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A Pragmatic Review of QoS Optimisations in IoT Driven Networks

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Abstract

With advent of Internet of Things (IoT) an exponential growth has been observed in recent times towards the use of fifth generation (5G) network to share data among anything and even everything around connected in billions. The exchange of large amount of data by these devices or objects accumulates network overhead in the IoT infrastructure in terms of energy, routing, battery charge, data rate, packet delivery/loss rate, availability, interoperability, congestion, scalability, cost and security. Hence it is highly essential to project optimal solutions to uphold thereby the quality of service (QoS) in available network. This study provides a thorough literature survey of diverse optimization techniques in IoT aided wireless networks like Mobile Ad-hoc NETwork (MANET) driven Internet of Mobile Things (IoMobT), Vehicular Ad-hoc NETwork (VANET) driven Internet of Vehicles (IoV), Flying Ad-hoc NETwork (FANET) driven Internet of Flying Things (IoF), Robot Ad-hoc NETwork (RANET) enabled Internet of Robots (IoR), Ship Ad-hoc NETwork (SANET) driven Internet of Ships (IoS) and Underwater or Underground Ad-hoc NETwork (UANET) in Internet of Underwater or Underground Things (IoU). It categorizes papers based on the issues resolved by the examined works and optimization strategies employed and then it contrasts and condenses the salient characteristics of each kind of publication. It also even sketches a preview of IoT along with its evolving trends and cutting-edge-solutions for improving QoS. Our survey attempts to give readers a better grasp of the principles behind various computing models and to examine QoS network optimization strategies across a range of IoT models.

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Abbreviations

3G/4G:

3Rd Generation/4th Generation

6LoWPAN:

IPv6 Low power Personal Area Networks

AAO:

Artificial Algae Optimization

ABC:

Artificial Bee Colony

ACO:

Ant Colony Optimization

ACS:

Ant Colony System

AEDE:

Adaptive EDE

AFSO:

Artificial Fish Swarm Optimization

AFSA:

Artificial Fish Schooling Algorithm

AGTO:

Artificial Gorilla Troops Optimizer

AGV:

Automated Guided Vehicles

AHP:

Analytic Hierarchy Process

AHO:

Artificial Hummingbird Optimization

ALO:

Ant Lion Optimizer

ANN:

Artificial Neural Network

AO:

Aquila Optimizer

AODV:

Ad hoc On-Demand Distance Vector

ARES:

Ant based energy Efficient routing algorithm for Sensor network

ASIoT:

Application Specicific IoT

AUV:

Autonomous Underwater Vehicles

AVO:

African Vultures Optimization

AWOA:

Aquila optimizer–Whale Optimization Algorithm

BFA:

Bacterial Foraging Algorithm

BFO:

Bacterial Foraging Optimization

BLE:

Bluetooth Low Energy

BOA:

Butterfly Optimisation Algorithm

BS:

Base Station

BSO:

Bat Swarm Optimisation

CAS:

Chaotic Ant Swarm

CatSO:

Cat Swarm Optimisation

CAVDO:

Clustering Algorithm for IoV based on Dragonfly Optimizer

CBCC-RDG3:

Contribution-Based Co-operative Co-evolution Recursive Differential Grouping

CH:

Cluster Head

ChSO:

Chicken Swarm Optimization

CI:

Computational Intelligence

CL:

Cloud Logistics

CLPSO:

Comprehensive Learning Particle Swarm Optimization

CoAP:

Constrained Application Protocol

CoRE:

Constrained RESTful Environments

CP:

Charged Particle

CS-HC:

Cuckoo Search with Hill Climbing

CSO:

Cuckoo Search Optimization

CSS:

Charged System Search

DE:

Differential Evolution

DECADE:

Distributed Emergent Cooperation through ADaptive Evolution

DEEM:

Differential Evolution Encoding Mechanism

DEM:

Differential Evolution Method

DEVIPS:

Differential Evolution algorithm with Variable Population Size based on a mutation strategy pool

DEVIPSK:

Differential Evolution algorithm with Varying Population Size created on a mutation tactic Pool initialized by K-Means

DGSC-DECC:

Differential Grouping with Spectral Clustering-Differential Evolution Co-operative Co-evolution

DIAMoND:

Distributed Intrusion/Anomaly Monitoring for Nonparametric Detection

DOA:

Dragonfly Optimization Algorithm

DPRA:

Delayed Power Ramping Algorithm

E. Coli:

Escherichia Coli

EA:

Evolutionary Algorithm

EADE:

Enhanced Adaptive Differential Evolution

EDE:

Enhanced Differential Evolution

EMA:

Exchange Market Algorithm

ENN:

Evolutionary Neural Network

EQSA:

Energy-centered and QoS-aware Services selection Algorithm

ESO:

Elephant Search/herd Optimization

ESS:

Efficient Scheduling Scheme

FANET:

Flying Ad-hoc NETwork

FCFS:

First-Come First-Served

fGA:

Flexible Genetic Algorithm

FH-ACO:

Fuzzy Heuristic Ant Colony Optimization

FIS:

Fuzzy Inference System

FL:

Fuzzy Logic

FO:

Firefly Optimization

FRA:

Firefly Routing Algorithm

GA:

Genetic Algorithm

GASS:

Genetic algorithm and simulated Annealing algorithm for edge Server Selection

GBCO:

Genetic Bee Colony Optimization (GA + ABC)

GOA:

Grasshoppers’ Optimization Algorithm

GP:

Genetic Programming

GROBO:

Grasshopper Optimization-based Bi-target Optimization

GSA:

Greedy and Simulated-annealing Algorithms

GSM:

Global System for Mobile Communication

GWOA:

Gray Wolf Optimization Algorithm

GwSO:

Glow-worm Swarm Optimisation

HBO:

Honey Bee Optimisation

HFLPSO:

Hybrid Fuzzy Levy flight Particle Swarm Optimization

HOLA:

Heuristic and Opportunistic Link Selection Algorithm

HONIED:

Hive Oversight for Network Intrusion Early Warning using DIAMoND

HS:

Harmony Search

iASEF:

Integrated Atom Swarm and Electromagnetic Force

IBFO:

Improved Bacterial Foraging Optimization

IETF:

Internet Engineering Task Force

IGAROT:

Improved Genetic Algorithm-based Route Optimization Technique

IIoT:

Internet of Industrial Things

iMOPSE:

