Blockchain-based resource allocation model in fog computing
Open accessAbstract
Fog computing makes up for the shortcomings of cloud computing. It brings many advantages, but various peculiarities must be perceived, such as security, resource management, storage, and other features at the same time. This paper investigates the resource contribution model between the fog node and cloud or users when fog computing introduces blockchain. The proposed model practices the reward and punishment mechanism of the blockchain to boost the fog nodes to contribute resources actively. The behavior of the fog node in contributing resources and the completion degree of the task also for contributing resources are packaged into blocks and stored in the blockchain system to form a transparent, open, and tamper-free service evaluation index. The differential game method is employed to model and solve the above process and address the interaction between the optimal resource contribution strategy of the fog node and the optimal benefit under the optimal resource contribution strategy. Indirectly, this service evaluation index also brings long-term economic benefits to fog service providers. Besides, taking advantage of the performance characteristics of the collective maintenance of blockchain and the ability to establish a credible consensus mechanism in an untrusted environment, fog computing nodes, under the proposed architecture, can have specific security protection capabilities.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
A full-featured blockchain cannot be implemented on Fog nodes due to their restricted storage and computing resources, wide distribution, heterogeneous network, and nodes' selfish behaviour.
“However, a full-featured blockchain cannot be implemented on the Fog nodes due to their restricted storage and computing resources, wide distribution, heterogeneous network, and nodes' selfish behaviour [155].”
From A survey on the adoption of blockchain in IoT: challenges and solutions (2021) · cites this paper as [155]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Bitcoin: A Peer-to-Peer Electronic Cash SystemPreprint
Squarepants, S. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.3977007 · Cited by 11,146 · see citing works
Fog computing and its role in the internet of things
Bonomi, F., Milito, R., Zhu, J., & Addepalli, S. (2012) · Proceedings of the first edition of the MCC workshop on Mobile cloud computing · doi:10.1145/2342509.2342513 · Cited by 6,244 · see citing works
Finding your Way in the Fog: Towards a Comprehensive Definition of Fog Computing
Vaquero, L. M., & Rodero-Merino, L. (2014) · ACM SIGCOMM Computer Communication Review · doi:10.1145/2677046.2677052 · Cited by 1,066 · see citing works
A Software Defined Fog Node Based Distributed Blockchain Cloud Architecture for IoT
Sharma, P. K., Chen, M.-Y., & Park, J. H. (2018) · IEEE Access · doi:10.1109/access.2017.2757955 · Cited by 676 · cited 1× · see citing works
“A blockchain-based distributed cloud architecture is proposed in [35].”
BackgroundFog Computing and Smart Gateway Based Communication for Cloud of Things
Aazam, M., & Huh, E.-N. (2014) · 2014 International Conference on Future Internet of Things and Cloud · doi:10.1109/ficloud.2014.83 · Cited by 620 · see citing works
Cloud/Fog Computing Resource Management and Pricing for Blockchain Networks
Xiong, Z., Feng, S., Wang, W., Niyato, D. T., Wang, P., & Han, Z. (2018) · IEEE Internet of Things Journal · doi:10.1109/jiot.2018.2871706 · Cited by 307 · cited 2× · see citing works
“Considering that the fog node is not able to complete the task with 100% probability, the success rate of introducing contributed resources is related to not only the power (in this paper, the power of fog node is used to characterize the equipment performance of fog node, that is, the larger αi is, the better the per…”
BackgroundEdgechain: an edge-IoT framework and prototype based on blockchain and smart contracts
Pan, J., Wang, J., Hester, A., Alqerm, I., Liu, Y., & Zhao, Y. (2019) · IEEE Internet of Things Journal · doi:10.1109/jiot.2018.2878154 · Cited by 297 · cited 2× · see citing works
“[42] designed a marginal Internet of things framework, called EdgeChain internal tokens.”
BackgroundProofs of Work and Bread Pudding Protocols(Extended Abstract)
Jakobsson, M., & Juels, A. (1999) · Springer eBooks · doi:10.1007/978-0-387-35568-9_18 · Cited by 288 · cited 1× · see citing works
“The consensus layer implements various consensus algorithms such as Proof of Work (PoW) [15] and Proof of Stake (PoS) [16] or Delegated Proof of Stake (DPoS) [17].”
MethodA User Authentication Scheme of IoT Devices using Blockchain-Enabled Fog Nodes
Almadhoun, R., Kadadha, M., Alhemeiri, M., Al-Shehhi, M. R., & Salah, K. (2018) · IEEE/ACS International Conference on Computer Systems and Applications (AICCSA) · doi:10.1109/aiccsa.2018.8612856 · Cited by 281 · cited 2× · see citing works
“[41] propose a user authentication scheme in a fog node that involves blockchain.”
