Journal of Information Processing Systems

  • 제15권4호
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  • Pages.833-852
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  • 2019
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  • 1976-913X(pISSN)
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  • 2092-805X(eISSN)
한국정보처리학회 (Korea Information Processing Society)
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DOI QR코드

DOI QR Code

Selection of Machine Learning Techniques for Network Lifetime Parameters and Synchronization Issues in Wireless Networks

  • 투고 : 2019.02.01
  • 심사 : 2019.04.18
  • 발행 : 2019.08.31
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⟨ 이전 논문 다음 논문 ⟩

초록

In real time applications, due to their effective cost and small size, wireless networks play an important role in receiving particular data and transmitting it to a base station for analysis, a process that can be easily deployed. Due to various internal and external factors, networks can change dynamically, which impacts the localisation of nodes, delays, routing mechanisms, geographical coverage, cross-layer design, the quality of links, fault detection, and quality of service, among others. Conventional methods were programmed, for static networks which made it difficult for networks to respond dynamically. Here, machine learning strategies can be applied for dynamic networks effecting self-learning and developing tools to react quickly and efficiently, with less human intervention and reprogramming. In this paper, we present a wireless networks survey based on different machine learning algorithms and network lifetime parameters, and include the advantages and drawbacks of such a system. Furthermore, we present learning algorithms and techniques for congestion, synchronisation, energy harvesting, and for scheduling mobile sinks. Finally, we present a statistical evaluation of the survey, the motive for choosing specific techniques to deal with wireless network problems, and a brief discussion on the challenges inherent in this area of research.

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참고문헌

  1. M. Bernas and B. Placzek, "Fully connected neural networks ensemble with signal strength clustering for indoor localization in wireless sensor networks," International Journal of Distributed Sensor Networks, vol. 11, no. 12, article no. 403242, 2015.
  2. X. Li, S. Ding, and Y. Li, "Outlier suppression via non-convex robust PCA for efficient localization in wireless sensor networks," IEEE Sensors Journal, vol. 17, no. 21, pp. 7053-7063, 2017. https://doi.org/10.1109/JSEN.2017.2754502
  3. W. Zhao, S. Su, and F. Shao, "Improved DV-hop algorithm using locally weighted linear regression in anisotropic wireless sensor networks," Wireless Personal Communications, vol. 98, no. 4, pp. 3335-3353, 2018. https://doi.org/10.1007/s11277-017-5017-2
  4. S. S. Banihashemian, F. Adibnia, and M. A. Sarram, "A new range-free and storage-efficient localization algorithm using neural networks in wireless sensor networks," Wireless Personal Communications, vol. 98, no. 1, pp. 1547-1568, 2018. https://doi.org/10.1007/s11277-017-4934-4
  5. A. El Assaf, S. Zaidi, S. Affes, and N. Kandil, "Robust ANNs-based WSN localization in the presence of anisotropic signal attenuation," IEEE Wireless Communications Letters, vol. 5, no. 5, pp. 504-507, 2016. https://doi.org/10.1109/LWC.2016.2595576
  6. S. Phoemphon, C. So-In, and D. T. Niyato, "A hybrid model using fuzzy logic and an extreme learning machine with vector particle swarm optimization for wireless sensor network localization," Applied Soft Computing, vol. 65, pp. 101-120, 2018. https://doi.org/10.1016/j.asoc.2018.01.004
  7. N. Baccar and R. Bouallegue, "Interval type 2 fuzzy localization for wireless sensor networks," EURASIP Journal on Advances in Signal Processing, vol. 2016, article no. 42, 2016.
  8. T. Tang, H. Liu, H. Song, and B. Peng, "Support vector machine based range-free localization algorithm in wireless sensor network," in Machine Learning and Intelligent Communications. Cham: Springer, 2016, pp. 150-158.
