Generic placeholder image

International Journal of Sensors, Wireless Communications and Control

Editor-in-Chief

ISSN (Print): 2210-3279
ISSN (Online): 2210-3287

Research Article

A Mobility Based Approach to Strengthen the Network Lifetime of Wireless Sensor Networks in 3D Region

Author(s): Neha Ahlawat* and Jasvinder Kaur

Volume 14, Issue 1, 2024

Published on: 13 December, 2023

Page: [36 - 44] Pages: 9

DOI: 10.2174/0122103279255197231020060356

Price: $65

conference banner
Abstract

Background: In this era of emerging technologies, Mobile Wireless Sensor Network (MWSN) has emerged as a powerful tool for many applications. Applications such as battlefield and traffic surveillance, agriculture and environment monitoring, smart homes and smart cities require a specific protocol to fulfill a specific purpose. WSN is composed of numerous tiny Sensor Nodes (SNs) along with one or more sinks, where sinks have unlimited sources of energy and SNs are battery- operated. SN tasks are to sense the data and transmit it to sink through the formation of dynamic topology. The SNs nearer to the sink rapidly exhaust their energy due to the heavy burden. Due to this, SNs became dead affecting the performance of the network lifespan. To overcome this problem, the concept of MWSN has been proposed. In MWSN, the sink can move from one location to another, and collect data from SNs. With the help of MWSN, the problem of energy holes can be resolved. An energy hole is a problem in which nodes are alive but they are not able to send the data due to low energy left. To overcome this problem, MWSN plays an important role. MSWN can move around the region and collect the data from SNs.

Methods: In this work, we have proposed a Mobile Sink (MS) that can move on fixed or random locations for data collection from SNs. The comparative analysis of various MS strategies such as MS on boundaries, 4 sojourn locations in the region, random position in the region and fixed path to collect the data has been done.

Results: SNs become dead in 2246 rounds in static approach. In the MS boundary approach, all SNs are dead in 2593 rounds. In the sojourn location, it lasts up to 4827. But in MS random and fixed location approaches, all SNs are dead in 11568 and 11513 rounds, respectively.

Conclusion: The simulation results depict that the MS strategies having fixed or random positions in the region enhanced the network lifetime 4 to 5 times more than the static sink.

Keywords: Mobile sink, MWSN, fixed position, sojourn location, random positions, static sink.

