Abstract
The quality of service is severely degraded by coverage holes in wireless sensor networks. This paper focuses on the coverage hole healing (CHH) problem for big data collection in a large-scale wireless sensor network (LS-WSN) where the LS-WSN containing both static sensors and mobile sensors with the topology control of LEACH algorithm. Meanwhile, the data volume transmitted by each sensor node may be inconsistent. Specifically, the target of the CHH problem is to find an optimal subset of mobile nodes from all mobile nodes while maximizing the transmission times (TT) that all dispatched mobile nodes can transmit in their lifetime. Hence, from the data-centric perspective, we propose a greedy healing algorithm (GHA) via the greedy-based heuristic strategy with low computational complexity to solve this CHH problem. Simulation results show that the proposed GHA can efficiently heal the coverage holes which significantly prolongs the network lifetime and observably enhances the quality of service (QoS) of WSNs while increasing the TT, transmitted data volume (TDV) and average residual energy of all dispatched mobile nodes.
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References
Oliveira LML, Rodrigues JJPC (2011) Wireless sensor networks: a survey on environmental monitoring. J Commun 6(2):143–151
Wang B (2011) Coverage problems in sensor networks: a survey. ACM Comput Surv 43(4):32
Arthur WB (2011) The second economy, McKinsey Quart
Liu Y, He Y, Li M, Wang J, Liu K, Li X (2013) Does wireless sensor network scale? A measurement study on green orbs. IEEE Trans Parallel Distrib Syst 24(10):1983–1993
Nasipuri A, Cox R, Conrad J, Van Der Zel L, Rodriguez B, and McKosky R (2010) Design considerations for a large-scale wireless sensor network for substation monitoring, in Proc. 5th IEEE Int. Workshop Pract. Issues Build. Sens. Netw. Appl., pp. 866–873
Jia M, Yin Z, Guo Q, Liu G, Gu X (2018) Toward improved offloading efficiency of data transmission in the IoT-cloud by leveraging secure truncating OFDM. IEEE Internet of Things J 99:1–8
Jia M, Yin Z, Guo Q, Liu G, Gu X (2018) Downlink design for spectrum efficient IoT network. IEEE Int of Things Journal 5(5):3397–3404
Jia M, Liu X, Gu X, Guo Q (2017) Joint cooperative spectrum sensing and channel selection optimization for satellite communication systems based on cognitive radio. Int J Satellite Commun Netw 35(2):139–150
Jia M, Gu X, Guo Q, Xiang W, Zhang N (2016) Broadband hybrid satellite-terre- strial communication systems based on cognitive radio towards 5G. IEEE Wirel Commun 23(6):96–106
Jia M, Liu X, Yin Z, Guo Q, Gu X (2016) Joint cooperative spectrum sensing and spectrum opportunity for satellite cluster communication networks. Ad Hoc Net 58:231–238
Jia M, Li D, Yin Z, Guo Q, Gu X (2018) High spectral efficiency secure communications with non-orthogonal physical and multiple access layers. IEEE Internet of Things J. 99:1–8
Cao X, Liu L, Cheng Y, Shen X (2018) Towards energy-efficient wireless networking in the big data era: a survey. IEEE Commun Surveys & Tutorials 20(1):303–332, First Quarter
Takaishi D, Nishiyama H, Kato N, Miura R (2014) Toward energy efficient big data gathering in densely distributed sensor networks. IEEE Trans Emerg Top Comput 2(3):388–397
Wu M, Tan L, Xiong N (2015) A structure fidelity approach for big data collection in wireless sensor networks. Sensors 15:248–273
Zhu J, Yin X, Bai J, Wang Y (2016) Mobility-assisted big data collecting in wireless sensor networks. Int J Distrib Sens Netw 12(8):18
Ding X, Tian Y, Yu Y (2016) A real-time big data gathering algorithm based on indoor wireless sensor networks for risk analysis of industrial operations. IEEE Trans. Ind. Inform. 12(3):1232–1242
Rani S, Ahmed SH, Talwar R, Malhotra J (2017) Can sensors collect big data? An energy-efficient big data gathering algorithm for a WSN. IEEE Trans Ind Inform 13(4):1961–1968
Din S, Ahmed A, Paul A, Rathore MMU, Jeon G (2017) A cluster-based data fusion technique to analyze big data in wireless multi-sensor system. IEEE Access 5:5069–5083
Ang KL, Seng JKP, Zungeru AM (2018) Optimizing energy consumption for big data collection in large-scale wireless sensor networks with mobile collectors. IEEE Syst J 12(1):616–626
Senouci MR, Mellouk A, Assnoune K (2014) Localized movement-assisted sensor deployment algorithm for hole detection and healing. IEEE Trans. Parallel Distrib. Syst. 25(5):1267–1277
Deng X, Tang Z, Yang LT, Lin M, Wang B (2018) Confident information coverage hole healing in hybrid industrial wireless sensor networks. IEEE Trans. Ind. Informat. 14(5):2220–2229
