Abstract
This paper explores the transformative integration of deep learning applications in the deployment of Wireless Sensor Networks (WSNs). As WSNs continue to play a pivotal role in diverse domains, the infusion of deep learning techniques offers unprecedented opportunities for enhanced data processing, analysis, and decision-making. The research problem addressed in this paper revolves around navigating the challenges associated with incorporating deep learning into WSN deployment. The methodology involves an extensive literature review, highlighting the increasing role of deep learning in addressing WSN challenges. Key findings underscore the potential improvements in energy efficiency, data processing speed, and accuracy achieved through deep learning-empowered WSNs. The implications of this research extend to diverse applications, including environmental monitoring, healthcare, industrial systems, and smart agriculture. As we delve into the future research agenda, the paper identifies the need for further exploration in areas such as adaptability to dynamic environments, privacy-preserving optimizations, and scalable deep learning models tailored to the unique constraints of WSNs.
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References
Zhou G, Zhang R, Huang S (2021) Generalized buffering algorithm. IEEE Access 9:27140–27157. https://doi.org/10.1109/ACCESS.2021.3057719
Ma K et al (2021) Reliability-constrained throughput optimization of industrial wireless sensor networks with energy harvesting relay. IEEE Internet Things J 8(17):13343–13354. https://doi.org/10.1109/JIOT.2021.3065966
Cao K, Wang B, Ding H, Lv L, Dong R, Cheng T, Gong F (2021) Improving physical layer security of uplink NOMA via energy harvesting jammers. IEEE Trans Inf Forensics Secur 16:786–799. https://doi.org/10.1109/TIFS.2020.3023277
Zou W, Sun Y, Zhou Y, Lu Q, Nie Y, Sun T, Peng L (2022) Limited sensing and deep data mining: a new exploration of developing city-wide parking guidance systems. IEEE Intell Transp Syst Mag 14(1):198–215. https://doi.org/10.1109/MITS.2020.2970185
Cao K, Ding H, Li W, Lv L, Gao M, Gong F, Wang B (2022) On the ergodic secrecy capacity of intelligent reflecting surface aided wireless powered communication systems. IEEE Wirel Commun Lett. https://doi.org/10.1109/LWC.2022.3199593
Wu H, Jin S, Yue W (2022) Pricing policy for a dynamic spectrum allocation scheme with batch requests and impatient packets in cognitive radio networks. J Syst Sci Syst Eng 31(2):133–149. https://doi.org/10.1007/s11518-022-5521-0
Jiang Y, Li X (2022) Broadband cancellation method in an adaptive co-site interference cancellation system. Int J Electron 109(5):854–874. https://doi.org/10.1080/00207217.2021.1941295
Mao Y, Sun R, Wang J, Cheng Q, Kiong LC, Ochieng WY (2022) New time-differenced carrier phase approach to GNSS/INS integration. GPS Solut 26(4):122. https://doi.org/10.1007/s10291-022-01314-3
Mao Y, Zhu Y, Tang Z, Chen Z (2022) A novel airspace planning algorithm for cooperative target localization. Electronics 11(18):2950. https://doi.org/10.3390/electronics11182950
Sun G, Xu Z, Yu H, Chen X, Chang V, Vasilakos AV (2020) Low-latency and resource-efficient service function chaining orchestration in network function virtualization. IEEE Internet Things J 7(7):5760–5772. https://doi.org/10.1109/JIOT.2019.2937110
Sun G, Li Y, Liao D, Chang V (2018) Service function chain orchestration across multiple domains: a full mesh aggregation approach. IEEE Trans Netw Serv Manage 15(3):1175–1191. https://doi.org/10.1109/TNSM.2018.2861717
Dai M, Luo L, Ren J, Yu H, Sun G (2022) PSACCF: prioritized online slice admission control considering fairness in 5G/B5G networks. IEEE Trans Netw Sci Eng 9(6):4101–4114. https://doi.org/10.1109/TNSE.2022.3195862
Zhang H, Wu H, Jin H, Li H (2023) High-dynamic and low-cost sensorless control method of high-speed brushless DC motor. IEEE Trans Industr Inf 19(4):5576–5584. https://doi.org/10.1109/TII.2022.3196358
Qu Z, Liu X, Zheng M (2022) Temporal-spatial quantum graph convolutional neural network based on schrödinger approach for traffic congestion prediction. IEEE Trans Intell Transp Syst. https://doi.org/10.1109/TITS.2022.3203791
Li Q, Lin H, Tan X, Du S (2020) H∞ Consensus for multiagent-based supply chain systems under switching topology and uncertain demands. IEEE Trans Syst, Man, Cybern: Syst 50(12):4905–4918. https://doi.org/10.1109/TSMC.2018.2884510
