Journal articles: 'Decentralized computing'

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Kelly, Terence. "Decentralized Computing." Queue 18, no. 5 (October 31, 2020): 41–53. http://dx.doi.org/10.1145/3434571.3436964.

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Farrens, Matt. "Distributed decentralized computing." ACM Computing Surveys 28, no. 4es (December 1996): 28. http://dx.doi.org/10.1145/242224.242259.

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YAMAGUCHI, A. "Autonomous Decentralized Control in Ubiquitous Computing." IEICE Transactions on Communications E88-B, no. 12 (December 1, 2005): 4421–26. http://dx.doi.org/10.1093/ietcom/e88-b.12.4421.

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Miller, Daniel E., and Edward J. Davison. "On Computing Quotient Decentralized Fixed Modes." IFAC Proceedings Volumes 44, no. 1 (January 2011): 2546–51. http://dx.doi.org/10.3182/20110828-6-it-1002.03142.

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Chen, Xu. "Decentralized Computation Offloading Game for Mobile Cloud Computing." IEEE Transactions on Parallel and Distributed Systems 26, no. 4 (April 1, 2015): 974–83. http://dx.doi.org/10.1109/tpds.2014.2316834.

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Aral, Atakan, and Tolga Ovatman. "A Decentralized Replica Placement Algorithm for Edge Computing." IEEE Transactions on Network and Service Management 15, no. 2 (June 2018): 516–29. http://dx.doi.org/10.1109/tnsm.2017.2788945.

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Kamath, Goutham, Lei Shi, Edmond Chow, Wenzhan Song, and Junjie Yang. "Decentralized multigrid for in-situ big data computing." Tsinghua Science and Technology 20, no. 6 (December 2015): 545–59. http://dx.doi.org/10.1109/tst.2015.7349927.

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Debe, Mazin, Khaled Salah, Muhammad Habib Ur Rehman, and Davor Svetinovic. "Blockchain-Based Decentralized Reverse Bidding in Fog Computing." IEEE Access 8 (2020): 81686–97. http://dx.doi.org/10.1109/access.2020.2991261.

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Wang, Yabin, Chenghao Guo, and Jin Yu. "Immune Scheduling Network Based Method for Task Scheduling in Decentralized Fog Computing." Wireless Communications and Mobile Computing 2018 (September 2, 2018): 1–8. http://dx.doi.org/10.1155/2018/2734219.

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Fog computing has changed the distributed computing rapidly by including the smart devices widely distributed at the network edges. It is able to provide less latency and is more capable of decreasing traffic jam in the network. However, it will bring more difficulties for resource managing and task scheduling especially in a decentralized ad hoc network. In this paper, we propose a method that takes advantages of the immune mechanism to schedule tasks in a decentralized way for fog computing. By using forward propagation and backward propagation in the ad hoc network, the power of distributed schedulers is used to generate the optimized scheduler strategies to deal with computing nodes overloaded and achieve the optimal task finishing time reducing. The experiment results show that our approach can beat similar methods.

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Vaidya, Chandu. "Statistical Approach for Load Distribution in Decentralized Cloud Computing." HELIX 8, no. 5 (August 31, 2018): 3884–87. http://dx.doi.org/10.29042/2018-3884-3887.

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Zhang, Daqiang, Zhijun Yang, Hongyu Huang, and Qin Zou. "Decentralized Checking Context Inconsistency in Ubiquitous Mobile Computing Environments." Physics Procedia 25 (2012): 700–707. http://dx.doi.org/10.1016/j.phpro.2012.03.146.

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Heiskanen, Pirja. "Decentralized method for computing Pareto solutions in multiparty negotiations." European Journal of Operational Research 117, no. 3 (September 1999): 578–90. http://dx.doi.org/10.1016/s0377-2217(98)00276-8.

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Darby, Paul J., and Nian-Feng Tzeng. "Decentralized QoS-Aware Checkpointing Arrangement in Mobile Grid Computing." IEEE Transactions on Mobile Computing 9, no. 8 (August 2010): 1173–86. http://dx.doi.org/10.1109/tmc.2010.80.