Intelligent Multi Objective Project Scheduling Environment

IoAT:

Internet of Animal Things

IoAuT:

Internet of Autonomous-Things

IoB:

Internet of Bins

IoBeauT:

Internet of Beautiful Things

IoBioT:

Internet of Biometric Things

IoBLT:

Internet of Battery-less Things

IoBT:

Internet of Battle (field) Things

IoET:

Internet of Every Thing

IoFT:

Internet of Flying Things

IoMiT:

Internet of Military Things

IoMobT:

Internet of Mobile Things

IoMT:

Internet of Medical Things

IoNT:

Internet of Nano Things

IoR:

Internet of Robots

IoRT:

Internet of Robotic Things

IoS:

Internet of Ships

IoT:

Internet of Things

IoTFS:

IoT-Fog System

IoUGT:

Internet of Underground Things

IoUWT:

Internet of Underwater Things

IoV:

Internet of Vehicles

IoWT:

Internet of Waste Things

IPSO:

Improved Particle Swam Optimization

IPv6:

Internet Protocol version 6

ISATOPSIS:

Improved Simulated Annealing Technique for Order Preference by Similarity to the Ideal Solution

ITS:

Intelligent Transport System

IWD:

Intelligent Water Drop

IWDRA:

Intelligent Water Drop Routing Algorithm

JADE:

J Adaptive Differential Evolution

JGGA:

Jumping Genes Genetic Algorithm

JSO:

Jellyfish Search Optimisation

KNN:

K Nearest Neighbors

LF-DCSO:

Lévy Flight-based Discrete Cuckoo Search Optimization

LGSO:

Large Scale Global Optimization

LISP:

List Processing

LOAD:

6LoWPAN Ad-hoc on-demand Distance vector

LS:

Local Search

LSA:

Lightning Search Algorithm

LTE:

Long Term Evolution

M2M:

Machine-to-Machine

MA:

Memetic Algorithms

MANET:

Mobile Ad-hoc NETwork

MDML-RP:

Metaheuristic Driven Machine Learning Routing Protocol

MEC:

Mobile Edge Computing

MFOA:

Moth Flame Optimization Algorithm

NFV:

Network Function Virtualization

MILP:

Mixed Integer Linear Programming

mIoT:

Massive IoT

MLOAD:

Multi-Path LOAD

MLSHADE-SPA:

Memetic Linear population Size reduction and Semi-Parameter Adaptation

moFIS-BFO:

Multiobjective Fuzzy Inference System Bacterial Foraging Optimization

MOCAS:

Multi-Objective Chaotic Ant Swarm

MOCAS:

Multi-Objective Chaotic Ant Swarm

MOCSA:

Multi-Objective Cuckoo Search Algorithm

MOGWO:

Multi-Objective Grey Wolf Optimizer

MOPSO:

Multi-Objective Particle Swarm Optimization

MOR4WSN:

Multi-Objective Routing for WSN

MS-RCPSP:

Multi Skill Resource-Constrained Project Scheduling Problem

MTS:

Modified Tabu Search

NJNP:

Nearest-Job-Next-with-Preemption

NSGA-II:

Non-dominated Sorting Genetic Algorithm II

NTS:

Novel Tabu Search

oppIoT:

Opportunistic IoT

OSEAP:

Optimal Secured Energy Aware Protocol

PenguinSO:

Penguin Search Optimisation

PID:

Proportional-Integrator-Differentiator / proportional-plus-integral-plus-derivative

PIO:

Pigeon Inspired Optimisation

PSO:

Particle Swarm Optimization

PSO-LSA:

Particle Swarm Optimization with Lightning Search Algorithm

PUF:

Physical Unclonable Function

QoS:

Quality of Service

QPSO:

Quantum Particle Swarm Optimization

QPSO:

Quantum stirred PSO

RA:

Resource Allocation

RACH:

Random Access Channel

RACH:

Random-Access Channel

RANET:

Robot Ad-hoc NETwork

RF:

Radio Frequency

RFID:

Radio Frequency Identification

RPL:

Routing Protocol for Low-Power and Lossy Networks

RREQ:

Route REQuest

RSPT:

Robust Shortest Path Tree

RW-DCSO:

Random Walk-based Discrete Cuckoo Search Optimization

SA:

Simulated Annealing

SA-LSA:

Simulated Annealing with Lightning Search Algorithm

SANET:

Ship Ad-hoc NETwork

SATOPSIS:

Simulated Annealing Technique for Order Preference by Similarity to the Ideal Solution

SAWS:

Simulated Annealing Weighted Sum

SBA:

Scenario-Based heuristic Algorithm

SDN:

Software Defined Networking

SEAP:

Secure Energy Aware-routing Protocol

SGN:

Stochastic Game Net

SSO:

Salp Swarm Optimisation

TLBO:

Teacher Learning Based Optimisation

TS:

Tabu Search

TSFIS-GWO:

Takagi–Sugeno Fuzzy Inference System Grey Wolf Optimizer

TSFM:

Three-Stage Fuzzy Metaheuristic

UANET:

Underwater Ad-hoc NETwork

UAV:

Unmanned/Uncrewed Aerial Vehicles

UgANET:

Underground Ad-hoc NETwork

UwANET:

Underwater Ad-hoc NETwork

V2I:

Vehicle to Infrastructure

V2V:

Vehicle to Vehicle

VANET:

Vehicular Ad-hoc NETwork

VCCA:

Variable Categorized Clustering Algorithm

VLGA:

Variable-Length Genetic Algorithm

VNF:

Virtual Network Function

WBAN:

Wearable Body area network

Wi-Fi:

Wireless Fidelity

WLAN:

Wireless Local Area Network

WMN:

Wireless Mesh Networks

WOA:

Whale Optimization Algorithm

WRSN:

Wireless Rechargeable Sensor Network

WSN:

Wireless Sensor Network

WSN-RFID:

Wireless Sensor Network Radio Frequency Identification

WUSN:

Wireless Underwater/Underground Sensor Network (WUwSN/WUgSN)

ZigBee:

Zonal Intercommunication Global-standard

ZRP:

Zone Routing Protocol

References

  1. Srinidhi, N. N., Kumar, S. D., & Venugopal, K. R. (2019). Network optimizations in the internet of things: A review. Engineering Science and Technology, an International Journal, 22(1), 1–21.