  9. J. Hong and T. Ohtsuki, "Signal eigenvector-based device-free passive localization using array sensor," IEEE Transactions on Vehicular Technology, vol. 64, no. 4, pp. 1354-1363, 2015. https://doi.org/10.1109/TVT.2015.2397436
  10. T. L. T. Nguyen, F. Septier, H. Rajaona, G. W. Peters, I. Nevat, and Y. Delignon, "A Bayesian perspective on multiple source localization in wireless sensor networks," IEEE Transactions on Signal Processing, vol. 64, no. 7, pp. 1684-1699, 2015. https://doi.org/10.1109/TSP.2015.2505689
  11. B. Sun, Y. Guo, N. Li, and D. Fang, "Multiple target counting and localization using variational Bayesian EM algorithm in wireless sensor networks," IEEE Transactions on Communications, vol. 65, no. 7, pp. 2985-2998, 2017. https://doi.org/10.1109/TCOMM.2017.2695198
  12. Y. Guo, B. Sun, N. Li, and D. Fang, "Variational Bayesian inference-based counting and localization for off-grid targets with faulty prior information in wireless sensor networks," IEEE Transactions on Communications, vol. 66, no. 3, pp. 1273-1283, 2017. https://doi.org/10.1109/tcomm.2017.2770139
  13. W. Sun, X. Yuan, J. Wang, Q. Li, L. Chen, and D. Mu, "End-to-end data delivery reliability model for estimating and optimizing the link quality of industrial WSNs," IEEE Transactions on Automation Science and Engineering, vol. 15, no. 3, pp. 1127-1137, 2017. https://doi.org/10.1109/tase.2017.2739342
  14. W. Kim, M. C. Stankovic, K. H. Johansson, and H. J. Kim, "A distributed support vector machine learning over wireless sensor networks," IEEE Transactions on Cybernetics, vol. 45, no. 11, pp. 2599-2611, 2015. https://doi.org/10.1109/TCYB.2014.2377123
  15. W. Elghazel, K. Medjaher, N. Zerhouni, J. Bahi, A. Farhat, C. Guyeux, and M. Hakem, "Random forests for industrial device functioning diagnostics using wireless sensor networks," in Proceedings of 2015 IEEE Aerospace Conference, Big Sky, MT, 2015, pp. 1-9.
  16. B. Yang, Y. Lei, and B. Yan, "Distributed multi-human location algorithm using naive Bayes classifier for a binary pyroelectric infrared sensor tracking system," IEEE Sensors Journal, vol. 16, no. 1, pp. 216-223, 2016. https://doi.org/10.1109/JSEN.2015.2477540
  17. J. Qin, W. Fu, H. Gao, and W. X. Zheng, "Distributed k-means algorithm and fuzzy c-means algorithm for sensor networks based on multiagent consensus theory," IEEE Transactions on Cybernetics, vol. 47, no. 3, pp. 772-783, 2017. https://doi.org/10.1109/TCYB.2016.2526683
  18. H. Chen, X. Li, and F. Zhao, "A reinforcement learning-based sleep scheduling algorithm for desired area coverage in solar-powered wireless sensor networks," IEEE Sensors Journal, vol. 16, no. 8, pp. 2763-2774, 2016. https://doi.org/10.1109/JSEN.2016.2517084
  19. E. Ancillotti, C. Vallati, R. Bruno, and E. Mingozzi, "A reinforcement learning-based link quality estimation strategy for RPL and its impact on topology management," Computer Communications, vol. 112, pp. 1-13, 2017. https://doi.org/10.1016/j.comcom.2017.08.005
  20. M. Xie, J. Hu, S. Han, and H. H. Chen, "Scalable hypergrid k-NN-based online anomaly detection in wireless sensor networks," IEEE Transactions on Parallel and Distributed Systems, vol. 24, no. 8, pp. 1661-1670, 2013. https://doi.org/10.1109/TPDS.2012.261
  21. A. Garofalo, C. Di Sarno, and V. Formicola, "Enhancing intrusion detection in wireless sensor networks through decision trees," in Dependable Computing. Heidelberg: Springer, 2013, pp. 1-15.
