Current researches for Internet of Things (IoT) QoS mainly focuses on the formulation of service level protocols, which improves some performance of resource scheduling, but there are still many shortcomings in resolving the real-time and personalized requirements of IoT. Aiming at the hierarchical resource scheduling algorithm of the IoT, the key issues of hierarchical resource scheduling are analyzed in detail. The hierarchical resource scheduling of the Internet of Things based on improved heuristic algorithm is deeply studied and explored. A cuckoo search algorithm based on adaptive Cauchy mutation is proposed. Because the algorithm is prone to premature, easy to fall into the local optimal solution, and unable to find the global optimal solution, by introducing mutation operator, the improved algorithm has a certain ability of local random search, while accelerating convergence to the optimal solution in the later period, maintaining the diversity of solutions. The simulation results show that the average service success rate of the proposed resource scheduling algorithm is close to 99%, which can effectively guarantee the relative fairness of user requests, meet the real-time and personalized needs of different users, and improve the utilization rate of resources.
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Boucetta C, Idoudi H, Saidane L A. (2016) Hierarchical cuckoo search-based routing in wireless sensor networks[C]// Computers & Communication.
Hui L, Zhou Q, Fei Z et al (2018) Scheduling based on interruption analysis and PSO for strictly periodic and preemptive partitions in integrated modular avionics [J]. IEEE Access 6(99):13523–13540
Jasper V (2018) Walraevens Joris, Maertens tom, et al. calculation of the performance region of an easy-to-optimize alternative for generalized processor sharing[J]. Eur J Oper Res 270(2):625–635
Zhu C, Hui Z, Han G, et al. (2015) BTDGS: binary-tree based data gathering scheme with Mobile sink for wireless multimedia sensor networks [J]. Mobile Networks & Applications, 20(5):604–622
Zhao Y, Zeng H (2017) An efficient schedulability analysis for optimizing systems with adaptive mixed-criticality scheduling [J]. Real-Time Systems 53(4):1–59
Olaniyan R , Maheswaran M (2018) Synchronous scheduling algorithms for edge coordinated internet of things[C]// 2018 IEEE 2nd international conference on fog and edge computing (ICFEC). IEEE.
Yue C, Long M, Wang J, et al. (2016) Correlation autoencoder hashing for supervised cross-modal search[C]// Acm on International Conference on Multimedia Retrieval.
Carlos Mera-Gomez, Francisco Ramirez, Rami Bahsoon, et al. (2018) A multi-agent elasticity management based on multi-tenant debt exchanges[C]// 2018 IEEE 12th International Conference on Self-Adaptive and Self-Organizing Systems (SASO).
Tan S, Hu L, Wang-Xu A et al (2016) Kernelized cross-modal hashing for multimedia retrieval[C]//. Intell Control Autom
Peng Y, Qi J, Xin H et al (2018) CCL: cross-modal correlation learning with multigrained fusion by hierarchical network[J]. IEEE Trans Multimedia 20(2):405–420
Nazir S, Shafiq S, Iqbal Z, et al. (2018) Cuckoo optimization algorithm based job scheduling using cloud and fog computing in smart grid[C]// the 10th International Conference on Intelligent Networking and Collaborative Systems (INCoS-2018).
Zhou F, Luo M, Tong Y, et al. (2017) Mitigating multi-tenant interference on mobile offloading servers: poster abstract[C]// Symposium.
Xing X, Shimada A, Taniguchi R I, et al. (2015) Coupled dictionary learning and feature mapping for cross-modal retrieval[C]// IEEE International Conference on Multimedia & Expo.
Chen W C, Chen C W, Hu M C. (2018) Syncgan: synchronize the latent spaces of cross-modal generative adversarial networks[C]// 2018 IEEE International Conference on Multimedia and Expo (ICME).
Nishimaki J, Hosokawa T, Fujiwara H. (2016) A scheduling method for hierarchical testability based on test environment generation results[C]// Workshop on design automation for understanding hardware designs.
Liang X, Peng P, Lu Y, et al. (2015) Cross-modal self-taught learning for image retrieval[C]// International Conference on Multimedia Modeling.
El-Ela AAA, El-Sehiemy RA, Abbas AS (2017) Optimal Placement and Sizing of Distributed Generation and Capacitor Banks in Distribution Systems Using Water Cycle Algorithm[J]. IEEE Syst J (99):1–8
Cao G, Iosifidis A, Member S et al (2018) Generalized multi-view embedding for visual recognition and cross-modal retrieval[J]. IEEE Transactions on Cybernetics 48(9):2542–2555
Tang J, Wang K, Shao L (2016) Supervised matrix factorization hashing for cross-modal retrieval[J]. IEEE Trans Image Process 25(7):3157–3166
Wu Y, Wang S, Zhang W, et al. (2017) Online low-rank similarity function learning with adaptive relative margin for cross-modal retrieval[C]// IEEE International Conference on Multimedia & Expo.
