Top

Wireless Networks

Published in:

01-11-2019

Design and evaluation of an LQI-based beaconless routing protocol for a heterogeneous MSN

Authors: Muhammad Taufiq Nuruzzaman, Huei-Wen Ferng

Published in: Wireless Networks | Issue 1/2020

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

search-config

AI-assisted search

Off

Abstract

In a mobile sensor network with a mobile sink, choosing the next hop depends on the current location of the sink. This necessitates a frequent update of routing paths within the network. In this paper, a link quality indicator (LQI) measured by a sensor when receiving a POLLING packet directly from the sink is employed to acquire the relative position of the sensor to the sink. By doing so, the sensor chooses the next hop with a higher LQI value (alternatively, closer to the mobile sink). Due to the heterogeneity of transmission power and for guaranteeing the reachability of the chosen next hop, an energy-efficient and reliable LQI-based beaconless routing (LQI-BLR) protocol is proposed in this paper. To avoid flooding REPOLLING packets, only the sensors with low LQI values are allowed to broadcast the REPOLLING packet to create a routing path for the sensors outside the transmission range of the sink. Through analytical and simulation approaches, the performance of LQI-BLR and the leader-based routing (LBR) Burgos et al. (Sensors 17(7):1587, 2017. https://doi.org/10.3390/s17071587) is compared. With extensive real-scenario simulations, we successfully show that LQI-BLR outperforms LBR Burgos et al. (Sensors 17(7):1587, 2017. https://doi.org/10.3390/s17071587) and the data-driven routing protocol (DDRP) Shi et al. (Int J Commun Syst 26(10):1341–1355, 2013. https://doi.org/10.3390/s17071587 in terms of packet delivery ratio, energy consumption, and packet delivery delay.

previous article Energy efficient scheduling in local area networks

next article Anti-scanning research for MPWFRFT communication signals

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

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"

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

Burgos, U., Amozarrain, U., Gómez-Calzado, C., & Lafuente, A. (2017). Routing in mobile wireless sensor networks: A leader-based approach. Sensors, 17(7), 1587. https://doi.org/10.3390/s17071587.CrossRef

Shi, L., Zhang, B., Mouftah, H. T., & Ma, J. (2013). DDRP: An efficient data-driven routing protocol for wireless sensor networks with mobile sinks. International Journal of Communication Systems, 26(10), 1341–1355. https://doi.org/10.1002/dac.2315.CrossRef

Hu, X., Bao, M., Zhang, X.P., Wen, S., Li, X., & Hu, Y.H., Quantized kalman filter tracking in directional sensor networks. IEEE Transactions on Mobile Computing (in press). https://doi.org/10.1109/TMC.2017.2742948.CrossRef

Mahboubi, H., Masoudimansour, W., Aghdam, A. G., & Sayrafian-Pour, K. (2017). An energy-efficient target-tracking strategy for mobile sensor networks. IEEE Transactions on Cybernetics, 47(2), 511–523. https://doi.org/10.1109/TCYB.2016.2519939.CrossRefMATH

Dominguez-Morales, J. P., Rios-Navarro, A., Dominguez-Morales, M., Tapiador-Morales, R., Gutierrez-Galan, D., Cascado-Caballero, D., et al. (2016). Wireless sensor network for wildlife tracking and behavior classification of animals in Donana. IEEE Communications Letters, 20(12), 2534–2537. https://doi.org/10.1109/LCOMM.2016.2612652.CrossRef

Gupta, H. P., Venkatesh, T., Rao, S. V., Dutta, T., & Iyer, R. R. (2017). Analysis of coverage under border effects in three-dimensional mobile sensor networks. IEEE Transactions on Mobile Computing, 16(9), 2436–2449. https://doi.org/10.1109/TMC.2016.2636832.CrossRef

Le, D. V., Oh, H., & Yoon, S. (2016). Environment learning-based coverage maximization with connectivity constraints in mobile sensor networks. IEEE Sensors Journal, 16(10), 3958–3971. https://doi.org/10.1109/JSEN.2016.2537840.CrossRef