BackgroundAuction Mechanisms in Cloud/Fog Computing Resource Allocation for Public Blockchain Networks
Jiao, Y., Wang, P., Niyato, D., & Suankaewmanee, K. (2019) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2019.2900238 · Cited by 262 · cited 2× · see citing works
“In the above research, scholars mostly use the characteristics of collective maintenance, traceability, and high security in blockchain technology to solve security problems in the fog computing environment [33–35,38,40], and resource allocation issues [39,42].”
BackgroundMethodEfficient and Privacy-Preserving Carpooling Using Blockchain-Assisted Vehicular Fog Computing
Li, M., Zhu, L., & Lin, X. (2018) · IEEE Internet of Things Journal · doi:10.1109/jiot.2018.2868076 · Cited by 229 · cited 1× · see citing works
“[40] consider that, in the fog-based car networking environment, the users may have a certain degree of security and privacy issues when sharing their sensitive information (such as identity and location) during carpooling.”
BackgroundTowards power consumption-delay tradeoff by workload allocation in cloud-fog computing
Deng, R., Lu, R., Lai, C., & Luan, T. H. (2015) · International Conference on Communications · doi:10.1109/icc.2015.7248934 · Cited by 204 · cited 1× · see citing works
“[31] developed a systematic framework to study the trade-off between power consumption and latency in cloud systems.”
BackgroundEvaluation of Logic-Based Smart Contracts for Blockchain Systems
Idelberger, F., Governatori, G., Riveret, R., & Sartor, G. (2016) · International Web Rule Symposium · doi:10.1007/978-3-319-42019-6_11 · Cited by 200 · cited 1× · see citing works
“The blockchain system generally consists of a data layer, a network layer, a consensus layer, an incentive layer, a contract layer, and an application layer [14].”
BackgroundFog computing architecture to enable consumer centric internet of things services
Datta, S. K., Bonnet, C., & Haerri, J. (2015) · 2015 International Symposium on Consumer Electronics (ISCE) · doi:10.1109/isce.2015.7177778 · Cited by 172 · see citing works
Service-oriented heterogeneous resource sharing for optimizing service latency in mobile cloud
Nishio, T., Shinkuma, R., Takahashi, T., & Mandayam, N. B. (2013) · doi:10.1145/2492348.2492354 · Cited by 170 · cited 1× · see citing works
“[21] proposed a service-oriented heterogeneous resource sharing scheme, which effectively reduces the service waiting time.”
BackgroundChen, M.-H., Dong, M., & Liang, B. (2018) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2018.2815533 · Cited by 169 · cited 1× · see citing works
“[29] designed a fog computing resource contribution system consisting of a remote cloud server, computing access point (CAP) and users, and designed the CAP as a mobile user.”
BackgroundA novel secure data transmission scheme in industrial internet of things
Hui, H., Zhou, C., Xu, S., & Lin, F. (2020) · China Communications · doi:10.23919/jcc.2020.01.006 · Cited by 163 · cited 1× · see citing works
“At the same time, it cryptographically guarantees the data’s irreversible and unforgeable characteristics and also protects the data security of users in the fog computing environment [43].”
BackgroundKumar, G., Saha, R., Rai, M. K., Thomas, R., & Kim, T.-H. (2019) · IEEE Internet of Things Journal · doi:10.1109/jiot.2019.2911969 · Cited by 145 · cited 2× · see citing works
“[37] proposed a statistical method to solve the expected value of a mathematical problem in the PoW consensus, to make the blockchain better suited for cloud computing or fog computing.”
BackgroundMethodA Hierarchical Game Framework for Resource Management in Fog Computing
Zhang, H., Zhang, Y., Gu, Y., Niyato, D. T., & Han, Z. (2017) · IEEE Communications Magazine · doi:10.1109/mcom.2017.1600896 · Cited by 119 · cited 1× · see citing works
“[19] proposed a game framework for resource management three-layered structure in data service operator (DSO), fog node (FN), and data service subscriber (ADSS) scenarios.”
BackgroundSteiner tree based optimal resource caching scheme in fog computing
Su, J., Lin, F., Zhou, X., & Lu, X. (2015) · China Communications · doi:10.1109/cc.2015.7224698 · Cited by 113 · cited 1× · see citing works
“[24] proved that the shortest path scheme based on Steiner tree theory is more efficient than the traditional scheme to minimize the path cost of the fog server contributing resources.”
MethodSmart collaborative distribution for privacy enhancement in moving target defense
Song, F., Zhou, Y., Wang, Y., Zhao, T., You, I., & Zhang, H. (2018) · Information Sciences · doi:10.1016/j.ins.2018.06.002 · Cited by 97 · cited 1× · see citing works
“Although the resources and computing power of idle resource devices have different degrees of restrictions [18], they can jointly cooperatively”
BackgroundAlsaffar, A. A., Pham, H. P., Hong, C. S., Huh, E., & Aazam, M. (2016) · Mobile Information Systems · doi:10.1155/2016/6123234 · Cited by 95 · cited 1× · see citing works
“[25] proposed a linear tree decision rule algorithm based on service size, completion time, and virtual machine capacity, which helps workload balancing and improves resource allocation.”
