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The future of wireless mesh network in next-generation communication: a perspective overview

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Abstract

Wireless mesh network (WMN) which evolves from ad-hoc network is a type of self-healing, self-configuration, and multi-hop wireless network. Without expensive and fixed base stations, WMN can be established fast, easily, and flexibly with low cost. With the fast development of wireless communication, the required data rate and amount increase. As WMN can always provide broad communication access with better scalability, flexibility, and robustness, WMN can still play an important role in future and can bring a wide range of applications in next-generation communication. From the beginning till now, the focus of WMN transfers from sparse connection to intelligent dense networking. The wireless network in future will be more complex with different demands. Considering the features of WMN, this paper provides an overview of WMN from the point of view of next-generation communication. The application scenes including space-air-ground integrated network, Internet of Things, and edge computing are provided. When deploying WMN, the technologies suitable for the demands in next-generation communication, such as blockchain, software defined networking, network functions virtualization, and machine learning are discussed. Further, the upcoming challenges and opportunities in future are also highlighted.

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References

  • 5G Edge Computing Whitepaper. https://transition.fcc.gov/bureaus/oet/tac/tacdocs/reports/2018/5G-Edge-Computing-Whitepaper-v6-Final.pdf

  • Adhinugraha K, Rahayu W, Hara T, Taniar D (2022) Measuring fault tolerance in IoT mesh networks using Voronoi diagram. J Netw Comput Appl 199:103297

    Article  Google Scholar 

  • Al-Hamadi H, Saoud M, Chen R, Cho JH (2020) Optimizing the lifetime of IoT-based star and mesh networks. IEEE Access 8:63090–63105

    Article  Google Scholar 

  • Alm A (2019) Internet of Things mesh network: Using the Thread networking protocol. Thesis in Faculty of Health, Science and Technology, Karlstads university

  • Al-Saadi A, Setchi R, Hicks Y, Allen SM (2016) Routing protocol for heterogeneous wireless mesh networks. IEEE Trans Veh Technol 65(12):9773–9786

    Article  Google Scholar 

  • Al-Turjman F (2019) Cognitive routing protocol for disaster-inspired internet of things. Futur Gener Comput Syst 92:1103–1115

    Article  Google Scholar 

  • Ammbr (2024) https://ammbr.com/

  • Asiful SM, Moh H (2022) Survey on computation offloading in UAV-Enabled mobile edge computing. J Netw Comput Appl 201:103341

    Article  Google Scholar 

  • Avallone S, Banchs A (2016) A channel assignment and routing algorithm for energy harvesting multiradio wireless mesh networks. IEEE J Sel Areas Commun 34(5):1463–1476

    Article  Google Scholar 

  • Bano M, Gilani SSA, Qayyum A (2018) A comparative analysis of hybrid routing schemes for SDN based wireless mesh networks. In: IEEE 20th International Conference on High Performance Computing and Communications, pp. 1189–1194

  • Benyamina D, Hafid A, Gendreau M (2011) Wireless mesh networks design—a survey. IEEE Commun Surv Tutor 14(2):299–310

    Article  Google Scholar 

  • Bhagat U, Gujar N, Patel S (2018) Implementation of IOT in development of intelligent campus lighting system using mesh network. In: IEEE International Conference on Smart Systems and Inventive Technology (ICSSIT), pp. 251–256

  • BlockMesh (2024) https://www.blockmesh.io/

  • Bote-Lorenzo ML, Gomez-Sanchez E, Mediavilla-Pastor C, Asensio-Perez JI (2018) Online machine learning algorithms to predict link quality in community wireless mesh networks. Comput Netw 132:68–80

    Article  Google Scholar 

  • Brincat AA, Lombardo A, Morabito G, Quattropani S (2019) On the use of Blockchain technologies in WiFi networks. Comput Netw 162:1–9

    Article  Google Scholar 

  • Burchard J, Chemodanov D, Gillis J, Calyam P (2017) Wireless mesh networking protocol for sustained throughput in edge computing. In: International Conference on Computing, Networking and Communications (ICNC), pp. 958–962

  • Chettibi S, Chikhi S (2014) Adaptive maximum-lifetime routing in mobile ad-hoc networks using temporal difference reinforcement learning. Evol Syst 5(2):89–108

