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Approximation algorithms for maximum weighted target cover problem with distance limitations

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Abstract

In this paper, we study approximation algorithms for the problem of maximum weighted target cover with distance limitations (MaxWTCDL). Given n targets \(T=\left\{ t_{1},t_{2},\ldots ,t_{n}\right\} \) on the plane and m mobile sensors \(S=\left\{ s_{1},s_{2},\ldots ,s_{m}\right\} \) randomly deployed on the plane, each target \(t_i\) has a weight \(w_{i}\) and the sensing radius of the mobile sensors is \(r_{s}\), suppose there is a movement distance constraint b for each sensor and a total movement distance constraint B, where \(B>b\), the goal of MaxWTCDL is to move the mobile sensors within the distance constraints b and B to maximize the weight of covered targets. We present two polynomial time approximation algorithms. One is greedy-based, achieving approximation ratio \(\frac{1}{2v}\) in time \(O(mn^2)\), where . The other is LP-based, achieving approximation ratio \(\frac{1}{v}(1-e^{-1})\) in time \(T_{LP}\), where \(T_{LP}\) is the time needed to solve the linear program.

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References

  • Chaturvedi P, Daniel AK (2022) A comprehensive review on scheduling based approaches for target coverage in WSN. Wirel Pers Commun 66:1–53

    Google Scholar 

  • Chekuri C, Kumar A (2004) Maximum coverage problem with group budget constraints and applications. In: Approximation, randomization, and combinatorial optimization. Algorithms and techniques, vol 3122. Berlin, pp 72–83

  • Chen Z, Gao X, Wu F, Chen G (2016) A PTAS to minimize mobile sensor movement for target coverage problem. In: IEEE INFOCOM 2016—the 35th annual IEEE international conference on computer communications, San Francisco, CA, USA, pp 1–9

  • Cherry A, Gudmundsson J, Mestre J (2017) Barrier coverage with uniform radii in 2d. In: Algorithms for sensor systems: 13th international symposium on algorithms and experiments for wireless sensor networks, ALGOSENSORS 2017, Vienna, Austria, September 7–8, 2017, Revised Selected Papers 13. Springer, pp 57—69

  • Czyzowicz J, Kranakis E, Krizanc D, Lambadaris I, Narayanan L, Opatrny J, Stacho L, Urrutia J, Yazdani M (2010) On minimizing the sum of sensor movements for barrier coverage of a line segment. In: Ad-hoc, mobile and wireless networks: 9th international conference, ADHOC-NOW 2010, Edmonton, AB, Canada, August 20–22, 2010. Proceedings 9. Springer, pp 29–42

  • Farbstein B, Levin A (2017) Maximum coverage problem with group budget constraints. J Combin Optim 34:725–735

    Article  MathSciNet  Google Scholar 

  • Guo L, Li M, Xu D (2019) Efficient approximation algorithms for maximum coverage with group budget constraints. Theor Comput Sci 788:53–65

    Article  MathSciNet  Google Scholar 

  • Guo L, Zou W, Wu C, Xu D, Du D (2021) Minsum movement of barrier and target coverage using sink-based mobile sensors on the plane. In: 2021 IEEE 41st international conference on distributed computing systems (ICDCS). IEEE, pp 696–706

  • Hochbaum DS, Maass W (1985) Approximation schemes for covering and packing problems in image processing and VLSI. J ACM 32(1):130–136

    Article  MathSciNet  Google Scholar 

  • Iida E, Yamashita M (2023) An infeasible interior-point arc-search method with Nesterov’s restarting strategy for linear programming problems. arXiv preprint arXiv:2303.01666

  • Kandris D, Nakas C, Vomvas D, Koulouras G (2020) Applications of wireless sensor networks: an up-to-date survey. Appl Syst Innov 3(1):14

    Article  Google Scholar 

  • Li X, Li D, Wan J, Vasilakos A, Lai C, Wang S (2017) A review of industrial wireless networks in the context of industry 4.0. Wirel Netw 23(1):23–41

    Article  Google Scholar 

  • Liang J, Liu M, Kui X (2014) A survey of coverage problems in wireless sensor networks. Sens Transducers 163(1):240

    Google Scholar 

  • Liao Z, Wang J, Zhang S, Cao J, Min G (2014) Minimizing movement for target coverage and network connectivity in mobile sensor networks. IEEE Trans Parallel Distrib Syst 26(7):1971–1983

    Article  Google Scholar 

  • Lombardo L, Corbellini S, Parvis M, Elsayed A, Angelini E, Grassini S (2017) Wireless sensor network for distributed environmental monitoring. IEEE Trans Instrum Meas 67(5):1214–1222

    Article  Google Scholar 

  • Mois G, Folea S, Sanislav T (2017) Analysis of three iot-based wireless sensors for environmental monitoring. IEEE Trans Instrum Meas 66(8):2056–2064

    Article  Google Scholar 

  • Quan LV, Hanh NT, Binh HTT, Toan VD, Ngoc DT, Lam BT (2023) A bi-population genetic algorithm based on multi-objective optimization for a relocation scheme with target coverage constraints in mobile wireless sensor networks. Expert Syst Appl 217:119486

    Article  Google Scholar 

  • Somasundara A, Ramamoorthy A, Srivastava M (2007) Mobile element scheduling with dynamic deadlines. IEEE Trans Mob Comput 4(6):395–410

    Article  Google Scholar 

  • Tan R, Xing G, Wang J, So HC (2009) Exploiting reactive mobility for collaborative target detection in wireless sensor networks. IEEE Trans Mob Comput 9(3):317–332

    Article  Google Scholar 

  • Wang B (2011) Coverage problems in sensor networks: a survey. ACM Comput Surv 43(4):1–53

    Article  Google Scholar 

  • Wongwattanakij N, Phetmak N, Jaikaeo C, Fakcharoenphol J (2023) An improved ptas for covering targets with mobile sensors. arXiv preprint arXiv:2305.03946

  • Wu W, Zhang Z, Lee W, Du D-Z (2020) Optimal coverage in wireless sensor networks, vol 162. Springer optimization and its applications. Springer, Cham

  • Zou W, Guo L, Hao C, Liu L (2023) Approximation algorithm for minsum linear barrier coverage with sink-based mobile sensors on the plane. Theor Comput Sci 941:121–130

    Article  MathSciNet  Google Scholar 

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Funding

This research is supported in part by National Natural Science Foundation of China (Grant Numbers U20A2068, 11901533).

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All authors contributed to the study conception and design. The first draft of the manuscript was written by Jianhong Jin and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Zhao Zhang.

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Jin, J., Ran, Y. & Zhang, Z. Approximation algorithms for maximum weighted target cover problem with distance limitations. J Comb Optim 47, 60 (2024). https://doi.org/10.1007/s10878-024-01166-2

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