Fei H, Cheng J, Zhang Y, et al. (2017) Towards sketch-based image retrieval with deep cross-modal correlation learning[C]// IEEE International Conference on Multimedia & Expo.
Haibo YU, Chao Z, Zuqiang D et al (2018) Economic optimization for configuration and sizing of micro integrated energy systems[J]. Journal of Modern Power Systems and Clean Energy 6(02):144–155
Wang J, Li G, Peng P et al (2017) Semi-supervised semantic factorization hashing for fast cross-modal retrieval[J]. Multimed Tools Appl 76(3):1–19
Yao L, Yuan Y, Huang Q, et al. (2016) Hashing for cross-modal similarity retrieval[C]// International Conference on Semantics.
Tan S, Hu L, Wang-Xu A, et al. (2016) Kernelized cross-modal hashing for multimedia retrieval[C]// Intelligent Control & Automation.
Yoosefi A, Naji HR. (2016) A hierarchical cluster-based model with run-time reconfigurable resource allocation on FPGAs[C]// International Conference on Vlsi Systems.
Khamis S, Fanello S, Rhemann C, et al. (2018) StereoNet: guided hierarchical refinement for real-time edge-aware depth prediction[C]// Europen Conference on Computer Vision (ECCV).
Gasmi K, Rebaya A, Amari I, et al. (2016) Workflow for multi-core architecture: from MATLAB/Simulink models to hardware mapping/scheduling[C]// International Conference on Sciences of Electronics.
George N, Chandrasekaran K, Binu A. (2016) Optimization-aware scheduling in cloud computing[C]// International Conference on Informatics & Analytics.
Hascoët J, Desnos K, Nezan J F, et al. (2017) Hierarchical dataflow model for efficient programming of clustered manycore processors[C]// IEEE International Conference on Application-specific systems.
Guo L, Chen H, Liu Q et al (2018) A computationally efficient and hierarchical control strategy for velocity optimization of on-road vehicles[J]. IEEE Trans Syst Man Cybern Syst 49(1):1–11
Sovannarith H, Chakchai S-I, Gia NT (2017) Distributed image compression architecture over wireless multimedia sensor networks[J]. Wirel Commun Mob Comput 2017(3):1–21
Pendleton M, Sebra R, Pang AWC, Ummat A, Franzen O, Rausch T, Stütz AM, Stedman W, Anantharaman T, Hastie A, Dai H, Fritz MHY, Cao H, Cohain A, Deikus G, Durrett RE, Blanchard SC, Altman R, Chin CS, Guo Y, Paxinos EE, Korbel JO, Darnell RB, McCombie WR, Kwok PY, Mason CE, Schadt EE, Bashir A (2015) Assembly and diploid architecture of an individual human genome via single-molecule technologies[J]. Nat Methods 12(8):780–786
Zhu C, Hui Z, Han G et al (2015) BTDGS: binary-tree based data gathering scheme with Mobile sink for wireless multimedia sensor networks[J]. Mobile Networks & Applications 20(5):604–622
Li Z, Zang C, Zeng P et al (2018) Fully distributed hierarchical control of parallel grid-supporting inverters in islanded AC microgrids[J]. IEEE Trans Ind Inf 14(2):679–690
Realy J, Sáezz S, Crespoy A (2016) Combined Scheduling of Time-Triggered Plans and Priority Scheduled Task Sets[J]. ACM SIGAda Ada Letters 36(1):68–76
This work was supported by the Natural Science Foundation of China (No. 61572074).
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This article is part of the Topical Collection: Special Issue on Fog/Edge Networking for Multimedia Applications
Guest Editors: Yong Jin, Hang Shen, Daniele D'Agostino, Nadjib Achir, and James Nightingale
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Zhang, C., Zeng, G., Wang, H. et al. Hierarchical resource scheduling method using improved cuckoo search algorithm for internet of things. Peer-to-Peer Netw. Appl. 12, 1606–1614 (2019). https://doi.org/10.1007/s12083-019-00801-8
- Hierarchical scheduling
- Internet of things
- Resource scheduling
- Improved cuckoo search algorithm
- Global optimal solution
- Mutation operator