International Journal of Computer Networks and Applications (IJCNA)

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International Journal of Computer Networks and Applications (IJCNA)

International Journal of Computer Networks and Applications (IJCNA)

Published By EverScience Publications

ISSN : 2395-0455

Hybrid Approaches to Address Various Challenges in Wireless Sensor Network for IoT Applications: Opportunities and Open Problems

Author NameAuthor Details

Pallavi Joshi, Ajay Singh Raghuvanshi

Pallavi Joshi[1]

Ajay Singh Raghuvanshi[2]

[1]Department of Electronics and Communication, National Institute of Technology Raipur, Raipur, Chhattisgarh, India

[2]Department of Electronics and Communication, National Institute of Technology Raipur, Raipur, Chhattisgarh, India

Cite this Article

DOI: 10.22247/ijcna/2021/209186

Pages : 151-187

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Abstract

Since the last decade, wireless sensor network (WSN) and Internet of Things (IoT) has proved itself a versatile technology in many real-time applications. The scalability, cost-effectiveness, and self-configuring nature of WSN make it the fittest technology for many network designs and scenarios. The traditional WSN algorithms are programmed for fixed parameters without any touch of Artificial Intelligence as well as the optimization technique. So, they suffer from a trade-off between various QoS parameters like network lifetime, energy efficiency, and others. To conquer the limitations of traditional WSN algorithms, machine learning has been introduced in wireless technology. But machine learning approaches also cannot solve all the problems in WSN solely. Some of the applications like target tracking, congestion control, and many more, do not give desired results even after applying the machine learning techniques. So, there is a need to introduce optimization in such cases. The paper gives an extensive survey on various optimization methods employed to solve many WSN issues from 2005 till 2020. It also gives a brief description of the usage of various machine learning techniques in WSNs from 2002 till 2020. The paper discusses the advantages, limitations, effects of these methods on various WSN techniques like topology, coverage, localization, network and node connectivity, routing, clustering, cluster head selection, cross-layer issues, intrusion detection, etc. This paper gives a lucid comparison of many state-of-the-art optimization algorithms and descriptive and statistical analysis for discussed issues and algorithms associated with them. It also elucidates some open issues for WSNs/IoT networks that can be solved using these approaches.