Yang X, Wang X, Wang S, Puig V (2023) Switching-based adaptive fault-tolerant control for uncertain nonlinear systems against actuator and sensor faults. J Franklin Inst 360(16):11462–11488. https://doi.org/10.1016/j.jfranklin.2023.08.042
Dai W, Zhou X, Li D, Zhu S, Wang X (2022) Hybrid parallel stochastic configuration networks for industrial data analytics. IEEE Trans Industr Inf 18(4):2331–2341. https://doi.org/10.1109/TII.2021.3096840
Wang Q, Dai W, Zhang C, Zhu J, Ma X (2023) A compact constraint incremental method for random weight networks and its application. IEEE Trans Neural Netw Learn Syst. https://doi.org/10.1109/TNNLS.2023.3289798
Li L, Yao L (2023) Fault tolerant control of fuzzy stochastic distribution systems with packet dropout and time delay. IEEE Trans Autom Sci Eng. https://doi.org/10.1109/TASE.2023.3266065
Guo Y, Zhang C, Wang C, Jia X (2023) Towards public verifiable and forward-privacy encrypted search by using blockchain. IEEE Trans Dependable Secure Comput 20(3):2111–2126. https://doi.org/10.1109/TDSC.2022.3173291
Fan W, Yang L, Bouguila N (2022) Unsupervised grouped axial data modeling via hierarchical bayesian nonparametric models with watson distributions. IEEE Trans Pattern Anal Mach Intell 44(12):9654–9668. https://doi.org/10.1109/TPAMI.2021.3128271
Zhou G, Xu C, Zhang H, Zhou X, Zhao D, Wu G, Zhang L (2022) PMT gain self-adjustment system for high-accuracy echo signal detection. Int J Remote Sens 43(19–24):7213–7235. https://doi.org/10.1080/01431161.2022.2155089
Zhou G, Zhou X, Li W, Zhao D, Song B, Xu C, Zou L (2022) Development of a lightweight single-band bathymetric LiDAR. Remote Sens 14(22):5880. https://doi.org/10.3390/rs14225880
Zheng W, Gong G, Tian J, Lu S, Wang R, Yin Z, Yin L (2023) Design of a modified transformer architecture based on relative position coding. Int J Comput Intell Syst 16(1):168. https://doi.org/10.1007/s44196-023-00345-z
Jannat MKA, Islam MS, Yang S, Liu H (2023) Efficient Wi-Fi-based human activity recognition using adaptive antenna elimination. IEEE Access 11:105440–105454. https://doi.org/10.1109/ACCESS.2023.3320069
Guo R, Liu H, Liu D (2023) When deep learning-based soft sensors encounter reliability challenges: a practical knowledge-guided adversarial attack and its defense. IEEE Trans Industr Inf. https://doi.org/10.1109/TII.2023.3297663
Wang Y, Sun R, Cheng Q, Ochieng WY (2023) Measurement quality control aided multi-sensor system for improved vehicle navigation in urban areas. IEEE Trans Industr Electron. https://doi.org/10.1109/TIE.2023.3288188
Bo C, Jiangping H, Bijoy G (2023) Finite-time observer based tracking control of heterogeneous multi-AUV systems with partial measurements and intermittent communication. Sci China Inf Sci. https://doi.org/10.1007/s11432-023-3903-6
Fu C, Yuan H, Xu H, Zhang H, Shen L (2023) TMSO-Net: texture adaptive multi-scale observation for light field image depth estimation. J Vis Commun Image Represent 90:103731. https://doi.org/10.1016/j.jvcir.2022.103731
Jiang Y, Liu S, Li M, Zhao N, Wu M (2022) A new adaptive co-site broadband interference cancellation method with auxiliary channel. Digit Commun Netw. https://doi.org/10.1016/j.dcan.2022.10.025
Mi C, Huang S, Zhang Y, Zhang Z, Postolache O (2022) Design and implementation of 3-D measurement method for container handling target. J Marine Sci Eng 10(12):1961. https://doi.org/10.3390/jmse10121961
Dai X, Xiao Z, Jiang H, Alazab M, Lui JCS, Dustdar S, Liu J (2023) Task co-offloading for D2D-assisted mobile edge computing in industrial internet of things. IEEE Trans Ind Inf 19(1):480–490. https://doi.org/10.1109/TII.2022.3158974
Jiang H, Dai X, Xiao Z, Iyengar AK (2022) Joint task offloading and resource allocation for energy-constrained mobile edge computing. IEEE Trans Mob Comput. https://doi.org/10.1109/TMC.2022.3150432
Dai X, Xiao Z, Jiang H, Lui JCS (2023) UAV-assisted task offloading in vehicular edge computing networks. IEEE Trans Mob Comput. https://doi.org/10.1109/TMC.2023.3259394
Hu J, Wu Y, Li T, Ghosh BK (2019) Consensus control of general linear multiagent systems with antagonistic interactions and communication noises. IEEE Trans Autom Control 64(5):2122–2127. https://doi.org/10.1109/TAC.2018.2872197
Chen B, Hu J, Zhao Y, Ghosh BK (2022) Finite-time velocity-free rendezvous control of multiple AUV systems with intermittent communication. IEEE Trans Syst, Man, Cybern: Syst 52(10):6618–6629. https://doi.org/10.1109/TSMC.2022.3148295
Zhang C, Xiao P, Zhao Z, Liu Z, Yu J, Hu X, Li G (2023) A wearable localized surface plasmons antenna sensor for communication and sweat sensing. IEEE Sens J 23(11):11591–11599. https://doi.org/10.1109/JSEN.2023.3266262