  22. Z. Feng, J. Fu, D. Du, F. Li, and S. Sun, "A new approach of anomaly detection in wireless sensor networks using support vector data description," International Journal of Distributed Sensor Networks, vol. 13, no. 1, article no. 1550147716686161, 2017.
  23. H. S. Emadi and S. M. Mazinani, "A novel anomaly detection algorithm using DBSCAN and SVM in wireless sensor networks," Wireless Personal Communications, vol. 98, no. 2, pp. 2025-2035, 2018. https://doi.org/10.1007/s11277-017-4961-1
  24. C. Titouna, M. Aliouat, and M. Gueroui, "Outlier detection approach using Bayes classifiers in wireless sensor networks," Wireless Personal Communications, vol. 85, no. 3, pp. 1009-1023, 2015. https://doi.org/10.1007/s11277-015-2822-3
  25. R. Feng, X. Han, Q. Liu, and N. Yu, "A credible Bayesian-based trust management scheme for wireless sensor networks," International Journal of Distributed Sensor Networks, vol. 11, no. 11, article no. 678926, 2015.
  26. M. Wazid and A. K, Das, "An efficient hybrid anomaly detection scheme using K-means clustering for wireless sensor networks," Wireless Personal Communications, vol. 90, no. 4, pp. 1971-2000, 2016. https://doi.org/10.1007/s11277-016-3433-3
  27. S. Shamshirband, A. Patel, N. B. Anuar, M. L. M. Kiah, and A. Abraham, "Cooperative game theoretic approach using fuzzy Q-learning for detecting and preventing intrusions in wireless sensor networks," Engineering Applications of Artificial Intelligence, vol. 32, pp. 228-241, 2014. https://doi.org/10.1016/j.engappai.2014.02.001
  28. S. Haque, M. Rahman, and S. Aziz, "Sensor anomaly detection in wireless sensor networks for healthcare," Sensors, vol. 15, no. 4, pp. 8764-878, 2015. https://doi.org/10.3390/s150408764
  29. T. Ma, F. Wang, J. Cheng, Y. Yu, and X. Chen, "A hybrid spectral clustering and deep neural network ensemble algorithm for intrusion detection in sensor networks," Sensors, vol. 16, no. 10, article no. 1701, 2016.
  30. S. Zidi, T. Moulahi, and B. Alaya, "Fault detection in wireless sensor networks through SVM classifier," IEEE Sensors Journal, vol. 18, no. 1, pp. 340-347, 2017. https://doi.org/10.1109/JSEN.2017.2771226
  31. M. Jiang, J. Luo, D. Jiang, J. Xiong, H. Song, and J. Shen, "A cuckoo search-support vector machine model for predicting dynamic measurement errors of sensors," IEEE Access, vol. 4, pp. 5030-5037, 2016. https://doi.org/10.1109/ACCESS.2016.2605041
  32. H. Zhang, J. Liu, and N. Kato, "Threshold tuning-based wearable sensor fault detection for reliable medical monitoring using Bayesian network model," IEEE Systems Journal, vol. 12, no. 2, pp. 1886-1896, 2018. https://doi.org/10.1109/JSYST.2016.2600582
  33. C. Titouna, M. Aliouat, and M. Gueroui, "FDS: fault detection scheme for wireless sensor networks," Wireless Personal Communications, vol. 86, no. 2, pp. 549-562, 2016. https://doi.org/10.1007/s11277-015-2944-7
  34. W. Meng, W. Li, Y. Xiang, and K. K. R. Choo, "A Bayesian inference-based detection mechanism to defend medical smartphone networks against insider attacks," Journal of Network and Computer Applications, vol. 78, pp. 162-169, 2017. https://doi.org/10.1016/j.jnca.2016.11.012
  35. P. Chanak and I. Banerjee, "Fuzzy rule-based faulty node classification and management scheme for large scale wireless sensor networks," Expert Systems with Applications, vol. 45, pp. 307-321, 2016. https://doi.org/10.1016/j.eswa.2015.09.040
  36. W. Li, P. Yi, Y. Wu, L. Pan, and J. Li, "A new intrusion detection system based on KNN classification algorithm in wireless sensor network," Journal of Electrical and Computer Engineering, vol. 2014, article no. 240217, 2014.