    Article  Google Scholar 

  • Chettibi S, Chikhi S (2016) Dynamic fuzzy logic and reinforcement learning for adaptive energy efficient routing in mobile ad-hoc networks. Appl Soft Comput 38:321–328

    Article  Google Scholar 

  • Chinchali S, Hu P, Chu T, Sharma M, Bansal M, Misra R, Pavone M, Katti S (2018) Cellular network traffic scheduling with deep reinforcement learning. In: Proceedings of the AAAI Conference on Artificial Intelligence, 32(1)

  • Cilfone A, Davoli L, Belli L, Ferrari G (2019) Wireless mesh networking: an IoT-oriented perspective survey on relevant technologies. Fut Internet 11(4):1–35

    Google Scholar 

  • Cotrim JR, Kleinschmidt JH (2020) LoRaWAN mesh networks: a review and classification of multihop communication. Sensors 20(15):1–21

    Article  Google Scholar 

  • Coutinho N, Matos R, Marques C, Reis A, Sargento S, Chakareski J, Kassler A (2015) Dynamic dual-reinforcement-learning routing strategies for quality of experience-aware wireless mesh networking. Comput Netw 88:269–285

    Article  Google Scholar 

  • Debroy S, Samanta P, Bashir A, Chatterjee M (2019) SpEED-IoT: spectrum aware energy efficient routing for device-to-device IoT communication. Futur Gener Comput Syst 93:833–848

    Article  Google Scholar 

  • Detti A, Pisa C, Salsano S, Blefari-Melazzi N (2013) Wireless mesh software defined networks (wmSDN). In: IEEE 9th international conference on wireless and mobile computing, networking and communications (WiMob), pp. 89-95

  • Di Pascale E, Macaluso I, Nag A, Kelly M, Doyle L (2018) The network as a computer: a framework for distributed computing over IoT mesh networks. IEEE Internet Things J 5(3):2107–2119

    Article  Google Scholar 

  • Dong Y, Wang J, Shim B, Kim DI (2016) DEARER: a distance-and-energy-aware routing with energy reservation for energy harvesting wireless sensor networks. IEEE J Sel Areas Commun 34(12):3798–3813

    Article  Google Scholar 

  • Dong S, Agrawal P, Sivalingam K (2007) Reinforcement learning based geographic routing protocol for UWB wireless sensor network. In: IEEE Global Telecommunications Conference, pp. 652-656

  • Dong F, Li O, Tong M (2018) Intelligent routing control for MANET based on reinforcement learning. In: MATEC Web of Conferences, 232

  • dos Santos WG, Costa WS, Faber MJ, Silva JA, Rocha HR, Segatto ME (2019) Sensor allocation in a hybrid star-mesh IoT network using genetic algorithm and K-medoids. In: IEEE Latin-American Conference on Communications (LATINCOM), pp. 1–6

  • Dutra DLC, Bagaa M, Taleb T, Samdanis K (2017) Ensuring end-to-end QoS based on multi-paths routing using SDN technology. In: IEEE Global Communications Conference (GLOBECOM), pp. 1–6

  • Elappila M, Chinara S (2018) Dynamic survivable path routing for fast changing IoT network topologies. in: 17th IEEE International Conference On Trust, Security And Privacy In Computing And Communications, pp. 92–97

  • Fadlullah ZM, Nakajo T, Nishiyama H, Owada Y, Hamaguchi K, Kato N (2015) Field measurement of an implemented solar powered BS-based wireless mesh network. IEEE Wirel Commun 22(3):137–143

    Article  Google Scholar 

  • Fan X, Zhao G, Tu H, Xu H, Huang H (2022) MASCOT: mobility-aware service function chain routing in mobile edge computing. In: 19th Annual IEEE International Conference on Sensing, COmmunication, and Networking, pp. 461-469

  • Fu L, Huang W, Gan X, Yang F, Wang X (2015) Capacity of wireless networks with social characteristics. IEEE Trans Wireless Commun 15(2):1505–1516

    Article  Google Scholar 

  • Gajjar A, Yang X (2017) A smart building system integrated with an edge computing algorithm and IoT mesh networks. In: Second ACM/IEEE Symposium on Edge Computing (SEC2017)