Li A, Masouros C, Swindlehurst AL, Yu W (2021) 1-Bit massive MIMO transmission: embracing interference with symbol-level precoding. IEEE Commun Mag 59(5):121–127. https://doi.org/10.1109/MCOM.001.2000601
Hou X, Zhang L, Su Y, Gao G, Liu Y, Na Z, Chen T (2023) A space crawling robotic bio-paw (SCRBP) enabled by triboelectric sensors for surface identification. Nano Energy 105:108013. https://doi.org/10.1016/j.nanoen.2022.108013
Min H, Li Y, Wu X, Wang W, Chen L, Zhao X (2023) A measurement scheduling method for multi-vehicle cooperative localization considering state correlation. Veh Commun. https://doi.org/10.1016/j.vehcom.2023.100682
Zhao X, Fang Y, Min H, Wu X, Wang W, Teixeira R (2024) Potential sources of sensor data anomalies for autonomous vehicles: an overview from road vehicle safety perspective. Expert Syst Appl 236:121358. https://doi.org/10.1016/j.eswa.2023.121358
Huang C, Tu Y, Han Z, Jiang F, Wu F, Jiang Y (2023) Examining the relationship between peer feedback classified by deep learning and online learning burnout. Comput Educ 207:104910. https://doi.org/10.1016/j.compedu.2023.104910
Mou J, Gao K, Duan P, Li J, Garg A, Sharma R (2023) A machine learning approach for energy-efficient intelligent transportation scheduling problem in a real-world dynamic circumstances. IEEE Trans Intell Transp Syst 24(12):15527–15539. https://doi.org/10.1109/TITS.2022.3183215
Hou X, Xin L, Fu Y, Na Z, Gao G, Liu Y, Chen T (2023) A self-powered biomimetic mouse whisker sensor (BMWS) aiming at terrestrial and space objects perception. Nano Energy 118:109034. https://doi.org/10.1016/j.nanoen.2023.109034
Mo J, Yang H (2023) Sampled value attack detection for busbar differential protection based on a negative selection immune system. J Mod Power Syst Clean Energy 11(2):421–433. https://doi.org/10.35833/MPCE.2021.000318
Liu C, Wu T, Li Z, Ma T, Huang J (2023) Robust online tensor completion for iot streaming data recovery. IEEE Trans Neural Netw Learn Syst 34(12):10178–10192. https://doi.org/10.1109/TNNLS.2022.3165076
Cao B, Zhao J, Gu Y, Fan S, Yang P (2020) Security-aware industrial wireless sensor network deployment optimization. IEEE Trans Industr Inf 16(8):5309–5316. https://doi.org/10.1109/TII.2019.2961340
Cao B, Zhao J, Yang P, Gu Y, Muhammad K, Rodrigues JJPC, de Albuquerque VHC (2020) Multiobjective 3-D topology optimization of next-generation wireless data center network. IEEE Trans Ind Inf 16(5):3597–3605. https://doi.org/10.1109/TII.2019.2952565
Tian G, Hui Y, Lu W, Tingting W (2023) Rate-distortion optimized quantization for geometry-based point cloud compression. J Electron Imaging 32(1):13047. https://doi.org/10.1117/1.JEI.32.1.013047
Lu J, Osorio C (2022) On the analytical probabilistic modeling of flow transmission across nodes in transportation networks. Transp Res Rec 2676(12):209–225. https://doi.org/10.1177/03611981221094829
Shi Y, Hou X, Na Z, Zhou J, Yu N, Liu S, Liu Y (2023) Bio-inspired attachment mechanism of dynastes hercules: vertical climbing for on-orbit assembly legged robots. J Bionic Eng. https://doi.org/10.1007/s42235-023-00423-0
Xu H, Han S, Li X, Han Z (2023) Anomaly traffic detection based on communication-efficient federated learning in space-air-ground integration network. IEEE Trans Wireless Commun 22(12):9346–9360. https://doi.org/10.1109/TWC.2023.3270179
Xing J, Yuan H, Hamzaoui R, Liu H, Hou J (2023) GQE-Net: a graph-based quality enhancement network for point cloud color attribute. IEEE Trans Image Process 32:6303–6317. https://doi.org/10.1109/TIP.2023.3330086
Sun R, Dai Y, Cheng Q (2023) An adaptive weighting strategy for multisensor integrated navigation in urban areas. IEEE Internet Things J 10(14):12777–12786. https://doi.org/10.1109/JIOT.2023.3256008
Chen J, Xu M, Xu W, Li D, Peng W, Xu H (2023) A flow feedback traffic prediction based on visual quantified features. IEEE Trans Intell Transp Syst 24(9):10067–10075. https://doi.org/10.1109/TITS.2023.3269794
Chen J, Wang Q, Peng W, Xu H, Li X, Xu W (2022) Disparity-based multiscale fusion network for transportation detection. IEEE Trans Intell Transp Syst 23(10):18855–18863. https://doi.org/10.1109/TITS.2022.3161977
Chen J, Wang Q, Cheng HH, Peng W, Xu W (2022) A review of vision-based traffic semantic understanding in ITSs. IEEE Trans Intell Transp Syst 23(11):19954–19979. https://doi.org/10.1109/TITS.2022.3182410
Xie J, Jiang H, Song W, Yang J (2023) A novel quality control method of time series ocean wave observation data combining deep learning prediction and statistical analysis. J Sea Res 195:102439