Index Terms

Wireless Sensor Networks

Internet of Things

Quality of Service

Artificial Intelligence

Optimization

Machine Learning

Reference

  1. 1.
    S. Verma, S. Member, and Y. Kawamoto, “A Survey on Network Methodologies for Real-Time Analytics of Massive IoT Data and Open Research Issues,” IEEE Communications Surveys & Tutorials., vol. 19, no. 3, pp. 1457–1477, 2017.
  2. 2.
    T. Seymour, “History of Wireless Communication,” Review of Business Information Systems (RBIS)., vol. 15, no. 2, pp. 37–42, 2011.
  3. 3.
    I. F. Akyildiz, W. Su, Y. Sankarasubramaniam, and E. Cayirci, “Wireless sensor networks?: a survey,” Computer networks., vol. 38, no. 4, pp. 393–422, 2002.
  4. 4.
    G. Dhand and S. S. Tyagi, “Data aggregation techniques in WSN?: Survey,” Procedia - Procedia Comput. Sci., vol. 92, pp. 378–384, 2016.
  5. 5.
    QF Hassan, editor. Internet of things A to Z: technologies and applications: John Wiley & Sons. 2018.
  6. 6.
    F. Javed, M. K. Afzal, S. Member, M. Sharif, B. Kim, and S. Member, “Internet of Things ( IoT ) Operating Systems Support , Networking Technologies , Applications , and Challenges?: A Comparative Review,” IEEE Commun. Surv. Tutorials, vol. 20, no. 3, pp. 2062–2100, 2018.
  7. 7.
    N. Sharma and V. Gupta,“Meta-heuristic based optimization of WSNs Localisation Problem- a Survey,” Procedia Comput. Sci., vol. 173, pp. 36–45, 2019.
  8. 8.
    Fei Z, Li B, Yang S, Xing C, Chen H, Hanzo L., “A Survey of Multi-Objective Optimization in Wireless Sensor Networks?: Metrics , Algorithms , and Open Problems,” IEEE Commun. Surv. Tutorials, vol. 19, no. 1, pp. 550–586, 2017.
  9. 9.
    R. Asorey-Cacheda, AJ. Garcia-Sanchez, F. García-Sánchez, J. García-Haro, “A survey on non-linear optimization problems in wireless sensor networks,” J. Netw. Comput. Appl., vol. 82, no. January, pp. 1–20, 2017.
  10. 10.
    SC. Chu, PW. Tsai, “Computational intelligence based on the behavior of cats,” International Journal of Innovative Computing, Information and Control, vol. 3, no. 1, pp. 163–173, 2007.
  11. 11.
    D. Ruan, “A PSO-Based Uneven Dynamic Clustering Multi-Hop routing protocol for wireless sensor networks. Sensors., vol. 19, no. 8, pp. 1835, 2019.
  12. 12.
    A. Nayyar, R. Singh, “Ant Colony Optimization (ACO) based Routing Protocols for Wireless Sensor Networks (WSN): A Survey,” Int. J. Adv. Comput. Sci. Appl., vol. 8, no. 2, pp. 148–155, 2017.
  13. 13.
    MB Krishna, MN Doja. "Swarm intelligence-based topology maintenance protocol for wireless sensor networks," IET wireless sensor systems., vol. 1, no. 4, pp. 181–190, 2011.
  14. 14.
    X. Liu, T. Qiu, S. Member, and B. Dai, “Swarm Intelligence-Based Rendezvous Selection via Edge Computing for Mobile Sensor Networks,” IEEE Internet of things journal, vol. 7, no. 10, pp. 9471-80, 2020.
  15. 15.
    M. S. Manshahia, “Swarm intelligence-based energy-efficient data delivery in WSAN to virtualise IoT in smart cities,” IET Wireless Sensor Systems, vol. 8, no. 6, pp. 256-9, 2018.
  16. 16.
    R. V Kulkarni, S. Member, A. Förster, G. K. Venayagamoorthy, and S. Member, “Computational Intelligence in Wireless Sensor Networks?: A Survey,” IEEE communications surveys & tutorials, vol. 13, no. 1, pp. 68–96, 2011.
  17. 17.
    M. A. Alsheikh, S. Lin, D. Niyato, H. Tan, and S. Member, “Machine Learning in Wireless Sensor Networks?:Algorithms,Strategies,and Applications,” IEEE Commun. Surv. Tutorials, vol. 16, no. 4, pp. 1996–2018, 2014.
  18. 18.
    W. Sun, M. Tang, L. Zhang, and Z. Huo, “A Survey of Using Swarm Intelligence Algorithms in IoT,” Sensors, vol. 20, no. 5, pp. 1420, 2020.
  19. 19.
    N Baba, “Convergence of a Random Optimization Method for Constrained Optimization Problems,” Journal of Optimization Theory and Applications, vol. 33, no. 4, pp. 451–461, 1981.
  20. 20.
    L. A. Hannah, “Stochastic Optimization,” International Encyclopedia of the Social & Behavioral Sciences., vol. 2, pp. 473-81, 2015.
  21. 21.
    P. Collet JP Rennard, “Introduction to Stochastic Optimization Algorithms. Handbook of Research on Nature-Inspired Computing for Economics and Management,” JP. Rennard, IDEA Group Inc.2006.
  22. 22.
    M. Lin, J. Tsai, and C. Yu, “A Review of Deterministic Optimization Methods in Engineering and Management,” Mathematical Problems in Engineering, 2012.
  23. 23.
    Marks R. Methodology platform for prediction of damage events for self-sensing aerospace panels subjected to real loading conditions (Doctoral dissertation, Cardiff University), 2016.
  24. 24.
    Akyol S, Alatas B,“Plant intelligence-based metaheuristic optimization algorithms,” Artificial Intelligence Review., vol. 47, no.4, pp. 417-62, 2017.
  25. 25.
    A. Hakanen, K. D. Interactive, J. Hakanen, J. Malmberg, V. Ojalehto, and K. Eyvindson, “Data-Driven Interactive Multiobjective Optimization using a Cluster-Based Surrogate in a Discrete Decision Space,” International Conference on Machine Learning, Optimization, and Data Science., pp. 104–115, 2019.
  26. 26.
    B. Alinia, M. H. Hajiesmaili, A. Khonsari, N. Crespi, and S. Member, “Maximum-Quality Tree Construction for Deadline-Constrained Aggregation in WSNs,” IEEE Sensors Journal., vol. 17, no. 12, pp. 3930–3943, 2017.
  27. 27.
    L. Bhasker, “Genetically derived secure cluster-based data aggregation in wireless sensor networks,” IET Information Security., vol. 8, no.1, pp. 1–7, 2014.
  28. 28.
    Ramakrishnan B, Rajesh RS, Shaji RS., “CBVANET: A cluster based vehicular adhoc network model for simple highway communication,” International Journal of Advanced Networking and Applications., vol. 2, no. 4, pp. 755-61, 2011.
