[1]WU Tingting,YU Zhiwen,XU Jian.Underwater crowd intelligence[J].CAAI Transactions on Intelligent Systems,2026,21(1):179-200.[doi:10.11992/tis.202506033]
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Underwater crowd intelligence

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 21 Number of periods: 2026 1 Page number: 179-200 Column: 学术论文—水下智能与海洋具身智能 Public date: 2026-03-05
Title:
Underwater crowd intelligence
Author(s):
WU Tingting1;  YU Zhiwen1; 2;  XU Jian3
1. College of Computer Science and Technology, Harbin Engineering University, Harbin 150001, China;
2. School of Computer Science, Northwestern Polytechnical University, Xi’an 710072, China;
3. College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin 150001, China
Keywords:
underwater crowd intelligence; communication-limited; multi-agent systems; underwater acoustic communication; intelligent sensing; intelligent computing; intelligent coordination; edge computing
CLC:
TP39
DOI:
10.11992/tis.202506033
Abstract:
Underwater intelligent systems play an irreplaceable role in key areas such as marine resource exploration, ecological monitoring, and national defense. Faced with the complexity and variability of the marine environment, traditional single-agent systems exhibit limitations in operational efficiency, environmental adaptability, and task coverage. Underwater crowd intelligence, based on multi-agent collaboration, offers a new technical pathway through distributed sensing, cooperative computing, and adaptive control. This paper systematically reviews the conceptual evolution and research progress of crowd intelligence, identifies the core challenges in underwater environments, and proposes a system architecture for underwater crowd intelligence based on the “sensing-computing-collaboration” paradigm. Within this framework, it elaborates on three key technologies: intelligent sensing, intelligent computing, and intelligent collaboration. Particular attention is given to emerging directions such as cooperative computing under communication constraints and intelligent decision-making across heterogeneous agent clusters. Finally, the paper outlines future prospects of underwater crowd intelligence by examining its applications in marine resource exploration, underwater environmental monitoring, and underwater security.
References:
[1] 陈健瑞, 王景璟, 侯向往, 等. 挺进深蓝: 从单体仿生到群体智能[J]. 电子学报, 2021, 49(12): 2458-2467 CHEN Jianrui, WANG Jingjing, HOU Xiangwang, et al. Advance into ocean: from bionic monomer to swarm intelligence[J]. Acta electronica sinica, 2021, 49(12): 2458-2467
[2] 柯冠岩, 吴涛, 李明, 等. 水下机器人发展现状和趋势[J]. 国防科技, 2013, 34(5): 44-47,6 KE Guanyan, WU Tao, LI Ming, et al. The improvements and trends of the unmanned underwater vehicles[J]. National defense technology, 2013, 34(5): 44-47,6
[3] 宋保维, 潘光, 张立川, 等. 自主水下航行器发展趋势及关键技术[J]. 中国舰船研究, 2022, 17(5): 27-44 SONG Baowei, PAN Guang, ZHANG Lichuan, et al. Development trend and key technologies of autonomous underwater vehicles[J]. Chinese journal of ship research, 2022, 17(5): 27-44
[4] 王凯朋, 孟祥尧, 滕月慧, 等. 海空跨域航行器流体动力特性研究[J]. 舰船科学技术, 2021, 43(19): 46-50 WANG Kaipeng, MENG Xiangyao, TENG Yuehui, et al. Study of aerodynamic and hydrodynamic characteristics for aerial underwater vehicle[J]. Ship science and technology, 2021, 43(19): 46-50
[5] 况阳, 顾颖闽. 基于几何力学的水面无人艇艇群控制技术[J]. 舰船科学技术, 2019, 41(12): 123-126 KUANG Yang, GU Yingmin. Control technology of USV group base on geometry mechanics[J]. Ship science and technology, 2019, 41(12): 123-126
[6] BONABEAU E, DORIGO M, THERAULAZ G. Swarm intelligence: from natural to artificial systems[M]. New York: Oxford University Press, 1999.