BackgroundSmart Collaborative Automation for Receive Buffer Control in Multipath Industrial Networks
Song, F., Ai, Z., Zhou, Y., You, I., Choo, K. R., & Zhang, H. (2019) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2019.2950109 · Cited by 94 · cited 1× · see citing works
“The main aim is to relocate some core functions of the cloud to the “near” location of the network edge, to overcome the defects of cloud computing regarding location awareness, mobility support, and real-time interaction [3,4].”
BackgroundScalable Fog Computing with Service Offloading in Bus Networks
Ye, D., Wu, M., Tang, S., & Yu, R. (2016) · IEEE International Conference on Cyber Security and Cloud Computing (CSCloud) · doi:10.1109/cscloud.2016.34 · Cited by 85 · cited 1× · see citing works
“[23] offload the computational tasks in the cloud to the fog server by bypassing the private cloud allocation strategy and use a genetic algorithm to find the optimal allocation scheme.”
MethodA Smart Collaborative Routing Protocol for Reliable Data Diffusion in IoT Scenarios
Ai, Z., Zhou, Y.-T., & Song, F. (2018) · Sensors · doi:10.3390/s18061926 · Cited by 73 · cited 1× · see citing works
“In recent years, with the continuous development of intelligent terminals and wireless network technology, we have entered the new era of the Internet of Everything from the interconnection of people and things, and the interconnection of things and things [1].”
BackgroundDLattice: A Permission-Less Blockchain Based on DPoS-BA-DAG Consensus for Data Tokenization
Zhou, T., Li, X., & Zhao, H. (2019) · IEEE Access · doi:10.1109/access.2019.2906637 · Cited by 62 · see citing works
Fog Computing as Enabler for Blockchain-Based IIoT App Marketplaces - A Case Study
Seitz, A., Henze, D., Miehle, D., Bruegge, B., Nickles, J., & Sauer, M. S. (2018) · International Conference on Internet of Things: Systems, Management and Security (IOTSMS) · doi:10.1109/iotsms.2018.8554484 · Cited by 61 · cited 2× · see citing works
“[36] introduced a decentralized market for industrial edge applications, Industrial Internet of Things (IIoT) Bazaar, which uses the blockchain’s collective maintenance, timing, and other features to create high transparency for users while ensuring edge device applications.”
BackgroundAn algorithm to optimise the load distribution of fog environments
Neto, E. C. P., Callou, G. R. D. A., & Aires, F. (2017) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc.2017.8122791 · Cited by 60 · cited 1× · see citing works
“[27] optimized the load balancing in a fog environment using the multi-tenant allocation algorithm (MtLDF), which takes into account the latency and priority of specific multi-tenant needs.”
MethodCombining Mobile and Fog Computing: Using CoAP to Link Mobile Device Clouds with Fog Computing
Shi, H. H., Chen, N., & Deters, R. (2015) · IEEE International Conference on Data Science and Data Intensive Systems · doi:10.1109/dsdis.2015.115 · Cited by 54 · cited 1× · see citing works
“[26] used the Representational State Transfer (REST) design pattern and the IoT Constrained Application Protocol (CoAP) to enable resource cooperation in the form of a planar architecture between fog nodes.”
MethodModeling Space-Terrestrial Integrated Networks with Smart Collaborative Theory
Song, F., Zhou, Y., Chang, L., & Zhang, H. (2019) · IEEE Network · doi:10.1109/mnet.2018.1800187 · Cited by 50 · cited 1× · see citing works
“In practical applications, the resource requirements of cloud service providers or users change dynamically, and contributing resources are not 100% successful [32] (or 100% total contribution).”
BackgroundA Fog Computing Architecture to Share Sensor Data by Means of Blockchain Functionality
Cech, H. L., Großmann, M., & Krieger, U. R. (2019) · IEEE International Conference on Fog Computing (ICFC) · doi:10.1109/icfc.2019.00013 · Cited by 50 · cited 1× · see citing works
“The authors of [33] analyze the rapid development of IoT services, carry out analysis concerning the innovative application of blockchain technology based on the fog computing architecture, and propose a fog computing architecture which is built on Hypriot Cluster Lab of Fog computing (HCL-BaFog) based on blockchain.”
MethodOptimization-Oriented Resource Allocation Management for Vehicular Fog Computing
Lin, F., Zhou, Y., Pau, G., & Collotta, M. (2018) · IEEE Access · doi:10.1109/access.2018.2879988 · Cited by 46 · cited 1× · see citing works
“Considering that the fog computing environment has the features of restricted resources, wide distribution, heterogeneous network, and selfish fog computing nodes, the proper management of these characteristics in fog nodes has become one of the main aims focus in the fog computing research field [7].”
BackgroundA Smart Collaborative Charging Algorithm for Mobile Power Distribution in 5G Networks
Ai, Z., Liu, Y., Song, F., & Zhang, H. (2018) · IEEE Access · doi:10.1109/access.2018.2818790 · Cited by 43 · cited 2× · see citing works
“Due to the DPoS consensus mechanis , t e authorized nodes involv d in bookkeeping n d to maintain a specific online time rate (usu lly 99%), which undoubtedly leads to the loss of power [45].”