  29. 29.
    X. Luo and X. Chang, “A Novel Data Fusion Scheme using Grey Model and Extreme Learning Machine in Wireless Sensor Networks,” International Journal of Control, Automation and Systems., vol. 13 no. 3, pp. 539-46, 2015.
  30. 30.
    C. Hua, T. P. Yum, and S. Member, “Optimal Routing and Data Aggregation for Maximizing Lifetime of Wireless Sensor Networks,” IEEE/ACM Transactions on networking., vol. 16, no. 4, pp. 892–903, 2008.
  31. 31.
    E. Fitzgerald, M. Pióro, S. Member, and A. Tomaszewski, “Energy-Optimal Data Aggregation and Dissemination for the Internet of Things,” IEEE Internet Things J., vol. 5, no. 2, pp. 955–969, 2018.
  32. 32.
    D. C. Hoang and R. K. S. Kumar, “Optimal data aggregation tree in wireless sensor networks based on intelligent water drops algorithm,” IET wireless sensor systems., vol. 2, no. 3, pp. 282–292, 2012.
  33. 33.
    Guo H, Low KS, Nguyen HA, “Optimizing the Localization of a Wireless Sensor Network in Real Time Based on a low-cost microcontroller,” IEEE transactions on industrial electronics., vol. 58, no. 3, pp. 741–749, 2011.
  34. 34.
    E. H. Houssein et al., “Optimal Sink Node Placement in Large Scale Wireless Sensor Networks Based on Harris ’ Hawk Optimization Algorithm,” IEEE Access., vol. 8 pp. 19381-97, 2020.
  35. 35.
    S. P. Singh and S. C. Sharma, “Implementation of a PSO Based Improved Localization Algorithm for Wireless Sensor Networks,” IETE Journal of Research., vol. 65, no. 4, pp. 502-14, 2019.
  36. 36.
    F. Shahzad, T. R. Sheltami, and E. M. Shakshuki, “Multi-Objective Optimization for a Reliable Localization Scheme in Wireless Sensor Networks,” Journal of communications and Networks. vol. 18, no. 5, pp. 796–805, 2016.
  37. 37.
    S. N. Ghorpade, M. Zennaro, S. Member, B. S. Chaudhari, and S. Member, “GWO Model for Optimal Localization of IoT-Enabled Sensor Nodes in Smart Parking Systems,” IEEE Transactions on Intelligent Transportation Systems, pp. 1–8, 2020.
  38. 38.
    C. Shieh, V. Sai, T. Lee, Q. Le, and Y. Lin, “Node Localization in WSN using Heuristic Optimization Approaches,” J. Netw. Intell., vol. 2, no. 3, pp. 275–286, 2017.
  39. 39.
    I. Strumberger, M. Minovic, and M. Tuba, “Performance of Elephant Herding Optimization and Tree Growth Algorithm Adapted for Node Localization in Wireless Sensor Networks,” Sensors., vol. 19 no. 11, pp. 2515, 2019.
  40. 40.
    Z. Wang, H. Xie, D. He, S. Chan. “Wireless Sensor Network Deployment Optimization Based on Two Flower Pollination Algorithms,” IEEE Access, vol. 7, pp. 180590–180608,2019.
  41. 41.
    M. Abo-zahhad, S. M. Ahmed, N. Sabor, and S. Sasaki, “Utilisation of multi-objective immune deployment algorithm for coverage area maximisation with limit mobility in wireless sensors networks,” IET Wireless Sensor Systems., vol. 5, no. 5, pp. 250–261, 2015.
  42. 42.
    J. Jia, J. Chen, G. Chang, Y. Wen, and J. Song, “Multi-objective optimization for coverage control in wireless sensor network with adjustable sensing radius,” Comput. Math. with Appl., vol. 57, no. 11–12, pp. 1767–1775, 2009.
  43. 43.
    Z. Sun, Y. Zhang, Y. Nie, W. Wei, and J. Lloret, “CASMOC?: a novel complex alliance strategy with multi-objective optimization of coverage in wireless sensor networks,” Wirel. Networks, vol. 23, no. 4, pp. 1201-22, 2017.
  44. 44.
    C. Titouna, N. Labraoui, J. Yves, and E. Wahabou, “Hybrid wireless sensors deployment scheme with connectivity and coverage maintaining in wireless sensor networks,” Wirel. Pers. Commun., vol. 112, no. 3, pp. 1893-917, 2020.
  45. 45.
    B. Mazari, “Multi-Objective WSN Deployment Using Genetic Algorithms Under Cost , Coverage , and Connectivity,” Wirel. Pers. Commun., vol. 94, no. 4, pp. 2739-68, 2017.
  46. 46.
    Y. Yue, L. Cao, and Z. Luo, “Hybrid Artificial Bee Colony Algorithm for Improving the Coverage and Connectivity of Wireless Sensor Networks,” Wirel. Pers. Commun., vol. 108, no. 3, pp. 1719-32, 2019.
  47. 47.
    C. Shivalingegowda, PV. Jayasree. Hybrid gravitational search algorithm-based model for optimizing coverage and connectivity in wireless sensor networks. Journal of Ambient Intelligence and Humanized Computing., vol. 12, no. 2, pp. 2835-48, 2021.
  48. 48.
    M. Alqahtani, A. Gumaei, H. Mathkour, M. Maher, and B. Ismail, “A Genetic-Based Extreme Gradient Boosting Model,” Sensors., vol. 19, no. 20, pp. 4383, 2019.
  49. 49.
    A. Ghosal and S. Halder, “A survey on energy efficient intrusion detection in wireless sensor networks,” J. Ambient Intell. Smart Environ., vol. 9, no. 2, pp. 239–261, 2017.
  50. 50.
    O. Can, O K Sahingoz, “A Survey of Intrusion Detection Systems in Wireless Sensor Networks,” In2015 6th international conference on modeling, simulation, and applied optimization (ICMSAO), pp. 1-6, 2015.
  51. 51.
    S. Tiwari, “A multilevel hybrid anomaly detection scheme for industrial wireless sensor networks,” International Journal of Network Management no., pp. 1–19, 2020.
  52. 52.
    S. Gavel, A. S. Raghuvanshi, and S. Tiwari, “A novel density estimation-based intrusion detection technique with Pearson’s divergence for Wireless Sensor Networks,” ISA Trans., 2020.
  53. 53.
    G. M. Borkar, L. H. Patil, D. Dalgade, and A. Hutke, “Sustainable Computing?: Informatics and Systems A novel clustering approach and adaptive SVM classifier for intrusion detection in WSN?: A data mining concept,” Sustain. Comput. Informatics Syst., vol. 23, pp. 120–135, 2019.
  54. 54.
    M. Stehlik, A. Saleh, A. Stetsko, and V. Maty, “Multi-Objective Optimization of Intrusion Detection Systems for Wireless Sensor Networks,” In Artificial Life Conference Proceedings., pp. 569–576, 2013.