  37. M. Zhao and T. W. Chow, "Wireless sensor network fault detection via semi-supervised local kernel density estimation," in Proceedings of 2015 IEEE International Conference on Industrial Technology (ICIT), Seville, Spain, 2015, pp. 1495-1500.
  38. G. Gennarelli and F. Soldovieri, "Performance analysis of incoherent RF tomography using wireless sensor networks," IEEE Transactions on Geoscience and Remote Sensing, vol. 54, no. 5, pp. 2722-2732, 2016 https://doi.org/10.1109/TGRS.2015.2505065
  39. A. Mehmood, Z. Lv, J. Lloret, and M. M. Umar, "ELDC: an artificial neural network based energy-efficient and robust routing scheme for pollution monitoring in WSNs," IEEE Transactions on Emerging Topics in Computing, 2017. http://doi.org/10.1109/TETC.2017.2671847.
  40. M. S. Gharajeh and S. Khanmohammadi, "DFRTP: dynamic 3D fuzzy routing based on traffic probability in wireless sensor networks," IET Wireless Sensor Systems, vol. 6, no. 6, pp. 211-219, 2016. https://doi.org/10.1049/iet-wss.2015.0008
  41. J. R. Srivastava ad T. S. B. Sudarshan, "A genetic fuzzy system based optimized zone based energy efficient routing protocol for mobile sensor networks (OZEEP)," Applied Soft Computing, vol. 37, pp. 863-886, 2015. https://doi.org/10.1016/j.asoc.2015.09.025
  42. Y. Lee, "Classification of node degree based on deep learning and routing method applied for virtual route assignment," Ad Hoc Networks, vol. 58, pp. 70-85, 2017. https://doi.org/10.1016/j.adhoc.2016.11.007
  43. F. Khan, S. Memon, and S. H. Jokhio, "Support vector machine based energy aware routing in wireless sensor networks," in Proceedings of 2016 2nd International Conference on Robotics and Artificial Intelligence (ICRAI), Rawalpindi, Pakistan, 2016, pp. 1-4.
  44. V. Jafarizadeh, A. Keshavarzi, and T. Derikvand, "Efficient cluster head selection using Naive Bayes classifier for wireless sensor networks," Wireless Networks, vol. 23, no. 3, pp. 779-785, 2017. https://doi.org/10.1007/s11276-015-1169-8
  45. Z. Liu, M. Zhang, and J. Cui, "An adaptive data collection algorithm based on a Bayesian compressed sensing framework," Sensors, vol. 14, no. 5, pp. 8330-8349, 2014. https://doi.org/10.3390/s140508330
  46. F. Kazemeyni, O. Owe, E. B. Johnsen, and I. Balasingham, "Formal modeling and analysis of learningbased routing in mobile wireless sensor networks," in Integration of Reusable Systems. Heidelberg: Springer, 2014, pp. 127-150.
  47. R. El Mezouary, A. Choukri, A. Kobbane, and M, El Koutbi, "An energy-aware clustering approach based on the k-means method for wireless sensor networks," in Advances in Ubiquitous Networking. Singapore: Springer, 2015, pp. 325-337.
