Internet is shown to preserve personal freedom of action and new opportunities for personal business. At the same time, it is shown that the world wide web of Internet networks carries new threats and risks in life activity associated with hiding the true faces of the participants in the common network and the existing, despite the encryption, the possibility of information leakage, its irrecovecability in the event of a misstatement. Particularly noted is the general problem of the dependence of the digital technology and technology used in Russia on the overwhelming borrowing of the electronic database. A warning is given to users who do not have special knowledge and means of protection, with widespread use of blockchain technology in business, financial activity, and personal information storage.

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24

Escalera, Alberto, Edgardo D. Castronuovo, Milan Prodanović, and Javier Roldán-Pérez. "Reliability Assessment of Distribution Networks with Optimal Coordination of Distributed Generation, Energy Storage and Demand Management." Energies 12, no. 16 (August 20, 2019): 3202. http://dx.doi.org/10.3390/en12163202.

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Modern power distribution networks assume the connection of Distributed Generators (DGs) and energy storage systems as well as the application of advanced demand management techniques. After a network fault these technologies and techniques can contribute individually to the supply restoration of the interrupted areas and help improve the network reliability. However, the optimal coordination of control actions between these resources will lead to their most efficient use, maximizing the network reliability improvement. Until now, the effect of such networks with optimal coordination has not been considered in reliability studies. In this paper, DGs, energy storage and demand management techniques are jointly modelled and evaluated for reliability assessment. A novel methodology is proposed for the calculation of the reliability indices. It evaluates the optimal coordination of energy storage and demand management in order to reduce the energy-not-supplied during outages. The formulation proposed for the calculation of the reliability indices (including the modelling of optimal coordination) is described in detail. The methodology is applied to two distribution systems combining DGs, energy storage and demand management. Results demonstrate the capability of the proposed method to assess the reliability of such type of networks and emphasise the impact of the optimal coordination on reliability.

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25

Wu, Jun, Mianxiong Dong, Kaoru Ota, and Zhenyu Zhou. "Regenerating Code based Secure Distributed Storage for Wireless Sensor Networks." Procedia Computer Science 21 (2013): 183–90. http://dx.doi.org/10.1016/j.procs.2013.09.025.

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26

Kon, Zhenning, Salah Aly, and Emina Soljanin. "Decentralized Coding Algorithms for Distributed Storage in Wireless Sensor Networks." IEEE Journal on Selected Areas in Communications 28, no. 2 (February 2010): 261–67. http://dx.doi.org/10.1109/jsac.2010.100215.

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27

Harshan, J. "Generalized Pyramid Codes for Versioned Data in Distributed Storage Networks." IEEE Communications Letters 21, no. 9 (September 2017): 1921–24. http://dx.doi.org/10.1109/lcomm.2017.2711007.

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28

Ribeiro Gonçalves, José António, Luís Pires Neves, and António Gomes Martins. "Multiobjective assessment of distributed energy storage location in electricity networks." International Journal of Sustainable Energy 36, no. 6 (July 28, 2015): 577–91. http://dx.doi.org/10.1080/14786451.2015.1066787.

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29

Miranda, Ismael, Helder Leite, and Nuno Silva. "Coordination of multifunctional distributed energy storage systems in distribution networks." IET Generation, Transmission & Distribution 10, no. 3 (February 18, 2016): 726–35. http://dx.doi.org/10.1049/iet-gtd.2015.0398.

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30

Fung, Park K. "InfoFlow: A Distributed Algorithm to Detect Communities According to the Map Equation." Big Data and Cognitive Computing 3, no. 3 (July 22, 2019): 42. http://dx.doi.org/10.3390/bdcc3030042.

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Formidably sized networks are becoming more and more common, including in social sciences, biology, neuroscience, and the technology space. Many network sizes are expected to challenge the storage capability of a single physical computer. Here, we take two approaches to handle big networks: first, we look at how big data technology and distributed computing is an exciting approach to big data storage and processing. Second, most networks can be partitioned or labeled into communities, clusters, or modules, thus capturing the crux of the network while reducing detailed information, through the class of algorithms known as community detection. In this paper, we combine these two approaches, developing a distributed community detection algorithm to handle big networks. In particular, the map equation provides a way to identify network communities according to the information flow between nodes, where InfoMap is a greedy algorithm that uses the map equation. We develop discrete mathematics to adapt InfoMap into a distributed computing framework and then further develop the mathematics for a greedy algorithm, InfoFlow, which has logarithmic time complexity, compared to the linear complexity in InfoMap. Benchmark results of graphs up to millions of nodes and hundreds of millions of edges confirm the time complexity improvement, while maintaining community accuracy. Thus, we develop a map equation based community detection algorithm suitable for big network data processing.