Wang N, Hossain E, Bhargava VK (2015) Backhauling 5G small cells: A radio resource management perspective. IEEE Wirel Commun 22:41–49
Giust F, Cominardi L, Bernardos CJ (2015) Distributed mobility management for future 5G networks: overview and analysis of existing approaches. IEEE Commun Mag 53:142–149
Agiwal M, Roy A, Saxena N (2016) Next generation 5G wireless networks: a comprehensive survey. IEEE Commun Surv Tutor 18:1617–1655
Gupta A, Jha RK (2015) A survey of 5G network: architecture and emerging technologies. IEEE Access 3:1206–1232
Zheng K et al (2016) Big data-driven optimization for mobile networks toward 5G. IEEE Netw 30:44–51
Jiang C et al (2017) Machine learning paradigms for next-generation wireless networks. IEEE Wirel Commun 24:98–105
Nguyen DD, Nguyen HX, White LB (2017) Reinforcement learning with network-assisted feedback for heterogeneous RAT selection. IEEE Trans Wirel Commun 16:6062–6076
Zhang, C., Zhou, P., Li, C. & Liu, L. A Convolutional Neural Network for Leaves Recognition Using Data Augmentation. in 2015 IEEE International Conference on Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing 2143–2150 (2015). doi:https://doi.org/10.1109/CIT/IUCC/DASC/PICOM.2015.318
Wang M, Cui Y, Wang X, Xiao S, Jiang J (2018) Machine learning for networking: workflow. Adv Oppor IEEE Netw 32:92–99
Alsheikh MA, Niyato D, Lin S, Tan H, Han Z (2016) Mobile big data analytics using deep learning and apache spark. IEEE Netw 30:22–29
Arulkumaran K, Deisenroth MP, Brundage M, Bharath AA (2017) Deep reinforcement learning: a brief survey. IEEE Signal Process Mag 34:26–38
Chen X-W, Lin X (2014) Big data deep learning: challenges and perspectives. IEEE Access 2:514–525
Gheisari, M., Wang, G. & Bhuiyan, M. Z. A. (2017) A Survey on Deep Learning in Big Data. in 2017 IEEE International Conference on Computational Science and Engineering (CSE) and IEEE International Conference on Embedded and Ubiquitous Computing (EUC) vol. 2 173–180
Yu S, Liu M, Dou W, Liu X, Zhou S (2017) Networking for big data: a survey. IEEE Commun Surv Tutorials 19:531–549
Alsheikh MA, Lin S, Niyato D, Tan H-P (2014) Machine learning in wireless sensor networks: algorithms, strategies, and applications. IEEE Commun Surv Tutorials 16:1996–2018
Tsai C-W, Lai C-F, Chiang M-C, Yang LT (2014) Data mining for internet of things: a survey. IEEE Commun Surv Tutorials 16:77–97
Cheng X, Fang L, Hong X, Yang L (2017) Exploiting mobile big data: sources, features, and applications. IEEE Netw 31:72–79
Bkassiny M, Li Y, Jayaweera SK (2013) A survey on machine-learning techniques in cognitive radios. IEEE Commun Surv Tutorials 15:1136–1159
Elijah O, Leow CY, Rahman TA, Nunoo S, Iliya SZ (2016) A comprehensive survey of pilot contamination in massive MIMO—5G system. IEEE Commun Surv Tutorials 18:905–923
Buzzi S et al (2016) A survey of energy-efficient techniques for 5G networks and challenges ahead. IEEE J Sel Areas Commun 34:697–709
Peng M, Li Y, Zhao Z, Wang C (2015) System architecture and key technologies for 5G heterogeneous cloud radio access networks. IEEE Netw 29:6–14
Foukas X, Patounas G, Elmokashfi A, Marina MK (2017) Network slicing in 5G: survey and challenges. IEEE Commun Mag 55:94–100
Taleb T et al (2017) On multi-access edge computing: a survey of the emerging 5G network edge cloud architecture and orchestration. IEEE Commun Surv Tutorials 19:1657–1681
Mach P, Becvar Z (2017) Mobile edge computing: a survey on architecture and computation offloading. IEEE Commun Surv Tutorials 19:1628–1656
Mao Y, You C, Zhang J, Huang K, Letaief KB (2017) A survey on mobile edge computing: the communication perspective. IEEE Commun Surv Tutorials 19:2322–2358
Wang Y et al (2017) A Data-driven architecture for personalized QoE management in 5G wireless networks. IEEE Wirel Commun 24:102–110
Han Q, Liang S, Zhang H (2015) Mobile cloud sensing, big data, and 5G networks make an intelligent and smart world. IEEE Netw 29:40–45
Chen X, Wu J, Cai Y, Zhang H, Chen T (2015) Energy-efficiency oriented traffic offloading in wireless networks: a brief survey and a learning approach for heterogeneous cellular networks. IEEE J Sel Areas Commun 33:627–640
Wu J, Guo S, Li J, Zeng D (2016) Big data meet green challenges: big data toward green applications. IEEE Syst J 10:888–900
Buda, T. S. et al. (2016) Can machine learning aid in delivering new use cases and scenarios in 5G? in NOMS 2016 - 2016 IEEE/IFIP Network Operations and Management Symposium 1279–1284 doi:https://doi.org/10.1109/NOMS.2016.7503003.