  48. A. Ray and D. De, "Energy efficient clustering protocol based on K-means (EECPK-means)-midpoint algorithm for enhanced network lifetime in wireless sensor network," IET Wireless Sensor Systems, vol. 6, no. 6, pp. 181-191, 2016. https://doi.org/10.1049/iet-wss.2015.0087
  49. B. Jain, G. Brar, and J. Malhotra, "EKMT-k-means clustering algorithmic solution for low energy consumption for wireless sensor networks based on minimum mean distance from base station," in Networking Communication and Data Knowledge Engineering. Singapore: Springer, 2018, pp. 113-123
  50. P. Guo, J. Cao, and X. Liu, "Lossless in-network processing in WSNs for domain-specific monitoring applications," IEEE Transactions on Industrial Informatics, vol. 13, no. 5, pp. 2130-2139, 2017. https://doi.org/10.1109/TII.2017.2691586
  51. M. A. Alsheikh, S. Lin, D. Niyato, and H. P. Tan, "Rate-distortion balanced data compression for wireless sensor networks," IEEE Sensors Journal, vol. 16, no. 12, pp. 5072-5083, 2016. https://doi.org/10.1109/JSEN.2016.2550599
  52. A. A. Rezaee and F. Pasandideh, "A fuzzy congestion control protocol based on active queue management in wireless sensor networks with medical applications," Wireless Personal Communications, vol. 98, no. 1, pp. 815-842, 2018. https://doi.org/10.1007/s11277-017-4896-6
  53. M. Gholipour, A. T. Haghighat, and M. R. Meybodi, "Hop-by-hop congestion avoidance in wireless sensor networks based on genetic support vector machine," Neurocomputing, vol. 223, pp. 63-76, 2017. https://doi.org/10.1016/j.neucom.2016.10.035
  54. S. H. Moon, S. Park, and S. J. Han, "Energy efficient data collection in sink-centric wireless sensor networks:a cluster-ring approach," Computer Communications, vol. 101, pp. 12-25, 2017. https://doi.org/10.1016/j.comcom.2016.07.001
  55. M. Rovcanin, E. De Poorter, I. Moerman, and P. Demeester, "A reinforcement learning based solution for cognitive network cooperation between co-located, heterogeneous wireless sensor networks," Ad Hoc Networks, vol. 17, pp. 98-11, 2014. https://doi.org/10.1016/j.adhoc.2014.01.009
  56. I. Mustapha, B. M. Ali, A. Sali, M. F. A. Rasid, and H. Mohamad, "An energy efficient reinforcement learning based cooperative channel sensing for cognitive radio sensor networks," Pervasive and Mobile Computing, vol. 35, pp. 165-184, 2017. https://doi.org/10.1016/j.pmcj.2016.07.007
  57. S. Kosunalp, Y. Chu, P. D. Mitchell, D. Grace, and T. Clarke, "Use of Q-learning approaches for practical medium access control in wireless sensor networks," Engineering Applications of Artificial Intelligence, vol. 55, pp. 146-154, 2016. https://doi.org/10.1016/j.engappai.2016.06.012
  58. K. H. Phung, B. Lemmens, M. Goossens, A. Nowe, L. Tran, and K. Steenhaut, "Schedule-based multichannel communication in wireless sensor networks: a complete design and performance evaluation," Ad Hoc Networks, vol. 26, pp. 88-102, 2015. https://doi.org/10.1016/j.adhoc.2014.11.008
  59. B. Alotaibi and K. Elleithy, "A new mac address spoofing detection technique based on random forests," Sensors, vol. 16, no. 3, article no. 281, 2016.