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31

Escoto, Miquel, Mario Montagud, Noemi González, Alejandro Belinchón, Adriana Valentina Trujillo, Julián Romero, Julio César Díaz-Cabrera, Marta Pellicer García, and Alfredo Quijano López. "Optimal Scheduling for Energy Storage Systems in Distribution Networks." Energies 13, no. 15 (July 31, 2020): 3921. http://dx.doi.org/10.3390/en13153921.

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Distributed energy storage may play a key role in the operation of future low-carbon power systems as they can help to facilitate the provision of the required flexibility to cope with the intermittency and volatility featured by renewable generation. Within this context, this paper addresses an optimization methodology that will allow managing distributed storage systems of different technology and characteristics in a specific distribution network, taking into account not only the technical aspects of the network and the storage systems but also the uncertainties linked to demand and renewable energy variability. The implementation of the proposed methodology will allow facilitating the integration of energy storage systems within future smart grids. This paper’s results demonstrate numerically the good performance of the developed methodology.

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32

Gong, Bo, Peng Cheng, Zhuo Chen, Ning Liu, Lin Gui, and Frank de Hoog. "Spatiotemporal Compressive Network Coding for Energy-Efficient Distributed Data Storage in Wireless Sensor Networks." IEEE Communications Letters 19, no. 5 (May 2015): 803–6. http://dx.doi.org/10.1109/lcomm.2015.2401008.

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33

Bochuan Yang, and Hu Wen. "Research on Email-based Distributed Storage Scheme." Journal of Convergence Information Technology 8, no. 7 (April 15, 2013): 1109–16. http://dx.doi.org/10.4156/jcit.vol8.issue7.136.

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Talluri, Laskhmi Siva Rama Krishna, Ragunathan Thirumalaisamy, Ramgopal Kota, Ram Prasad Reddy Sadi, Ujjwal KC, Ranesh Kumar Naha, and Aniket Mahanti. "Providing Consistent State to Distributed Storage System." Computers 10, no. 2 (February 15, 2021): 23. http://dx.doi.org/10.3390/computers10020023.

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In cloud storage systems, users must be able to shut down the application when not in use and restart it from the last consistent state when required. BlobSeer is a data storage application, specially designed for distributed systems, that was built as an alternative solution for the existing popular open-source storage system-Hadoop Distributed File System (HDFS). In a cloud model, all the components need to stop and restart from a consistent state when the user requires it. One of the limitations of BlobSeer DFS is the possibility of data loss when the system restarts. As such, it is important to provide a consistent start and stop state to BlobSeer components when used in a Cloud environment to prevent any data loss. In this paper, we investigate the possibility of BlobSeer providing a consistent state distributed data storage system with the integration of checkpointing restart functionality. To demonstrate the availability of a consistent state, we set up a cluster with multiple machines and deploy BlobSeer entities with checkpointing functionality on various machines. We consider uncoordinated checkpoint algorithms for their associated benefits over other alternatives while integrating the functionality to various BlobSeer components such as the Version Manager (VM) and the Data Provider. The experimental results show that with the integration of the checkpointing functionality, a consistent state can be ensured for a distributed storage system even when the system restarts, preventing any possible data loss after the system has encountered various system errors and failures.

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35

Ndawula, Mike, Sasa Djokic, and Ignacio Hernando-Gil. "Reliability Enhancement in Power Networks under Uncertainty from Distributed Energy Resources." Energies 12, no. 3 (February 7, 2019): 531. http://dx.doi.org/10.3390/en12030531.

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This paper presents an integrated approach for assessing the impact that distributed energy resources (DERs), including intermittent photovoltaic (PV) generation, might have on the reliability performance of power networks. A test distribution system, based on a typical urban MV and LV networks in the UK, is modelled and used to investigate potential benefits of the local renewable generation, demand-manageable loads and coordinated energy storage. The conventional Monte Carlo method is modified to include time-variation of electricity demand profiles and failure rates of network components. Additionally, a theoretical interruption model is employed to assess more accurately the moment in time when interruptions to electricity customers are likely to occur. Accordingly, the impact of the spatio-temporal variation of DERs on reliability performance is quantified in terms of the effect of network outages. The potential benefits from smart grid functionalities are assessed through both system- and customer-oriented reliability indices, with special attention to energy not supplied to customers, as well as frequency and duration of supply interruptions. The paper also discusses deployment of an intelligent energy management system to control local energy generation-storage-demand resources that can resolve uncertainties in renewable-based generation and ensure highly reliable and continuous supply to all connected customers.

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Ning, Zuoting, Lijun Xiao, Wei Liang, Weiqi Shi, and Kuan-Ching Li. "On the Exploitation of Blockchain for Distributed File Storage." Journal of Sensors 2020 (December 12, 2020): 1–11. http://dx.doi.org/10.1155/2020/8861688.