Imran A, Zoha A, Abu-Dayya A (2014) Challenges in 5G: how to empower SON with big data for enabling 5G. IEEE Netw 28:27–33
Keshavamurthy, B. & Ashraf, M. Conceptual design of proactive SONs based on the Big Data framework for 5G cellular networks: A novel Machine Learning perspective facilitating a shift in the SON paradigm. in 2016 International Conference System Modeling & Advancement in Research Trends (SMART) 298–304 (2016). doi:https://doi.org/10.1109/SYSMART.2016.7894539
Klaine PV, Imran MA, Onireti O, Souza RD (2017) A survey of machine learning techniques applied to self-organizing cellular networks. IEEE Commun Surv Tutorials 19:2392–2431
Li R et al (2017) Intelligent 5G: when cellular networks meet artificial intelligence. IEEE Wirel Commun 24:175–183
Bui N et al (2017) A survey of anticipatory mobile networking: context-based classification, prediction methodologies, and optimization techniques. IEEE Commun Surv Tutorials 19:1790–1821
Atat R et al (2018) Big data meet cyber-physical systems: a panoramic survey. IEEE Access 6:73603–73636
Cheng X, Fang L, Yang L, Cui S (2017) Mobile big data: the fuel for data-driven wireless. IEEE Internet Things J 4:1489–1516
Kato N et al (2017) The deep learning vision for heterogeneous network traffic control: proposal, challenges, and future perspective. IEEE Wirel Commun 24:146–153
Zorzi M, Zanella A, Testolin A, De Grazia MDF, Zorzi M (2015) Cognition-based networks: a new perspective on network optimization using learning and distributed intelligence. IEEE Access 3:1512–1530
Fadlullah ZM et al (2017) State-of-the-art deep learning: evolving machine intelligence toward tomorrow’s intelligent network traffic control systems. IEEE Commun Surv Tutorials 19:2432–2455
Mohammadi M, Al-Fuqaha A, Sorour S, Guizani M (2018) Deep learning for iot big data and streaming analytics: a survey. IEEE Commun Surv Tutorials 20:2923–2960
Mao Q, Hu F, Hao Q (2018) Deep learning for intelligent wireless networks: a comprehensive survey. IEEE Commun Surv Tutorials 20:2595–2621
Gharaibeh A et al (2017) Smart cities: a survey on data management, security, and enabling technologies. IEEE Commun Surv Tutorials 19:2456–2501
Szegedy, C. et al. Going deeper with convolutions. in 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 1–9 (2015). doi:https://doi.org/10.1109/CVPR.2015.7298594
Mao, J., Chen, X., Nixon, K. W., Krieger, C. & Chen, Y. MoDNN: Local distributed mobile computing system for Deep Neural Network. in Design, Automation & Test in Europe Conference & Exhibition (DATE), 2017 1396–1401 (2017). doi:https://doi.org/10.23919/DATE.2017.7927211
Mukherjee M, Shu L, Wang D (2018) Survey of fog computing: fundamental, network applications, and research challenges. IEEE Commun Surv Tutorials 20:1826–1857
Hinton GE, Osindero S, Teh Y (2006) A fast learning algorithm for deep belief nets. Neural Comput 18:1527–1554
He, K., Zhang, X., Ren, S. & Sun, J. Deep Residual Learning for Image Recognition. in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 770–778 (2016). doi:https://doi.org/10.1109/CVPR.2016.90
Ji S, Xu W, Yang M, Yu K (2013) 3D Convolutional neural networks for human action recognition. IEEE Trans Pattern Anal Mach Intell 35:221–231
Huang, G., Liu, Z., Maaten, L. Van Der & Weinberger, K. Q. Densely Connected Convolutional Networks. in 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2261–2269 (2017). doi:https://doi.org/10.1109/CVPR.2017.243
Hinton GE (2002) Training products of experts by minimizing contrastive divergence. Neural Comput 14:1771–1800
Mao B et al (2017) Routing or computing? the paradigm shift towards intelligent computer network packet transmission based on deep learning. IEEE Trans Comput 66:1946–1960
Raghavendra, R. & Busch, C. Learning deeply coupled autoencoders for smartphone based robust periocular verification. in 2016 IEEE International Conference on Image Processing (ICIP) 325–329 (2016). doi:https://doi.org/10.1109/ICIP.2016.7532372
Jeon, Y. & Kim, J. Active Convolution: Learning the Shape of Convolution for Image Classification. in 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 1846–1854 (2017). doi:https://doi.org/10.1109/CVPR.2017.200
Dai, J. et al. Deformable Convolutional Networks. in 2017 IEEE International Conference on Computer Vision (ICCV) 764–773 (2017). doi:https://doi.org/10.1109/ICCV.2017.89
Bengio Y, Simard P, Frasconi P (1994) Learning long-term dependencies with gradient descent is difficult. IEEE Trans Neural Networks 5:157–166
Graves, A., Jaitly, N. & Mohamed, A. Hybrid speech recognition with Deep Bidirectional LSTM. in 2013 IEEE Workshop on Automatic Speech Recognition and Understanding 273–278 (2013). doi:https://doi.org/10.1109/ASRU.2013.6707742