    Google Scholar 

  2. Subash, K., Ramya, D. J., & Arockiam, L. (2019). Quality of Service in the Internet of Things (IoT)–A Survey. TIRUCHIRAPPALLI-620 002, TAMIL NADU, INDIA

  3. Hussain, S. A., Yusof, K. M., Hussain, S. M., & Singh, A. V. (2019, February). A review of quality of service issues in internet of vehicles (IoV). In 2019 Amity international conference on artificial intelligence (AICAI) (pp. 380–383). IEEE.

  4. Alhasan, A., Audah, L., Alhadithi, O. S., & Alwan, M. H. (2019). Quality of service mechanisms in internet of things: A comprehensive survey. Journal of Advanced Research in Dynamical and Control Systems, 11(2), 858–875.

    Google Scholar 

  5. Chowdhury, A., & Raut, S. A. (2018). A survey study on internet of things resource management. Journal of Network and Computer Applications, 120, 42–60.

    Google Scholar 

  6. Chenna, K. B., & Srinivasan, C. K. (2018, June). Survey on optimization in IoT. In 2018 second international conference on intelligent computing and control systems (ICICCS) (pp. 1924–1928). IEEE

  7. Sun, W., Tang, M., Zhang, L., Huo, Z., & Shu, L. (2020). A survey of using swarm intelligence algorithms in IoT. Sensors, 20(5), 1420.

    Google Scholar 

  8. Shah, A. S., Nasir, H., Fayaz, M., Lajis, A., & Shah, A. (2019). A review on energy consumption optimization techniques in IoT based smart building environments. Information, 10(3), 108.

    Google Scholar 

  9. Qu, Z., Wang, Y., Sun, L., Peng, D., & Li, Z. (2020). Study QoS optimization and energy saving techniques in cloud, fog, edge, and IoT. Complexity, 2020, 1–16.

    Google Scholar 

  10. Begović, M., Čaušević, S., & Avdagić-Golub, E. (2021). QoS management in software defined networks For IoT environment: An overview. International Journal for Quality Research, 15(1), 171–188. https://doi.org/10.24874/IJQR15.01-10

    Article  Google Scholar 

  11. Srivastava, A., & Kumar, A. (2022). A review of network optimization on the internet of things. Innovations in Computer Science and Engineering: Proceedings of the Ninth ICICSE, 2021, 49–57.

    Google Scholar 

  12. Panigrahy, S. K., & Emany, H. (2023). A survey and tutorial on network optimization for intelligent transport system using the internet of vehicles. Sensors, 23(1), 555.

    Google Scholar 

  13. Mokabberi, A., Iranmehr, A., & Golsorkhtabaramiri, M. (2023, February). A review of energy-efficient QoS-aware composition in the internet of things. In 2023 8th international conference on technology and energy management (ICTEM) (pp. 1–6). IEEE

  14. Charde, P., & Lonkar, B. B. (2023, July). An empirical review of machine learning models for energy optimizations in IoT networks. In 2023 14th international conference on computing communication and networking technologies (ICCCNT) (pp. 1–7). IEEE

  15. Rostami, M., & Goli-Bidgoli, S. (2024). An overview of QoS-aware load balancing techniques in SDN-based IoT networks. Journal of Cloud Computing, 13(1), 89.

    Google Scholar 

  16. Zainaddin, D. A., Hanapi, Z. M., Othman, M., Ahmad Zukarnain, Z., & Abdullah, M. D. H. (2024). Recent trends and future directions of congestion management strategies for routing in IoT-based wireless sensor network: a thematic review. Wireless Networks, 30(3), 1–45.

    Google Scholar 

  17. Ashton, K. (2019). That internet of things thing. RFiD J., 22(7), 97–114.

    Google Scholar 

  18. Bellavista, P., Cardone, G., Corradi, A., & Foschini, L. (2013). Convergence of MANET and WSN in IoT urban scenarios. IEEE Sensors Journal, 13(10), 3558–3567.

    Google Scholar 

  19. Ang, K. L. M., & Seng, J. K. P. (2019). Application Specific Internet of Things (ASIoTs): Taxonomy, Applications, Use Case and Future Directions. IEEE Access, 7, 56577–56590. https://doi.org/10.1109/ACCESS.2019.2907793

    Article  Google Scholar 

  20. Kott, A., Swami, A., & West, B. J. (2016). The internet of battle things. Computer, 49(12), 70–75.

    Google Scholar 

  21. Stephen Russell and Tarek Abdelzaher. (2018). The internet of battlefield things: The next generation of command, control, communications and intelligence (C3I) decision-making. milcom track 5––Big data and machine learning

  22. Vishnu, S., Ramson, S. J., & Jegan, R. (2020, March). Internet of medical things (IoMT)-An overview. In 2020 5th international conference on devices, circuits and systems (ICDCS) (pp. 101–104). IEEE

  23. Benaissa, S., Plets, D., Tanghe, E., Trogh, J., Martens, L., Vandaele, L., Verloock, L., Tuyttens, F. A. M., Sonck, B., & Joseph, W. (2017). Internet of animals: characterisation of LoRa sub-GHz off-body wireless channel in dairy barns. Electronics Letters, 53(18), 12811283.

    Google Scholar 

  24. Medvedev, A., Fedchenkov, P., Zaslavsky, A., Anagnostopoulos, T., & Khoruzhnikov, S. (2015). Waste management as an IoT-enabled service in smart cities. in Proc. Int. Conf. Next Gener. Wired/Wireless Netw. (pp. 104_115)

  25. Namahoot, C. S., Brückner, M., Kim, Y., & Pinijkitcharoenkul, S. (Mar 2020)Cost-effective waste collection system based on the internet of wasted things (IoWT). https://doi.org/10.1007/978-981-15-2612-1_26, In book: International conference on communication, computing and electronics systems (pp.277–286)

  26. Domingo, M. C. (2012). An overview of the internet of underwater things. Journal of Network and Computer Applications, 35(6), 18791890.

    Google Scholar 

  27. Kao, C.-C., Lin, Y.-S., Wu, G.-D., & Huang, C.-J. (2017). A comprehensive study on the Internet of underwater things: Applications, challenges, and channel models. Sensors, 17(7), 1477.