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Yin, Hao, Dongchao Guo, Kai Wang, Zexun Jiang, Yongqiang Lyu, and Ju Xing. "Hyperconnected Network: A Decentralized Trusted Computing and Networking Paradigm." IEEE Network 32, no. 1 (January 2018): 112–17. http://dx.doi.org/10.1109/mnet.2018.1700172.

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Zhang, Daqiang, Min Chen, Hongyu Huang, and Minyi Guo. "Decentralized checking of context inconsistency in pervasive computing environments." Journal of Supercomputing 64, no. 2 (August 9, 2011): 256–73. http://dx.doi.org/10.1007/s11227-011-0661-x.

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Jiang, Jingyan, Liang Hu, Chenghao Hu, Jiate Liu, and Zhi Wang. "BACombo—Bandwidth-Aware Decentralized Federated Learning." Electronics 9, no. 3 (March 5, 2020): 440. http://dx.doi.org/10.3390/electronics9030440.

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The emerging concern about data privacy and security has motivated the proposal of federated learning. Federated learning allows computing nodes to only synchronize the locally- trained models instead of their original data in distributed training. Conventional federated learning architecture, inherited from the parameter server design, relies on highly centralized typologies and large nodes-to-server bandwidths. However, in real-world federated learning scenarios, the network capacities between nodes are highly uniformly distributed and smaller than that in data centers. As a result, how to efficiently utilize network capacities between computing nodes is crucial for conventional federated learning. In this paper, we propose Bandwidth Aware Combo (BACombo), a model segment level decentralized federated learning, to tackle this problem. In BACombo, we propose a segmented gossip aggregation mechanism that makes full use of node-to-node bandwidth for speeding up the communication time. Besides, a bandwidth-aware worker selection model further reduces the transmission delay by greedily choosing the bandwidth-sufficient worker. The convergence guarantees are provided for BACombo. The experimental results on various datasets demonstrate that the training time is reduced by up to 18 times that of baselines without accuracy degrade.

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Froiz-Míguez, Iván, Paula Fraga-Lamas, and Tiago M. Fernández-Caramés. "Decentralized P2P Broker for M2M and IoT Applications." Proceedings 54, no. 1 (August 20, 2020): 24. http://dx.doi.org/10.3390/proceedings2020054024.

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The recent increase in the number of connected IoT devices, as well as the heterogeneity of the environments where they are deployed, has derived into the growth of the complexity of Machine-to-Machine (M2M) communication protocols and technologies. In addition, the hardware used by IoT devices has become more powerful and efficient. Such enhancements have made it possible to implement novel decentralized computing architectures like the ones based on edge computing, which offload part of the central server processing by using multiple distributed low-power nodes. In order to ease the deployment and synchronization of decentralized edge computing nodes, this paper describes an M2M distributed protocol based on Peer-to-Peer (P2P) communications that can be executed on low-power ARM devices. In addition, this paper proposes to make use of brokerless communications by using a distributed publication/subscription protocol. Thanks to the fact that information is stored in a distributed way among the nodes of the swarm and since each node can implement a specific access control system, the proposed system is able to make use of write access mechanisms and encryption for the stored data so that the rest of the nodes cannot access sensitive information. In order to test the feasibility of the proposed approach, a comparison with an Message-Queuing Telemetry Transport (MQTT) based architecture is performed in terms of latency, network consumption and performance.

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Nyamtiga, Sicato, Rathore, Sung, and Park. "Blockchain-Based Secure Storage Management with Edge Computing for IoT." Electronics 8, no. 8 (July 25, 2019): 828. http://dx.doi.org/10.3390/electronics8080828.

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As a core technology to manage decentralized systems, blockchain is gaining much popularity to deploy such applications as smart grid and healthcare systems. However, its utilization in resource-constrained mobile devices is limited due to high demands of resources and poor scalability with frequent-intensive transactions. Edge computing can be integrated to facilitate mobile devices in offloading their mining tasks to cloud resources. This integration ensures reliable access, distributed computation and untampered storage for scalable and secure transactions. It is imperative therefore that crucial issues of security, scalability and resources management be addressed to achieve successful integration. Studies have been conducted to explore suitable architectural requirements, and some researchers have applied the integration to deploy some specific applications. Despite these efforts, however, issues of anonymity, adaptability and integrity still need to be investigated further to attain a practical, secure decentralized data storage. We based our study on peer-to-peer and blockchain to achieve an Internet of Things (IoT) design supported by edge computing to acquire security and scalability levels needed for the integration. We investigated existing blockchain and associated technologies to discover solutions that address anonymity, integrity and adaptability issues for successful integration of blockchain in IoT systems. The discovered solutions were then incorporated in our conceptual design of the decentralized application prototype presented for secure storage of IoT data and transactions.