  60. X. Song, C. Wang, J. Gao, and X. Hu, "DLRDG: distributed linear regression-based hierarchical data gathering framework in wireless sensor network," Neural Computing and Applications, vol. 23, no. 7-8, pp. 1999-2013, 2013. https://doi.org/10.1007/s00521-012-1248-z
  61. Y. Li and L. E. Parker, "Nearest neighbor imputation using spatial-temporal correlations in wireless sensor networks," Information Fusion, vol. 15, pp. 64-79, 2014. https://doi.org/10.1016/j.inffus.2012.08.007
  62. F. Edwards-Murphy, M. Magno, P. M. Whelan, J. O'Halloran, and E. M. Popovici, "b+ WSN: smart beehive with preliminary decision tree analysis for agriculture and honey bee health monitoring," Computers and Electronics in Agriculture, vol. 124, pp. 211-219, 2016. https://doi.org/10.1016/j.compag.2016.04.008
  63. H. He, Z. Zhu, and E. Makinen, "Task-oriented distributed data fusion in autonomous wireless sensor networks," Soft Computing, vol. 19, no. 8, pp. 2305-2319, 2015. https://doi.org/10.1007/s00500-014-1421-7
  64. C. Habib, A. Makhoul, R. Darazi, and C. Salim, "Self-adaptive data collection and fusion for health monitoring based on body sensor networks," IEEE Transactions on Industrial Informatics, vol. 12, no. 6, pp. 2342-2352, 2016. https://doi.org/10.1109/TII.2016.2575800
  65. A. De Paola, P. Ferraro, S. Gaglio, G. L. Re, and S. K. Das, "An adaptive Bayesian system for context-aware data fusion in smart environments," IEEE Transactions on Mobile Computing, vol. 16, no. 6, pp. 1502-1515, 2017. https://doi.org/10.1109/TMC.2016.2599158
  66. S. Hwang, R. Ran, J. Yang, and D. K. Kim, "Multivariated Bayesian compressive sensing in wireless sensor networks," IEEE Sensors Journal, vol. 16, no. 7, pp. 2196-2206, 2015. https://doi.org/10.1109/JSEN.2015.2508670
  67. Y. Wang, A. Yang, Z. Li, X. Chen, P. Wang, and H. Yang, "Blind drift calibration of sensor networks using sparse Bayesian learning," IEEE Sensors Journal, vol. 16, no. 16, pp. 6249-6260, 2016. https://doi.org/10.1109/JSEN.2016.2582539
  68. H. Harb, A. Makhoul, and R. Couturier, "An enhanced K-means and ANOVA-based clustering approach for similarity aggregation in underwater wireless sensor networks," IEEE Sensors Journal, vol. 15, no. 10, pp. 5483-5493, 2015. https://doi.org/10.1109/JSEN.2015.2443380
  69. X. Xu, R. Ansari, A. Khokhar, and A. V. Vasilakos, "Hierarchical data aggregation using compressive sensing (HDACS) in WSNs," ACM Transactions on Sensor Networks (TOSN), vol. 11, no. 3, article no. 45, 2015.
  70. A. Morell, A. Correa, M. Barcelo, and J. L. Vicario, "Data aggregation and principal component analysis in WSNs," IEEE Transactions on Wireless Communications, vol. 15, no. 6, pp. 3908-3919, 2016. https://doi.org/10.1109/TWC.2016.2531041
  71. M. I. Chidean, E. Morgado, E. del Arco, J. Ramiro-Bargueno, and A. J. Caamano, "Scalable data-coupled clustering for large scale WSN," IEEE Transactions on Wireless Communications, vol. 14, no. 9, pp. 4681-4694, 2015. https://doi.org/10.1109/TWC.2015.2424693
  72. M. Wu, L. Tan, and N. Xiong, "Data prediction, compression, and recovery in clustered wireless sensor networks for environmental monitoring applications," Information Sciences, vol. 329, pp. 800-818, 2016. https://doi.org/10.1016/j.ins.2015.10.004
  73. A. R. Pinto, C. Montez, G. Araujo, F. Vasques, and P. Portugal, "An approach to implement data fusion techniques in wireless sensor networks using genetic machine learning algorithms," Information Fusion, vol. 15, pp. 90-101, 2014. https://doi.org/10.1016/j.inffus.2013.05.003