Priyadarshi Rahul (2023) Energy-efficient routing in wireless sensor networks: a meta-heuristic and artificial intelligence-based approach: a comprehensive review. Arch Computat Methods Eng. https://doi.org/10.1007/s11831-023-10039-6
Li, J. et al. Perceptual Generative Adversarial Networks for Small Object Detection. in 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 1951–1959 (2017). doi:https://doi.org/10.1109/CVPR.2017.211
Li, Y., Liu, S., Yang, J. & Yang, M.-H. Generative Face Completion. in 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 5892–5900 (2017). doi:https://doi.org/10.1109/CVPR.2017.624
Priyadarshi R, Singh L, Singh A, Thakur A (2018) SEEN: stable energy efficient network for wireless sensor network. In: 2018 5th international conference on signal processing and integrated networks (SPIN), pp. 338–342
Liu Y-J, Tang L, Tong S, Chen CLP, Li D-J (2015) Reinforcement learning design-based adaptive tracking control with less learning parameters for nonlinear discrete-time MIMO systems. IEEE Trans Neural Netw Learn Syst 26:165–176
Gupta T, Kumar A, Priyadarshi R (2020) A novel hybrid precoding technique for millimeter wave. In Nanoelectronics, circuits and communication systems: proceeding of NCCS 2018, pp. 481–493
Nie, L., Jiang, D., Yu, S. & Song, H. Network Traffic Prediction Based on Deep Belief Network in Wireless Mesh Backbone Networks. in 2017 IEEE Wireless Communications and Networking Conference (WCNC) 1–5 (2017). doi:https://doi.org/10.1109/WCNC.2017.7925498
Priyadarshi R, Gupta B (2020) Coverage area enhancement in wireless sensor network. Microsyst Technol 26(5):1417–1426
Huang, C.-W., Chiang, C.-T. & Li, Q. A study of deep learning networks on mobile traffic forecasting. in 2017 IEEE 28th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC) 1–6 (2017). doi:https://doi.org/10.1109/PIMRC.2017.8292737
Anurag A, Priyadarshi R, Goel A, Gupta B (2020) 2-D coverage optimization in WSN using a novel variant of particle swarm optimisation. In 2020 7th international conference on signal processing and integrated networks (SPIN), pp. 663–668
Navabi, S., Wang, C., Bursalioglu, O. Y. & Papadopoulos, H. Predicting Wireless Channel Features Using Neural Networks. in 2018 IEEE International Conference on Communications (ICC) 1–6 (2018). doi:https://doi.org/10.1109/ICC.2018.8422221
Priyadarshi R, Singh L, Singh A et al. (2018) A novel HEED protocol for wireless sensor networks. In 2018 5th international conference on signal processing and integrated networks (SPIN), pp. 296–300
Wang, W., Zhu, M., Zeng, X., Ye, X. & Sheng, Y. Malware traffic classification using convolutional neural network for representation learning. in 2017 International Conference on Information Networking (ICOIN) 712–717 (2017). doi:https://doi.org/10.1109/ICOIN.2017.7899588
Pandey A, Kumar D, Priyadarshi R, Nath V (2022) Development of smart village for better lifestyle of farmers by crop and health monitoring system. In: Microelectronics, communication systems, machine learning and internet of things: select proceedings of MCMI 2020. Springer: Singapore, pp. 689–694
Priyadarshi R, Soni SK, Sharma P (2019) An enhanced GEAR protocol for wireless sensor networks. In: Nath V, Mandal J (eds) Nanoelectronics, circuits and communication systems. Lecture notes in electrical engineering, vol 511. Springer, Singapore. https://doi.org/10.1007/978-981-13-0776-8_27
Rawat P, Chauhan S, Priyadarshi R (2021) A novel heterogeneous clustering protocol for lifetime maximization of wireless sensor network. Wireless Pers Commun 117:825–841
Feng J, Chen X, Gao R, Zeng M, Li Y (2018) DeepTP: an end-to-end neural network for mobile cellular traffic prediction. IEEE Netw 32:108–115
Priyadarshi, R., Yadav, S., Bilyan, D. (2019). Performance and Comparison Analysis of MIEEP Routing Protocol Over Adapted LEACH Protocol. https://doi.org/10.1007/978-981-13-6295-8_20
Li, H. & Trocan, M. Personal Health Indicators by Deep Learning of Smart Phone Sensor Data. in 2017 3rd IEEE International Conference on Cybernetics (CYBCONF) 1–5 (2017). doi:https://doi.org/10.1109/CYBConf.2017.7985800
Priyadarshi R, Gupta B (2023) 2-D coverage optimization in obstacle-based FOI in WSN using modified PSO. J Supercomput 79(5):4847–4869
Khan, U. M., Kabir, Z., Hassan, S. A. & Ahmed, S. H. A Deep Learning Framework Using Passive WiFi Sensing for Respiration Monitoring. in GLOBECOM 2017 - 2017 IEEE Global Communications Conference 1–6 (2017). doi:https://doi.org/10.1109/GLOCOM.2017.8255027