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Jia, Xudong, Ning Hu, Shi Yin, Yan Zhao, Chi Zhang, and Xinda Cheng. "A2 Chain: A Blockchain-Based Decentralized Authentication Scheme for 5G-Enabled IoT." Mobile Information Systems 2020 (December 21, 2020): 1–19. http://dx.doi.org/10.1155/2020/8889192.

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The fifth-generation mobile communication technology (5G) provides high-bandwidth and low-latency data channels for massive IoT terminals to access the core business network. At the same time, it also brings higher security threats and challenges. Terminal identity authentication is an important security mechanism to ensure the core business network; however, most of the existing solutions adopt a centralized authentication model. Once the number of authentication requests exceeds the processing capacity of the authentication center service, it will cause authentication request congestion or deadlock. The decentralized authentication model can effectively solve the above problems. This article proposes a decentralized IoT authentication scheme called A2 Chain. First, A2 Chain uses edge computing to decentralize the processing of authentication requests and eliminate the burden on authentication services and the network. Second, to implement cross-domain identity verification of IoT devices, A2 Chain uses blockchain, and sidechain technologies are used to securely share the identity verification information of IoT devices. Additionally, A2 Chain replaces public key infrastructure (PKI) algorithm with identity-based cryptography (IBC) algorithm to eliminate the management overhead caused by centralized authentication model.

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SHENG, Yiqiang, Jinlin WANG, Chaopeng LI, and Weining QI. "Max-Min-Degree Neural Network for Centralized-Decentralized Collaborative Computing." IEICE Transactions on Communications E99.B, no. 4 (2016): 841–48. http://dx.doi.org/10.1587/transcom.2015adp0013.

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Patil, Mohini Tanaji. "A Survey on Different Techniques Used in Decentralized Cloud Computing." International Journal of Science and Engineering Applications 5, no. 2 (March 19, 2016): 92–94. http://dx.doi.org/10.7753/ijsea0502.1007.

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JIANG, Yan-hua. "Decentralized approach for metadata management in computing resource sharing platform." Journal of Computer Applications 31, no. 2 (April 6, 2011): 462–65. http://dx.doi.org/10.3724/sp.j.1087.2011.00462.

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Sangwan, Arun, Gaurav Kumar, and Sorabh Gupta. "To Convalesce Task Scheduling in a Decentralized Cloud Computing Environment." Review of Computer Engineering Research 3, no. 1 (2016): 25–34. http://dx.doi.org/10.18488/journal.76/2016.3.1/76.1.25.34.

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Harjula, Erkki, Pekka Karhula, Johirul Islam, Teemu Leppanen, Ahsan Manzoor, Madhusanka Liyanage, Jagmohan Chauhan, Tanesh Kumar, Ijaz Ahmad, and Mika Ylianttila. "Decentralized Iot Edge Nanoservice Architecture for Future Gadget-Free Computing." IEEE Access 7 (2019): 119856–72. http://dx.doi.org/10.1109/access.2019.2936714.

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Josilo, Sladana, and Gyorgy Dan. "Decentralized Algorithm for Randomized Task Allocation in Fog Computing Systems." IEEE/ACM Transactions on Networking 27, no. 1 (February 2019): 85–97. http://dx.doi.org/10.1109/tnet.2018.2880874.

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Qu, Youyang, Longxiang Gao, Tom H. Luan, Yong Xiang, Shui Yu, Bai Li, and Gavin Zheng. "Decentralized Privacy Using Blockchain-Enabled Federated Learning in Fog Computing." IEEE Internet of Things Journal 7, no. 6 (June 2020): 5171–83. http://dx.doi.org/10.1109/jiot.2020.2977383.