[7] WANG Zhenning, CAO Yue, JIANG Kai, et al. When crowdsensing meets smart cities: a comprehensive survey and new perspectives[J]. IEEE communications surveys & tutorials, 2025, 27(2): 1101-1151
[8] NGUYEN T H, JUNG J J. Swarm intelligence-based green optimization framework for sustainable transportation[J]. Sustainable cities and society, 2021, 71: 102947
[9] TANG Jun, DUAN Haibin, LAO Songyang. Swarm intelligence algorithms for multiple unmanned aerial vehicles collaboration: a comprehensive review[J]. Artificial intelligence review, 2023, 56(5): 4295-4327
[10] JIANG Liangyu, LI Yichen, YU Wenbin, et al. Cooperative localization for asynchronous AUVs using time difference of communication in underwater anchor-free environments[J]. IEEE transactions on cybernetics, 2024, 54(11): 6531-6544
[11] HUANG Yupei, LI Peng, MA Shaoxuan, et al. Visual-inertial-acoustic sensor fusion for accurate autonomous localization of underwater vehicles[J]. IEEE transactions on cybernetics, 2025, 55(2): 880-896
[12] GOU Yu, ZHANG Tong, LIU Jun, et al. Achieving fair-effective communications and robustness in underwater acoustic sensor networks: a semi-cooperative approach[J]. IEEE transactions on mobile computing, 2024, 23(5): 5722-5739
[13] GJANCI P, PETRIOLI C, BASAGNI S, et al. Path finding for maximum value of information in multi-modal underwater wireless sensor networks[J]. IEEE transactions on mobile computing, 2018, 17(2): 404-418
[14] HU Xinyu, CAO Zhe, CHEN Yanli. Dynamic task assignment and path optimization for multi-AUVs system[J]. IEEE transactions on intelligent transportation systems, 2025, 26(6): 8233-8246
[15] ZHU Danjie, YANG S X. Current effect-eliminated optimal target assignment and motion planning for a multi-UUV system[J]. IEEE transactions on intelligent transportation systems, 2024, 25(8): 8419-8428
[16] CONTINI M, ILLIEN V, JULIEN M, et al. Seatizen Atlas: a collaborative dataset of underwater and aerial marine imagery[J]. Scientific data, 2025, 12: 67
[17] ZHOU Aolong, ZHANG Wen, LI Xiaoyong, et al. A novel noise-aware deep learning model for underwater acoustic denoising[J]. IEEE transactions on geoscience and remote sensing, 2023, 61: 4202813
[18] KUMAR M, DASS S. A total variation-based algorithm for pixel-level image fusion[J]. IEEE transactions on image processing, 2009, 18(9): 2137-2143
[19] WANG Kuan, LIU Zhijian, LIN Yujun, et al. HAQ: hardware-aware automated quantization with mixed precision[C]//2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2020: 8604-8612.
[20] WANG Shengbo, LIN Chuan, HAN Guangjie, et al. Multi-AUV cooperative underwater multi-target tracking based on dynamic-switching-enabled multi-agent reinforcement learning[J]. IEEE transactions on mobile computing, 2025, 24(5): 4296-4311
[21] YAN Tao, XU Zhe, YANG S X. Consensus formation tracking for multiple AUV systems using distributed bioinspired sliding mode control[J]. IEEE transactions on intelligent vehicles, 2023, 8(2): 1081-1092
[22] 邱志明, 孟祥尧, 马焱, 等. 海上无人系统发展及关键技术研究[J]. 中国工程科学, 2023, 25(3): 74-83 QIU Zhiming, MENG Xiangyao, MA Yan, et al. Development and key technologies of maritime unmanned systems[J]. Strategic study of CAE, 2023, 25(3): 74-83
[23] 闫敬, 关新平. 海上无人系统跨域集群发展现状及其关键技术[J]. 自动化学报, 2025, 51(4): 744-761 YAN Jing, GUAN Xinping. Development status and key techniques for cross-domain swarm of maritime unmanned systems[J]. Acta automatica sinica, 2025, 51(4): 744-761
[24] BENI G, WANG Jing. Swarm intelligence in cellular robotic systems[C]//Robots and Biological Systems: Towards a New Bionics? . Berlin: Springer, 1993: 703-712.
[25] DORIGO M. Optimization, learning and natural algorithms[J]. Thesis, Politecnico di Milano, 1992.
[26] KENNEDY J, EBERHART R. Particle swarm optimization[C]//Proceedings of ICNN’95 - International Conference on Neural Networks. Piscataway: IEEE, 2002: 1942-1948.