  55. 55.
    M. Safaldin, M. Otair, and L. Abualigah, “Improved binary gray wolf optimizer and SVM for intrusion detection system in wireless sensor networks,” J. Ambient Intell. Humaniz. Comput., vol. 12,no. 2, pp. 1559-76, 2021.
  56. 56.
    S. A. Elsaid, “An optimized collaborative intrusion detection system for wireless sensor networks,” Soft Comput., pp. 1-5, 2020.
  57. 57.
    Z. Wang, H. Ding, and B. O. Li, “An Energy Efficient Routing Protocol Based on Improved Artificial Bee Colony Algorithm for Wireless Sensor Networks,” IEEE Access, vol. 8, pp. 133577-96, 2020.
  58. 58.
    R. Arya, S. C. Sharma, “Optimization approach for energy minimization and bandwidth estimation of WSN for data centric protocols,” Int. J. Syst. Assur. Eng. Manag., vol. 9, no. 1, pp. 2–11, 2018.
  59. 59.
    S. B. Sasi and R. Santhosh, “Multiobjective routing protocol for wireless sensor network optimization using ant colony conveyance algorithm,” International Journal of Communication Systems., vol. 34, no. 6, pp. 1–13, 2019.
  60. 60.
    I. S Akila, R Venkatesan, “A Fuzzy Based Energy-aware Clustering Architecture for Cooperative Communication in WSN,” The Computer Journal, vol. 59, no. 10, pp. 1551-1562, 2016.
  61. 61.
    P. Jiang, Y. Feng, F. Wu, S. Yu, and H. Xu, “Dynamic Layered Dual-Cluster Heads Routing Algorithm Based on Krill Herd Optimization in UWSNs,” Sensors., vol. 16, no. 9, pp. 1379, 2016.
  62. 62.
    B. Ramakrishnan, “Adaptive Routing Protocol based on Cuckoo Search algorithm ( ARP-CS ) for secured Vehicular Ad hoc network ( VANET ),” International Journal of computer networks and applications., vol. 2, no. 4, pp. 173–178, 2015.
  63. 63.
    M. Pavani and P. T. Rao, “Novel Two-Fold Data Aggregation and MAC Scheduling to Support Energy Efficient Routing in Wireless Sensor Network,” IEEE Access, vol. 7, pp.1260-74, 2018.
  64. 64.
    J. Wang, X. Ren, F. C. Yankun, and C. Guobao, “On MAC optimization for large-scale wireless sensor network,” Wirel. Networks, vol. 22, no. 6, pp. 1877-89, 2016.
  65. 65.
    B. Li, X. Guo, R. Zhang, X. Du, and M. Guizani, “Performance Analysis and Optimization for the MAC Protocol in UAV-based IoT Network,” IEEE Transactions on Vehicular Technology., vol. 69, no. 8, pp. 8925-37, 2020.
  66. 66.
    A. Chehri, “Energy-Efficient Modified DCC-MAC Protocol for IoT in E-Health Applications,” Internet of Things, pp. 100119, 2019.
  67. 67.
    A. Rahman, A. T. Asyhari, I. Febry, J. Ali, M. M. Rahman, and M. Karim, “A scalable hybrid MAC strategy for traffic-differentiated IoT-enabled intra-vehicular networks,” Comput. Commun., vol. 157, pp. 320–328, 2020.
  68. 68.
    M. Ram, S. Kumar, V. Kumar, A. Sikandar, and R. Kharel, “Enabling Green Wireless Sensor Networks?: Energy E ffi cient T-MAC Using Markov Chain Based Optimization,” Electronics., vol. 8, no. 5, pp.534, 2019.
  69. 69.
    Z. G. Al-mekhlafi, Z. M. Hanapi, and A. M. S. Saleh, “Firefly-Inspired Time Synchronization Mechanism For Self-Organizing Energy-Efficient Wireless Sensor Networks?: A survey,” IEEE Access, vol. 7, pp. 115229-48, 2019.
  70. 70.
    B. Zhou and M. C. Vuran, “Towards Optimal Synchronization Scheduling in Internet of ( Heterogeneous ) Things,” In2019 IEEE Global Communications Conference (GLOBECOM), pp. 1–6, 2019.
  71. 71.
    Y. Zou, S. Member, H. Liu, S. Member, and Q. Wan, “Joint Synchronization and Localization in Wireless Sensor Networks Using Semidefinite Programming,” IEEE Internet of Things Journal., vol. 5, no. 1, pp. 199–205, 2017.
  72. 72.
    S. Tahmasebi, M. Safi, S. Zolfi, and M. R. Maghsoudi, “Placement of SDN Controllers for Optimizing the Network Performance in WSNs,” Sensors, vol. 20, no. 11, pp. 1–19, 2020.
  73. 73.
    A. Tan, Y. Peng, X. Su, H. Tong, and Q. Deng, “A Novel Synchronization Scheme Based on a Dynamic Superframe for an Industrial Internet of,” Sensors., vol. 19, no. 3, pp. 1–22, 2019.
  74. 74.
    S. Jayanthi, N. Stalin, and S. Sutha, “Internet of Things ( IOT ) Based Generous Transformational Optimization Algorithm ( GTOA ) for Hybrid Renewable Energy System Synchronization and Status Monitioring,” Wirel. Pers. Commun., vol. 102, no. 4, pp. 2597-618, 2018.
  75. 75.
    Liu H, Wang S, Gong S, Zhao N, An J, Quek TQ., “Hybrid LMMSE Transceiver Optimization for Distributed IoT Sensing Networks with Different Levels of Synchronization,” IEEE Internet of Things Journal, vol. 4662, pp. 1–13, 2021.
  76. 76.
    A. Zervopoulos et al., “Wireless Sensor Network Synchronization for Precision Agriculture Applications,” Agriculture., vol. 10, no. 3, pp. 1–20, 2020.
  77. 77.
    Singh K, Singh K, Aziz A. “Congestion control in wireless sensor networks by hybrid multi-objective optimization algorithm,” Computer Networks., vol. 138, pp. 90-107, 2018.
  78. 78.
    B. Sharma, G. Srivastava, and J. C. Lin, “A bidirectional congestion control transport protocol for the internet of drones,” Comput. Commun., vol. 153, pp. 102–116, 2019.
  79. 79.
    Q. Pham and W. Hwang, “Network Utility Maximization based Congestion Control over Wireless Networks?: A Survey and potential directives,”. IEEE Communications Surveys & Tutorials., vol. 19, no. 2, pp. 1173-200, 2016.
  80. 80.
    D. Pandey and V. Kushwaha, “An exploratory study of congestion control techniques in Wireless Sensor Networks,” Comput. Commun., vol. 157, pp. 257–283, 2020.
  81. 81.
    S. Gheisari and E. Tahavori, “CCCLA?: A cognitive approach for congestion control in Internet of Things using a game of learning automata,” Comput. Commun., vol. 147, pp. 40–49, 2019.
  82. 82.