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Chen, Hao, Yu Ye, Ming Xiao, Mikael Skoglund, and H. Vincent Poor. "Coded Stochastic ADMM for Decentralized Consensus Optimization With Edge Computing." IEEE Internet of Things Journal 8, no. 7 (April 1, 2021): 5360–73. http://dx.doi.org/10.1109/jiot.2021.3058116.

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Wang, Xiaoying, Xiaojing Liu, Lihua Fan, and Xuhan Jia. "A Decentralized Virtual Machine Migration Approach of Data Centers for Cloud Computing." Mathematical Problems in Engineering 2013 (2013): 1–10. http://dx.doi.org/10.1155/2013/878542.

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As cloud computing offers services to lots of users worldwide, pervasive applications from customers are hosted by large-scale data centers. Upon such platforms, virtualization technology is employed to multiplex the underlying physical resources. Since the incoming loads of different application vary significantly, it is important and critical to manage the placement and resource allocation schemes of the virtual machines (VMs) in order to guarantee the quality of services. In this paper, we propose a decentralized virtual machine migration approach inside the data centers for cloud computing environments. The system models and power models are defined and described first. Then, we present the key steps of the decentralized mechanism, including the establishment of load vectors, load information collection, VM selection, and destination determination. A two-threshold decentralized migration algorithm is implemented to further save the energy consumption as well as keeping the quality of services. By examining the effect of our approach by performance evaluation experiments, the thresholds and other factors are analyzed and discussed. The results illustrate that the proposed approach can efficiently balance the loads across different physical nodes and also can lead to less power consumption of the entire system holistically.

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He, Dong, Qingyu Xiong, Xuyang Zhang, Yunchuang Dai, and Ziyan Jiang. "A Decentralized, Flat-Structured Control System for Chiller Plants." Applied Sciences 9, no. 22 (November 10, 2019): 4811. http://dx.doi.org/10.3390/app9224811.

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This paper presents a novel control system for chiller plants that is decentralized and flat-structured. Each device in chiller plant system is fitted with a smart node. It is a smart agent, which collects, handles and sends out information to its neighbours. All the smart nodes form a network that can realize self-organization and self-recognition. Different kinds of control strategies can be converted into series of decentralized computing processes carried on by the smart nodes. The principle and mechanism of this decentralized, flat-structured control system for chiller plants are described in detail. Then a case study is presented to show how to build the decentralized, flat-structured control system actually. The measured data shows that the decentralized control method is energy efficiency. Moreover, it is much more flexible and scalable compared with the traditional centralized control method.

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Tsamoura, Efthymia, Anastasios Gounaris, and Yannis Manolopoulos. "Optimal Service Ordering in Decentralized Queries Over Web Services." International Journal of Knowledge-Based Organizations 1, no. 2 (April 2011): 1–16. http://dx.doi.org/10.4018/ijkbo.2011040101.

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The problem of ordering expensive predicates (or filter ordering) has recently received renewed attention due to emerging computing paradigms such as processing engines for queries over remote Web Services, and cloud and grid computing. The optimization of pipelined plans over services differs from traditional optimization significantly, since execution takes place in parallel and thus the query response time is determined by the slowest node in the plan, which is called the bottleneck node. Although polynomial algorithms have been proposed for several variants of optimization problems in this setting, the fact that communication links are typically heterogeneous in wide-area environments has been largely overlooked. The authors propose an attempt to optimize linear orderings of services when the services communicate directly with each other and the communication links are heterogeneous. The authors propose a novel optimal algorithm to solve this problem efficiently. The evaluation of the proposal shows that it can result in significant reductions of the response time.

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Hu, Chunyang, Jingchen Li, Haobin Shi, Bin Ning, and Qiong Gu. "Decentralized Offloading Strategies Based on Reinforcement Learning for Multi-Access Edge Computing." Information 12, no. 9 (August 25, 2021): 343. http://dx.doi.org/10.3390/info12090343.

