@article{Zanbouri2025, 
 title = {A Comprehensive Survey of Wireless Time-Sensitive Networking (TSN): Architecture,  Technologies,  Applications,  and Open Issues}, 
 volume = {27},
 ISSN = {2373-745X},
 url = {http://dx.doi.org/10.1109/COMST.2024.3486618},
 DOI = {10.1109/comst.2024.3486618},
 number = {4},
 journal = {IEEE Communications Surveys & Tutorials},
 publisher = {Institute of Electrical and Electronics Engineers (IEEE)},
 author = {Zanbouri,  Kouros and Noor-A-Rahim,  Md. and John,  Jobish and Sreenan,  Cormac J. and Vincent Poor,  H. and Pesch,  Dirk},
 year = {2025},
 month = Aug,
 pages = {2129–2155} 
 }

@INPROCEEDINGS{10978126,
 author={Zanbouri, Kouros and Noor-A-Rahim, Md. and Pesch, Dirk},
 booktitle={2025 IEEE Wireless Communications and Networking Conference (WCNC)}, 
 title={Comparative Performance Evaluation of 5G-TSN Applications in Indoor Factory Environments},
 year={2025},
 volume={},
 number={},
 pages={1-6}, 
 keywords={5G mobile communication;Wireless networks;Simulation;Scalability;Quality of service;Ultra reliable low latency communication;Production facilities;Real-time systems;Reliability;Testing;5G;TSN;Wireless TSN;Industrial Networks;Indoor Factory}, 
 doi={10.1109/WCNC61545.2025.10978126}
 }

@INPROCEEDINGS{10978486,
 author={Zanbouri, Kouros and Noor-A-Rahim, Md. and Pesch, Dirk},
 booktitle={2025 IEEE Wireless Communications and Networking Conference (WCNC)},
 title={Scalability Analysis of 5G-TSN Applications in Indoor Factory Settings},
 year={2025},
 volume={},
 number={},
 pages={1-6},
 keywords={Wireless communication;Wireless sensor networks;5G mobile communication;Scalability;Ethernet;Ultra reliable low latency communication;Production facilities;Robustness;Indoor environment;Fourth Industrial Revolution;5G;TSN;Industry 4.0;Wireless TSN;Industrial Networks;Indoor Factory;Smart Factory},
 doi={10.1109/WCNC61545.2025.10978486}
 }

@article{https://doi.org/10.1002/dac.5886,
 author = {Zanbouri, Kouros and Darbandi, Mehdi and Nassr, Mohammad and Heidari, Arash and Navimipour, Nima Jafari and Yalcın, Senay},
 title = {A GSO-based multi-objective technique for performance optimization of blockchain-based industrial Internet of things},
 journal = {International Journal of Communication Systems},
 volume = {37},
 number = {15},
 pages = {e5886},
 keywords = {blockchain, Glowworm Swarm Optimization, industry, internet of things, multi-objective optimization},
 doi = {https://doi.org/10.1002/dac.5886},
 url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/dac.5886},
 eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1002/dac.5886},
 abstract = {Summary The latest developments in the industrial Internet of things (IIoT) have opened up a collection of possibilities for many industries. To solve the massive IIoT data security and efficiency problems, a potential approach is considered to satisfy the main needs of IIoT, such as high throughput, high security, and high efficiency, which is named blockchain. The blockchain mechanism is considered a significant approach to boosting data protection and performance. In the quest to amplify the capabilities of blockchain-based IIoT, a pivotal role is accorded to the Glowworm Swarm Optimization (GSO) algorithm. Inspired by the collaborative brilliance of glowworms in nature, the GSO algorithm offers a unique approach to harmonizing these conflicting aims. This paper proposes a new approach to improve the performance optimization of blockchain-based IIoT using the GSO algorithm due to the blockchain's contradictory objectives. The proposed blockchain-based IIoT system using the GSO algorithm addresses scalability challenges typically associated with blockchain technology by efficiently managing interactions among nodes and dynamically adapting to network demands. The GSO algorithm optimizes the allocation of resources and decision-making, reducing inefficiencies and bottlenecks. The method demonstrates considerable performance improvements through extensive simulations compared to traditional algorithms, offering a more scalable and efficient solution for industrial applications in the context of the IIoT. The extensive simulation and computational study have shown that the proposed method using GSO considerably improves the objective function and blockchain-based IIoT systems' performance compared to traditional algorithms. It provides more efficient and secure systems for industries and corporations.},
 year = {2024}
 }