    V. Narawade and U. D. Kolekar, “ACSRO?: Adaptive cuckoo search based rate adjustment for optimized congestion avoidance and control in wireless sensor networks,” Alexandria Eng. J., vol. 57, no. 1, pp. 131–145, 2018.
  83. 83.
    P. Antoniou, A. Pitsillides, T. Blackwell, and A. Engelbrecht, “Congestion Control in Wireless Sensor Networks based on Bird Flocking Behavior,” Computer Networks, vol. 57, no. 5, pp. 1167-91, 2013.
  84. 84.
    S. K. Dhurandher, S. Misra, H. Mittal, A. Agarwal, and I. Woungang, “Using ant-based agents for congestion control in ad-hoc wireless sensor networks,” Cluster Computing., vol. 14, no. 1 pp. 41–53, 2011.
  85. 85.
    V. Srivastava, S. Tripathi, K. Singh, and L. Hoang, “Energy efficient optimized rate based congestion control routing in wireless sensor network,” J. Ambient Intell. Humaniz. Comput., vol. 11, no. 3, pp. 1325–1338, 2020.
  86. 86.
    M. Tahir, S. S. Yousaf, and B. Zikria, “Congestion Avoidance and Fault Detection in WSNs using Data Science Congestion avoidance and fault detection in WSNs using data science techniques,” Transactions on Emerging Telecommunications Technologies., , pp. 3756, 2019.
  87. 87.
    M. S. Manshahia, M. Dave, and S. B. Singh, “Improved Bat Algorithm Based Energy Efficient Congestion Control Scheme for Wireless Sensor Networks,” Wireless Sensor Network., vol. 8, no. 11, pp. 229-41, 2016.
  88. 88.
    E. Masazade, R. Niu, PK. Varshney, "Dynamic bit allocation for object tracking in wireless sensor networks," IEEE Transactions on Signal Processing., vol. 60, no. 10, pp. 5048-63, 2012.
  89. 89.
    F. Liu, W. Xiao, S. Chen, C. Jiang. “Adaptive Dynamic Programming-Based Multi-Sensor Scheduling for Collaborative Target Tracking in Energy Harvesting Wireless Sensor Networks,” Sensors., vol. 18, no. 12, pp. 4090, 2018.
  90. 90.
    X. Wang, L. Ding, and D. Bi, “Reputation-Enabled Self-Modification for Target Sensing in Wireless Sensor Networks,” IEEE Transactions on Instrumentation and Measurement., vol. 59, no. 1, pp. 171–179, 2009.
  91. 91.
    J. Zheng, Z. Alam, S. Liang, X. Xing, and G. Wang, “Auction-based adaptive sensor activation algorithm for target tracking in wireless sensor networks,” Futur. Gener. Comput. Syst., pp. 88-99, 2013.
  92. 92.
    M. A. Khan, M. A. Khan, and A. U. Rahman, “Exploiting cooperative sensing for accurate target tracking in industrial Internet of things,” International Journal of Distributed Sensor Networks., vol. 15, no. 12, 2019.
  93. 93.
    M. Anvaripour and M. Saif, “A Novel Approach to Reliable Sensor Selection and Target Tracking in Sensor Networks,” IEEE Transactions on Industrial Informatics., vol. 16, no. 1, pp. 171-82, 2019.
  94. 94.
    N. Cao, S. Member, S. Choi, and E. Masazade, “Sensor Selection for Target Tracking in Wireless Sensor Networks With Uncertainty,” IEEE Transactions on signal Processing., vol. 64, no. 20, pp. 5191–5204, 2016.
  95. 95.
    M. M. Alam, A. Razzaque, and C. S. Hong, “Energy-Aware QoS Provisioning for Wireless Sensor Networks?: Analysis and Protocol,” Journal of Communications and Networks., vol. 11, no. 4, pp. 390–405, 2009.
  96. 96.
    H. Tian, Z. Qian, X. Wang, and X. Liang, “QoI-Aware DODAG Construction in RPL-Based Event Detection Wireless Sensor Networks,” Journal of Sensors., 2017.
  97. 97.
    K. Liu, Y. Zhuang, Z. Wang, and J. Ma, “Spatiotemporal Correlation Based Fault-Tolerant Event Detection in Wireless Sensor Networks,” International Journal of Distributed Sensor Networks., vol. 11, no. 10, 2015.
  98. 98.
    J. Wang, L. Cheng, and J. Liu, “A Complex Event Detection Method for multi- probability RFID Event Stream,” Journal of Software., vol. 9, no. 4, pp. 834–840, 2014.
  99. 99.
    F. Tashtarian, M. Hossein, Y. Moghaddam, and S. Member, “On Maximizing the Lifetime of Wireless Sensor Networks in Event-Driven Applications With Mobile Sinks,” IEEE Trans. Veh. Technol., vol. 64, no. 7, pp. 3177–3189, 2015.
  100. 100.
    S. Li, S. Zhao, P. Yang, P. Andriotis, L. Xu, and Q. Sun, “Distributed Consensus Algorithm for Events Detection in Cyber Physical Systems,” IEEE Internet Things J., vol. 6, no. 2, pp. 2299-308, 2019.
  101. 101.
    G. Naddafzadeh-shirazi, S. Member, L. Lampe, and S. Member, “Lifetime Maximization in UWB Sensor Networks for Event Detection,” IEEE transactions on signal processing., vol. 59, no. 9, pp. 4411–4423, 2011.
  102. 102.
    A. Castagnetti, A. Pegatoquet, T. N. Le, and M. Auguin, “A Joint Duty-Cycle and Transmission Power Management for Energy Harvesting WSN,” IEEE Transactions on Industrial Informatics., vol. 10, no. 2, pp. 928-36, 2014.
  103. 103.
    Y. Xiong, G. Chen, M. Lu, X. Wan, and M. Wu, “A Two-phase Lifetime-enhancing Method for Hybrid Energy-harvesting Wireless Sensor Network,” IEEE Sens. J., vol. 20, no. 4, pp. 1934-46, 2019.
  104. 104.
    A. Mehrabi, S. M. Ieee, K. Kim, and S. M. Ieee, “Maximizing Data Collection Throughput on a Path in Energy Harvesting Sensor Networks Using a Mobile Sink,” IEEE Transactions on Mobile Computing., vol. 15, no. 3, pp. 690-704, 2015.
  105. 105.
    S. Galmés, “Analytical Model for the Duty Cycle in Solar-Based EH-WSN for Environmental Monitoring,” Sensors., vol. 18, no. 8, pp. 1–32, 2018.
  106. 106.
    X. Bao, H. Liang, Y. Liu, and F. Zhang, “A Stochastic Game Approach for Collaborative Beamforming in SDN-Based Energy Harvesting Wireless Sensor Networks,” IEEE Internet Things J., vol. 6, no. 6, pp. 9583-95, 2019.
  107. 107.
    TG. Nguyen, C. So-In, DB, Ha, “Secrecy Performance Analysis of Energy Harvesting Wireless Sensor Networks With a Friendly Jammer,” IEEE Access., vol. 5, pp. 25196-206, 2020.
  108. 108.