Graphical Abstract
[1]
Sethi D, Bhattacharya PP. A study on energy efficient and reliable data transfer (EERDT) protocol for WBAN. 2016 Second International Conference on Computational Intelligence & Communication Technology (CICT). 254-8.
[http://dx.doi.org/10.1109/CICT.2016.57]
[2]
Sethi D. An approach to optimize homogeneous and heterogeneous routing protocols in WSN using sink mobility. MPAN J Metrol Soc India 2020; 35(2): 241-50.
[http://dx.doi.org/10.1007/s12647-020-00366-5]
[3]
Pasupuleti V, Balaswamy C. Performance analysis of fractional earthworm optimization algorithm for optimal routing in wireless sensor networks. EAI Endorsed Transactions on Scalable Information Systems 2021; 8(32)
[4]
Sethi D, Pratim Bhattacharya P. Revised multi-chain PEGASIS for wireless sensor networks. Int J Sensors Wirel Commun Control 2016; 6(1): 12-7.
[http://dx.doi.org/10.2174/2210327905666150914225227]
[5]
Anand J, Sethi D. Comparative analysis of energy efficient routing in WBAN. 2017 3rd International Conference on Computational Intelligence & Communication Technology (CICT) 2017; Feb 9: IEEE 1-6.
[http://dx.doi.org/10.1109/CIACT.2017.7977373]
[6]
Mishra D, Sethi D, Bhattacharya PP. Modeling and simulation of a clustered wsn for precision agriculture. Int J Comput Sci. Inf Technol Control Eng (IJCSITCE) 2016; 3(1/2): 25-34. [IJCSITCE].
[7]
Prakash PS. Machine learning-based optimized hierarchical routing protocols for wsn lifetime: A review. Information Technology In Industry 2021; 9(2): 289-307.
[http://dx.doi.org/10.17762/itii.v9i2.346]
[8]
Sethi D, Bhattacharya PP. A Comparative analysis of various mobile sink routing protocols and performance comparison of clustered routing protocols in mobile sink scenario. Majlesi Journal of Electrical Engineering 2018; 12(3): 11-22.
[9]
More SS, Patil DD. Wireless sensor networks optimization using machine learning to increase the network lifetime. InInnovative Data Communication Technologies and Application. Proceedings of ICIDCA 2020; 2021: 319-29.
[10]
Sethi D, Anand J. Big data and WBAN: prediction and analysis of the patient health condition in a remote area. Engineering and Applied Science Research 2019; 46(3): 248-55.
[11]
Anand J, Khandelwal J, Sethi D, Choudhary S. An analysis to empower IoT devices through FOG computing. 2021 10th International Conference on Internet of Everything, Microwave Engineering, Communication and Networks (IEMECON) 2021; Dec 1: IEEE 01-4.
[http://dx.doi.org/10.1109/IEMECON53809.2021.9689125]
[12]
Deepak S, Jyoti A, Meenu S, Ankita T. A DMA-WSN based routing strategy to maximize efficiency and reliability in a ship to communicate data on coronavirus. Recent Adv Electr Electron Eng 2023; 16: 1-20.
[13]
Sethi DP, Bhattacharya P. Artificial neural network based base station localization for energy efficient routing in WSN. Recent Pat Comput Sci 2016; 9(3): 248-59.
[http://dx.doi.org/10.2174/2213275909666160816161408]
[14]
Sharma H, Haque A, Blaabjerg F. Machine learning in wireless sensor networks for smart cities: a survey. Electronics 2021; 10(9): 1012.
[http://dx.doi.org/10.3390/electronics10091012]
[15]
Shukla M, Sethi D, Bindal L, Mani K, Upadhyay K, Sharma M. Patient monitoring system using blockchain and IoT technology. Recent Adv Electr Electron Eng 2023; 16(4): 449-59.
[http://dx.doi.org/10.2174/2352096516666221026092345]
[16]
Neha Kaur J. Banita. A comparative analysis of ANN-based homogeneous and heterogeneous routing protocols for selection of cluster head in WSN. Recent Adv Electr Electron Eng 2023; 16: 1-15.
[17]
Salim C, Mitton N. Machine learning based data reduction in WSN for smart agriculture. Proceedings of the 34th International Conference on Advanced Information Networking and Applications (AINA-2020). 127-38.
[http://dx.doi.org/10.1007/978-3-030-44041-1_12]
[18]
Alruhaily NM. M D. A multi-layer machine learning-based intrusion detection system for wireless sensor networks. Int J Adv Comput Sci Appl 2021; 12(4): 281-8.
[http://dx.doi.org/10.14569/IJACSA.2021.0120437]
[19]
Sarkunavathi A, Srinivasan V, Ramalingam M. Comprehensive analysis of intrusion prevention and detection system and dataset used in WSN using machine learning & deep learning. Mathematical Statistician and Engineering Applications 2022; 71(3s2): 638-57.
[20]
Anh Khoa T, Quang Minh N, Hai Son H, et al. Wireless sensor networks and machine learning meet climate change prediction. Int J Commun Syst 2021; 34(3): e4687.
[http://dx.doi.org/10.1002/dac.4687]
[21]
Kim JM, Park SH, Han YJ, Chung TM. CHEF: cluster head election mechanism using fuzzy logic in wireless sensor networks. 2008 10th international conference on advanced communication technology 2008; Feb 171: 654-9.
[http://dx.doi.org/10.1109/ICACT.2008.4493846]
[22]
Mann PS, Singh S, Kumar A. Computational intelligence based metaheuristic for energy-efficient routing in wireless sensor networks. 2016 IEEE congress on evolutionary computation (CEC) 2016; Jul 24 IEEE: 4460-7.