[27] XIAO Limin. An optimizing method based on autonomous animats: fish-swarm algorithm[J]. Systems engineering-theory & practice, 2002, 22(11): 32-38
[28] 吴虎胜, 张凤鸣, 吴庐山. 一种新的群体智能算法——狼群算法[J]. 系统工程与电子技术, 2013, 35(11): 2430-2438 WU Husheng, ZHANG Fengming, WU Lushan. New swarm intelligence algorithm——wolf pack algorithm[J]. Systems engineering and electronics, 2013, 35(11): 2430-2438
[29] PUGH J, MARTINOLI A. Inspiring and modeling multi-robot search with particle swarm optimization[C]//2007 IEEE Swarm Intelligence Symposium. Piscataway: IEEE, 2007: 332-339.
[30] WILSON S, PAVLIC T P, KUMAR G P, et al. Design of ant-inspired stochastic control policies for collective transport by robotic swarms[J]. Swarm intelligence, 2014, 8(4): 303-327
[31] YANG Lidong, JIANG Jialin, GAO Xiaojie, et al. Autonomous environment-adaptive microrobot swarm navigation enabled by deep learning-based real-time distribution planning[J]. Nature machine intelligence, 2022, 4(5): 480-493
[32] SONG Zhilong, FAN Linfeng, LU Shuaihua, et al. Inverse design of promising electrocatalysts for CO2 reduction via generative models and bird swarm algorithm[J]. Nature communications, 2025, 16: 1053
[33] NITTI A, DE TULLIO M D, FEDERICO I, et al. A collective intelligence model for swarm robotics applications[J]. Nature communications, 2025, 16: 6572
[34] ARBEL E, BUISE L, VAN WAES C, et al. A mechanical route for cooperative transport in autonomous robotic swarms[J]. Nature communications, 2025, 16: 7519
[35] DENG Yong, ZHANG Yazhou, SHI Xianming. Refining swarm behaviors with human-swarm interaction strategies: an improved monkey algorithm for multidimensional optimization problems[J]. Scientific reports, 2025, 15: 31197
[36] LUO Yi, LIU Yanmin, CHEN Jianjie, et al. Multi-objective particle swarm algorithm based on angular segmentation archive and dynamic update tactics[J]. Scientific reports, 2025, 15: 31012
[37] PAN Yunhe. Heading toward artificial intelligence 2.0[J]. Engineering, 2016, 2(4): 409-413
[38] 刘云浩. 群智感知计算[J]. 中国计算机学会通讯, 2012, 8(10): 38-41 LIU Yunhao. Crowd sensing and computing[J]. Communications of the China computer federation, 2012, 8(10): 38-41
[39] GUO Bin, WANG Zhu, YU Zhiwen, et al. Mobile crowd sensing and computing: the review of an emerging human-powered sensing paradigm[J]. ACM computing surveys, 2015, 48(1): 1-31
[40] YU Zhiwen, MA Huadong, GUO Bin, et al. Crowdsensing 2.0[J]. Communications of the ACM, 2021, 64(11): 76-80.
[41] 郭斌, 於志文. 人机物融合群智计算[J]. 中国计算机学会通讯, 2021, 17(2): 36-41 GUO Bin, YU Zhiwen. Crowd intelligence with the deep fusion of human, machine, and things[J]. Communications of the China computer federation, 2021, 17(2): 36-41
[42] 梅宏, 曹东刚, 谢涛. 泛在操作系统: 面向人机物融合泛在计算的新蓝海[J]. 中国科学院院刊, 2022, 37(1): 30-37 MEI Hong, CAO Donggang, XIE Tao. Ubiquitous operating system: toward the blue ocean of human-cyber-physical ternary ubiquitous computing[J]. Bulletin of the Chinese academy of sciences, 2022, 37(1): 30-37
[43] LIU Yimeng, YU Zhiwen, CUI Helei, et al. SafeCity: a heterogeneous mobile crowd sensing system for urban public safety[J]. IEEE internet of things journal, 2023, 10(20): 18330-18345
[44] BLENDER T, BUCHNER T, FERNANDEZ B, et al. Managing a mobile agricultural robot swarm for a seeding task[C]//IECON 2016 - 42nd Annual Conference of the IEEE Industrial Electronics Society. Piscataway: IEEE, 2016: 6879-6886.
[45] UTAMIMA A, SULASTRI M J, YUNIARTI L, et al. Optimizing multi-machine path planning for crop precision seeding with Lovebird Algorithm[J]. Computers and electronics in agriculture, 2025, 235: 110207
[46] WANG Hao, LIU C H, DAI Zipeng, et al. Energy-efficient 3D vehicular crowdsourcing for disaster response by distributed deep reinforcement learning[C]//Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining. New York: ACM, 2021: 3679-3687.