    S. Hariharan and N. B. Shroff, “Maximizing Aggregated Information in Sensor,” IEEE transactions on automatic control., vol. 56, no. 10, pp. 2369–2380, 2011.
  109. 109.
    K. Cao, G. Xu, J. Zhou, T. Wei, M. Chen, and S. Hu, “QoS-Adaptive Approximate Real-Time Computation for Mobility-Aware IoT Lifetime Optimization,” IEEE Trans. Comput. Des. Integr. Circuits Syst., vol. 38, no. 10, pp. 1799-810, 2018.
  110. 110.
    W. Xu, S. Member, Y. Zhang, Q. Shi, and X. Wang, “Energy Management and Cross Layer Optimization for Wireless Sensor Network Powered by Heterogeneous Energy Sources,” IEEE Transactions on Wireless Communications., vol. 14, no. 5, pp. 2814-26, 2015.
  111. 111.
    C. Lin and T. Kokkinos, “Cross-Layer Solutions for Extended-Range Wireless Sensor Networks,” IEEE Sensors Journal., vol. 13, no. 3, pp. 1044–1054, 2012.
  112. 112.
    J. Tan, A. Liu, M. Zhao, H. Shen, and M. Ma, “Cross-layer design for reducing delay and maximizing lifetime in industrial wireless sensor networks,” EURASIP Journal on Wireless Communications and Networking., pp. 1-26, 2018.
  113. 113.
    D. Kalaiselvi and R. Radhakrishnan, “Multiconstrained QoS Routing Using a Differentially Guided Krill Herd Algorithm in Mobile Ad Hoc Networks,” Mathematical Problems in Engineering., 2015.
  114. 114.
    A. Ahmad, S. Ahmad, M. H. Rehmani, and N. U. Hassan, “A Survey on Radio Resource Allocation in Cognitive Radio Sensor Networks,” IEEE Communications Surveys & Tutorials., vol. 17, no. 2, pp. 888-917, 2015.
  115. 115.
    P. Asghari, A. Masoud, R. Hamid, and H. Seyyed, “Privacy ? aware cloud service composition based on QoS optimization in Internet of Things,” J. Ambient Intell. Humaniz. Comput., 1-26, 2020.
  116. 116.
    P. Dani, P. Adi, and A. Kitagawa, “Quality of Service and Power Consumption Optimization on the IEEE 802 . 15 . 4 Pulse Sensor Node based on Internet of Things,” International Journal of Advanced Computer Science and Applications (IJACSA)., vol. 10, no. 5, pp. 144–154, 2019.
  117. 117.
    A. H. Sodhro, M. S. Obaidat, Q. H. Abbasi, P. Pace, S. Pirbhulal, and A. Yasar, “Quality of Service Optimization in an IoT-Driven Intelligent Transportation System,” IEEE Wirel. Commun., vol. 26, no.6, pp. 10–17, 2019.
  118. 118.
    M. Amjad, A. Ahmed, M. Naeem, M. Awais, W. Ejaz, and A. Anpalagan, “Resource Management in Energy Harvesting Cooperative IoT Network under QoS Constraints,” Sensors., vol. 18, no. 10, pp. 3560, 2018.
  119. 119.
    DG Costa, LA. Guedes, “A Survey on Multimedia-Based Cross-Layer Optimization in Visual Sensor Networks,” Sensors., vol. 11, no. 5, pp. 5439-68, 2011.
  120. 120.
    I. Al-anbagi, M. Erol-kantarci, and H. T. Mouftah, “A Survey on Cross-layer Quality of Service Approaches in WSNs for Delay and Reliability-Aware Applications,” IEEE Communications Surveys & Tutorials, vol. 18, no. 1, pp. 525-52, 2014.
  121. 121.
    P. Siddavaatam and R. S. B, “A Delta-Diagram Based Synthesis for Cross Layer Optimization Modeling of IoT,” In Transactions on Computational Science XXXIII, pp. 1-24, Springer, Berlin, Heidelberg, 2018.
  122. 122.
    Y. Zhou, Z. Sheng, and C. Mahapatra, “Topology design and cross-layer optimization for wireless body sensor networks,” Ad Hoc Networks, 48-62, 2017.
  123. 123.
    H. A. Khattak, Z. Ameer, I. U. Din, and M. K. Khan, “Cross-layer Design and Optimization Techniques in Wireless Multimedia Sensor Networks for Smart Cities,” Computer Science and Information Systems., vol. 16, no. 1, pp. 1–17, 2019.
  124. 124.
    X. Xu, M. Yuan, X. Liu, and A. Liu, “A cross-layer optimized opportunistic routing scheme for loss-and-delay sensitive WSNs,” Sensors., vol. 18, no. 5, 2018.
  125. 125.
    T. Le et al., “A Bayesian Perspective on Multiple Source Localization in Wireless Sensor Networks,” IEEE Transactions on Signal Processing., vol. 64, no. 7, pp. 1684-99, 2016.
  126. 126.
    B. Sun, Y. Guo, N. Li, and D. Fang, “Multiple Target Counting and Localization using Variational Bayesian EM Algorithm in Wireless,” IEEE Transactions on Communications., vol. 65, no. 7, pp. 2985-98, 2017.
  127. 127.
    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.
  128. 128.
    V. Jafarizadeh, A. Keshavarzi, T. Derikvand, “Efficient cluster head selection using Naïve Bayes classifier for wireless sensor networks,” Wirel. Networks, vol. 23, no. 3, pp. 779-85, 2017.
  129. 129.
    C. Titouna, M. Aliouat, and M. Gueroui, “Outlier Detection Approach Using Bayes Classifiers in Wireless Sensor Networks,” Wirel. Pers. Commun., vol. 85, no. 3, pp. 1009-23, 2015.
  130. 130.
    L. Gispan, A. Leshem, and Y. Be, “Decentralized estimation of regression coefficients in sensor,” Digit. Signal Process., vol. 68, pp. 16–23, 2017.
  131. 131.
    X. Song, C. Wang, and J. Gao, “DLRDG?: distributed linear regression-based hierarchical data gathering framework in wireless sensor network,” Neural Computing and Applications., vol. 23, no. 7, pp. 1999-2013, 2013.
  132. 132.
    W. Sun, Yuan X, Wang J, Li Q, Chen L, Mu D, “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. 1127-37, 2017.
  133. 133.
    X. Chang, J. Huang, S. Liu, G. Xing, H. Zhang, and J. Wang, “Accuracy-aware Interference Modeling and Measurement in Wireless Sensor Networks,” IEEE Transactions on Mobile Computing., vol. 15, no. 2, pp. 278-91, 2015.
  134. 134.
    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, pp. 1550147716686161, 2017.
  135. 135.
    N. Shahid, I. Haider, N. Saad, and B. Qaisar, “One-class support vector machines?: analysis of outlier detection for wireless sensor networks in harsh environments,” Artificial Intelligence Review., vol. 43, no. 4, pp. 515-63, 2015.
  136. 136.