  • Ghaffari A (2017) Real-time routing algorithm for mobile ad hoc networks using reinforcement learning and heuristic algorithms. Wireless Netw 23(3):703–714

    Article  Google Scholar 

  • Guo W, Yan C, Lu T (2019) Optimizing the lifetime of wireless sensor networks via reinforcement-learning-based routing. Int J Distrib Sensor Netw 15:2

    Article  Google Scholar 

  • Gupta P, Kumar PR (2000) The capacity of wireless networks. IEEE Trans Inf Theory 46(2):388–404

    Article  MathSciNet  Google Scholar 

  • Haseeb K, Din IU, Almogren A, Islam N, Altameem A (2020) Rts: a robust and trusted scheme for iot-based mobile wireless mesh networks. IEEE Access 8:68379–68390

    Article  Google Scholar 

  • Huang H, Li P, Guo S, Zhuang W (2015) Software-defined wireless mesh networks: architecture and traffic orchestration. IEEE Netw 29(4):24–30

    Article  Google Scholar 

  • Huh H, Kim JY (2019) Lora-based mesh network for iot applications. In: IEEE 5th World Forum on Internet of Things (WF-IoT), pp. 524–527

  • Jiang W (2022) Graph-based deep learning for communication networks: A survey. Comput Commun 185:40–54

    Article  Google Scholar 

  • ** W, Gu R, Ji Y (2019) Reward function learning for q-learning-based geographic routing protocol. IEEE Commun Lett 23(7):1236–1239

    Article  Google Scholar 

  • Karunaratne S, Gacanin H (2019) An overview of machine learning approaches in wireless mesh networks. IEEE Commun Mag 57(4):102–108

    Article  Google Scholar 

  • Kashyap R, Azman M, Panicker JG (2019) Ubiquitous mesh: a wireless mesh network for IoT systems in smart homes and smart cities. In: IEEE International Conference on Electrical, Computer and Communication Technologies (ICECCT), pp. 1-5

  • Kim H (2014) Low power routing and channel allocation method of wireless video sensor networks for Internet of Things (IoT). In: IEEE World Forum on Internet of Things (WF-IoT), pp. 446-451

  • Kim J, Lee J (2012) Cluster-based mobility supporting WMN for IoT networks. In: IEEE International Conference on Green Computing and Communications, pp. 700–703

  • Kishi K, Suzuki H, Koyama A (2017) A communication method for wireless mesh networks suitable to IoT communication environment. In: Conference on Complex, Intelligent, and Software Intensive Systems, pp. 117–127

  • Labraoui M, Boc MM, Fladenmuller A (2016) Software defined networking-assisted routing in wireless mesh networks. In: International Wireless Communications and Mobile Computing Conference (IWCMC), pp. 377–382

  • Lech P, Wlodarski P (2017) Analysis of the IoT WiFi mesh network. In: Computer Science On-line Conference, pp. 272-280

  • Lee HC, Ke KH (2018) Monitoring of large-area IoT sensors using a LoRa wireless mesh network system: design and evaluation. IEEE Trans Instrum Meas 67(9):2177–2187

    Article  Google Scholar 

  • Li J, Silva BN, Diyan M, Cao Z, Han K (2018) A clustering based routing algorithm in IoT aware wireless mesh networks. Sustain Cities Soc 40:657–666

    Article  Google Scholar 

  • Li F, Song X, Chen H, Li X, Wang Y (2018) Hierarchical routing for vehicular ad hoc networks via reinforcement learning. IEEE Trans Veh Technol 68(2):1852–1865

    Article  Google Scholar 

  • Li J, Khan M, Lee B, Han K (2019) Load balancing and interference delay aware routing in IoT aware wireless mesh networks. J Internet Technol 20(1):293–300

    Google Scholar 

  • Liao Z, Cheng S (2023) RVC: A reputation and voting based blockchain consensus mechanism for edge computing-enabled IoT systems. J Netw Comput Appl 209:103510

    Article  Google Scholar 

  • Liu Y, Tong KF, Wong KK (2019) Reinforcement learning based routing for energy sensitive wireless mesh IoT networks. Electron Lett 55(17):966–968