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Using reinforcement learning technologies to learn offloading strategies for multi-access edge computing systems has been developed by researchers. However, large-scale systems are unsuitable for reinforcement learning, due to their huge state spaces and offloading behaviors. For this reason, this work introduces the centralized training and decentralized execution mechanism, designing a decentralized reinforcement learning model for multi-access edge computing systems. Considering a cloud server and several edge servers, we separate the training and execution in the reinforcement learning model. The execution happens in edge devices of the system, and edge servers need no communication. Conversely, the training process occurs at the cloud device, which causes a lower transmission latency. The developed method uses a deep deterministic policy gradient algorithm to optimize offloading strategies. The simulated experiment shows that our method can learn the offloading strategy for each edge device efficiently.

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Kratzke, Nane. "A Brief History of Cloud Application Architectures." Applied Sciences 8, no. 8 (August 14, 2018): 1368. http://dx.doi.org/10.3390/app8081368.

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This paper presents a review of cloud application architectures and its evolution. It reports observations being made during a research project that tackled the problem to transfer cloud applications between different cloud infrastructures. As a side effect, we learned a lot about commonalities and differences from plenty of different cloud applications which might be of value for cloud software engineers and architects. Throughout the research project, we analyzed industrial cloud standards, performed systematic mapping studies of cloud-native application-related research papers, did action research activities in cloud engineering projects, modeled a cloud application reference model, and performed software and domain-specific language engineering activities. Two primary (and sometimes overlooked) trends can be identified. First, cloud computing and its related application architecture evolution can be seen as a steady process to optimize resource utilization in cloud computing. Second, these resource utilization improvements resulted over time in an architectural evolution of how cloud applications are being built and deployed. A shift from monolithic service-oriented architectures (SOA), via independently deployable microservices towards so-called serverless architectures, is observable. In particular, serverless architectures are more decentralized and distributed, and make more intentional use of separately provided services. In other words, a decentralizing trend in cloud application architectures is observable that emphasizes decentralized architectures known from former peer-to-peer based approaches. This is astonishing because, with the rise of cloud computing (and its centralized service provisioning concept), the research interest in peer-to-peer based approaches (and its decentralizing philosophy) decreased. However, this seems to change. Cloud computing could head into the future of more decentralized and more meshed services.

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Wu, Ning, Chengyin Liu, Yukun Guo, and Jianhua Zhang. "On-Board Computing for Structural Health Monitoring with Smart Wireless Sensors by Modal Identification Using Hilbert-Huang Transform." Mathematical Problems in Engineering 2013 (2013): 1–9. http://dx.doi.org/10.1155/2013/509129.

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Smart wireless sensors have been recognized as a promising technology to overcome many inherent difficulties and limitations associated with traditional wired structural health monitoring (SHM) systems. Despite the advances in smart sensor technologies, on-board computing capability of smart sensors has been considered as one of the most difficult challenges in the application of the smart sensors in SHM. Taking the advantage of recent developments in microprocessor which provides powerful on-board computing functionality for smart sensors, this paper presents a new decentralized data processing approach for modal identification using the Hilbert-Huang transform (HHT) algorithm, which is based on signal decomposition technique. It is shown that this method is suitable for implementation in the intrinsically distributed computing environment found in wireless smart sensor networks (WSSNs). The HHT-based decentralized data processing is, then, programmed and implemented on the Crossbow IRIS mote sensor platform. The effectiveness of the proposed techniques is demonstrated through a set of numerical studies and experimental validations on an in-house cable-stayed bridge model in terms of the accuracy of identified dynamic properties.

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Liu, Bang Fan, Hui Hui Zhong, and Meng Wang. "How to Design the Cloud Computing Used in E-Government’s Information Security." Applied Mechanics and Materials 536-537 (April 2014): 616–19. http://dx.doi.org/10.4028/www.scientific.net/amm.536-537.616.

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Information security is a core issue to ensure the smooth implementation of e-government. On the one hand, cloud computing promote information security management from centralized to decentralized. Help to improve the ability of the information security of e-government. On the other hand, it can not be ignored that the unprecedented openness and complexity of cloud computing also threat to information security of e-government. In this paper, we shall discuss the building of the cloud computing information security of e-government from three aspects of technical, management and law.

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B.Rathod, Suresh, and V. Krishna Reddy. "Decision Making Framework for Decentralized Virtual Machine Placement in Cloud Computing." International Journal of Engineering & Technology 7, no. 2.7 (March 18, 2018): 705. http://dx.doi.org/10.14419/ijet.v7i2.7.10926.