    H. S. Emadi and S. M. Mazinani, “A Novel Anomaly Detection Algorithm Using DBSCAN and SVM in Wireless Sensor Networks,” Wirel. Pers. Commun., vol. 98, no. 2, pp. 2025-35, 2018.
  137. 137.
    S. Zidi, T. Moulahi, and B. Alaya, “Fault detection in Wireless Sensor Networks through SVM classifier,” IEEE Sensors Journal., vol. 6, no. 1, pp. 340-7, 2017.
  138. 138.
    A. S. Raghuvanshi, R. Tripathi, and S. Tiwari, “Machine Learning Approach for Anomaly Detection in Wireless Sensor Data,” Int. J. Adv. Eng. Technol., vol. 47, no. 4, pp. 47–61, 2011.
  139. 139.
    S. Gavel and A. S. Raghuvanshi, “Comparative Study of Anomaly Detection in Wireless Sensor Networks Using Different Kernel Functions Comparative study of Anomaly Detection in Wireless sensor networks using different kernel functions,” In Advances in VLSI, Communication, and Signal Processing, Springer, Singapore, 2020.
  140. 140.
    H. He, Z. Zhu, and E. Mäkinen, “Task-oriented distributed data fusion in autonomous wireless sensor networks,” Soft Computing, vol. 19, no. 8, pp. 2305-19, 2015.
  141. 141.
    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 q,” Comput. Electron. Agric., vol. 124, pp. 211–219, 2016.
  142. 142.
    A. Garofalo, C. Di Sarno, and V. Formicola, “Enhancing Intrusion Detection in Wireless Sensor Networks through Decision Trees,” InEuropean Workshop on Dependable Computing, pp. 1–15, Springer, Berlin, Heidelberg, 2013.
  143. 143.
    W. Elghazel, R. A. Savary, J. Bahi, and M. Hakem, “Random Forests for Industrial Device Functioning Diagnostics Using Wireless Sensor Networks,” In2015 IEEE Aerospace Conference, pp. 1-9, 2015.
  144. 144.
    B. Alotaibi and K. Elleithy, “A New MAC Address Spoofing Detection Technique,” Sensors. 2016, vol. 16, no. 3, pp. 281, 2016.
  145. 145.
    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., vol. 8, no. 1, pp. 106-14, 2017.
  146. 146.
    A. El Assaf and S. Zaidi, “Robust ANNs-based WSN Localization in the Presence of Anisotropic Signal Attenuation,” IEEE Wireless Communications Letters., vol. 5, no. 5, pp. 504-7, 2016.
  147. 147.
    C. Li, X. Xie, Y. Huang, H. Wang, and C. Niu, “Distributed Data Mining Based on Deep Neural Network for Wireless Sensor Network,” International Journal of Distributed Sensor Networks., vol. 11, no. 7, pp. 1–7, 2015.
  148. 148.
    H. R. Dhasian and P. Balasubramanian, “Survey of data aggregation techniques using soft computing in wireless sensor networks,” IET Inf. Secur., vol. 7, no. 4, pp. 336–342, 2013.
  149. 149.
    R. Das, I. Turkoglu, and A. Sengur, “Expert Systems with Applications Effective diagnosis of heart disease through neural networks ensembles,” Expert Syst. Appl., vol. 36, no. 4, pp. 7675–7680, 2009.
  150. 150.
    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, Springer, Singapore pp. 113–123, 2018.
  151. 151.
    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, 2016.
  152. 152.
    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 International Symposium on Ubiquitous Networking, Springer, Singapore, pp. 325–337, 2015.
  153. 153.
    A. Ray and D. De, “Energy efficient clustering protocol based on for enhanced network lifetime in wireless sensor network,” IET Wireless Sensor Systems., vol. 6, no. 6, pp. 181-91, 2016.
  154. 154.
    P. Neamatollahi, S. Abrishami, M. Naghibzadeh, MH. Moghaddam, O. Younis., “Hierarchical Clustering-Task Scheduling Policy in Cluster-Based Wireless Sensor Networks,” IEEE Transactions on Industrial Informatics., vol. 14, no. 5, pp. 1876–1886, 2017.
  155. 155.
    J. R. Srivastava and 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. 863-86, 2015.
  156. 156.
    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. 2017.
  157. 157.
    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. 211-9, 2016.
  158. 158.
    Y. Li, H. Chen, M. Lv, and Y. Li, “Event-based k-nearest neighbors query processing over distributed sensory data using fuzzy sets,” Soft Comput., vol. 23, no. 2, pp. 483-95, 2019.
  159. 159.
    P. Chanak and I. Banerjee, “Fuzzy rule-based faulty node classification and management scheme for large scale wireless sensor networks,” Expert Syst. Appl., vol. 45, pp. 307-21, 2016.
  160. 160.
    C. Anagnostopoulos and S. Hadjiefthymiades, “Advanced Principal Component-Based Compression Schemes for,” ACM Transactions on Sensor Networks (TOSN)., vol. 11, no. 1, pp. 1–34, 2014.
  161. 161.
    S. Liu, L. Feng, J. Wu, G. Hou, and G. Han, “Concept drift detection for data stream learning based on angle optimized global embedding and principal component analysis in sensor networks,” Comput. Electr. Eng., vol. 58, pp. 327-36, 2017.
  162. 162.
    M. I. Chidean, E. Morgado, M. Sanroman-Junquera, J. Ramiro-Bargueno, J. Ramos, and A. J. Caamano, “Energy Efficiency and Quality of Data Reconstruction Through Data-Coupled Clustering for Self-Organized Large-Scale WSNs,” IEEE Sens. J., vol. 16, no. 12, pp. 5010–5020, 2016.
  163. 163.
    T. Yu, S. Member, X. Wang, A. Shami, and S. Member, “Recursive Principal Component Analysis-Based Data Outlier Detection and Sensor Data Aggregation in IoT Systems,” IEEE Internet of Things Journal., vol. 4, no. 6, pp. 2207–2216, 2017.
  164. 164.
    S. Kosunalp, Y. Chu, P. D. Mitchell, D. Grace, and T. Clarke, “Engineering Applications of Artificial Intelligence Use of Q-learning approaches for practical medium access control in wireless sensor networks,” Eng. Appl. Artif. Intell., vol. 55, pp. 146–154, 2016.
  165. 165.
    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. 2763-74, 2016.
  166. 166.