    Article  Google Scholar 

  • Liu X, Fu L, Wang J, Wang X, Chen G (2019) Multicast scaling of capacity and energy efficiency in heterogeneous wireless sensor networks. ACM Trans Sensor Netw (TOSN) 15(3):1–32

    Google Scholar 

  • Liu Y, Lu H, Li X, Zhang Y, ** L, Zhao D (2021) Dynamic service function chain orchestration for NFV/MEC-enabled iot networks: a deep reinforcement learning approach. IEEE Internet Things J 8(9):7450–7465

    Article  Google Scholar 

  • Liu H, Long S, Li Z, Fu Y, Zuo Y, Zhang X (2023) Revenue maximizing online service function chain deployment in multi-tier computing network. IEEE Trans Parallel Distrib Syst 34(3):781–796

    Article  Google Scholar 

  • Liu X, Abdelhakim M, Krishnamurthy P, Tipper D (2018) Identifying malicious nodes in multihop iot networks using diversity and unsupervised learning. In: IEEE International Conference on Communications (ICC), pp. 1–6

  • Malandra F, Sanso B (2017) Performance analysis of large scale RF-mesh networks for smart cities and IoT. In: IEEE International Conference on Smart Grid Communications (SmartGridComm), pp. 13-18

  • Maleki M, Hakami V, Dehghan M (2017) A model-based reinforcement learning algorithm for routing in energy harvesting mobile ad-hoc networks. Wireless Pers Commun 95(3):3119–3139

    Article  Google Scholar 

  • Mao B, Fadlullah ZM, Tang F, Kato N, Akashi O, Inoue T, Mizutani K (2017) Routing or computing? The paradigm shift towards intelligent computer network packet transmission based on deep learning. IEEE Trans Comput 66(11):1946–1960

    Article  MathSciNet  Google Scholar 

  • Misra S, Krishna PV, Bhiwal A, Chawla AS, Wolfinger BE, Lee C (2012) A learning automata-based fault-tolerant routing algorithm for mobile ad hoc networks. J Supercomput 62(1):4–23

    Article  Google Scholar 

  • Muhendra R, Rinaldi A, Budiman M (2017) Development of WiFi mesh infrastructure for Internet of Things applications. Proc Eng 170:332–337

    Article  Google Scholar 

  • Nolan KE, Kelly MY, Nolan M, Brady J, Guibene W (2016) Techniques for resilient real-world IoT. In: IEEE International Wireless Communications and Mobile Computing Conference (IWCMC), pp. 222-226

  • Nurlan Z, Zhukabayeva T, Othman M, Adamova A, Zhakiyev N (2021) Wireless sensor network as a mesh: vision and challenges. IEEE Access 10:46–67

    Article  Google Scholar 

  • Ok D, Ahmed F, Agnihotri M, Cavdar C (2017) Self-organizing mesh topology formation in internet of things with heterogeneous devices. In: European Conference on Networks and Communications (EuCNC), pp. 1–5

  • Owada Y, Sato G, Temma K, Kuri T, Inoue M, Nagano T (2019) An Implementation of Layer 2 Overlay Mesh Network and Edge Computing Platform for IoT. In: IEEE Twelfth International Conference on Mobile Computing and Ubiquitous Network (ICMU), pp. 1-6

  • Paris S, Nita-Rotaru C, Martignon F, Capone A (2012) Cross-layer metrics for reliable routing in wireless mesh networks. IEEE/ACM Trans Netw 21(3):1003–1016

    Article  Google Scholar 

  • Peng Y, Guo L, Deng Q, Ning Z, Zhang L (2015) A novel hybrid routing forwarding algorithm in SDN enabled wireless mesh networks. In: IEEE 17th International Conference on High Performance Computing and Communications, pp. 1806-1811

  • Pirmagomedov R, Ometov A, Moltchanov D, Lu X, Kovalchukov R, Olshannikova E, Andreev S, Koucheryavy Y, Dohler M (2020) Applying blockchain technology for user incentivization in mmWave-based mesh networks. IEEE Access 8:50983–50994

    Article  Google Scholar 

  • Pratesi M, Valentini F, Santucci F (2015) An Android-based testbed for hybrid Wireless Mesh Networks in the IoT perspective. In: IEEE 1st International Forum on Research and Technologies for Society and Industry Leveraging a better tomorrow (RTSI), pp. 1-6