BackgroundA New Resource Allocation Mechanism for Security of Mobile Edge Computing System
Hui, H., Zhou, C., An, X., & Lin, F. (2019) · IEEE Access · doi:10.1109/access.2019.2936374 · Cited by 43 · see citing works
Zhu, X., & Badr, Y. (2018) · IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData) · doi:10.1109/cybermatics_2018.2018.00234 · Cited by 33 · see citing works
Content Recommendation Algorithm for Intelligent Navigator in Fog Computing Based IoT Environment
Lin, F., Zhou, Y., You, I., Lin, J., An, X., & Lü, X. (2019) · IEEE Access · doi:10.1109/access.2019.2912897 · Cited by 31 · cited 1× · see citing works
“Nevertheless, fog computing, as an emerging computing model, has brought new problems and challenges to the computing service field due to its “fog node as a resource provider” [6].”
BackgroundA Blockchain-Based Offloading Approach in Fog Computing Environment
Tang, W., Zhao, X., Rafique, W., & Dou, W. (2018) · IEEE International Symposium on Parallel and Distributed Processing with Applications (ISPA) · doi:10.1109/bdcloud.2018.00056 · Cited by 28 · see citing works
Real time resource allocation on a dynamic two level symbiotic fog architecture
Kochar, V., & Sarkar, A. (2016) · International Symposium on Embedded Computing and System Design (ISED) · doi:10.1109/ised.2016.7977053 · Cited by 25 · cited 1× · see citing works
“Kochar and Sarkar [28] constructed a two-tier edge computing resource sharing framework that takes into account the benefits of the fog service provider, resulting in higher system utilization.”
BackgroundA coalitional game based mechanism for resource sharing in geo-distributed mobile cloud computing
Zhao, Y., Huang, Z., Zhang, X., Liu, W., Zhang, Q., & Zhu, Z. (2017) · Chinese Control and Decision Conference (CCDC) · doi:10.1109/ccdc.2017.7979158 · Cited by 3 · cited 1× · see citing works
“[20] proposed a resource sharing mechanism based on the alliance game by studying the resource contribution between cooperative cloud providers.”
BackgroundDong, X., Zheng, J., Cai, Y., Yang, J., & Wang, Y. (2017) · International Conference on Wireless Communications and Signal Processing (WCSP) · doi:10.1109/wcsp.2017.8171081 · Cited by 3 · cited 1× · see citing works
“[22] studied the optimization of energy and communication resource sharing between mobile devices with limited energy and restricted data in mobile cloud computing.”
Background
Flagged first, then by citations
2 of the 42 references couldn’t be matched to a known paper.
Research works printed but not linked: 14
- Ai, Z.; Zhou, Y.; Song, F. A smart collaborative routing protocol for reliable data diffusion in IoT scenarios. Sensors 2018, 18, 1926.
- Bonomi, F. Connected vehicles, the internet of things, and fog computing. In Proceedings of the Eighth ACM International Workshop on Vehicular Inter-Networking (VANET), Las Vegas, NV, USA, 19-23 September 2011; pp. 13-15.
- Vaquero, L.M.; Rodero-Merino, L. Finding your way in the fog: Towards a comprehensive definition of fog computing. ACM SIGCOMM Comput. Commun. Rev. 2014, 44, 27-32. · DOI
- Lin, F.; Zhou, Y.; You, I.; Lin, J.; An, X.; Lv, X. Content recommendation algorithm for intelligent navigator in fog computing based iot environment. IEEE Access 2019, 7, 53677-53686.
- Bonomi, F. Connected vehicles, the internet of things, and fog computing. In Proceedings of the Eighth ACM International Workshop on Vehicular Inter-Networking (VANET), Las Vegas, NV, USA, 19-23 September 2011; pp. 13-15.
- Bonomi, F.; Milito, R.; Zhu, J.; Addepalli, S. Fog computing and its role in the internet of things. In Proceedings of the First Edition of the MCC Workshop on Mobile Cloud Computing, Helsinki, Finland, 17 August 2012; pp. 13-16.
- Song, F.; Ai, Z.; Zhou, Y.; You, I.; Choo, R.; Zhang, H. Smart Collaborative Automation for Receive Buffer Control in Multipath Industrial Networks. IEEE Trans. Ind. Inform. 2019. [CrossRef] · DOI
- Lin, F.; Zhou, Y.; Pau, G.; Collotta, M. Optimization-oriented resource allocation management for vehicular fog computing. IEEE Access 2018, 6, 69294-69303. [CrossRef] · DOI
- Ai, Z.; Liu, Y.; Chang, L.; Lin, F.; Song, F. A Smart Collaborative Authentication Framework for Multi-dimensional Fine-grained Control. IEEE Access. in press.