@ARTICLE{10056387,
 author={Zanbouri, Kouros and Al-Khafaji, Hamza Mohammed Ridha and Navimipour, Nima Jafari and Yalcın, Şenay},
 journal={IEEE MultiMedia},
 title={A New Fog-Based Transmission Scheduler on the Internet of Multimedia Things Using a Fuzzy-Based Quantum Genetic Algorithm},
 year={2023},
 volume={30},
 number={3},
 pages={74-86},
 keywords={Genetic algorithms;Internet of Things;Optimization;Multimedia systems;Cloud computing;Quantum computing;Edge computing;Audio systems;Streaming media},
 doi={10.1109/MMUL.2023.3247522}
 }

@Article{electronics11223769,
 AUTHOR = {Zanbouri, Kouros and Razoughi Bastak, Mostafa and Alizadeh, Seyed Mehdi and Jafari Navimipour, Nima and Yalcin, Senay},
 TITLE = {A New Energy-Aware Method for Gas Lift Allocation in IoT-Based Industries Using a Chemical Reaction-Based Optimization Algorithm},
 JOURNAL = {Electronics},
 VOLUME = {11},
 YEAR = {2022},
 NUMBER = {22},
 ARTICLE-NUMBER = {3769},
 URL = {https://www.mdpi.com/2079-9292/11/22/3769},
 ISSN = {2079-9292},
 ABSTRACT = {The Internet of Things (IoT) has recently developed opportunities for various industries, including the petrochemical industry, that allow for intelligent manufacturing with real-time management and the analysis of the produced big data. In oil production, extracting oil reduces reservoir demand, causing oil supply to fall below the economically viable level. Gas lift is a popular artificial lift system that is both efficient and cost-effective. If gas supplies in the gas lift process are not limited, a sufficient amount of gas may be injected into the reservoir to reach the highest feasible production rate. Because of the limited supply of gas, it is essential to achieve the sustainable utilization of our limited resources and manage the injection rate of the gas into each well in order to enhance oil output while reducing gas injection. This study describes a novel IoT-based chemical reaction optimization (CRO) technique to solve the gas lift allocation issue. The CRO algorithm is inspired by the interaction of molecules with each other and achieving the lowest possible state of free energy from an unstable state. The CRO algorithm has excellent flexibility, enabling various operators to modify solutions and a favorable trade-off between intensification and diversity. A reasonably fast convergence rate serves as a powerful motivator to use as a solution. The extensive simulation and computational study have presented that the proposed method using CRO based on IoT systems significantly improves the overall oil production rate and reduces gas injection, energy consumption and cost compared to traditional algorithms. Therefore, it provides a more efficient system for the petroleum production industry.},
 DOI = {10.3390/electronics11223769}
 }

@article{https://doi.org/10.1002/dac.4259,
 author = {Zanbouri, Kouros and Jafari Navimipour, Nima},
 title = {A cloud service composition method using a trust-based clustering algorithm and honeybee mating optimization algorithm},
 journal = {International Journal of Communication Systems},
 volume = {33},
 number = {5},
 pages = {e4259},
 keywords = {cloud computing, service composition, honeybee mating optimization algorithm, trust, clustering algorithm, computing time},
 doi = {https://doi.org/10.1002/dac.4259},
 url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/dac.4259},
 eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1002/dac.4259},
 note = {e4259 IJCS-19-0113.R3},
 abstract = {Summary One of the most critical issues in using service-oriented technologies is the combination of services, which has become an important challenge in the present. There are some significant challenges in the service composition, most notable is the quality of service (QoS), which is more challenging due to changing circumstances in dynamic service environments. Also, trust value in the case of selection of more reliable services is another challenge in the service composition. Due to NP-hard complexity of service composition, many metaheuristic algorithms have been used so far. Therefore, in this paper, the honeybee mating optimization algorithm as one of the powerful metaheuristic algorithms is used for achieving the desired goals. To improve the QoS, inspirations from the mating stages of the honeybee, the interactions between honeybees and queen bee mating and the selection of the new queen from the relevant optimization algorithm have been used. To address the trust challenge, a trust-based clustering algorithm has also been used. The simulation results using C# language have shown that the proposed method in small scale problem acts better than particle swarm optimization algorithm, genetic algorithm, and discrete gbest-guided artificial bee colony algorithm. With the clustering and reduction of the search space, the response time is improved; also, more trusted services are selected. The results of the simulation on a large-scale problem have indicated that the proposed method is exhibited worse performance than the average results of previous works in computation time.},
 year = {2020}
 }