    Google Scholar 

  28. Chinonso Okereke, Nur Haliza, Abdul Wahab, Mohd Murtadha Mohamad, S H Zaleha. Autonomous underwater vehicle in internet of underwater things: A survey. Conference paper , https://www.researchgate.net/publication/349427247, Feb 2021

  29. Salam, A., Raza, U., Salam, A., & Raza, U. (2020). Current advances in internet of underground things. Signals in the Soil: Developments in Internet of Underground Things. https://doi.org/10.1007/978-3-030-50861-6

    Article  Google Scholar 

  30. Akyildiz, I. F., & Jornet, J. M. (2010). The Internet of nano-things. IEEE Wireless Commun., 17(6), 5863.

    Google Scholar 

  31. Akhtar, N., & Perwej, Y. (2020). The internet of nano things (IoNT) existing state and future prospects. GSC Advanced Research and Reviews, 05(02), 131–150.

    Google Scholar 

  32. Althagafi, A. M., & Azim, M. M. (Dec, 2019) Internet of Beautiful Things (IoBT): Towards improving human’s behaviors. https://doi.org/10.1109/GCIoT47977.2019.9058405, Conference: 2019 IEEE global conference on internet of things (GCIoT)

  33. Kantarci, B., Erol-Kantarci, M., & Schuckers, S. (2015). Towards secure cloud-centric Internet of Biometric Things. IEEE 4th International Conference on Cloud Networking (CloudNet)

  34. Shah, D., & Haradi, V. (2016). IoT based biometrics implementation on Raspberry Pi. Procedia Computer Science, 79, 328336.

    Google Scholar 

  35. Qianao, Ju., Sun, Geng, Li, Hongsheng, & Zhang, Ying. (2019). Collaborative in-network processing for internet of battery-less things. IEEE INTERNET OF THINGS JOURNAL, 6(3), 5184.

    Google Scholar 

  36. Qianao Ju, Geng Sun, Hongsheng Li, and Ying Zhang. Latency-aware in-network computing for internet of battery-less things. 978–1–5386–6358–5/18/$31.00 ©2018 IEEE, 2018

  37. Sisinni, Emiliano, Saifullah, Abusayeed, Han, Song, Jennehag, Ulf, & Gidlund, Mikael. (2018). Industrial internet of things: Challenges, opportunities, and directions. IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, 14(11), 4724.

    Google Scholar 

  38. Seetharaman, A., Patwa, N., Saravanan, A. S., & Sharma, A. (2019). Customer expectation from industrial internet of things (IIOT). Journal of Manufacturing Technology Management, 30(8), 1161–1178. https://doi.org/10.1108/JMTM-08-2018-0278

    Article  Google Scholar 

  39. Nahrstedt, K., Li, H., Nguyen, P., Chang, S., & Vu, L. Internet of mobile things: Mobility-driven challenges, designs and implementations. in Proc. IEEE 1st Int. Conf. Internet-Things Design Implement., pp. 2536 (2016)

  40. Hatim, S. M., Elias, S. J., Awang, N., & Darus, M. Y. (2018). VANETs and internet of things (IoT): A discussion. Indonesian Journal of Electrical Engineering and Computer Science, 12(1), 218–224.

    Google Scholar 

  41. Manjinder Kaur, Jyoteesh Malhotra, Pankaj Deep Kaur. A VANET-IoT based Accident Detection and Management System for the Emergency Rescue Services in a Smart City. 2020 8th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO), Amity University, Noida, India, June 4–5, 2020.

  42. Pigatto, D. F., Rodrigues, M., de Carvalho Fontes, J. V., Pinto, A. S. R., Smith, J., & Branco, K. R. L. J. C. (2018). The internet of flying things internet of things A to Z: Technologies and applications F Qusay Eds Hassan The institute of electrical and electronics engineers John Wiley & Sons

  43. Zaidi Sofiane, and Carlos Tavares Calafate. Internet of flying things (IoFT): A Survey. Article in computer communications, https://www.researchgate.net/publication/345744959, Jan 2021

  44. Liu, G., Perez, R., Muñoz, J. A., & Regueira, F. (2016). Internet of ships: The future ahead. World Journal of Engineering and Technology, 4, 220–227.

    Google Scholar 

  45. Aslam, Sheraz, Michaelides, Michalis P., & Herodotou, Herodotos. (2020). Internet of ships: A survey on architectures, emerging applications, and challenges. IEEE INTERNET OF THINGS JOURNAL, 7(10), 9714–9727.

    Google Scholar 

  46. Alatas, B. (2011). ACROA: Artificial chemical reaction optimization algorithm for global optimization. Expert Systems with Applications, 38, 13170–13180.

    Google Scholar 

  47. Carvalho, I. A., Noronha, T. F., Duhamel, C., & Vieira, L. F. (2016). A scenario based heuristic for the robust shortest path tree problem. IFAC-PapersOnLine, 49(12), 443–448.

    Google Scholar 

  48. Dhondge, K., Shorey, R., & Tew, J. (2016): Heuristic and opportunistic link selection algorithm for energy efficiency in industrial internet of things (IIoT) systems. in 8th international conference on communication systems and networks (COMSNETS), pp. 1–6

  49. Shailendra, S., Rao, A., Panigrahi, B., Rath, H. K., & Simha, A. (2017). Power efficient RACH mechanism for dense IoT deployment. in IEEE international conference on communications workshops (ICC Workshops), pp. (373–378)

  50. Korczynski, M., Hamieh, A., Huh, J. H., Holm, H., Rajagopalan, S. R., & Fefferman, N. H. (2016). Hive oversight for network intrusion early warning using diamond: A bee-inspired method for fully distributed cyber defense’. IEEE Communications Magazine, 54(6), 60–67.