- Yuan, Y.; Wang, F. Blockchain: The state of the art and future trends. Acta Autom. Sin. 2016, 42, 481-494.
- Agarwal, S.; Yadav, S.; Yadav, A.K. An architecture for elastic resource allocation in Fog computing. Int. J. Comput. Sci. Commun. 2015, 6, 201-207.
- Houy, N. The Bitcoin mining game. 2014. Available online: https://papers.ssrn.com/sol3/papers.cfm?abstract_ id=2407834(accessed on 24 September 2019).
- Song, F.; Zhu, M.; Zhou, Y.; You, I.; Zhang, H. Smart collaborative tracking for ubiquitous power IoT in edge-cloud interplay domain. IEEE Internet Things J.. in press.
- Yeung, D.W.K.; Petrosjan, L.A. Cooperative Stochastic Differential Games; Springer Science & Business Media: Berlin, Germany, 2006.
Cited by
Sorted by citations
A survey on the adoption of blockchain in IoT: challenges and solutions
Uddin, M. A., Stranieri, A., Gondal, I., & Balasubramanian, V. (2021) · Blockchain: Research and Applications · doi:10.1016/j.bcra.2021.100006 · Cited by 365 · see citing works
A Review on IEEE 802.11p for Intelligent Transportation Systems
Arena, F., Pau, G., & Severino, A. G. (2020) · Journal of Sensor and Actuator Networks · doi:10.3390/jsan9020022 · Cited by 236 · see citing works
Ferrag, M. A., & Shu, L. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3078072 · Cited by 228 · see citing works
“Wang et al. [74] uses the differential game to model the introduction of blockchain technology in traditional fog computing.”
Bukhari, M. M., Ghazal, T. M., Abbas, S., Khan, M. A., Farooq, U., Wahbah, H., Ahmad, M., & Khan, M. A. (2022) · Computational Intelligence and Neuroscience · doi:10.1155/2022/3606068 · Cited by 99 · see citing works
“It must be resolved to enhance the fog performance by offloading certain tasks to nearby nodes to make sure of the fair utilization of the underlying resources [29].”
BackgroundAn Overview on the Current Status and Future Perspectives of Smart Cars
Arena, F., Pau, G., & Severino, A. G. (2020) · Infrastructures · doi:10.3390/infrastructures5070053 · Cited by 76 · see citing works
“The 5G, with a theoretical bandwidth from 100 Mbps to 10 Gbps, reduced energy consumption, enormously decreased latency time, and unprecedented reliability, will be the network that will connect the world of tomorrow [24].”
BackgroundBlockchain technology as a Fog computing security and privacy solution: An overview
Alzoubi, Y. I., Al‐Ahmad, A., & Kahtan, H. (2021) · Computer Communications · doi:10.1016/j.comcom.2021.11.005 · Cited by 76 · see citing works
A Survey on Blockchain-Fog Integration Approaches
Baniata, H., & Kertész, A. (2020) · IEEE Access · doi:10.1109/access.2020.2999213 · Cited by 66 · see citing works
“Authors of [35] proposed a model in which BC is deployed in IoT-Fog-Cloud environment, to hold information about the contributed resources by fog nodes.”
BackgroundAn overview of big data analysis
Arena, F., & Pau, G. (2020) · Bulletin of Electrical Engineering and Informatics · doi:10.11591/eei.v9i4.2359 · Cited by 60 · see citing works
“IoT applications cover many features, from health [13-15] to home automation [16-19], from transport [20-24] to security [25-29].”
BackgroundAlzoubi, Y. I., Gill, A., & Mishra, A. (2022) · Journal of Cloud Computing · doi:10.1186/s13677-022-00353-y · Cited by 60 · see citing works
“The differential game technique was used to describe and solve the aforementioned process, as well as to handle the relationship between the fog node’s optimal resource contribution strategy and the optimal benefit under that strategy [205].”
BackgroundMethodReliable Resource Allocation and Management for IoT Transportation Using Fog Computing
Atiq, H. U., Ahmad, Z., Zaman, S. K. U., Khan, M. A., Shaikh, A. A., & Al‐Rasheed, A. (2023) · Electronics · doi:10.3390/electronics12061452 · Cited by 47 · see citing works
Computational Resource Allocation in Fog Computing: A Comprehensive Survey
Bachiega, J., Costa, B., de Carvalho, L. R., Rosa, M., & Araújo, A. (2023) · ACM Computing Surveys · doi:10.1145/3586181 · Cited by 45 · see citing works
“12.9% Game [71, 80, 91, 93, 96, 99, 101, 184, 189, 199, 200] Uses mathematical models to make optimal decisions under con-flictingconditions.”
BackgroundInfluentialBlockchain consensus mechanisms comparison in fog computing: A systematic review
Alzoubi, Y. I., & Mishra, A. (2024) · ICT Express · doi:10.1016/j.icte.2024.02.008 · Cited by 33 · see citing works
HIDRA: A Distributed Blockchain-Based Architecture for Fog/Edge Computing Environments
Núñez‐Gómez, C., Caminero, B., & Carrión, C. (2021) · IEEE Access · doi:10.1109/access.2021.3082197 · Cited by 32 · see citing works
“In [40], a blockchain-based fog computing resource contribution model is proposed.”
