Top

The Journal of Supercomputing

Published in:

17-09-2020

Two-phase node deployment for target coverage in rechargeable WSNs using genetic algorithm and integer linear programming

Authors: Mahsa Zameni, Aref Rezaei, Leili Farzinvash

Published in: The Journal of Supercomputing | Issue 4/2021

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

Node deployment is a matter of considerable concern in designing wireless sensor networks (WSNs). This paper studies this issue in the context of rechargeable WSNs (RWSNs). We propose an efficient algorithm, namely node deployment for target coverage in RWSNs (NDTCR), which determines the number and positions of installed sensors in two phases. The first phase applies genetic algorithm to construct a mesh over a subset of positions. The mentioned mesh covers the targets and connects them to the sink. In the second phase of NDTCR, we propose an integer linear programming (ILP) model to install some sensors at each position of the mesh. The advantage of applying the first phase is that it prunes the solution space considerably. Therefore, the proposed ILP model can be solved in a reasonable time. The experimental results demonstrate that NDTCR requires 29% fewer sensors on average in comparison with the previous approaches.

previous article Ultrasound image analysis technology under deep belief networks in evaluation on the effects of diagnosis and chemotherapy of cervical cancer

next article A novel attention fusion network-based framework to ensemble the predictions of CNNs for lymph node metastasis detection

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Yick J, Mukherjee B, Ghosal D (2008) Wireless sensor network survey. Comput Netw 52(12):2292–2330. https://doi.org/10.1016/j.comnet.2008.04.002CrossRef

Sah DK, Amgoth T (2020) Renewable energy harvesting schemes in wireless sensor networks: a Survey. Inform Fusion 63:223–247. https://doi.org/10.1016/j.inffus.2020.07.005CrossRef

Wu Y, Liu W, Shen O (2017) Joint optimal placement, routing, and energy allocation in wireless sensor networks with a shared energy harvesting module. Int J Distrib Sens N. https://doi.org/10.1177/1550147717709440CrossRef

Sengupta S, Das S, Nasir MD, Panigrahi BK (2013) Multi-objective node deployment in WSNs: in search of an optimal trade-off among coverage, lifetime, energy consumption, and connectivity. Eng Appl Artif Intel 26:405–416. https://doi.org/10.1016/j.engappai.2012.05.018CrossRef

Benatia MA, Sahnoun M, Baudry D, Louis A, El-Hami A, Mazari B (2017) Multi-objective WSN deployment using genetic algorithms under cost, coverage, and connectivity constraints. Wirel Pers Commun 94(4):2739–2768. https://doi.org/10.1007/s11277-017-3974-0CrossRef

Zhang Y-H, Gong Y-J, Gu T-L, Li Y, Zhang J (2017) Flexible genetic algorithm: a simple and generic approach to node placement problems. Appl Soft Comput 52:457–470. https://doi.org/10.1016/j.asoc.2016.10.022CrossRef

Li Q, Liu N (2020) Monitoring area coverage optimization algorithm based on nodes perceptual mathematical model in wireless sensor networks. Comput Commun 155:227–234. https://doi.org/10.1016/j.comcom.2019.12.040CrossRef

Yang C, Chin K-W (2017) On nodes placement in energy harvesting wireless sensor networks for coverage and connectivity. IEEE Trans Ind Inform 13(1):27–36. https://doi.org/10.1109/TII.2016.2603845CrossRef

Li Y, Chen Y, Chen CS, Wang Z, Zhu Y-H (2019) Simultaneous sensor placement and scheduling for fusion-based detection in RF-powered sensor networks. IEEE Internet Things J 6(3):5595–5606. https://doi.org/10.1109/JIOT.2019.2903847CrossRef

10.

Zhu X, Li J, Zhou M, Chen X (2019) Optimal deployment of energy-harvesting directional sensor networks for target coverage. IEEE Syst J 13(1):377–388. https://doi.org/10.1109/JSYST.2018.2820085CrossRef

11.

Liu Y, Chin K-W, Yang C, He T (2019) Nodes deployment for coverage in rechargeable wireless sensor networks. IEEE Trans Veh Technol 68(6):6064–6073. https://doi.org/10.1109/TVT.2019.2912188CrossRef

12.