    Google Scholar 

  51. Raz, N. R., & Akbarzadeh-T, M. R. (2014). A Bio-Inspired model for emergence of cooperation among nanothings. in Iranian Conference on Intelligent Systems (ICIS), (pp. 1–6)

  52. Bilal Alatas, Umit Can. (January, 2015). Physics based Metaheuristic Optimization Algorithms for Global Optimization. https://www.researchgate.net/publication/330703172, Article

  53. Anupam Biswas, K. K., Mishra, Shailesh Tiwari, & Misra, A. K. (2013). Physics-inspired optimization algorithms: A survey hindawi publishing corporation. Journal of Optimization. https://doi.org/10.1155/2013/438152

    Article  Google Scholar 

  54. Dohare, Indu, & Singh, Karan. (2020). Green communication in sensor enabled IoT: Integrated physics inspired meta-heuristic optimization based approach. Wireless Networks. https://doi.org/10.1007/s11276-020-02263-w

    Article  Google Scholar 

  55. Quwaider, M., & Shatnawi, Y. (2020). Neural network model as internet of things congestion control using PID controller and immune-hill-climbing algorithm. Simulation Modelling Practice and Theory. https://doi.org/10.1016/j.simpat.2019.102022

    Article  Google Scholar 

  56. Xu Liu_, Jingzhi Huy, Hongliang Zhangz, Boya Diy, and Lingyang Song. (2021) Deployment Optimization for Meta-material Based Internet of Things. Electrical Engineering and Systems Science > Signal Processing. arXiv: 2107.01452v1 [eess.SP] 3 Jul 2021

  57. Hassan Daryanavard and Abbas Harifi. (2019) UAV Path Planning for Data Gathering of IoT Nodes: Ant Colony or Simulated Annealing Optimization’, Third International Conference on Internet of Things and Applications, University of Isfahan, Isfahan, Iran, 978–1–7281–3477–2/19/$31.00 ©2019 IEEE

  58. Ji, J., Guohua, Wu., Shuai, J., Zhang, Z., Wang, Z., & Ren, Y. (2019). (2019) Heuristic approaches for enhancing the privacy of the leader in IoT networks. Sensors, 19, 3886. https://doi.org/10.3390/s19183886

    Article  Google Scholar 

  59. Amer, H., Salman, N., Hawes, M., Chaqfeh, M., Mihaylova, L., & Mayfield, M. (2016). (2016) An improved simulated annealing technique for enhanced mobility in smart cities. Sensors, 16, 1013. https://doi.org/10.3390/s16071013

    Article  Google Scholar 

  60. Chakraborti, Subhamoy, & Sanyal, Sugata. (2015). An elitist simulated annealing algorithm for solving multi objective optimization problems in internet of things design. International Journal of Advanced Networking and Applications, 07(03), 2784–2789.

    Google Scholar 

  61. Sharma, A., Sharma, S., & Gupta, D. (2021). Design of modifed tabu search (MTS) algorithm, an optimization technique for intelligent routing of an IOT network with an aim to improving the effciency. Research Square. https://doi.org/10.21203/rs.3.rs-554510

    Article  Google Scholar 

  62. Revathy, G., Kavitha, N. S., Senthilvadivu, K., Sathya, D., & Logeshwari, P. (2020). Girl child safety using IoT sensors and tabu search optimization. International Journal of Recent Technology and Engineering (IJRTE), 8(5), E6093-018520. https://doi.org/10.35940/ijrte

    Article  Google Scholar 

  63. Xing, L., Liu, Y., Li, H., Chin-Chia, Wu., Lin, W.-C., & Chen, X. (2020). (2020) A novel tabu search algorithm for multi-agv routing problem. Mathematics, 8, 279. https://doi.org/10.3390/math8020279

    Article  Google Scholar 

  64. Téllez, N., Salazar, A., Jimeno, M., & Nino-Ruiz, E. D. (2018). A tabu search method for load balancing in fog computing. International Journal of Artificial Intelligence, 16(2), 78–105.

    Google Scholar 

  65. Kaveh, A., & Talatahari, S. (2010). (2010) A novel heuristic optimization method: Charged system search. Acta Mechanica, 213, 267–289. https://doi.org/10.1007/s00707-009-0270-4

    Article  Google Scholar 

  66. Asadieh, B., & Afshar, A. (2019). (2019) Optimization of water-supply and hydropower reservoir operation using the charged system search algorithm. Hydrology, 6, 5. https://doi.org/10.3390/hydrology6010005

    Article  Google Scholar 

  67. Kasi, S. K., Kasi, M. K., Ali, K., Raza, M., Afzal, H., Lasebae, A., Naeem, Islam, S Ul. B., & Rodrigues, J. J. P. C. (2020). Heuristic edge server placement in industrial internet of things and cellular networks. IEEE Internet of Things Journal. https://doi.org/10.1109/JIOT.2020.3041805

    Article  Google Scholar 

  68. Dhumane, A. V., Prasad, R. S., & Prasad, J. R. (2017). An optimal routing algorithm for internet of things enabling technologies. International Journal of Rough Sets and Data Analysis (IJRSDA), 4(3), 1–16.

    Google Scholar 

  69. Martins, J., Mazayev, A., Correia, N., Schütz, G., & Barradas, A. (2017). Gacn: Self-clustering genetic algorithm for constrained networks. IEEE Communications Letters, 21(3), 628–631.

    Google Scholar 

  70. I. Khan, J. Sahoo, S. Han, R. Glitho, N. Crespi. (2016) A genetic algorithm-based solution for efficient in-network sensor data annotation in virtualized wireless sensor networks. in 13th IEEE annual consumer communications & networking conference (CCNC), (pp. 321–322)

  71. Aydogan, E., Yilmaz, S., Sen, S., Butun, I., Forsström, S., & Gidlund, M. (2019) A Central Intrusion Detection System for RPL-Based Industrial Internet of Things. 2019 15th IEEE International Workshop on Factory Communication Systems (WFCS), https://doi.org/10.1109/WFCS.2019.8758024.

  72. Umeda, T., Shibagaki, K., Nozaki, Y., & Yoshikawa, M. (2018) Lethal genes aware genetic programming analysis for RO PUF. 2018 IEEE 7th global conference on consumer electronics (GCCE), https://doi.org/10.1109/GCCE.2018.8574699

  73. Yu, Y., Choi, T. M., Au, K. F., & Sun, Z. L. (2010). Applications of evolutionary neural networks for sales forecasting of fashionable products. In handbook of research on machine learning applications and trends: Algorithms, methods, and techniques (pp. 387–403). IGI Global

  74. Zhang, B. Y., Hu, W., Feng, J., & Sun, W. H. (2013). Data classification in internet of things based on evolutionary neural network. Advances in Materials Research, 659, 202–207.

    Google Scholar 

  75. Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-ii. IEEE Transactions on Evolutionary Computation, 6(2), 182–197.

    Google Scholar 

  76. A. Rodriguez, P. Falcarin, A. Ordonez. (2015) Energy optimization in wireless sensor networks based on genetic algorithms. In SAI intelligent systems conference (IntelliSys), (pp. 470–474)

  77. Rodriguez, A., Ordóñez, A., Ordoñez, H., & Segovia, R. (2015). Adapting NSGA-ii for hierarchical sensor networks in the IoT. Procedia Computer Science, 61, 355–360.