MethodLin, L. Z., Wu, J., Zhou, Z., Zhao, J., Li, P., & Xiong, J. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3358379 · Cited by 30 · see citing works
“This gap further diminishes with the reduction of the reward ratio τ [as per (17)].”
BackgroundBolívar, M. P. R., Sepe, F., Nanu, L., & Roberto, F. (2024) · Journal of Hospitality and Tourism Technology · doi:10.1108/jhtt-03-2024-0193 · Cited by 29 · see citing works
Auction based cost‐efficient resource allocation by utilizing blockchain in fog computing
Jain, V., & Kumar, B. (2022) · Transactions on Emerging Telecommunications Technologies · doi:10.1002/ett.4469 · Cited by 20 · see citing works
“For simulation we consider that a fog service provider can offer any of four different types of resources which are categorized as (small, medium, larger, extra-large) where the weightage of each resource type is ( 1 , 2 , 4 , 8 ) similar to the work presented in Reference 34.”
MethodInfluentialBlockchain-Fog Computing Integration Applications: A Systematic Review
Alzoubi, Y. I., & Aljaafreh, A. (2023) · Cybernetics and Information Technologies · doi:10.2478/cait-2023-0001 · Cited by 22 · see citing works
A Blockchain assisted fog computing for secure distributed storage system for IoT Applications
Apat, H. K., & Sahoo, B. (2024) · Journal of Industrial Information Integration · doi:10.1016/j.jii.2024.100739 · Cited by 20 · see citing works
Blockchain-enabled cooperative computing strategy for resource sharing in fog networks
Rani, S., Gupta, D., Herencsár, N., & Srivastava, G. (2022) · Internet of Things · doi:10.1016/j.iot.2022.100672 · Cited by 18 · see citing works
A Data Integrity Verification Scheme for Centralized Database Using Smart Contract and Game Theory
He, S., Xing, X., Wang, G., & Sun, Z. (2023) · IEEE Access · doi:10.1109/access.2023.3284850 · Cited by 17 · see citing works
“Some solutions focus on optimizing computing resource management, such as realizing decentralized scheduling of multiple reusable energy sources in an energy local network (ELN) system [31], and optimizing resource allocation in a fog computing environment [32].”
BackgroundTrust Management and Resource Optimization in Edge and Fog Computing Using the CyberGuard Framework
Alwakeel, A. M., & Alnaim, A. K. (2024) · Sensors · doi:10.3390/s24134308 · Cited by 16 · see citing works
“[13]”
BackgroundLiu, X. (2021) · Wireless Personal Communications · doi:10.1007/s11277-021-08213-9 · Cited by 13 · see citing works
RetractedBlock-RAS: A P2P Resource Allocation Scheme in 6G Environment with Public Blockchains
Shukla, A., Gupta, R., Tanwar, S., Kumar, N., & Rodrigues, J. J. P. C. (2020) · Globecom · doi:10.1109/globecom42002.2020.9348008 · Cited by 12 · see citing works
“Here, blockchain ensures the secure and fair resource allocation among all the devices connected to the 6G communication network with complete transparency [8].”
BackgroundCloud continuum testbeds and next-generation ICTs: Trends, challenges, and perspectives
Casino, F., López-Iturri, P., & Patsakis, C. (2024) · Computer Science Review · doi:10.1016/j.cosrev.2024.100696 · Cited by 11 · see citing works
Liu, Y., Zhang, Y., & Zheng, D. (2025) · Financial Innovation · doi:10.1186/s40854-024-00737-w · Cited by 11 · see citing works
“And BCT can be integrated with an IoT sys-tem, guaranteeing its immutability and efficiency (Wang et al. 2019b).”
Distributed Ledger Technologies for Cellular Networks and Beyond 5G: a survey
Moussaoui, M., Aryal, N., Bertin, E., & Crespi, N. (2022) · Conference on Blockchain Research & Applications for Innovative Networks and Services (BRAINS) · doi:10.1109/brains55737.2022.9908598 · Cited by 8 · see citing works
“In order to motivate nodes to engage in fog resource contribution, [43] introduces a BC-based model that employs a punishment/ reward mechanism to assess the satisfaction level of the delivered fog services by the providers.”
BackgroundPelican optimization algorithm with blockchain for secure load balancing in fog computing
Premkumar, N., & Santhosh, R. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-17632-8 · Cited by 8 · see citing works
“Haoyu Wang et al. [14] studied the asset assurance model between the fog center and the cloud or clients.”
Dynamic stochastic game-based security of edge computing based on blockchain
Wang, H., & An, J. (2023) · The Journal of Supercomputing · doi:10.1007/s11227-023-05289-x · Cited by 7 · see citing works
“In addition, according to [24], due to the unique nature of the distributed system, the larger N is, the lower the efficiency, thus reducing the experience of marginal users and indirectly affecting the benefits of defenders.”