[47] HAN Lei, TU Chunyu, YU Zhiwen, et al. Collaborative route planning of UAVs, workers, and cars for crowdsensing in disaster response[J]. IEEE/ACM transactions on networking, 2024, 32(4): 3606-3621
[48] ZHANG Shuzhu, LEE C K M, CHAN H K, et al. Swarm intelligence applied in green logistics: a literature review[J]. Engineering applications of artificial intelligence, 2015, 37: 154-169
[49] MATARAZZO T J, KONDOR D, MILARDO S, et al. Crowdsourcing bridge dynamic monitoring with smartphone vehicle trips[J]. Communications engineering, 2022, 1: 29
[50] ZHOU Xin, WEN Xiangyong, WANG Zhepei, et al. Swarm of micro flying robots in the wild[J]. Science robotics, 2022, 7(66): eabm5954
[51] ZHOU Boyu, XU Hao, SHEN Shaojie. RACER: rapid collaborative exploration with a decentralized multi-UAV system[J]. IEEE transactions on robotics, 2023, 39(3): 1816-1835
[52] BAI Yang, TRAN NGOC P T, NGUYEN H D, et al. Swarm navigation of cyborg-insects in unknown obstructed soft terrain[J]. Nature communications, 2025, 16: 221
[53] LING Zhenhua, AI Yang, GU Yu, et al. Waveform modeling and generation using hierarchical recurrent neural networks for speech bandwidth extension[J]. IEEE/ACM transactions on audio, speech, and language processing, 2018, 26(5): 883-894
[54] ESKIMEZ S E, KOISHIDA K. Speech super resolution generative adversarial network[C]//2019 IEEE International Conference on Acoustics, Speech and Signal Processing. Piscataway: IEEE, 2019: 3717-3721.
[55] ZHANG Tingwei, RUAN S J. VM-ASR: a lightweight dual-stream U-Net model for efficient audio super-resolution[J]. IEEE transactions on audio, speech and language processing, 2025, 33: 666-677
[56] TIAN Ye, WANG Zhe, SUN Jianguo, et al. Time-frequency domain fusion enhancement for audio super-resolution[C]//Proceedings of the 32nd ACM International Conference on Multimedia. New York: ACM, 2024: 2879-2887.
[57] LU Yexin, AI Yang, LING Zhenhua. Explicit estimation of magnitude and phase spectra in parallel for high-quality speech enhancement[J]. Neural networks, 2025, 189: 107562
[58] BERMAN D, LEVY D, AVIDAN S, et al. Underwater single image color restoration using haze-lines and a new quantitative dataset[J]. IEEE transactions on pattern analysis and machine intelligence, 2021, 43(8): 2822-2837
[59] ZHOU Jingchun, LIU Qian, JIANG Qiuping, et al. Underwater camera: improving visual perception via adaptive dark pixel prior and color correction[J]. International journal of computer vision, 2025, 133(11): 8215-8233
[60] ZHANG Weidong, ZHUANG Peixian, SUN Haihan, et al. Underwater image enhancement via minimal color loss and locally adaptive contrast enhancement[J]. IEEE transactions on image processing, 2022, 31: 3997-4010
[61] ZHOU Jingchun, WANG Shiyin, LIN Zifan, et al. A pixel distribution remapping and multi-prior retinex variational model for underwater image enhancement[J]. IEEE transactions on multimedia, 2024, 26: 7838-7849
[62] ZHANG Weibo, WANG Hao, REN Peng, et al. MACT: Underwater image color correction via minimally attenuated channel transfer[J]. Pattern recognition letters, 2025, 187: 28-34
[63] QI Qi, LI Kunqian, ZHENG Haiyong, et al. SGUIE-net: semantic attention guided underwater image enhancement with multi-scale perception[J]. IEEE transactions on image processing, 2022, 31: 6816-6830
[64] WANG Hao, K?SER K, REN Peng. Large foundation model empowered discriminative underwater image enhancement[J]. IEEE transactions on geoscience and remote sensing, 2025, 63: 5609317
[65] PENG Lintao, ZHU Chunli, BIAN Liheng. U-shape transformer for underwater image enhancement[J]. IEEE transactions on image processing, 2023, 32: 3066-3079
[66] WANG Hao, ZHANG Weibo, BAI Lu, et al. Metalantis: a comprehensive underwater image enhancement framework[J]. IEEE transactions on geoscience and remote sensing, 2024, 62: 5618319
[67] LI Kunqian, WU Li, QI Qi, et al. Beyond single reference for training: underwater image enhancement via comparative learning[J]. IEEE transactions on circuits and systems for video technology, 2023, 33(6): 2561-2576
[68] LI Yuanyuan, MI Zetian, WANG Yulin, et al. TAFormer: a transmission-aware transformer for underwater image enhancement[J]. IEEE transactions on circuits and systems for video technology, 2025, 35(1): 601-616
[69] ZHANG Wen, LI Xiaoyong, ZHOU Aolong, et al. Underwater acoustic source separation with deep Bi-LSTM networks[C]//2021 4th International Conference on Information Communication and Signal Processing. Piscataway: IEEE, 2021: 254-258.