Priyadarshi R, Rawat P, Nath V, Acharya B, Shylashree N (2020) Three level heterogeneous clustering protocol for wireless sensor network. Microsyst Technol 26:3855–3864
Sood SK, Agrewal M (2023) Quantum machine learning for computational methods in engineering: a systematic review. Arch Comput Methods Eng. https://doi.org/10.1007/s11831-023-10027-w
Priyadarshi, R., Yadav, S., Bilyan, D. (2019). Performance Analysis of Adapted Selection Based Protocol Over LEACH Protocol. https://doi.org/10.1007/978-981-13-6295-8_21
Rawat P, Chauhan S, Priyadarshi R (2020) Energy-efficient clusterhead selection scheme in heterogeneous wireless sensor network. J Circ Syst Comput 29(13):2050204
Alkhateeb A et al (2018) Deep learning coordinated beamforming for highly-mobile millimeter wave systems. IEEE Access 6:37328–37348
Jain, V., Randheer, Priyadarshi, R., Thakur, A. (2019). Performance Analysis of Block Matching Algorithms. In: Nath, V., Mandal, J. (eds) Proceedings of the Third International Conference on Microelectronics, Computing and Communication Systems. Lecture Notes in Electrical Engineering, vol 556. Springer, Singapore. https://doi.org/10.1007/978-981-13-7091-5_7
Su, X., Zhang, D., Li, W. & Zhao, K. A Deep Learning Approach to Android Malware Feature Learning and Detection. in 2016 IEEE Trustcom/BigDataSE/ISPA 244–251 (2016). doi:https://doi.org/10.1109/TrustCom.2016.0070
R. Priyadarshi, M. P. Singh, A. Bhardwaj and P. Sharma, "Amount of fading analysis for composite fading channel using holtzman approximation," 2017 Fourth International Conference on Image Information Processing (ICIIP), Shimla, India, 2017, pp. 1–5, doi: https://doi.org/10.1109/ICIIP.2017.8313759
Fei-Fei L, Fergus R, Perona P (2006) One-shot learning of object categories. IEEE Trans Pattern Anal Mach Intell 28:594–611
Edel, M. & Köppe, E. Binarized-BLSTM-RNN based Human Activity Recognition. in 2016 International Conference on Indoor Positioning and Indoor Navigation (IPIN) 1–7 (2016). doi:https://doi.org/10.1109/IPIN.2016.7743581
Priyadarshi R, Soni SK, Bhadu R, Nath V (2018) Performance analysis of diamond search algorithm over full search algorithm. Microsyst Technol 24:2529–2537
Tekouabou SCK, Gherghina ŞC, Kameni ED et al (2023) AI-based on machine learning methods for urban real estate prediction: a systematic survey. Arch Computat Methods Eng. https://doi.org/10.1007/s11831-023-10010-5
Nguyen, K. K. et al. Cyberattack detection in mobile cloud computing: A deep learning approach. in 2018 IEEE Wireless Communications and Networking Conference (WCNC) 1–6 (2018). doi:https://doi.org/10.1109/WCNC.2018.8376973
Yuan Z, Lu Y, Xue Y (2016) Droiddetector: android malware characterization and detection using deep learning. Tsinghua Sci Technol 21:114–123
Wang X, Gao L, Mao S (2016) CSI phase fingerprinting for indoor localization with a deep learning approach. IEEE Internet Things J 3:1113–1123
Priyadarshi R, Gupta B, Anurag A (2020) Deployment techniques in wireless sensor networks: a survey, classification, challenges, and future research issues. J Supercomput 76:7333–7373
Zhou Y, Fadlullah ZM, Mao B, Kato N (2018) A Deep-learning-based radio resource assignment technique for 5G ultra dense networks. IEEE Netw 32:28–34
McGraw, I. et al. Personalized speech recognition on mobile devices. in 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 5955–5959 (2016). doi:https://doi.org/10.1109/ICASSP.2016.7472820
Priyadarshi R, Rawat P, Nath V (2019) Energy dependent cluster formation in heterogeneous wireless sensor network. Microsyst Technol 25:2313–2321
Seneviratne S et al (2017) A survey of wearable devices and challenges. IEEE Commun Surv Tutorials 19:2573–2620
Priyadarshi R, Singh A, Agarwal D, Verma UC, Singh A (2023) Emerging smart manufactory: industry 40 and manufacturing in india: the next wave. In: Nath V, Mandal JK (eds) Microelectronics, communication systems, machine learning and internet of things. Lecture notes in electrical engineering, vol 887. Springer, Singapore
Fang S-H, Fei Y-X, Xu Z, Tsao Y (2017) Learning transportation modes from smartphone sensors based on deep neural network. IEEE Sens J 17:6111–6118
Sateesh, V.A., Kumar, A., Priyadarshi, R., Nath, V. (2021). A Novel Deployment Scheme to Enhance the Coverage in Wireless Sensor Network. In: Nath, V., Mandal, J.K. (eds) Proceedings of the Fourth International Conference on Microelectronics, Computing and Communication Systems. Lecture Notes in Electrical Engineering, vol 673. Springer, Singapore. https://doi.org/10.1007/978-981-15-5546-6_82
Priyadarshi R, Soni SK, Nath V (2018) Energy efficient cluster head formation in wireless sensor network. Microsyst Technol 24:4775–4784