  • Rademacher M, Jonas K, Siebertz F, Rzyska A, Schlebusch M, Kessel M (2017) Software-defined wireless mesh networking: current status and challenges. Comput J 60(10):1520–1535

    Article  MathSciNet  Google Scholar 

  • Ramezani M, Jahanshahi M (2017) Load-aware multicast routing in multi-radio wireless mesh networks using fca-cmac neural network. Computing 4:1–29

    Google Scholar 

  • Rapate GS, Naveen NC (2018) Energy and routing efficiency in IoT: proposal for combined approach. In: International Conference on Electrical, Electronics, Communication, Computer, and Optimization Techniques (ICEECCOT), pp. 451-454

  • Rausch MJ, Krishna VB, Gu P, Chandra R, Feddersen B, Fawaz A, Sanders W H (2018) Peer-to-peer detection of DoS attacks on city-scale IoT mesh networks. In: IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm), pp. 1-6

  • Ray PP (2022) A review on 6G for space-air-ground integrated network: key enablers, open challenges, and future direction. J King Saud Univ-Comput Inf Sci 34(9):6949–6976

    Google Scholar 

  • Rescati M, Scapini E, DeMatteis M, Schettini R, Pau D, Paganoni M, Baschirotto A (2019) HAEMS: implementation of an intelligent event-driven edge mesh IoT architecture. In: IEEE 5th International forum on Research and Technology for Society and Industry (RTSI), pp. 39-44

  • RightMesh (2024) https://rightmesh.io/

  • Rodrigues TK, Suto K, Nishiyama H, Liu J, Kato N (2019) Machine learning meets computation and communication control in evolving edge and cloud: challenges and future perspective. IEEE Commun Surv Tutori 22(1):38–67

    Article  Google Scholar 

  • Rosyidi L, Asvial M, Sari RF (2019) Lightweight and energy-aware wireless mesh routing for autonomic internet of things network. Int J Control Autom 12(2):33–44

    Article  Google Scholar 

  • Sahni Y, Cao J, Zhang S, Yang L (2017) Edge mesh: a new paradigm to enable distributed intelligence in internet of things. IEEE ACCESS 5:16441–16458

    Article  Google Scholar 

  • Sajjadi D, Ruby R, Tanha M, Pan J (2018) Fine-grained traffic engineering on SDN-aware Wi-Fi mesh networks. IEEE Trans Veh Technol 67(8):7593–7607

    Article  Google Scholar 

  • Sajjadi D, Tanha M, Pan J (2016) A comparative study of channel switching latency for conventional and SDN-based routing in multi-hop multi-radio wireless mesh networks. In 13th IEEE Annual Consumer Communications and Networking Conference (CCNC), pp. 330–334

  • Sendra S, Rego A, Lloret J, Jimenez JM, Romero O (2017) Including artificial intelligence in a routing protocol using software defined networks. In: IEEE International Conference on Communications Workshops (ICC Workshops), pp. 670–674

  • Sgora A, Vergados DD, Chatzimisios P (2016) A survey on security and privacy issues in wireless mesh networks. Secur Commun Netw 9(13):1877–1889

    Article  Google Scholar 

  • Shang W, Yu Y, Droms R, Zhang L (2016) Challenges in IoT networking via TCP/IP architecture. NDN Project

  • Sharif S, Zeadally S, Ejaz W (2023) Space-aerial-ground-sea integrated networks: resource optimization and challenges in 6G. J Netw Comput Appl 215:103647

    Article  Google Scholar 

  • Smarter with Gartner. https://www.gartner.com/smarterwithgartner/what-edge-computing-means-for-infrastructure-and-operations-leaders/

  • SmartMesh whitepaper. https://smartmesh.io/SmartMeshWhitePaperEN.pdf

  • Stampa G, Arias M, Sanchez-Charles D, Muntes-Mulero V, Cabellos A (2017) A deep-reinforcement learning approach for software-defined networking routing optimization. ar**v:1709.07080

  • Sun Y, Lin Y, Tang Y (2017) A reinforcement learning-based routing protocol in VANETs. In: International Conference in Communications, Signal Processing, and Systems, pp. 2493–2500