[70] LUO Yi, MESGARANI N. Conv-TasNet: surpassing ideal time–frequency magnitude masking for speech separation[J]. IEEE/ACM transactions on audio, speech, and language processing, 2019, 27(8): 1256-1266
[71] TANG Jianxun, CHEN Zhe, CHEN Mingsong. A novel underwater acoustic signal denoising model based on complex convolution dual-branch multi-scale attention network[C]//2025 IEEE International Conference on Acoustics, Speech and Signal Processing. Piscataway: IEEE, 2025: 1-5.
[72] VELURI B, CHAN J, ITANI M, et al. Real-time target sound extraction[C]//2023 IEEE International Conference on Acoustics, Speech and Signal Processing. Piscataway: IEEE, 2023: 1-5.
[73] ASHRAF H, SHAH B, SOOMRO A M, et al. Ambient-noise free generation of clean underwater ship engine audios from hydrophones using generative adversarial networks[J]. Computers and electrical engineering, 2022, 100: 107970
[74] TANG Chuanxin, LUO Chong, ZHAO Zhiyuan, et al. Joint time-frequency and time domain learning for speech enhancement[C]//Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence. Yokohama: IJCAI, 2020: 3816-3822.
[75] YANG Lei, LIU Wei, WANG Weiqin. TFPSNet: time-frequency domain path scanning network for speech separation[C]//2022 IEEE International Conference on Acoustics, Speech and Signal Processing. Piscataway: IEEE, 2022: 6842-6846.
[76] LI Yi, SUN Yang, WANG Wenwu, et al. U-shaped transformer with frequency-band aware attention for speech enhancement[J]. IEEE/ACM transactions on audio, speech, and language processing, 2023, 31: 1511-1521
[77] ZHOU Xingyue, YANG Kunde. A denoising representation framework for underwater acoustic signal recognition[J]. The journal of the acoustical society of America, 2020, 147(4): EL377-EL383
[78] ZHU Pengsen, GAO Lina, ZHANG Yonggang, et al. RGDNet: recognition-guided underwater acoustic signal denoising via mask integration and signal decoupling[J]. IEEE transactions on geoscience and remote sensing, 2025, 63: 4204422
[79] CHEN Lu, LUO Xinwei, ZHOU Hanlu. A ship-radiated noise classification method based on domain knowledge embedding and attention mechanism[J]. Engineering applications of artificial intelligence, 2024, 127: 107320
[80] DUAN Yichen, SHEN Xiaohong, WANG Haiyan. Time-domain anti-interference method for ship radiated noise signal[J]. EURASIP journal on advances in signal processing, 2022, 2022(1): 65
[81] ZHU Pengsen, ZHANG Yonggang, HUANG Yulong, et al. SFC-sup: robust two-stage underwater acoustic target recognition method based on supervised contrastive learning[J]. IEEE transactions on geoscience and remote sensing, 2023, 61: 4209023
[82] ZHU Pengsen, ZHANG Yonggang, HUANG Yulong, et al. Underwater acoustic target recognition based on spectrum component analysis of ship radiated noise[J]. Applied acoustics, 2023, 211: 109552
[83] XING Chuanxi, WU Yaowen, XIE Lixiang, et al. A sparse dictionary learning-based denoising method for underwater acoustic sensors[J]. Applied acoustics, 2021, 180: 108140
[84] LI Yuxing, LI Yaan, CHEN Xiao, et al. A new underwater acoustic signal denoising technique based on CEEMDAN, mutual information, permutation entropy, and wavelet threshold denoising[J]. Entropy, 2018, 20(8): 563
[85] YANG Hong, LAI Minyang, LI Guohui. Novel underwater acoustic signal denoising: Combined optimization secondary decomposition coupled with original component processing algorithms[J]. Chaos, solitons & fractals, 2025, 193: 116098.