    Google Scholar 

  78. Song, L., Chai, K. K., Chen, Y., Schormans, J., Loo, J., & Vinel, A. (2017). Qos-aware energy-efficient cooperative scheme for cluster-based IoT systems. IEEE Systems Journal, 11(3), 1447–1455.

    Google Scholar 

  79. S. Ageev, Y. Kopchak, I. Kotenko, I. Saenko. (2015) Abnormal traffic detection in networks of the internet of things based on fuzzy logical inference. in XVIII international conference on soft computing and measurements (SCM), (pp. 5–8)

  80. Kwon, J. H., Cha, M., Lee, S. B., & Kim, E. J. (2019). Variable-categorized clustering algorithm using fuzzy logic for internet of things local networks. Multimedia Tools and Applications, 78, 2963–2982.

    Google Scholar 

  81. Choi, J.-Y., & Jeong, J. (2015). Design and performance analysis of cost-optimized handoff scheme based on fuzzy logic for building smart car IoT applications. International Information Institute (Tokyo), 18(10), 4339.

    Google Scholar 

  82. Li, Y., Sun, Z., Han, L., & Mei, N. (2017). Fuzzy comprehensive evaluation method for energy management systems based on an internet of things. IEEE Access., 5, 21312.

    Google Scholar 

  83. Mao, Y., Li, J., Chen, M.-R., Liu, J., Xie, C., & Zhan, Y. (2016). Fully secure fuzzy identity based encryption for secure IoT communications. Computer Standards & Interfaces, 44, 117–121.

    Google Scholar 

  84. Alireza Askarzadeh, Esmat Rashedi. (2017) Harmony Search Algorithm. Chapter ·March, https://doi.org/10.4018/978-1-5225-2322-2.ch001, https://www.researchgate.net/publication/314523255

  85. Hamza, K. S., & Amir, F. (2016) Evolutionary clustering for integrated WSN-RFID networks. in 10th international conference on informatics and systems, (pp. 267–272)

  86. Qureshi, T. N., Javaid, N., Al-mogren, A., Khan, A. U., Almajed, H., & Mohiuddin, I. (2021). An adaptive enhanced differential evolution strategies for topology robustness in internet of things. International Journal of Web and Grid Services. https://doi.org/10.1504/IJWGS.2021.10040852

    Article  Google Scholar 

  87. Goudos, S. K., Boursianis, A. D., Mohamed, A. W., Wan, S., Sarigiannidis, P., Karagiannidis, G. K., & Suganthan, P. N. (2021) Large Scale Global Optimization Algorithms for IoT Networks: A Comparative Study. Neural and Evolutionary Computing (cs.NE), arXiv: 2102.11275v1 [cs.NE].

  88. Bin, Xu., Zhang, Lu., Zipeng, Xu., Liu, Y., Chai, J., Qin, S., & Sun, Y. (2021). Energy optimization in multi-UAV-assisted edge data collection system. Computers Materials & Continua Tech Science Press. https://doi.org/10.32604/cmc.2021.018395

    Article  Google Scholar 

  89. Quoc, H. D., The, L. N., Doan, C. N., Thanh, T. P., & Xiong, N. N. (2020). Intelligent differential evolution scheme for network resources in IoT. Scientific Programming, 2020(1), 8860384. https://doi.org/10.1155/2020/8860384

    Article  Google Scholar 

  90. Huang, P.-Q., Wang, Y., Wang, K., & Yang, K. (2019). Differential evolution with a variable population size for deployment optimization in a UAV-assisted IoT data collection system. IEEE Transactions on Emerging Topics in Computational Intelligence. https://doi.org/10.1109/TETCI.2019.2939373

    Article  Google Scholar 

  91. da Silva Fré, G. L., de Carvalho Silva, J., Reis, F. A., & Mendes, L. D. P. (2015) Particle Swarm optimization implementation for minimal transmission power providing a fully-connected cluster for the internet of things. In International Workshop on Telecommunications (IWT), pp. 1–7

  92. Hu, Y., Ding, Y., Hao, K., Ren, L., & Han, H. (2014). An immune orthogonal learning particle swarm optimisation algorithm for routing recovery of wireless sensor networks with mobile sink. International Journal of Systems Science, 45(3), 337–350.

    Google Scholar 

  93. Song, L., Chai, K. K., Chen, Y., Loo, J., Jimaa, S., & Schormans, J. (2016) Qpso-based energyaware clustering scheme in the capillary networks for internet of things systems. in IEEE Wireless Communications and Networking Conference (WCNC), pp. 1–6.

  94. Sung, W.-T., & Hsu, C.-C. (2013). Iot system environmental monitoring using IPSO weight factor estimation. Sensor Review, 33(3), 246–256.

    Google Scholar 

  95. Kumrai, T., Ota, K., Dong, M., Kishigami, J., & Sung, D. K. (2017). Multi-objective optimization in cloud brokering systems for connected internet of things. IEEE Internet of Things Journal, 4(2), 404–413.

    Google Scholar 

  96. Verma, A., Kaushal, S., & Sangaiah, A. K. (2017). Computational intelligence based heuristic approach for maximizing energy efficiency in internet of things. Intelligent decision support systems for sustainable computing: Paradigms and applications, 53-76

  97. Reddy, P. K., & Babu, R. (2017). An evolutionary secure energy efficient routing protocol in internet of things. Int. J. Intell. Eng. Syst., 10(3), 337–346.

    Google Scholar 

  98. Ismail, N. H. A., & Hassan, R. (2013). 6lowpan local repair using bio inspired artificial bee colony routing protocol. Procedia Technology, 11, 281–287.

    Google Scholar 

  99. Arulanantham, D., Palanisamy, C., Pradeepkumar, G., & Kavitha, S. (2021). An energy efficient path selection using swarm intelligence in IoT SN. Journal of Physics: Conference Series, 1916, 012102. https://doi.org/10.1088/1742-6596/1916/1/012102

    Article  Google Scholar 

  100. Zhao, H. Y., Wang, J. C., Guan, X., Wang, Z. H., He, Y. H., & Xie, H. L. (2020). Ant colony system for energy consumption optimization in mobile IoT networks. Journal of circuits, systems and computers, 29(09), 2050150. https://doi.org/10.1142/S0218126620501509

    Article  Google Scholar 

  101. Hongyu Zhu, Zhuzhi Jia, Haipeng Peng, Lixiang Li. (2007) Chaotic ant swarm. Third international conference on natural computation (ICNC 2007)’, https://doi.org/10.1109/ICNC.2007.296.