  • Tang F, Mao B, Fadlullah ZM, Kato N, Akashi O, Inoue T, Mizutani K (2017) On removing routing protocol from future wireless networks: a real-time deep learning approach for intelligent traffic control. IEEE Wirel Commun 25(1):154–160

    Article  Google Scholar 

  • Tang F, Fadlullah ZM, Mao B, Kato N (2018) An intelligent traffic load prediction-based adaptive channel assignment algorithm in SDN-IoT: a deep learning approach. IEEE Internet Things J 5(6):5141–5154

    Article  Google Scholar 

  • Ul Hasan N, Ejaz W, Anpalagan A (2019) Distributed energy-efficient channel assignment in cognitive mesh network for IoT systems. Trans Emerg Telecommun Technol 30(10):1–15

    Google Scholar 

  • Van Moffaert K, Drugan MM, Nowe A (2013) Scalarized multi-objective reinforcement learning: Novel design techniques. In: IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning (ADPRL), pp. 191–199

  • Vittecoq S (2013) A radio mesh platform for the IoT. In: IEEE Seventh International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing, pp. 530-534

  • Wang X, **: a cooperative dual-agent deep reinforcement learning approach. IEEE Trans Parallel Distrib Syst 34(8):2479–2497

    Article  Google Scholar 

  • Wei Y, Yu FR, Song M, Han Z (2018) Joint optimization of caching, computing, and radio resources for fog-enabled IoT using natural actor-critic deep reinforcement learning. IEEE Internet Things J 6(2):2061–2073

    Article  Google Scholar 

  • Wu Y, Yang K (2018) Cooperative reinforcement learning based throughput optimization in energy harvesting wireless sensor networks. In: 27th Wireless and Optical Communication Conference (WOCC), pp. 1–6

  • Xu H, Li XY, Huang L, Deng H, Huang H, Wang H (2017) Incremental deployment and throughput maximization routing for a hybrid SDN. IEEE/ACM Trans Netw 25(3):1861–1875

    Article  Google Scholar 

  • Xu C, Wang K, Li P, **a R, Guo S, Guo M (2018) Renewable energy-aware big data analytics in geo-distributed data centers with reinforcement learning. IEEE Trans Netw Sci Eng 7(1):205–215

    Article  MathSciNet  Google Scholar 

  • Yang F, Gondi V, Hallstrom JO, Wang KC, Eidson G (2014) OpenFlow-based load balancing for wireless mesh infrastructure. In: IEEE 11th Consumer Communications and Networking Conference (CCNC), pp. 444–449

  • Yao Q, Huang A, Shan H, Quek TQ, Wang W (2016) Delay-aware wireless powered communication networks-Energy balancing and optimization. IEEE Trans Wireless Commun 15(8):5272–5286

    Article  Google Scholar 

  • Yin J, Yang Z, Cao H, Liu T, Zhou Z, Wu C (2019) A survey on Bluetooth 5.0 and mesh: new milestones of IoT. ACM Trans Sens Netw (TOSN) 15(3):1–29

    Article  Google Scholar 

  • Zhang P, Zhang Y, Kumar N, Hsu C (2023) Deep reinforcement learning algorithm for latency-oriented IIoT resource orchestration. IEEE Internet Things J 10(8):7153–7163

    Article  Google Scholar 

  • Zhou H, Ouyang X, Zhao Z (2020) ALLSTAR: a blockchain based decentralized ecosystem for cloud and edge computing. In: IEEE International Conference on Joint Cloud Computing, pp. 55–62

  • Zhu M, Cao J, Pang D, He Z, Xu M (2015) SDN-based routing for efficient message propagation in VANET. In: International Conference on Wireless Algorithms, Systems, and Applications, pp. 788-797

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  1. School of Computer Science and Technology, Guangdong University of Technology, Guangzhou, 510006, China

    Yuan Chai

  2. Department of Computer Science, the University of Manchester, Manchester, M13 9PL, UK

    **ao-Jun Zeng

  3. Southampton Business School, the University of Southampton, Southampton, UK

    Zixu Liu

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Chai, Y., Zeng, XJ. & Liu, Z. The future of wireless mesh network in next-generation communication: a perspective overview. Evolving Systems (2024). https://doi.org/10.1007/s12530-024-09583-8

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