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In distributed cloud environment hosts are configured with Local Resource Monitors (LRM). This LRM monitors the underlying hosts’ resource usage, runs independently and balances the underling host’s load by migrating Virtual Machine (VM) instance. For the dynamic environment, each hosts has varying resource requirement, hosts load cannot remain constant. LRM at each host takes decision for VM migration considering static threshold on its own and other hosts current CPU utilization. This result in chances of getting selected same host for VM placement by multiple hosts to reduce resource usage of underlying hosts. The decision making at each server causes the problem of same host identification by multiple hosts during VM placement and consumes extra CPU power and network bandwidth consumption towards each server. This paper addresses the above said issue by proposing decentralized decision making framework for cloud considering hybrid Peer to Peer (P2P) network topology. Proposed solution results avoiding above said issues and balances the load across servers in DC.

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Hou, Shoulu, Wei Ni, Shuai Zhao, Bo Cheng, Shiping Chen, and Junliang Chen. "Decentralized Real-Time Optimization of Voltage Reconfigurable Cloud Computing Data Center." IEEE Transactions on Green Communications and Networking 4, no. 2 (June 2020): 577–92. http://dx.doi.org/10.1109/tgcn.2020.2987063.

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Yu, Rong, Jiefei Ding, Sabita Maharjan, Stein Gjessing, Yan Zhang, and Danny H. K. Tsang. "Decentralized and Optimal Resource Cooperation in Geo-Distributed Mobile Cloud Computing." IEEE Transactions on Emerging Topics in Computing 6, no. 1 (January 2018): 72–84. http://dx.doi.org/10.1109/tetc.2015.2479093.

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SHAFIQ, M. O. "Autonomous Semantic Grid: Principles of Autonomous Decentralized Systems for Grid Computing." IEICE Transactions on Information and Systems E88-D, no. 12 (December 1, 2005): 2640–50. http://dx.doi.org/10.1093/ietisy/e88-d.12.2640.

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Cui, Laizhong, Shu Yang, Ziteng Chen, Yi Pan, Zhong Ming, and Mingwei Xu. "A Decentralized and Trusted Edge Computing Platform for Internet of Things." IEEE Internet of Things Journal 7, no. 5 (May 2020): 3910–22. http://dx.doi.org/10.1109/jiot.2019.2951619.

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Bonnah, Ernest, and Ju Shiguang. "DecChain: A decentralized security approach in Edge Computing based on Blockchain." Future Generation Computer Systems 113 (December 2020): 363–79. http://dx.doi.org/10.1016/j.future.2020.07.009.

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Xiang, Bin, Jocelyne Elias, Fabio Martignon, and Elisabetta Di Nitto. "Resource calendaring for Mobile Edge Computing: Centralized and decentralized optimization approaches." Computer Networks 199 (November 2021): 108426. http://dx.doi.org/10.1016/j.comnet.2021.108426.

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NITIN, DR, Neha Agarwal, and Piyush Chauhan. "Fault Tolerant Heterogeneous Limited Duplication Scheduling algorithm for Decentralized Grid." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 4, no. 3 (April 30, 2013): 765–75. http://dx.doi.org/10.24297/ijct.v4i3.4204.

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Fault tolerance is one of the most desirable property in decentralized grid computing systems, where computational resources are geographically distributed. These resources collaborate in order to execute workflow applications as fast as possible. In workflow applications, tasks are dependent on each other, so it becomes extremely vital that scheduling techniques should also have some decentralized fault tolerant mechanism. In this paper, we have proposed a decentralized fault tolerant mechanism which utilize the checkpoint concept; for Heterogeneous Limited Duplication (HLD) algorithm. HLD is based on task duplication scheduling in heterogeneous environment. There are two fold benefits firstly; if node failure occurs then rest of grid nodes sustain the execution of application. Secondly, less makespan of application is obtained using checkpoint concept. Therefore, application scheduled over decentralized grid systems (which are known for their unreliable behavior) will yield results fast utilizing algorithm proposed in this paper.

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Foster, L. A., and L. Silverberg. "On-Off Decentralized Control of Flexible Structures." Journal of Dynamic Systems, Measurement, and Control 113, no. 1 (March 1, 1991): 41–47. http://dx.doi.org/10.1115/1.2896357.