Mini S, Udgata SK, Sabat SL (2014) Sensor deployment and scheduling for target coverage problem in wireless sensor networks. IEEE Sens J 14(3):636–644. https://doi.org/10.1109/JSEN.2013.2286332CrossRef

13.

Gupta SK, Kuila P, Jana PK (2016) Genetic algorithm approach for k-coverage and m-connected node placement in target based wireless sensor networks. Comput Electr Eng 56:544–556. https://doi.org/10.1016/j.compeleceng.2015.11.009CrossRef

14.

Yarinezhad R, Hashemi SN (2020) A sensor deployment approach for target coverage problem in wireless sensor networks. J Ambient Intell Human Comput. https://doi.org/10.1007/s12652-020-02195-5CrossRef

15.

Karatas M (2018) Optimal deployment of heterogeneous sensor networks for a hybrid point and barrier coverage application. Comput Netw 132:129–144. https://doi.org/10.1016/j.comnet.2018.01.001CrossRef

16.

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. https://doi.org/10.1007/s11227-020-03166-5CrossRef

17.

Boukerche A, Sun P (2018) A novel hierarchical two-tier node deployment strategy for sustainable wireless sensor networks. IEEE Trans Sustain Comput 3(4):236–247. https://doi.org/10.1109/TSUSC.2018.2816465CrossRef

18.

Mehajabin N, Razzaque MA, Hassan MM, Almogren A, Alamri A (2016) Energy-sustainable relay node deployment in wireless sensor networks. Comput Netw 104:108–121. https://doi.org/10.1016/j.comnet.2016.05.014CrossRef

19.

Meng Y, Aimin W, Sun G, Zhang Y (2018) Deploying charging nodes in wireless rechargeable sensor networks based on improved firefly algorithm. Comput Electr Eng 72:719–731. https://doi.org/10.1016/j.compeleceng.2017.11.021CrossRef

20.

He T, Chin K-W, Soh S (2018) On maximizing min flow rates in rechargeable wireless sensor networks. IEEE Trans Ind Inform 14(7):2962–2972. https://doi.org/10.1109/TII.2017.2771288CrossRef

21.

Ding X, Wang Y, Sun G, Luo C, Li D, Chen W, Hu Q (2020) Optimal charger placement for wireless power transfer. Comput Netw 170:107123. https://doi.org/10.1016/j.comnet.2020.107123CrossRef

22.

Yang F, Shu L, Huang K, Li K, Han G, Liu Y (2020) A partition-based node deployment strategy in solar insecticide lamp internet of things. IEEE Internet Things J. https://doi.org/10.1109/JIOT.2020.2996514CrossRef

23.

Lin C-C, Chen Y-C, Chen J-L, Deng D-J, Wang S-B, Jhong S-Y (2017) Lifetime enhancement of dynamic heterogeneous wireless sensor networks with energy-harvesting sensors. Mobile Netw Appl 22(5):931–942. https://doi.org/10.1007/s11036-017-0861-6CrossRef

24.

DeWitt J, Shi H (2017) Barrier coverage in energy harvesting sensor networks. Ad Hoc Netw 56:72–83. https://doi.org/10.1016/j.adhoc.2016.11.014CrossRef

25.

Yang C, Chin K-W, Liu Y, Zhang J, He T (2019) Robust targets coverage for energy harvesting wireless sensor networks. IEEE Trans Veh Technol 68(6):5884–5892. https://doi.org/10.1109/TVT.2019.2908584CrossRef

26.

Li C, Chin K-W, Yang C (2020) Complete target coverage in radio frequency and solar-powered sensor networks. IEEE Syst J. https://doi.org/10.1109/JSYST.2020.2997300CrossRef

27.

Lu T, Liu G, Li W, Chang S, Guo W (2017) Distributed sampling rate allocation for data quality maximization in rechargeable sensor networks. J Netw Comput Appl 80:1–9. https://doi.org/10.1016/j.jnca.2016.12.021CrossRef

28.