  102. Huang, Jun, Liqian, Xu., Xing, Cong-cong, & Duan, Qiang. (2015). A novel bioinspired multiobjective optimization algorithm for designing wireless sensor networks in the internet of things Hindawi publishing corporation. Journal of Sensors, 2015, 1–16. https://doi.org/10.1155/2015/192194

    Article  Google Scholar 

  103. Joshi, A. S., Kulkarni, O., Kakandikar, G. M., & Nandedkar, V. M. (2017). Cuckoo search optimization-a review international conference on advancements in aeromechanical materials for manufacturing. Materials Today Proceedings. https://doi.org/10.1016/j.matpr.2017.07.055

    Article  Google Scholar 

  104. Ramzanpoor, Y., Shirvani, M. H., & Golsorkhtabaramiri, M. (2021). Multi-objective fault-tolerant optimization algorithm for deployment of IoT applications on fog computing infrastructure. Complex & Intelligent Systems. https://doi.org/10.1007/s40747-021-00368-z

    Article  Google Scholar 

  105. Bello-Salau, H., Onumanyi, A. J., Abu-Mahfouz, A. M., Adejo, A. O., & MU’AZu, M. B. (2020). New discrete cuckoo search optimization algorithms for effective route discovery in IoT-based vehicular Ad-Hoc networks. Digital Object Identifier. https://doi.org/10.1109/ACCESS.2020.3014736

    Article  Google Scholar 

  106. Shaji, K. A., Theodore, M., Samira, & Revathy, G. (2021). Firefly optimization in IOT applications for wireless mesh networks. Turkish Journal of Computer and Mathematics Education, 12(2), 2487–2491.

    Google Scholar 

  107. Sharmaa, N., Batraa, U., & Zafar, S. (2020). Remit accretion in IOT networks encircling ingenious firefly algorithm correlating water drop algorithm. Procedia Computer Science, 167(2020), 551–561.

    Google Scholar 

  108. Mirjalili, S., & Lewis, A. (2016). The whale optimization algorithm. Advances in engineering software, 95, 51–67.

    Google Scholar 

  109. Sangaiah, A. K., Hosseinabadi, A. A. R., Shareh, M. B., Rad, S. Y. B., Zolfagharian, A., & Chilamkurti, N. (2020). IoT resource allocation and optimization based on heuristic algorithm. Sensors, 20, 539. https://doi.org/10.3390/s20020539

    Article  Google Scholar 

  110. T. A. Al-Janabi and H. S. Al-Raweshidy. (2017) Efficient whale optimisation algorithm-based SDN clustering for IoT focused on node density. 16th annual mediterranean Ad Hoc networking workshop, 978–1–5386–2077–9/17/$31.00 ©2017 IEEE

  111. Ullah, Ibrar, Khitab, Zar, Khan, Muhammad Naeem, & Hussain, Sajjad. (2019). An efficient energy management in office using bio-inspired energy optimization algorithms. Processes, 7, 142. https://doi.org/10.3390/pr7030142

    Article  Google Scholar 

  112. Lan, Xu., Yiliu, Tu., & Zhang, Yuting. (2020). A grasshopper optimization-based approach for task assignment in cloud logistics. Hindawi Mathematical Problems in Engineering, 2020, 1–10. https://doi.org/10.1155/2020/3298460

    Article  Google Scholar 

  113. Tlili, S., Mnasri, S., & Val, T. (2021). A multi-objective gray wolf algorithm for routing in IoT collection networks with real experiments. National Computing Colleges Conference (NCCC). https://doi.org/10.1109/NCCC49330.2021.9428865

    Article  Google Scholar 

  114. Manshahia, M. S. (2019). Grey wolf algorithm based energy-efficient data transmission in internet of things. The 6th international symposium on emerging information, communication and networks (EICN 2019). Procedia Computer Science, 160, 604–609.

    Google Scholar 

  115. Valluru, S. K., Sehgal, K., & Thareja, H (2021) Evaluation of moth-flame optimization, genetic and simulated annealing tuned pid controller for steering control of autonomous underwater vehicle. 2021 IEEE international IOT, electronics and mechatronics conference (IEMTRONICS)| 978–1–6654–4067–7/21/$31.00 ©2021 IEEE| https://doi.org/10.1109/IEMTRONICS52119.2021.9422632

  116. Sadrishojaei, M., Navimipour, N. J., Reshadi, M., & Hosseinzadeh, M. (2021). Clustered routing method in the internet of things using a moth-flame optimization algorithm. International Journal of Communication Systems, 2021, e4964. https://doi.org/10.1002/dac.4964

    Article  Google Scholar 

  117. Nallakaruppan, M. K., & Senthil Kumaran, U. (2020). Hybrid swarm intelligence for feature selection on IoT-based infrastructure. Int. J. Cloud Computing, 9(2/3), 216. https://doi.org/10.1504/IJCC.2020.109375

    Article  Google Scholar 

  118. Mirjalili, S. (2016). (2015) Dragonfly algorithm: A new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems. Neural Computing and Applications, 27, 1053–1073. https://doi.org/10.1007/s00521-015-1920-1

    Article  Google Scholar 

  119. Wang, Lin, Shi, Ronghua, & Dong, Jian. (2021). A hybridization of dragonfly algorithm optimization and angle modulation mechanism for 0–1 knapsack problems. Entropy, 23, 598. https://doi.org/10.3390/e23050598

    Article  MathSciNet  Google Scholar 

  120. Aadil, F., Ahsan, W., Rehman, Z. U., Shah, P. A., Rho, S., & Mehmood, I. (2018). Clustering algorithm for internet of vehicles (IoV) based on dragonfly optimizer (CAVDO). Journal of Supercomput. https://doi.org/10.1007/s11227-018-2305-x

    Article  Google Scholar 

  121. Yang, G. (2017). (2017) Game theory-inspired evolutionary algorithm for global optimization. Algorithms, 10, 111. https://doi.org/10.3390/a10040111www.mdpi.com/journal/algorithms

    Article  Google Scholar 

  122. Na, J., Lin, K. J., Huang, Z., & Zhou, S. (2015) An Evolutionary Game Approach on IoT service selection for balancing device energy consumption. in IEEE 12th International Conference on e-Business Engineering, (pp. 331–338)

  123. Borah, S. J., Dhurandher, S. K., Woungang, I., & Kumar, V. (2017). A game theoretic contextbased routing protocol for opportunistic networks in an IoT scenario. Computer Networks, 129(2), 572–584.