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A new near minimum fuel method for on-off decentralized control of flexible structures is introduced. Fuel minimization is achieved by turning on actuators when local velocities are in the neighborhood of a maximum and when local displacements are in the neighborhood of a minimum. Maximum velocity and minimum displacement neighborhoods at each actuator location are determined by recursively computing standard deviations of displacements and velocities over running intervals of time. The fuel consumed by on-off control is shown to be 20 percent lower than by linear optimal control for the same reductions in energy over the same periods of time. On-off control of a uniform beam illustrates the method.

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Meng, Xian Yong, Zhong Chen, and Xiang Yu Meng. "Privacy-Preserving Decentralized Key-Policy Attribute-Based Signcryption in Cloud Computing Environments." Applied Mechanics and Materials 475-476 (December 2013): 1144–49. http://dx.doi.org/10.4028/www.scientific.net/amm.475-476.1144.

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In this paper, a novel decentralized key-policy attribute-based signcryption (ABS) scheme is proposed, where each authority can generate secret-public key pair for the user independently without any cooperation and a centralized authority. In the proposed scheme, each authority can join or leave the system randomly without reinitializing the system,and issue secret-public keys to user respectively. Therefore, it is clear that the multi-authority attribute-based access control scheme can reduce the communication cost and the collaborative computing cost. Additionally, the attribute-based signcryption scheme is efficient in terms of both the identification authentication and the confidential communication, and can realize security secret sharing in cloud computing environments.

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Xu, Ronghua, Seyed Yahya Nikouei, Deeraj Nagothu, Alem Fitwi, and Yu Chen. "BlendSPS: A BLockchain-ENabled Decentralized Smart Public Safety System." Smart Cities 3, no. 3 (September 1, 2020): 928–51. http://dx.doi.org/10.3390/smartcities3030047.

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Due to the recent advancements in the Internet of Things (IoT) and Edge-Fog-Cloud Computing technologies, the Smart Public Safety (SPS) system has become a more realistic solution for seamless public safety services that are enabled by integrating machine learning (ML) into heterogeneous edge computing networks. While SPS facilitates convenient exchanges of surveillance data streams among device owners and third-party applications, the existing monolithic service-oriented architecture (SOA) is unable to provide scalable and extensible services in a large-scale heterogeneous network environment. Moreover, traditional security solutions rely on a centralized trusted third-party authority, which not only can be a performance bottleneck or the single point of failure, but it also incurs privacy concerns on improperly use of private information. Inspired by blockchain and microservices technologies, this paper proposed a BLockchain-ENabled Decentralized Smart Public Safety (BlendSPS) system. Leveraging the hybrid blockchain fabric, a microservices based security mechanism is implemented to enable decentralized security architecture, and it supports immutability, auditability, and traceability for secure data sharing and operations among participants of the SPS system. An extensive experimental study verified the feasibility of the proposed BlendSPS that possesses security and privacy proprieties with limited overhead on IoT based edge networks.

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Firdaus, Muhammad, Sandi Rahmadika, and Kyung-Hyune Rhee. "Decentralized Trusted Data Sharing Management on Internet of Vehicle Edge Computing (IoVEC) Networks Using Consortium Blockchain." Sensors 21, no. 7 (March 31, 2021): 2410. http://dx.doi.org/10.3390/s21072410.

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The emergence of the Internet of Vehicles (IoV) aims to facilitate the next generation of intelligent transportation system (ITS) applications by combining smart vehicles and the internet to improve traffic safety and efficiency. On the other hand, mobile edge computing (MEC) technology provides enormous storage resources with powerful computing on the edge networks. Hence, the idea of IoV edge computing (IoVEC) networks has grown to be an assuring paradigm with various opportunities to advance massive data storage, data sharing, and computing processing close to vehicles. However, the participant’s vehicle may be unwilling to share their data since the data-sharing system still relies on a centralized server approach with the potential risk of data leakage and privacy security. In addition, vehicles have difficulty evaluating the credibility of the messages they received because of untrusted environments. To address these challenges, we propose consortium blockchain and smart contracts to accomplish a decentralized trusted data sharing management system in IoVEC. This system allows vehicles to validate the credibility of messages from their neighboring by generating a reputation rating. Moreover, the incentive mechanism is utilized to trigger the vehicles to store and share their data honestly; thus, they will obtain certain rewards from the system. Simulation results substantially display an efficient network performance along with forming an appropriate incentive model to reach a decentralized trusted data sharing management of IoVEC networks.