    R. O. Y. C. Hsu, C. Liu, and H. Wang, “A Reinforcement Learning-Based ToD Provisioning Dynamic Power Management for Sustainable Operation of Energy Harvesting Wireless Sensor Node,” IEEE Transactions on Emerging Topics in Computing. vol. 2, no. 2, pp. 181–191, 2014.
  167. 167.
    AP. Renold, 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. 5061-79, 2017.
  168. 168.
    F. A. Aoudia, M. Gautier, and O. Berder, “RLMan?: an Energy Manager Based on Reinforcement Learning for Energy Harvesting Wireless Sensor Networks,” IEEE Transactions on Green Communications and Networking., vol. 2, no. 2, pp. 408-17, 2018.
  169. 169.
    T. Ma, F. Wang, J. Cheng, Y. Yu, and X. Chen, “A Hybrid Spectral Clustering and Deep Neural Network Ensemble Algorithm for Intrusion Detection,” Sensors., vol. 16, no. 10, pp. 1701, 2016.
  170. 170.
    Y, Wang, A. Yang, X. Chan, P. Wang, Y. Wang, H. Yang, "A deep learning approach for blind drift calibration of sensor networks," IEEE Sensors Journal., vol. 17, no. 13, pp. 4158-71, 2017.
  171. 171.
    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.
  172. 172.
    W. Kim and H. J. Kim, “Distributed estimation using online semi-supervised particle filter for mobile sensor networks,” IET Control Theory & Applications., vol. 9, no. 3, pp. 418–427, 2015.
  173. 173.
    S. Kumar, S. N. Tiwari, and R. M. Hegde, “Ad Hoc Networks Sensor node tracking using semi-supervised Hidden Markov Models,” AD HOC NETWORKS, vol. 33, pp. 55-70, 2015.
  174. 174.
    B. Yang, J. Xu, J. Yang, and M. Li, “Localization algorithm in wireless sensor networks based on semi-supervised manifold learning and its application,” Cluster Computing., vol. 13, no. 4, pp. 435–446, 2010.
  175. 175.
    D. P. Kumar, T. Amgoth, C. Sekhara, and R. Annavarapu, “Machine learning algorithms for wireless sensor networks?: A survey,” Inf. Fusion, vol. 49, pp. 1–25, 2019.
  176. 176.
    K. A. Yau, Y. Elkhatib, A. Hussain, and A. L. A. Al-fuqaha, “Unsupervised Machine Learning for Networking?: Techniques , Applications and Research Challenges,” IEEE Access, vol. 7, pp. 65579–65615, 2019.
  177. 177.
    C. Mendes and P. Baltus, “Design Methodology for Industrial Internet-of-Things Wireless Systems,” IEEE Sensors Journal., vol. 21, no. 4, pp. 5529-42, 2020.
  178. 178.
    Y. Zhang, J. Liang, S. Jiang, and W. Chen, “A Localization Method for Underwater Wireless Sensor Networks Based on Mobility Prediction and Particle Swarm Optimization Algorithms,” Sensors., vol. 16, no. 2, pp. 212, 2016.
  179. 179.
    W. Twayej, M. Khan, S. Member, and S. Member, “Network Performance Evaluation of M2M With Self Organising Cluster Head to Sink Mapping,” IEEE Sensors Journal., vol. 17, no. 15, pp. 4962-74, 2017.
  180. 180.
    N. Kumar and D. P. Vidyarthi, “A Green Routing Algorithm for IoT-Enabled Software Defined Wireless Sensor Network,” IEEE Sens. J., vol. 18, no. 22, pp. 9449–9460, 2018.
  181. 181.
    L. Ben Saad, B. Beferull-lozano, and S. Member, “Accurate Graph Filtering in Wireless Sensor Networks,” IEEE Internet of Things Journal., vol. 7, no. 12, pp. 11431-45, 2020.
  182. 182.
    H. Kharrufa, S. Member, H. Al-kashoash, A. H. Kemp, and S. Member, “A Game Theoretic Optimization of RPL for Mobile Internet of Things Applications,” IEEE Sensors Journal., vol. 18, no. 6, pp. 2520-30, 2018.
  183. 183.
    X. Liu, T. Qiu, S. Member, and T. Wang, “Load-Balanced Data Dissemination for Wireless Sensor Networks?: A Nature-Inspired Approach,” IEEE Internet Things J., vol. 6, no. 6, pp. 9256–9265, 2019.
  184. 184.
    R. Chaudhry, S. Tapaswi, and N. Kumar, “FZ enabled Multi-objective PSO for multicasting in IoT based Wireless Sensor Networks,” Inf. Sci. (Ny)., vol. 498, pp. 1–20, 2019.
  185. 185.
    J. S. Raj, “QoS optimization of energy efficient routing in iot wireless sensor networks,” Journal of ISMAC., vol. 01, no. 01, pp. 12–23, 2019.
  186. 186.
    S. Yao, Z. Li, J. Guan, and Y. Liu, “Stochastic Cost Minimization Mechanism based on Identifier Network for IoT Security,” IEEE Internet Things J., vol. 7, no. 5, pp. 3923-34, 2019.
  187. 187.
    R. Mitra and T. Khan, “Secure and Reliable Data Transmission in Wireless Sensor Network?: A Survey,” International Journal of Computational Engineering Research., vol. 2, no. 3, pp. 748–754, 2012.
  188. 188.
    H. Keshmiri and H. Bakhshi, “A New 2-Phase Optimization-Based Guaranteed Connected Target Coverage for Wireless Sensor Networks,” IEEE Sensors Journal., vol. 20, no. 13, pp. 7472-86, 2020.
  189. 189.
    D. G. Costa, C. Duran-faundez, D. C. Andrade, J. B. Rocha-junior, J. Paulo, and J. Peixoto, “TwitterSensing?: An Event-Based Approach for Wireless Sensor Networks Optimization Exploiting Social Media in Smart City Applications,” Sensors., vol.18, no. 4, pp. 1080, 2018.
  190. 190.
    J. Park. Bhat, A. Nk, CS. Geyik, UY. Ogras, HG. Lee, "Energy per operation optimization for energy-harvesting wearable IoT devices," Sensors., vol. 20, no. 3, pp. 764, 2020.
  191. 191.
    B. Ji, Z. Chen, B. Zhou, C. Li, and H. Wen, “Joint optimization for ambient backscatter communication system with energy harvesting for IoT,” Mech. Syst. Signal Process., vol. 135, no. 2, pp. 106412, 2020.
  192. 192.
    S. M. Shimly, D. B. Smith, and S. Movassaghi, “Experimental Analysis of Cross-layer Optimization for Distributed Wireless Body-to-Body Networks,” IEEE Sens. J., vol. 19, no. 24, pp. 12494-509, 2019.
  193. 193.
    L. Tao, X. M. Zhang, and W. Liang, “Efficient Algorithms for Mobile Sink Aided Data Collection From Dedicated and Virtual Aggregation Nodes in Energy Harvesting Wireless Sensor Networks,” IEEE Trans. Green Commun. Netw., vol. 3, no. 4, pp. 1058–1071, 2019.
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