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Distributed file storage aims to support credible access to data on distributed nodes. There are some application scenarios, for example, data centers, peer-to-peer (P2P) storage systems, and storage in wireless networks. Nevertheless, among these applications, data blocks are inevitably replaced and inaccessible when there exists nodes failure. As a result, data integrity and credibility is absent. To overcome such a challenge, blockchain is explored to protect the distributed data. Through analysis and evaluation, we demonstrate that blockchain advocates data integrity and credibility for distributed file storage, as well as the application of blockchain technology for distributed file storage.

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Dandapat, Sourav Kumar, Sanyam Jain, Romit Roy Choudhury, and Niloy Ganguly. "Distributed content storage for just-in-time streaming." ACM SIGCOMM Computer Communication Review 42, no. 4 (September 24, 2012): 77–78. http://dx.doi.org/10.1145/2377677.2377689.

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Moysiadis, Vasileios, Panagiotis Sarigiannidis, and Ioannis Moscholios. "Towards Distributed Data Management in Fog Computing." Wireless Communications and Mobile Computing 2018 (September 2, 2018): 1–14. http://dx.doi.org/10.1155/2018/7597686.

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In the emerging area of the Internet of Things (IoT), the exponential growth of the number of smart devices leads to a growing need for efficient data storage mechanisms. Cloud Computing was an efficient solution so far to store and manipulate such huge amount of data. However, in the next years it is expected that Cloud Computing will be unable to handle the huge amount of the IoT devices efficiently due to bandwidth limitations. An arising technology which promises to overwhelm many drawbacks in large-scale networks in IoT is Fog Computing. Fog Computing provides high-quality Cloud services in the physical proximity of mobile users. Computational power and storage capacity could be offered from the Fog, with low latency and high bandwidth. This survey discusses the main features of Fog Computing, introduces representative simulators and tools, highlights the benefits of Fog Computing in line with the applications of large-scale IoT networks, and identifies various aspects of issues we may encounter when designing and implementing social IoT systems in the context of the Fog Computing paradigm. The rationale behind this work lies in the data storage discussion which is performed by taking into account the importance of storage capabilities in modern Fog Computing systems. In addition, we provide a comprehensive comparison among previously developed distributed data storage systems which consist of a promising solution for data storage allocation in Fog Computing.

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Li, Xiaoning, Chuanhai Wang, Gang Chen, Xing Fang, Pingnan Zhang, and Wenjuan Hua. "Distributed-Framework Basin Modeling System: Ⅲ. Hydraulic Modeling System." Water 13, no. 5 (February 28, 2021): 649. http://dx.doi.org/10.3390/w13050649.

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A distributed-framework basin modeling system (DFBMS) was developed to simulate the runoff generation and movement on a basin scale. This study is part of a series of papers on DFBMS that focuses on the hydraulic calculation methods in runoff concentration on underlying surfaces and flow movement in river networks and lakes. This paper introduces the distributed-framework river modeling system (DF-RMS) that is a professional modeling system for hydraulic modeling. The DF-RMS contains different hydrological feature units (HFUs) to simulate the runoff movement through a system of rivers, storage units, lakes, and hydraulic structures. The river network simulations were categorized into different types, including one-dimensional river branch, dendritic river network, loop river network, and intersecting river network. The DF-RMS was applied to the middle and downstream portions of the Huai River Plain in China using different HFUs for river networks and lakes. The simulation results showed great consistency with the observed data, which proves that DF-RMS is a reliable system to simulate the flow movement in river networks and lakes.

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40

Li, Xi, Zehua Wang, Victor C. M. Leung, Hong Ji, Yiming Liu, and Heli Zhang. "Blockchain-empowered Data-driven Networks." ACM Computing Surveys 54, no. 3 (June 2021): 1–38. http://dx.doi.org/10.1145/3446373.

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The paths leading to future networks are pointing towards a data-driven paradigm to better cater to the explosive growth of mobile services as well as the increasing heterogeneity of mobile devices, many of which generate and consume large volumes and variety of data. These paths are also hampered by significant challenges in terms of security, privacy, services provisioning, and network management. Blockchain, which is a technology for building distributed ledgers that provide an immutable log of transactions recorded in a distributed network, has become prominent recently as the underlying technology of cryptocurrencies and is revolutionizing data storage and processing in computer network systems. For future data-driven networks (DDNs), blockchain is considered as a promising solution to enable the secure storage, sharing, and analytics of data, privacy protection for users, robust, trustworthy network control, and decentralized routing and resource managements. However, many important challenges and open issues remain to be addressed before blockchain can be deployed widely to enable future DDNs. In this article, we present a survey on the existing research works on the application of blockchain technologies in computer networks and identify challenges and potential solutions in the applications of blockchains in future DDNs. We identify application scenarios in which future blockchain-empowered DDNs could improve the efficiency and security, and generally the effectiveness of network services.