[http://dx.doi.org/10.1109/CEC.2016.7744357]
[23]
Pathak A. A proficient bee colony-clustering protocol to prolong lifetime of wireless sensor networks. J Comput Netw Commun 2020; 2020: 1-9.
[http://dx.doi.org/10.1155/2020/1236187]
[24]
Li H. LEACH-HPR: An energy efficient routing algorithm for Heterogeneous WSN. 2010 IEEE International Conference on Intelligent Computing and Intelligent Systems 2010; Oct 29; IEEE 2: 507-11.
[25]
Kour H, Sharma AK. Hybrid energy efficient distributed protocol for heterogeneous wireless sensor network. Int J Comput Appl 2010; 4(6): 1-5.
[http://dx.doi.org/10.5120/828-1173]
[26]
Chithra A, Kumari RSS. A novel 3- level energy heterogeneity clustering protocol with hybrid routing for a concentric circular wireless sensor network. Cluster Comput 2019; 22(S5): 11101-8.
[http://dx.doi.org/10.1007/s10586-017-1310-9]
[27]
Chowdary KM, Kuppili V. Enhanced clustering and intelligent mobile sink path construction for an efficient data gathering in wireless sensor networks. Arab J Sci Eng 2021; 46(9): 8329-44.
[http://dx.doi.org/10.1007/s13369-021-05415-y]
[28]
Wang J, Cao J, Sherratt RS, Park JH. An improved ant colony optimization-based approach with mobile sink for wireless sensor networks. J Supercomput 2018; 74(12): 6633-45.
[http://dx.doi.org/10.1007/s11227-017-2115-6]
[29]
Al-Janabi TA, Al-Raweshidy HS. An energy efficient hybrid MAC protocol with dynamic sleep-based scheduling for high density IoT networks. IEEE Internet Things J 2019; 6(2): 2273-87.
[http://dx.doi.org/10.1109/JIOT.2019.2905952]
[30]
Roy NR, Chandra P. Threshold sensitive clustering in SEP. Sustainable Computing: Informatics and Systems 2020; 25: 100367.
[31]
Mehra PS. Lbecr: load balanced, efficient clustering and routing protocol for sustainable internet of things in smart cities. J Ambient Intell Humaniz Comput 2022; 14(3): 1-23.
[32]
Binu GS, Shajimohan B. A novel heuristic based energy efficient routing strategy in wireless sensor network. Peer-to-Peer Netw Appl 2020; 13(6): 1853-71.
[http://dx.doi.org/10.1007/s12083-020-00939-w]
[33]
Zhang Y, Zhang X, Ning S, Gao J, Liu Y. Energy-efficient multilevel heterogeneous routing protocol for wireless sensor networks. IEEE Access 2019; 7: 55873-84.
[http://dx.doi.org/10.1109/ACCESS.2019.2900742]
[34]
Khan MK, Shiraz M, Zrar Ghafoor K, Khan S, Safaa Sadiq A, Ahmed G. energy-efficient multistage routing protocol for wireless sensor networks. Wirel Commun Mob Comput 2018; 2018: 1-13.
[http://dx.doi.org/10.1155/2018/6839671]
[35]
El Alami H, Najid A. ECH: An enhanced clustering hierarchy approach to maximize lifetime of wireless sensor networks. IEEE Access 2019; 7: 107142-53.
[http://dx.doi.org/10.1109/ACCESS.2019.2933052]
[36]
Sharma D, Bhondekar AP. Traffic and energy aware routing for heterogeneous wireless sensor networks. IEEE Commun Lett 2018; 22(8): 1608-11.
[http://dx.doi.org/10.1109/LCOMM.2018.2841911]
[37]
Manzoor K, Jokhio SH, Khanzada TJ, Jokhio IA. Enhanced TL-LEACH routing protocol for large-scale WSN applications. 2019 Cybersecurity and Cyberforensics Conference (CCC). 35-9.
[http://dx.doi.org/10.1109/CCC.2019.00-12]
[38]
Wu L, Nie L, Liu B, Cui J, Xiong N. An energy-balanced cluster head selection method for clustering routing in WSN. Journal of Internet Technology 2018; 19(1): 115-25.
[39]
Jiang B, Huang G, Wang T, Gui J, Zhu X. Trust based energy efficient data collection with unmanned aerial vehicle in edge network. Trans Emerg Telecommun Technol 2022; 33(6): e3942.
[http://dx.doi.org/10.1002/ett.3942]
[40]
Ma X, Zhang X, Yang R. Reliable energy-aware routing protocol in delay-tolerant mobile sensor networks. Wirel Commun Mob Comput 2019; 2019: 1-11.
[http://dx.doi.org/10.1155/2019/5746374]
[41]
Mostafaei H. Energy-efficient algorithm for reliable routing of wireless sensor networks. IEEE Trans Ind Electron 2019; 66(7): 5567-75.
[http://dx.doi.org/10.1109/TIE.2018.2869345]
[42]
Shukry S. Stable routing and energy-conserved data transmission over wireless sensor networks. EURASIP J Wirel Commun Netw 2021; 2021(1): 36.
[http://dx.doi.org/10.1186/s13638-021-01925-3]
[43]
Shankar T, Eappen G, Rajalakshmi S. Optimized routing algorithm for wireless sensor networks. Advances in Automotive Technologies: Select Proceedings of ICPAT 2019; 83-96.
[http://dx.doi.org/10.1007/978-981-15-5947-1_8]
[44]
Yang G, Liang T, He X, Xiong N. Global and local reliability-based routing protocol for wireless sensor networks. IEEE Internet Things J 2019; 6(2): 3620-32.
[http://dx.doi.org/10.1109/JIOT.2018.2889379]
[45]
Senthil Kumaran R, Dhanyasri R, Loga K, Harinee MP. A novel scheme for energy efficiency and secure routing protocol in wireless sensor networks. Recent Trends in Communication and Intelligent Systems: Proceedings of ICRTCIS 2020; 95-104.

Rights & Permissions Print Cite
© 2024 Bentham Science Publishers | Privacy Policy