  74. S. N. Das, S. Misra, B. E. Wolfinger, and M. S. Obaidat, "Temporal-correlation-aware dynamic selfmanagement of wireless sensor networks," IEEE Transactions on Industrial Informatics, vol. 12, no. 6, pp. 2127-2138, 2016. https://doi.org/10.1109/TII.2016.2594758
  75. P. Braca, P. Willett, K. LePage, S. Marano, and V. Matta, "Bayesian tracking in underwater wireless sensor networks with port-starboard ambiguity," IEEE Transactions on Signal Processing, vol. 62, no. 7, pp. 1864-1878, 2014. https://doi.org/10.1109/TSP.2014.2305640
  76. B. Zhou, Q. Chen, T. Li, and P. Xiao, "Online variational Bayesian filtering-based mobile target tracking in wireless sensor networks," Sensors, vol. 14, no. 11, pp. 21281-21315, 2014. https://doi.org/10.3390/s141121281
  77. B. Xue, L. Zhang, W. Zhu, and Y. Yu, "A new sensor selection scheme for Bayesian learning based sparse signal recovery in WSNs," Journal of the Franklin Institute, vol. 355, no. 4, pp. 1798-1818, 2018. https://doi.org/10.1016/j.jfranklin.2017.06.009
  78. H. Chen, R. Wang, L. Cui, and L. Zhang, "Easidslt: a two-layer data association method for multitarget tracking in wireless sensor networks," IEEE Transactions on Industrial Electronics, vol. 62, no. 1, pp. 434-443, 2015. https://doi.org/10.1109/TIE.2014.2331026
  79. P. Oikonomou, A. Botsialas, A. Olziersky, I. Kazas, I. Stratakos, S. Katsikas, D. Dimas, K. Mermikli, G. Sotiropoulos, D. Goustouridis, et al., "A wireless sensing system for monitoring the workplace environment of an industrial installation," Sensors and Actuators B: Chemical, vol. 224, pp. 266-274, 2016. https://doi.org/10.1016/j.snb.2015.10.043
  80. Z. Wei, Y. Zhang, X. Xu, L. Shi, and L. Feng, "A task scheduling algorithm based on Q-learning and shared value function for WSNs," Computer Networks, vol. 126, pp. 141-149, 2017. https://doi.org/10.1016/j.comnet.2017.06.005
  81. M. Elhoseny, A. Tharwat, A. Farouk, and A. E. Hassanien, "K-coverage model based on genetic algorithm to extend WSN lifetime," IEEE Sensors Letters, vol. 1, no. 4, pp. 1-4, 2017.
  82. C. P. Chen, S. C. Mukhopadhyay, C. L. Chuang, T. S. Lin, M. S. Liao, Y. C. Wang, and J. A. Jiang, "A hybrid memetic framework for coverage optimization in wireless sensor networks," IEEE Transactions on Cybernetics, vol. 45, no. 10, pp. 2309-2322, 2015. https://doi.org/10.1109/TCYB.2014.2371139
  83. M. Collotta, G. Pau, and A. V. Bobovich, "A fuzzy data fusion solution to enhance the QoS and the energy consumption in wireless sensor networks," Wireless Communications and Mobile Computing, vol. 2017, article ID 341828, 2017.
  84. W. Sun, W. Lu, Q. Li, L. Chen, M. Mu, and X. Yuan, "WNN-LQE: wavelet-neural-network-based link quality estimation for smart grid WSNs," IEEE Access, vol. 5, pp. 12788-12797, 2017. https://doi.org/10.1109/ACCESS.2017.2723360
  85. E. K. Lee, H. Viswanathan, and D. Pompili, "RescueNet: reinforcement-learning-based communication framework for emergency networking," Computer Networks, vol. 98, pp. 14-28, 2016. https://doi.org/10.1016/j.comnet.2016.01.011
  86. A. P. Renold and S. Chandrakala, "MRL-SCSO: multi-agent reinforcement learning-based self-configuration and self-optimization protocol for unattended wireless sensor networks," Wireless Personal Communications, vol. 96, no. 4, pp. 5061-5079, 2017. https://doi.org/10.1007/s11277-016-3729-3
  87. L. Ren, W. Wang, and H. Xu, "A reinforcement learning method for constraint-satisfied services composition," IEEE Transactions on Services Computing, 2017. http://doi.org/10.1109/TSC.2017.2727050.