BackgroundMethodA blockchain-based cyber-security for connected networks
Choudhary, D., & Pahuja, R. (2023) · Peer-to-Peer Networking and Applications · doi:10.1007/s12083-023-01506-9 · Cited by 7 · see citing works
“The fundamental model of Blockchain technology revolves around cryptographic hash functions in their most primitive form [14].”
BackgroundA New Blockchain-Based Auction Method for Resource Allocation in Fog Computing Environment
Ghasemi, A. (2024) · Journal of Network and Systems Management · doi:10.1007/s10922-024-09883-z · Cited by 6 · see citing works
“In [21], a resource allocation approach based on differential game theory in the cloud-fog computing environment using blockchain to balance the amount of participation of nodes and appropriate profit is proposed.”
A New Approach to the Resource Allocation Problem in Fog Computing Based on Learning Automata
Pourian, R. E., Fartash, M., & Torkestani, J. A. (2022) · Cybernetics & Systems · doi:10.1080/01969722.2022.2145653 · Cited by 6 · see citing works
“Wang et al. (2019) pre-sent a blockchain-based model for resource sharing in FC.”
Game-theoretic Designs for Blockchain-based IoT: Taxonomy and Research Directions
Erfan, F., Bellaïche, M., & Halabi, T. (2022) · IEEE International Conference on Decentralized Applications and Infrastructures (DAPPS) · doi:10.1109/dapps55202.2022.00012 · Cited by 6 · see citing works
“For instance, Wang et al. [27] used differential games to model the blockchain on top of fog computing to solve the resource contribution problem.”
BackgroundMethodDynamic Game‐Based Computation Offloading and Resource Allocation in LEO‐Multiaccess Edge Computing
Wang, H., Wang, H., & An, J. (2021) · Wireless Communications and Mobile Computing · doi:10.1155/2021/9486818 · Cited by 6 · see citing works
An Efficient SDFRM Security System for Blockchain Based Internet of Things
Vivekraj, M., & Ramanathan, T. (2022) · Intelligent Automation & Soft Computing · doi:10.32604/iasc.2023.027675 · Cited by 5 · see citing works
Smart offload chain: a proposed architecture for blockchain assisted fog offloading in smart city
Patel, M. P., Gohil, B. N., Chaudhary, S., & Garg, S. (2022) · International Journal of Power Electronics and Drive Systems/International Journal of Electrical and Computer Engineering · doi:10.11591/ijece.v12i4.pp4137-4145 · Cited by 5 · see citing works
Xu, L., & Yin, J. (2025) · Computer Networks · doi:10.1016/j.comnet.2025.111618 · Cited by 4 · see citing works
Adaptive Spatial-Temporal Regularization for Correlation Filters Based Visual Object Tracking
Chen, F., & Wang, X. (2021) · Symmetry · doi:10.3390/sym13091665 · Cited by 4 · see citing works
Fog Computing and Edge Computing: Open Issues, Critical Challenges and the Road Ahead for Future
Nair, M. M., Mishra, A. K., & Tyagi, A. K. (2023) · International Conference on Contemporary Computing · doi:10.1145/3607947.3607962 · Cited by 3 · see citing works
“Moreover, Blockchain would assure all its user’s complete privacy and security by eliminating the middlemen who usually has control over the data [22].”
BackgroundLight-HIDRA: Scalable and decentralized resource orchestration in Fog-IoT environments
Núñez‐Gómez, C., de Vos, M., Decouchant, J., Pouwelse, J. A., Caminero, B., & Carrión, C. (2024) · Future Generation Computer Systems · doi:10.1016/j.future.2024.05.041 · Cited by 3 · see citing works
A Sustainable Resource Allocation Techniques for Fog Computing
Singh, J., & Singh, P. (2021) · Lecture notes in civil engineering · doi:10.1007/978-981-15-9554-7_13 · Cited by 3 · see citing works
Consortium blockchain-based IoT identity management for smart systems
Hanumantharaju, R., Shreenath, K. N., Sowmya, B. J., Supreeth, S., Dayananda, P., Shivashankar, R., & Shruthi, G. (2025) · Cogent Engineering · doi:10.1080/23311916.2025.2573142 · Cited by 2 · see citing works
“Another study (Wang et al., 2019) proposed a reward-4 punishment system in blockchain to improve fog node performance, with future work aimed at addressing fog computing challenges in resource management.”
Abubacker, N. F., Rana, M. E., & Raheem, M. (2023) · Advances in computational intelligence and robotics book series · doi:10.4018/978-1-6684-4466-5.ch003 · Cited by 2 · see citing works
Blockchain in Evolving Computing Paradigms: A Beginner's Guide for Review and Future Directions
Mahapatra, A., Mishra, K., Majhi, S. K., & Pradhan, R. (2023) · IEEE Region Humanitarian Technology Conference · doi:10.1109/r10-htc57504.2023.10461800 · Cited by 2 · see citing works
“(2019) [7] presented a model for allocating resources in Fog computing platforms that makes use of blockchain technology.”