Qiu C, Shen H, and Chen K (2015) An energy-efficient and distributed cooperation mechanism for k-coverage hole detection and healing in WSNs, in Proc. IEEE 12th Int. Conf. Mobile Ad Hoc Sens. Syst., Oct. pp. 73–81
Han G, Liu L, Jiang J, Shu L, Hancke G (2017) Analysis of energy-efficient connected target coverage algorithms for industrial wireless sensor networks. IEEE Trans Ind Informat 13(1):135–143
Latif K, Javaid N, Ahmad A, Khan ZA, Alrajeh N, Khan MI (2016) On energy hole and coverage hole avoidance in underwater wireless sensor networks. IEEE Sensors J 16(11):4431–4442
Wang YC, Hu CC, Tseng YC (2008) Efficient placement and dispatch of sensors in a wireless sensor network. IEEE Trans Mobile Comput 7(2):262–274
Yan F, Vergne A, Martins P, Decreusefond L (2015) Homology-based distributed coverage hole detection in wireless sensor networks. IEEE/ACM Trans Netw 23(6):1705–1718
Sahoo PK, Liao W (2015) HORA: a distributed coverage hole repair algorithm for wireless sensor networks. IEEE Trans Mob Comput 14(7):1397–1410
Abolhasan M, Maali Y, Rafiei A, Ni W (2016) Distributed hybrid coverage hole recovery in wireless sensor networks. IEEE Sensors J 16(23):8640–8648
Li W, Wu Y (2016) Tree-based coverage hole detection and healing method in wireless sensor networks. Comput Netw 103(24):33–43
Liu B, Ren F, Shen J, Chen H (2010) Advanced self-correcting time synchronization in wireless sensor networks. IEEE Commun Lett 14(4):309–311
Zhang W, Yin Q, Chen H, Gao F, Ansari N (2013) Distributed angle estimation for localization in wireless sensor networks. IEEE Trans Wirel Commun 12(2):527–537
Liu B, Chen H, Zhong Z, Poor HV (2010) Asymmetrical round trip based synchronization-free localization in large-scale underwater sensor networks. IEEE Trans Wirel Commun 9(11):3532–3542
Wang G, Chen H, Li Y, Jin M (2012) On received-signal-strength based localization with unknown transmit power and path loss exponent. IEEE Wirel Commun Lett 1(5):536–539
Chen H, Liu B, Huang P, Liang J, Gu Y (2012) Mobility-assisted node localization based on TOA measurements without time synchronization in wireless sensor networks. ACM Mob Net App 17(1):90–99
Chen L, Chen W, Wang B, Zhang X, Chen H, Yang D (2011) System-level simulation methodology and platform for mobile cellular systems. IEEE Commun Mag 49(7):148–155
Wang G, Chen H (2011) An importance sampling method for TDOA-based source localization. IEEE Trans Wirel Commun 10(5):1560–1568
Chen H, Gao F, Marins MHT, Huang P, Liang J (2013) Accurate and efficient node localization for mobile sensor networks. ACM Mob. Net. App. 18(1):141–147
Chen H, Wang GWZ, So HC, Poor HV (2011) Non-line-of-sight node localization based on semi-definite programming in wireless sensor networks. IEEE Trans Wirel Commun 11(1):108–116
Huang P, Chen H, Xing G, Tan Y (2009) SGF: a state-free gradient-based forwarding protocol for wireless sensor networks. ACM Trans Sensor Net 5(2):1–14
Chen H, Shi Q, Tan R, Poor HV, Sezaki K (2010) Mobile element assisted cooperative localization for wireless sensor networks with obstacles. IEEE Trans Wirel Commun 9(3):956–963
Heinzelman WB, Chandrakasan AP, Balakrishnan H (2002) An application- specific protocol architecture for wireless microsensor networks. IEEE Trans Wirel Commun 1(4):660–670
Amundson I, Koutsoukos XD (2009) A survey on localization for mobile wireless sensor networks, in Proc. 2nd Int. Conf. Mobile Entity Localization Tracking GPS-Less Environ., pp. 235–254
Liu Y, Yang Z (2010) Location, localization, and localizability: location- awareness Technology for Wireless Networks. Springer, New York
Acknowledgments
This research was supported by the National Natural Science Foundation of China (61671165, 6176060053), the Guangxi Natural Science Foundation (2016GXNSFGA380009), the Fund of Key Laboratory of Cognitive Radio and Information Processing (Guilin University of Electronic Technology), Ministry of Education, China and the Guangxi Key Laboratory of Wireless Wideband Communication and Signal Processing (CRKL170101), and the Innovation Project of GUET Graduate Education (2017YJCX27).
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Feng, J., Chen, H. Healing Coverage Holes for Big Data Collection in Large-Scale Wireless Sensor Networks. Mobile Netw Appl 24, 1975–1984 (2019). https://doi.org/10.1007/s11036-019-01334-3
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DOI: https://doi.org/10.1007/s11036-019-01334-3