    Google Scholar 

  124. Ali, Z., Abbas, Z. H., & Li, F. Y. (2016). A stochastic routing algorithm for distributed IoT with unreliable wireless links. In 2016 IEEE 83rd Vehicular Technology Conference (VTC Spring) (pp. 1-5)

  125. Jiang, N., Deng, Y., Kang, X., & Nallanathan, A. (2018). Random access analysis for massive IoT networks under a new spatio-temporal model: A stochastic geometry approach. IEEE Transactions on Communications, 66(11), 5788–5803.

    Google Scholar 

  126. Kaur, R., Kaur, N., & Sood, S. K. (2017). Security in IoT network based on stochastic game net model. International Journal of Network Management, 27(4), e1975.

    Google Scholar 

  127. Gharbieh, M., ElSawy, H., Bader, A., & Alouini, M. S. (2017). Spatiotemporal stochastic modeling of IoT enabled cellular networks: Scalability and stability analysis. IEEE Transactions on Communications, 65(8), 3585–3600.

    Google Scholar 

  128. Kuppusamy, P., & Kalaavathi, B. (2016). Novel authentication based framework for smart transportation using IoT and memetic algorithm. Asian Journal of Research in Social Sciences and Humanities, 6(10), 674–690.

    Google Scholar 

  129. Kuś, W., & Mucha, W. (2016) Memetic inverse problem solution in cyber-physical systems. Adv. Tech. Diagn. 335–341

  130. Tunc, C., & Akar, N. (2017). Markov fluid queue model of an energy harvesting IoT device with adaptive sensing. Performance Evaluation, 111, 1–16.

    Google Scholar 

  131. Sun, F., Wu, C., & Sheng, D. (2017). Bayesian networks for intrusion dependency analysis in water controlling systems. J. Inform. Sci. Eng., 33, 4.

    MathSciNet  Google Scholar 

  132. Khanouche, M. E., Amirat, Y., Chibani, A., Kerkar, M., & Yachir, A. (2016). Energy-centered and QoS-aware services selection for internet of things. IEEE Transactions on Automation Science and Engineering, 13(3), 1256–1269.

    Google Scholar 

  133. Zhang, Y.-W., Zhang, W.-M., Peng, K., Yan, D.-C., & Qi-lin, Wu. (2020). A novel edge server selection method based on combined genetic algorithm and simulated annealing algorithm. Automatika, 62(1), 32–43. https://doi.org/10.1080/00051144.2020.1837499

    Article  Google Scholar 

  134. Iwendi, C., Maddikunta, P. K. R., Gadekallu, T. R., Lakshmanna, K., Bashir, A. K., & Piran, M. J. (2020). A metaheuristic optimization approach for energy efficiency in the IoT networks. Pract Exper. https://doi.org/10.1002/spe.2797

    Article  Google Scholar 

  135. Senthil, G. A., Raaza, A., & Kumar, N. (2021). Internet of things energy efficient cluster-based routing using hybrid particle swarm optimization for wireless sensor network. Research Square. https://doi.org/10.21203/rs.3.rs-512199/v1

    Article  Google Scholar 

  136. Kesavan, S. P., Sivaraj, K., Palanisamy, A., & Murugasamy, R. (2019). Distributed localization algorithm using hybrid cuckoo search with hill climbing (CS-HC) algorithm for internet of things. International Journal of Psychosocial Rehabilitation, 23(4), 1171–1179. https://doi.org/10.37200/IJPR/V23I4/PR190443

    Article  Google Scholar 

  137. Shokouhifar, M. (2021). FH-ACO: Fuzzy heuristic-based ant colony optimization for joint virtual network function placement and routing. Applied Soft Computing, 107, 107401.

    Google Scholar 

  138. Moharamkhani, E., Zadmehr, B., Memarian, S., Saber, M. J., & Shokouhifar, M. (2021). Multiobjective fuzzy knowledge-based bacterial foraging optimization for congestion control in clustered wireless sensor networks. International Journal of Communication Systems, 34(16), e4949.

    Google Scholar 

  139. Fanian, F., & Rafsanjani, M. K. (2023). Three-stage fuzzy-metaheuristic algorithm for smart cities: Scheduling mobile charging and automatic rule tuning in WRSNs. Applied Soft Computing, 145, 110599.

    Google Scholar 

  140. Aryai, P., Khademzadeh, A., Jassbi, S. J., Hosseinzadeh, M., Hashemzadeh, O., & Shokouhifar, M. (2023). Real-time health monitoring in WBANs using hybrid metaheuristic-driven machine learning routing protocol (MDML-RP). AEU-Int J Electron Commun, 168, 154723.

    Google Scholar 

  141. Hemavathi, S., & Latha, B. (2023). HFLFO: Hybrid fuzzy levy flight optimization for improving QoS in wireless sensor network. Ad Hoc Networks, 142, 103110.

    Google Scholar 

  142. Memarian, S., Behmanesh-Fard, N., Aryai, P., Shokouhifar, M., Mirjalili, S., & del Carmen Romero-Ternero, M. (2024). TSFIS-GWO: Metaheuristic-driven takagi-sugeno fuzzy system for adaptive real-time routing in WBANs. Applied Soft Computing, 155, 111427.

    Google Scholar 

  143. Salehnia, T., Montazerolghaem, A., Mirjalili, S., Khayyambashi, M. R., & Abualigah, L. (2024). SDN-based optimal task scheduling method in Fog-IoT network using combination of AO and WOA. In Handbook of Whale Optimization Algorithm (pp. 109–128). Academic Press

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    Satyabrat Sahoo, Satya Prakash Sahoo & Manas Ranjan Kabat

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Sahoo, S., Sahoo, S.P. & Kabat, M.R. A Pragmatic Review of QoS Optimisations in IoT Driven Networks. Wireless Pers Commun 137, 325–366 (2024). https://doi.org/10.1007/s11277-024-11412-9

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