BackgroundEnabling 6G Networks for Advances Challenges and Traffic Engineering for Future Connectivity
Ahmed, M., Waqqas, F., Fatima, M., Khan, A. M., Naz, M. A., & Ahmed, M. (2024) · VFAST Transactions on Software Engineering · doi:10.21015/vtse.v12i4.2017 · Cited by 2 · see citing works
FogChainFlow: On-off blockchain data management for optimizing IoT healthcare
Karthikeyan, P., & Brindha, K. (2025) · Cluster Computing · doi:10.1007/s10586-025-05698-y · Cited by 2 · see citing works
Aplicaciones de blockchain en IoT y computación en la niebla
Caminero, B., Núñez‐Gómez, C., & Carrión, C. (2021) · Actas del Congreso Internacional de Ingeniería de Sistemas · doi:10.26439/ciis2021.5548 · Cited by 1 · see citing works
R, S. K. Y., & Champa, H. N. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-024-18322-9 · Cited by 1 · see citing works
Blockchain-Based Fog Computing For Internet of Things: A Review of Trends and Challenges
Abdullahi, I., Adeshina, S. A., & Kalu, F. (2021) · International Conference on Multidisciplinary Engineering and Applied Science (ICMEAS) · doi:10.1109/icmeas52683.2021.9692369 · Cited by 1 · see citing works
Geetha, R., Rajamani, V., & Parthasarathy, V. (2022) · Research Square · doi:10.21203/rs.3.rs-1366799/v1 · Cited by 1 · see citing works
Integrating Blockchain and Fog Computing Technologies for Efficient Privacy-preserving Systems
Baniata, H. (2023) · doi:10.14232/phd.11555 · Cited by 1 · see citing works
Blockchain Based Trust for the Internet of Things: A Review
Shehada, D. J. M., Amour, M. A., Muammar, S., & Gawanmeh, A. (2022) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-97652-1_21
Towards Intelligent Control of Beaconing Power and Beaconing Rate in Vehicular Ad Hoc Networks
Omar, D. A., Garmani, H., Amrani, M. E., Azougaghe, E.-S., Baslam, M., & Jourhmane, M. (2022) · Journal of Interconnection Networks · doi:10.1142/s0219265922500013
Implementation of IoT and Blockchain in Traffic Control by using Matrix AlgorithmPreprint
Ahmed, M., -ul-Haque, M. I., Ahmed, A., Kamel, H., Radi, W., & Amin, U. (2023) · Research Square · doi:10.21203/rs.3.rs-2960312/v1
Rana, M. E., & Abubacker, N. F. (2023) · Advances in computational intelligence and robotics book series · doi:10.4018/978-1-6684-4466-5.ch001
Elhaou, H., Elmiraouy, O., bourigue, R., & Azougaghe, E.-S. (2026) · Communications in computer and information science · doi:10.1007/978-3-032-16281-6_15
Blockchain-Based Fog Environment for Self-Sustaining Fog Networks
Alshuaibi, E. A., & Hussain, F. K. (2026) · Springer handbooks · doi:10.1007/978-3-031-92372-2_27
- Authors
- Wang, H., Wang, L., Zhou, Z., Tao, X., Pau, G., & Arena, F.
- Affiliations
- University of Science and Technology Beijing (CN)
- Asiainfo Technology Co., Ltd.; Asiainfo Technology Co., Ltd. Northwest Wang Dong Road, Haidian District, Building #19, Beijing 100193, China
- Institute of Medical Support Technology, Academy of Systems Engineering, Tianjin 300050, China
- Università degli Studi di Enna Kore (IT)
- Corresponding author
- Wang, Lina
- Zhou, Zhichao
- Venue
- Applied Sciences
- Publisher
- MDPI AG
- Published
- 2019-12-16
- Volume
- 9
- Issue
- 24
- Pages
- 5538
- ISSN
- 2076-3417 (ISSN-L)
- OpenAlex
- W2996749605
- Cited by
- 58
- References
- 42
- Citations by year
- 2026: 2 · 2025: 4 · 2024: 10 · 2023: 13 · 2022: 12 · 2021: 10 · 2020: 7
- FWCI
- 3.97
- Type
- journal article
- Language
- en
- Open access
- gold
- dx.doi.org
- Licence
- Locations
- Applied Sciences (publishedVersion, cc-by)
- Funders
- National Key R&D Program of China (2017YFC0806404)
- National Natural Science Foundation of China (61701020)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Physical Sciences › Computer Science › Information Systems › Blockchain Technology Applications and Security
- Physical Sciences › Computer Science › Information Systems › Cloud Computing and Resource Management
- Keywords
- blockchain, fog computing, resource allocation, fog nodes, reward and punishment system, differential games, consensus algorithms
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