Li H, Ota K, Dong M (2018) Learning IoT in edge: deep learning for the internet of things with edge computing. IEEE Netw 32:96–101
Kumar S, Soni SK, Randheer Priyadarshi, R. (2020) Performance analysis of novel energy aware routing in wireless sensor network. In: Nath V, Mandal J (eds) Nanoelectronics, circuits and communication systems NCCS 2018. Lecture notes in electrical engineering, vol 642. Springer, Singapore. https://doi.org/10.1007/978-981-15-2854-5_44
Liu J, Krishnamachari B, Zhou S, Niu Z (2018) DeepNap: data-driven base station sleeping operations through deep reinforcement learning. IEEE Internet Things J 5:4273–4282
Priyadarshi, Rahul, and Abhyuday Bhardwaj. "NODE NON-UNIFORMITY FOR ENERGY EFFECTUAL COORDINATION IN WSN." International Journal on Information Technologies & Security 9, no. 4 (2017)
Mennes, R., Camelo, M., Claeys, M. & Latré, S. A neural-network-based MF-TDMA MAC scheduler for collaborative wireless networks. in 2018 IEEE Wireless Communications and Networking Conference (WCNC) 1–6 (2018). doi:https://doi.org/10.1109/WCNC.2018.8377044
Priyadarshi R, Gupta B, Anurag A (2020) Wireless sensor networks deployment: a result oriented analysis. Wirel Pers Commun 113:843–866
Priyadarshi R, Rana H, Srivastava A, Nath V (2023) A novel approach for sink route in wireless sensor network. In: Nath V, Mandal JK (eds) Microelectronics, communication systems, machine learning and internet of things. Lecture notes in electrical engineering, vol 887. Springer, Singapore
Wang J, Zhang X, Gao Q, Yue H, Wang H (2017) Device-free wireless localization and activity recognition: a deep learning approach. IEEE Trans Veh Technol 66:6258–6267
Mohammadi M, Al-Fuqaha A, Guizani M, Oh J-S (2018) Semisupervised deep reinforcement learning in support of IoT and smart city services. IEEE Internet Things J 5:624–635
Priyadarshi R, Bhardwaj P, Gupta P, Nath V (2022) Utilization of smartphone-based wireless sensors in agricultural science: a state of art. Microelectron Commun Syst Mach Learn IoT 2020:681–688
Lee W, Kim M, Cho D-H (2019) Deep learning based transmit power control in underlaid device-to-device communication. IEEE Syst J 13:2551–2554
Singh L, Kumar A, Priyadarshi R (2020) Performance and comparison analysis of image processing based forest fire detection. In: Nath V, Mandal J (eds) Nanoelectronics, circuits and communication systems nccs 2018. Lecture notes in electrical engineering, vol 642. Springer, Singapore. https://doi.org/10.1007/978-981-15-2854-5_41
Wang X, Gao L, Mao S, Pandey S (2017) CSI-based fingerprinting for indoor localization: a deep learning approach. IEEE Trans Veh Technol 66:763–776
Chen H, Zhang Y, Li W, Tao X, Zhang P (2017) ConFi: convolutional neural networks based indoor Wi-Fi localization using channel state information. IEEE Access 5:18066–18074
Priyadarshi, R., Kumar, R.R. (2021). An Energy-Efficient LEACH Routing Protocol for Wireless Sensor Networks. In: Nath, V., Mandal, J.K. (eds) Proceedings of the Fourth International Conference on Microelectronics, Computing and Communication Systems. Lecture Notes in Electrical Engineering, vol 673. Springer, Singapore. https://doi.org/10.1007/978-981-15-5546-6_35
Li X et al (2018) Intelligent power control for spectrum sharing in cognitive radios: a deep reinforcement learning approach. IEEE Access 6:25463–25473
Xiao C, Yang D, Chen Z, Tan G (2017) 3-D BLE Indoor localization based on denoising autoencoder. IEEE Access 5:12751–12760
Luo, T. & Nagarajan, S. G. Distributed Anomaly Detection Using Autoencoder Neural Networks in WSN for IoT. in 2018 IEEE International Conference on Communications (ICC) 1–6 (2018). doi:https://doi.org/10.1109/ICC.2018.8422402
Sun W et al (2017) WNN-LQE: wavelet-neural-network-based link quality estimation for smart grid WSNs. IEEE Access 5:12788–12797
Kang J, Park Y-J, Lee J, Wang S-H, Eom D-S (2018) Novel leakage detection by ensemble CNN-SVM and graph-based localization in water distribution systems. IEEE Trans Ind Electron 65:4279–4289
Priyadarshi R, Gupta B (2021) Area coverage optimization in three-dimensional wireless sensor network. Wirel Pers Commun 117:843–865
Assaf AE, Zaidi S, Affes S, Kandil N (2016) Robust ANNs-based WSN localization in the presence of anisotropic signal attenuation. IEEE Wirel Commun Lett 5:504–507
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Qiu, Y., Ma, L. & Priyadarshi, R. Deep Learning Challenges and Prospects in Wireless Sensor Network Deployment. Arch Computat Methods Eng (2024). https://doi.org/10.1007/s11831-024-10079-6
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DOI: https://doi.org/10.1007/s11831-024-10079-6