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41

Bassil, Chirine, Hussein EL GHOR, Jawad Khalife, and Nizar Hamadeh. "A New Clustering Routing Protocol for Homogeneous Wireless Sensor Networks Powered by Renewable Energy Sources." Scalable Computing: Practice and Experience 21, no. 4 (December 20, 2020): 637–48. http://dx.doi.org/10.12694/scpe.v21i4.1787.

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The technology of wireless sensor networks (WSNs) is in constant development and it made great progress in many applications. One of the most popular problems in WSNs is the limited energy storage power at every sensor node. This paper aims to propose and develop a new distributed clustering algorithm for energy harvesting wireless sensor networks denoted by DEH-WSN (Energy Harvesting for Distributed Clustering Wireless Sensor Networks Protocol) that relies on matching between clustering and energy harvesting in a distributed topology. DEH-WSN uses initial and residual energy capacity of the nodes to choose cluster heads. Simulation results prove that the proposed method increases network lifetime and the effective throughput.

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Ortega, Victor, and Jose F. Monserrat. "Semantic Distributed Data for Vehicular Networks Using the Inter-Planetary File System." Sensors 20, no. 22 (November 10, 2020): 6404. http://dx.doi.org/10.3390/s20226404.

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Abstract:

Vehicular networks provide means to distribute data among intelligent vehicles, increasing their efficiency and the safety of their occupants. While connected to these networks, vehicles have access to various kinds of information shared by other vehicles and road-side units (RSUs). This information includes helpful resources, such as traffic state or remote sensors. An efficient and fast system to get access to this information is important but unproductive if the data are not appropriately structured, accessible, and easy to process. This paper proposes the creation of a semantic distributed network using content-addressed networking and peer-to-peer (P2P) connections. In this open and collaborative network, RSUs and vehicles use ontologies to semantically represent information and facilitate the development of intelligent autonomous agents capable of navigating and processing the shared data. In order to create this P2P network, this paper makes use of the Inter-Planetary File System (IPFS), an open source solution that provides secure, reliable, and efficient content-addressed distributed storage over standard IP networks using the new QUIC protocol. This paper highlights the feasibility of this proposal and compares it with the state-of-the-art. Results show that IPFS is a promising technology that offers a great balance between functionality, performance, and security.

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Zhou, L., Teck Yoong Chai, C. V. Saradhi, Y. Wang, V. Foo, Q. Qiang, J. Biswas, and C. Lu. "WDM optical network testbed and distributed storage application." IEEE Communications Magazine 44, no. 2 (February 2006): 23–29. http://dx.doi.org/10.1109/mcom.2006.1593546.

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YANG, Jun, Guo-hua ZHAO, Ke-jia WANG, and Jun-de SONG. "A modern service-oriented distributed storage solution." Journal of China Universities of Posts and Telecommunications 16 (September 2009): 120–26. http://dx.doi.org/10.1016/s1005-8885(08)60344-1.

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Yu, Hongliang, Fan Zhang, and Yongwei Wu. "Granary: A sharing oriented distributed storage system." Future Generation Computer Systems 38 (September 2014): 47–60. http://dx.doi.org/10.1016/j.future.2013.08.001.

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Chen, Haiwen, Jiaping Yu, Fang Liu, Zhiping Cai, and Jing Xia. "Archipelago: A Medical Distributed Storage System for Interconnected Health." IEEE Internet Computing 24, no. 2 (March 1, 2020): 28–38. http://dx.doi.org/10.1109/mic.2019.2963182.

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Jun, Wang, Yi Qiong, Wang Yue, and Jia Yingying. "Distributed data storage solution under sink failures in wireless sensor networks." Journal of China Universities of Posts and Telecommunications 24, no. 2 (April 2017): 72–102. http://dx.doi.org/10.1016/s1005-8885(17)60201-2.

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Pedersen, Jesper, Alexandre Graell i Amat, Iryna Andriyanova, and Fredrik Brannstrom. "Distributed Storage in Mobile Wireless Networks With Device-to-Device Communication." IEEE Transactions on Communications 64, no. 11 (November 2016): 4862–78. http://dx.doi.org/10.1109/tcomm.2016.2605681.

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Siavoshani, Mahdi Jafari, Ali Pourmiri, and Seyed Pooya Shariatpanahi. "Storage, Communication, and Load Balancing Trade-off in Distributed Cache Networks." IEEE Transactions on Parallel and Distributed Systems 29, no. 4 (April 1, 2018): 943–57. http://dx.doi.org/10.1109/tpds.2017.2781242.

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Prasad, D. Krishna, and Krishna Chaitanya Katkam. "Secure Storage of Profile Matching of Distributed-Based Mobile Social Networks." International Journal of Computer Trends and Technology 13, no. 4 (July 25, 2014): 154–59. http://dx.doi.org/10.14445/22312803/ijctt-v13p132.

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