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Yuan, Yuan, Zongrui Zou, Dong Li, Li Yan, and Dongxiao Yu. "D-(DP)2SGD: Decentralized Parallel SGD with Differential Privacy in Dynamic Networks." Wireless Communications and Mobile Computing 2021 (March 23, 2021): 1–14. http://dx.doi.org/10.1155/2021/6679453.

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Decentralized machine learning has been playing an essential role in improving training efficiency. It has been applied in many real-world scenarios, such as edge computing and IoT. However, in fact, networks are dynamic, and there is a risk of information leaking during the communication process. To address this problem, we propose a decentralized parallel stochastic gradient descent algorithm (D-(DP)2SGD) with differential privacy in dynamic networks. With rigorous analysis, we show that D-(DP)2SGD converges with a rate of O 1 / K n while satisfying ε -DP, which achieves almost the same convergence rate as previous works without privacy concern. To the best of our knowledge, our algorithm is the first known decentralized parallel SGD algorithm that can implement in dynamic networks and take privacy-preserving into consideration.

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et al., Alraddady. "Dependability in fog computing: Challenges and solutions." International Journal of ADVANCED AND APPLIED SCIENCES 8, no. 4 (April 2021): 82–88. http://dx.doi.org/10.21833/ijaas.2021.04.010.

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The tremendous increase in IoT devices and the amount of data they produced is very expensive to be processed at cloud data centers. Therefore, fog computing was introduced in 2012 by Cisco as a decentralized computing environment that is considered to be more efficient in handling such a plethora in the number of requests. Fog computing is a distributed computing paradigm that focuses on bringing data processing at the network peripheral to reduce response time and increase the quality of service. Dependability challenges of such distributed and heterogeneous computing environments are considered in this paper. Because fog computing is a new computing paradigm, several studies have been presented to tackle its challenges and issues. However, dependability in specific did not receive much attention. In the paper, we explore several solutions to increase dependability in fog computing such as fault tolerance techniques, placement policies, middleware, and data management mechanisms aiming to help system designers choose the most appropriate solution.

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Ali, H. Arafat, A. I. Saleh, Amany M. Sarhan, and Abdulrahman A. Azab. "Peer-to-Peer Desktop Grids Based on an Adaptive Decentralized Scheduling Mechanism." International Journal of Grid and High Performance Computing 2, no. 1 (January 2010): 1–20. http://dx.doi.org/10.4018/jghpc.2010092801.

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This article proposes an adaptive fuzzy logic based decentralized scheduling mechanism that will be suitable for dynamic computing environment in which matchmaking is achieved between resource requirements of outstanding tasks and resource capabilities of available workers. Feasibility of the proposed method is done via real time system. Experimental results show that implementing the proposed fuzzy matchmaking based scheduling mechanism maximized the resource utilization of executing workers without exceeding the maximum execution time of the task. It is concluded that the efficiency of FMA-based decentralized scheduling, in the case of parallel execution, is reduced by increasing the number of subtasks.

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Margariti, Spiridoula V., Vassilios V. Dimakopoulos, and Georgios Tsoumanis. "Modeling and Simulation Tools for Fog Computing—A Comprehensive Survey from a Cost Perspective." Future Internet 12, no. 5 (May 16, 2020): 89. http://dx.doi.org/10.3390/fi12050089.

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Fog computing is an emerging and evolving technology, which bridges the cloud with the network edges, allowing computing to work in a decentralized manner. As such, it introduces a number of complex issues to the research community and the industry alike. Both of them have to deal with many open challenges including architecture standardization, resource management and placement, service management, Quality of Service (QoS), communication, participation, to name a few. In this work, we provide a comprehensive literature review along two axes—modeling with an emphasis in the proposed fog computing architectures and simulation which investigates the simulation tools which can be used to develop and evaluate novel fog-related ideas.

Journal articles on the topic 'Decentralized computing'

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