Ashraf N, Hasan A, Khaliq Qureshi H, Lestas M (2019) Combined data rate and energy management in harvesting enabled tactile IoT sensing devices. IEEE Trans Ind Inform 15(5):3006–3015. https://doi.org/10.1109/TII.2019.2900795CrossRef

29.

Lu T, Liu G, Chang S (2018) Energy-efficient data sensing and routing in unreliable energy-harvesting wireless sensor network. Wirel Netw 24(2):611–625. https://doi.org/10.1007/s11276-016-1360-6CrossRef

30.

Li F, Xiong M, Wang L, Peng H, Hua J, Liu X (2018) A novel energy-balanced routing algorithm in energy harvesting sensor networks. Phys Commun-AMST 27:181–187. https://doi.org/10.1016/j.phycom.2018.02.010CrossRef

31.

Hu J, Luo J, Zheng Y, Li K (2019) Graphene-grid deployment in energy harvesting cooperative wireless sensor networks for green IoT. IEEE Trans Ind Inform 15(3):1820–1829. https://doi.org/10.1109/TII.2018.2871183CrossRef

32.

Li S, Fu L, He S, Sun Y (2018) Near-optimal co-deployment of chargers and sink stations in rechargeable sensor networks. ACM Trans Embed Comput S. https://doi.org/10.1145/3070721CrossRef

33.

Heinzelman WB, Chandrakasan AP, Balakrishnan H (2002) An application-specific protocol architecture for wireless microsensor networks. IEEE Trans Wirel Commun 1:660–670. https://doi.org/10.1109/TWC.2002.804190CrossRef

34.

Tabibi S, Ghaffari A (2019) Energy-efficient routing mechanism for mobile sink in wireless sensor networks using particle swarm optimization algorithm. Wirel Pers Commun 104:199–216. https://doi.org/10.1007/s11277-018-6015-8CrossRef

35.

Mosavvar I, Ghaffari A (2019) Data aggregation in wireless sensor networks using firefly algorithm. Wirel Pers Commun 104:307–324. https://doi.org/10.1007/s11277-018-6021-xCrossRef

36.

Asorey-Cacheda R, Garcia-Sanchez A-J, Garcia-Sanchez F, Garcia-Haro J (2017) A survey on non-linear optimization problems in wireless sensor networks. J Netw Comput Appl 82:1–20CrossRef

37.

Goldberg DE (1989) Genetic algorithms in search, optimization, and machine learning, 1st edn. Addison-Wesley Professional, Boston. https://doi.org/10.1016/j.jnca.2017.01.001CrossRefMATH

38.

Nikokheslat HD, Ghaffari A (2017) Protocol for controlling congestion in wireless sensor networks. Wireless Pers Commun 95:3233–3251. https://doi.org/10.1007/s11277-017-3992-yCrossRef

39.

Ghaffari A (2015) Congestion control mechanisms in wireless sensor networks: a survey. J Netw Comput Appl 52:101–115. https://doi.org/10.1016/j.jnca.2015.03.002CrossRef

Title: Two-phase node deployment for target coverage in rechargeable WSNs using genetic algorithm and integer linear programming
Authors: Mahsa Zameni
Aref Rezaei
Leili Farzinvash
Publication date: 17-09-2020
Publisher: Springer US
Published in: The Journal of Supercomputing / Issue 4/2021
Print ISSN: 0920-8542
Electronic ISSN: 1573-0484
DOI: https://doi.org/10.1007/s11227-020-03431-7

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft"

Springer Professional "Technik"

Springer Professional "Wirtschaft+Technik"

Other articles of this Issue 4/2021

On the use of many-core Marvell ThunderX2 processor for HPC workloads

Ensemble-based clustering of large probabilistic graphs using neighborhood and distance metric learning

Improving the learning of self-driving vehicles based on real driving behavior using deep neural network techniques

Suspicious activity detection using deep learning in secure assisted living IoT environments

A novel enhanced region proposal network and modified loss function: threat object detection in secure screening using deep learning

Centroid-Based Routing protocol with moving sink node for uniform and non-uniform distribution of wireless sensor nodes

Premium Partner