  88. M. A. Razzaque, M. H. U. Ahmed, C. S. Hong, and S. Lee, "QoS-aware distributed adaptive cooperative routing in wireless sensor networks," Ad Hoc Networks, vol. 19, pp. 28-42, 2014. https://doi.org/10.1016/j.adhoc.2014.02.002
  89. D. Capriglione, D. Casinelli, and L. Ferrigno, "Analysis of quantities influencing the performance of time synchronization based on linear regression in low cost WSNs," Measurement, vol. 77, pp. 105-116, 2016. https://doi.org/10.1016/j.measurement.2015.08.039
  90. J. J. Perez-Solano and S. Felici-Castell, "Adaptive time window linear regression algorithm for accurate time synchronization in wireless sensor networks," Ad Hoc Networks, vol. 24, pp. 92-108, 2015. https://doi.org/10.1016/j.adhoc.2014.08.002
  91. G. Betta, D. Casinelli, and L. Ferrigno, "Some notes on the performance of regression-based time synchronization algorithms in low cost WSNs," in Sensors. Cham: Springer, 2015, pp. 439-443.
  92. V. P. Illiano and E. C. Lupu, "Detecting malicious data injections in event detection wireless sensor networks," IEEE Transactions on Network and service management, vol. 12, no. 3, pp. 496-510, 2015. https://doi.org/10.1109/TNSM.2015.2448656
  93. Y. Li, H. Chen, M. Lv, and Y. Li, "Event-based k-nearest neighbors query processing over distributed sensory data using fuzzy sets," Soft Computing, vol. 23, no. 2, pp. 483-495, 2019. https://doi.org/10.1007/s00500-017-2821-2
  94. Y. Han, J. Tang, Z. Zhou, M. Xiao, L. Sun, and Q. Wang, "Novel itinerary-based KNN query algorithm leveraging grid division routing in wireless sensor networks of skewness distribution," Personal and Ubiquitous Computing, vol. 18, no. 8, pp. 1989-2001, 2014. https://doi.org/10.1007/s00779-014-0795-y
  95. T. Wang, J. Zeng, Y. Lai, Y. Cai, H. Tian, Y. Chen, and B. Wang, "Data collection from WSNs to the cloud based on mobile Fog elements," Future Generation Computer Systems, 2017. https://doi.org/10.1016/j.future.2017.07.031.
  96. F. Tashtarian, M. Y. Moghaddam, K. Sohraby, and S. Effati, "ODT: optimal deadline-based trajectory for mobile sinks in WSN: a decision tree and dynamic programming approach," Computer Networks, vol. 77, pp. 128-143, 2015. https://doi.org/10.1016/j.comnet.2014.12.003
  97. S. Kim and D. Y. Kim, "Efficient data-forwarding method in delay-tolerant P2P networking for IoT services," Peer-to-Peer Networking and Applications, vol. 11, no. 6, pp. 1176-1185, 2018. https://doi.org/10.1007/s12083-017-0614-0
  98. S. W. Awan and S. Saleem, "Hierarchical clustering algorithms for heterogeneous energy harvesting wireless sensor networks," in Proceedings of 2016 International Symposium on Wireless Communication Systems (ISWCS), Poznan, Poland, 2016, pp. 270-274.
  99. A. Sharma and A. Kakkar, "Forecasting daily global solar irradiance generation using machine learning," Renewable and Sustainable Energy Reviews, vol. 82, pp. 2254-2269, 2018. https://doi.org/10.1016/j.rser.2017.08.066
  100. W. M. Tan, P. Sullivan, H. Watson, J. Slota-Newson, and S. A. Jarvis, "An indoor test methodology for solar-powered wireless sensor networks," ACM Transactions on Embedded Computing Systems (TECS), vol. 16, no. 3, article no. 82, 2017.