[86] LIU Yanyu, ZHOU Dongming, NIE Rencan, et al. TSE_Fuse: two stage enhancement method using attention mechanism and feature-linking model for infrared and visible image fusion[J]. Digital signal processing, 2022, 123: 103387
[87] HUANG Zhenyu, NIU Guocheng, LIU Xiao, et al. Learning with noisy correspondence for cross-modal matching[J]. Advances in neural information processing systems, 2021, 34: 29406-29419
[88] HUANG Runhui, LONG Yanxin, HAN Jianhua, et al. NLIP: noise-robust language-image pre-training[J]. Proceedings of the AAAI conference on artificial intelligence, 2023, 37(1): 926-934
[89] WANG Yunbo, WU Yujie, DAI Zhien, et al. Noisy correspondence rectification via asymmetric similarity learning[J]. Proceedings of the AAAI conference on artificial intelligence, 2025, 39(20): 21384-21392
[90] LIU Chengliang, WEN Jie, WU Zhihao, et al. Information recovery-driven deep incomplete multiview clustering network[J]. IEEE transactions on neural networks and learning systems, 2024, 35(11): 15442-15452
[91] YIN Jun, WANG Pei, SUN Shiliang, et al. Incomplete multi-view clustering via multi-level contrastive learning[J]. IEEE transactions on knowledge and data engineering, 2025, 37(8): 4716-4727
[92] WANG Ziyu, DU Yiming, WANG Yao, et al. Deep incomplete multi-view clustering via multi-level imputation and contrastive alignment[J]. Neural networks, 2025, 181: 106851
[93] LI Shaoyuan, JIANG Yuan, ZHOU Zhihua. Partial multi-view clustering[J]. Proceedings of the AAAI conference on artificial intelligence, 2014, 28(1): 1-7
[94] ZHAO Handong, LIU Hongfu, FU Y R. Incomplete multi-modal visual data grouping[C]//Proceedings of the International Joint Conference on Artificial Intelligence, New York: IJCAI, 2016: 2392-2398.
[95] LIU Chengliang, WEN Jie, XU Yong, et al. Reliable representation learning for incomplete multi-view missing multi-label classification[J]. IEEE transactions on pattern analysis and machine intelligence, 2025, 47(6): 4940-4956
[96] YANG T J, CHEN Y H, SZE V. Designing energy-efficient convolutional neural networks using energy-aware pruning[C]//2017 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2017: 6071-6079.
[97] DIKSTEIN L, LAPID A, NETZER A, et al. Data generation for hardware-friendly post-training quantization[C]//2025 IEEE/CVF Winter Conference on Applications of Computer Vision. Tucson: IEEE, 2025: 5103-5113.
[98] GONG Ruihao, LIU Xianglong, LI Yuhang, et al. Pushing the limit of post-training quantization[J]. IEEE transactions on pattern analysis and machine intelligence, 2025, 47(7): 5556-5570
[99] LIN Ji, TANG Jiaming, TANG Haotian, et al. AWQ: activation-aware weight quantization for LLM compression and acceleration[EB/OL]. (2023-06-01)[2025-06-27]. https://arxiv.org/abs/2306.00978.
[100] ESHRATIFAR A E, ESMAILI A, PEDRAM M. BottleNet: a deep learning architecture for intelligent mobile cloud computing services[C]//2019 IEEE/ACM International Symposium on Low Power Electronics and Design. Piscataway: IEEE, 2019: 1-6.
[101] SHAO Jiawei, ZHANG Jun. BottleNet++: an end-to-end approach for feature compression in device-edge co-inference systems[C]//2020 IEEE International Conference on Communications Workshops. Piscataway: IEEE, 2020: 1-6.
[102] AHN H, LEE M, SEONG S, et al. ScissionLite: accelerating distributed deep learning with lightweight data compression for IIoT[J]. IEEE transactions on industrial informatics, 2024, 20(10): 11950-11960
[103] ULICNY C, RAUCH R, GAZDA J, et al. Split computing in autonomous mobility for efficient semantic segmentation using transformers[C]//2025 IEEE Symposia on Computational Intelligence for Energy, Transport and Environmental Sustainability. Piscataway: IEEE, 2025: 1-8.
[104] QI Huamei, REN Fang, WANG Leilei, et al. Multi-compression scale DNN inference acceleration based on cloud-edge-end collaboration[J]. ACM transactions on embedded computing systems, 2024, 23(1): 1-25
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