Wireless Sensor Network Based Monitoring System: Implementation, Constraints, and Solution

Apip Miptahudin - Institute of Sepuluh Nopember Technology, Surabaya, Indonesia
Titiek Suryani - Institute of Sepuluh Nopember Technology, Surabaya, Indonesia
Wirawan Wirawan - Institute of Sepuluh Nopember Technology, Surabaya, Indonesia

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DOI: http://dx.doi.org/10.30630/joiv.6.4.1530


Wireless Sensor Network (WSN) is a collection of sensors communicating at close range by forming a wireless-based network (wireless). Since 2015 research related to the use of WSN in various health, agriculture, security industry, and other fields has continued to grow. One interesting research case is the use of WSN for the monitoring process by collecting data using sensors placed and distributed in locations based on a wireless system. Sensors with low power, multifunction, supported by a combination of wireless network, microcontroller, memory, operating system, radio communication, and energy source in the form of an integrated battery enable a monitoring process of the monitoring area to run properly. The implementation of the wireless sensor network includes five main parts, namely sender, receiver, wireless transmission media, data/information, network architecture/configuration, and network management. Network management itself includes network configuration management, network performance management, network failure management, network security management, and network financing management. The main obstacles in implementing a wireless sensor network include three things: an effective and efficient data sending/receiving process, limited and easily depleted sensor energy/power, network security, and data security that is vulnerable to eavesdropping and destruction. This paper presents a taxonomy related to the constraints in implementing Wireless Sensor Networks. This paper also presents solutions from existing studies related to the constraints of implementing the WSN. Furthermore, from the results of the taxonomy mapping of these constraints, new gaps were identified related to developing existing research to produce better solutions.


Wireless sensor network; taxonomy; configuration; energy; network security; optimization.

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C. G. Thuy, “Flexible configuration of wireless sensor network for monitoring of rainfall-induced landslide,” Indones. J. Electr. Eng. Comput. Sci., vol. 12, no. 3, pp. 1030–1036, 2018.

M. Carlos-Mancilla, E. López-Mellado, and M. Siller, “Wireless sensor networks formation: approaches and techniques,” J. Sensors, vol. 2016, 2016.

D. Zhao, G. Lun, and R. Xue, “Coding-aware opportunistic routing for sparse underwater wireless sensor networks,” IEEE Access, vol. 9, pp. 50170–50187, 2021.

S. Karim et al., “Corrections to ‘GCORP: Geographic and Cooperative Opportunistic Routing Protocol for Underwater Sensor Networks,’” IEEE Access, vol. 9, pp. 67734–67735, 2021.

U. C. Cabuk, G. Dalkilic, and O. Dagdeviren, “CoMAD: Context-aware mutual authentication protocol for drone networks,” IEEE Access, vol. 9, pp. 78400–78414, 2021.

S.-W. Lee, J.-H. Kwon, X. Zhang, and E.-J. Kim, “Traffic-Adaptive CFP Extension for IEEE 802.15. 4 DSME MAC in Industrial Wireless Sensor Networks,” IEEE Access, vol. 9, pp. 94454–94469, 2021.

R. Elhabyan, W. Shi, and M. St-Hilaire, “Coverage protocols for wireless sensor networks: Review and future directions,” J. Commun. Networks, vol. 21, no. 1, pp. 45–60, 2019.

W. Wang, S. L. Capitaneanu, D. Marinca, and E.-S. Lohan, “Comparative analysis of channel models for industrial IoT wireless communication,” IEEE Access, vol. 7, pp. 91627–91640, 2019.

G. Caso, Ö. Alay, L. De Nardis, A. Brunstrom, M. Neri, and M.-G. Di Benedetto, “Empirical models for NB-IoT path loss in an urban scenario,” IEEE Internet Things J., vol. 8, no. 17, pp. 13774–13788, 2021.

B. Clerckx and E. Bayguzina, “Waveform design for wireless power transfer,” IEEE Trans. Signal Process., vol. 64, no. 23, pp. 6313–6328, 2016.

Y. Huang and B. Clerckx, “Waveform design for wireless power transfer with limited feedback,” IEEE Trans. Wirel. Commun., vol. 17, no. 1, pp. 415–429, 2017.

M. Mahbub, “Comparative link-level analysis and performance estimation of channel models for IIoT (industrial-IoT) wireless communications,” Internet of things, vol. 12, p. 100315, 2020.

J. D. Rodríguez, A. Pérez, and J. A. Lozano, “Sensitivity Analysis of k-Fold Cross Validation in Prediction Error Estimation,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 32, no. 3, 2010, doi: 10.1109/TPAMI.2009.187.

J. Liu, Z. Zhao, J. Ji, and M. Hu, “Research and application of wireless sensor network technology in power transmission and distribution system,” Intell. Converg. Networks, vol. 1, no. 2, pp. 199–220, 2020.

J. Chen, C. W. Yu, and W. Ouyang, “Efficient wireless charging pad deployment in wireless rechargeable sensor networks,” IEEE Access, vol. 8, pp. 39056–39077, 2020.

S. Khisa and S. Moh, “Survey on recent advancements in energy-efficient routing protocols for underwater wireless sensor networks,” IEEE Access, vol. 9, pp. 55045–55062, 2021.

Q. Qian, A. Y. S. Pandiyan, and D. E. Boyle, “Optimal recharge scheduler for drone-to-sensor wireless power transfer,” IEEE Access, vol. 9, pp. 59301–59312, 2021.

C.-L. Hu, S.-Z. Huang, Z. Zhang, and L. Hui, “Energy-Balanced Optimization on Flying Ferry Placement for Data Gathering in Wireless Sensor Networks,” IEEE Access, vol. 9, pp. 70906–70923, 2021.

A. J. Williams, M. F. Torquato, I. M. Cameron, A. A. Fahmy, and J. Sienz, “Survey of energy harvesting technologies for wireless sensor networks,” IEEE Access, vol. 9, pp. 77493–77510, 2021.

J. Li, F. Safaei, and others, “Throughput analysis of in-band full-duplex transmission networks with wireless energy harvesting enabled sources,” IEEE Access, vol. 9, pp. 74989–75002, 2021.

S.-J. Hsiao and W.-T. Sung, “Employing blockchain technology to strengthen security of wireless sensor networks,” IEEE Access, vol. 9, pp. 72326–72341, 2021.

Y. Liu and Y. Wu, “Employ DBSCAN and neighbor voting to screen selective forwarding attack under variable environment in event-driven wireless sensor networks,” IEEE Access, vol. 9, pp. 77090–77105, 2021.

M. A. Al-Naeem, “Prediction of Re-Occurrences of Spoofed ACK Packets Sent to Deflate a Target Wireless Sensor Network Node by DDOS,” IEEE Access, vol. 9, pp. 87070–87078, 2021.

R. Alturki et al., “Sensor-cloud architecture: A taxonomy of security issues in cloud-assisted sensor networks,” IEEE Access, vol. 9, pp. 89344–89359, 2021.

A. M. Wilson, T. Panigrahi, B. P. Mishra, and S. L. Sabat, “Adaptive Geman-McClure estimator for robust distributed channel estimation,” IEEE Access, vol. 9, pp. 93691–93702, 2021.

S. Zafar et al., “A systematic review of bio-cyber interface technologies and security issues for internet of bio-nano things,” IEEE Access, 2021.


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