Tunca, C., Isik, S., Donmez, M., & Ersoy, C. (2014). Distributed mobile sink routing for wireless sensor networks: A survey. IEEE Communications Surveys Tutorials, 16(2), 877–897. https://doi.org/10.1109/SURV.2013.100113.00293.CrossRef

Yu, S., Zhang, B., Li, C., & Mouftah, H. (2014). Routing protocols for wireless sensor networks with mobile sinks: A survey. IEEE Communications Magazine, 52(7), 150–157. https://doi.org/10.1109/MCOM.2014.6852097.CrossRef

10.

Yun, Y., & Xia, Y. (2010). Maximizing the lifetime of wireless sensor networks with mobile sink in delay-tolerant applications. IEEE Transactions on Mobile Computing, 9(9), 1308–1318. https://doi.org/10.1109/TMC.2010.76.CrossRef

11.

Yun, Y., Xia, Y., Behdani, B., & Smith, J. C. (2013). Distributed algorithm for lifetime maximization in a delay-tolerant wireless sensor network with a mobile sink. IEEE Transactions on Mobile Computing, 12(10), 1920–1930. https://doi.org/10.1109/TMC.2012.152.CrossRef

12.

Jiang, D., Huo, L., Lv, Z., Song, H., & Qin, W. (2018). A joint multi-criteria utility-based network selection approach for vehicle-to-infrastructure networking. IEEE Transactions on Intelligent Transportation Systems, 19(10), 3305–3319. https://doi.org/10.1109/TITS.2017.2778939.CrossRef

13.

Moteiv Corporation: TMote Sky: Ultra low power ieee 802.15.4 compliant wireless sensor module (2006). http://www.snm.ethz.ch/Projects/TmoteSky.

14.

Texas Instruments: CC2538 Powerful Wireless Microcontroller System-On-Chip for 2.4-GHz IEEE 802.15.4,6LoWPAN, and ZigBee\(\textregistered \) Applications (2015). http://www.ti.com/lit/ds/symlink/cc2538.pdf.

15.

Texas Instruments: CC2640 SimpleLink\(^{{tm}}\) Bluetooth\(\textregistered \) Wireless MCU (2016). http://www.ti.com/lit/ds/symlink/cc2640.pdf.

16.

Jiang, D., Zhang, P., Lv, Z., & Song, H. (2016). Energy-efficient multi-constraint routing algorithm with load balancing for smart city applications. IEEE Internet of Things Journal, 3(6), 1437–1447. https://doi.org/10.1109/JIOT.2016.2613111.CrossRef

17.

Moussaoui, A., & Boukeream, A. (2015). A survey of routing protocols based on link-stability in mobile ad hoc networks. Journal of Network and Computer Applications, 47, 1–10. https://doi.org/10.1016/j.jnca.2014.09.007.CrossRef

18.

Noura, M., Atiquzzaman, M., & Gaedke, M. (2019). Interoperability in Internet of Things: Taxonomies and open challenges. Mobile Networks and Applications, 24(3), 796–809. https://doi.org/10.1007/s11036-018-1089-9.CrossRef

19.

Nguyen, L. T., Defago, X., Beuran, R., & Shinoda, Y. (2008) An energy efficient routing scheme for mobile wireless sensor networks. In Proceedings of the IEEE international symposium on wireless communication systems (pp. 568–572). https://doi.org/10.1109/ISWCS.2008.4726120.

20.

Kumar, G. S., Vinu, P. M. V., & Jacob, K. P. (2008). Mobility metric based LEACH-Mobile protocol. In Proceedings of the international conference on advanced computing and communications (pp. 248–253). https://doi.org/10.1109/ADCOM.2008.4760456.

21.

Carroll, A., & Heiser, G. (2010) An analysis of power consumption in a smartphone. In Proceedings of the USENIX conference on USENIX annual technical conference (pp. 21–21).

22.

PackStatus: GPS tracking and sensoring devices (2019). https://www.packstatus.com/gps-tracking-sensoring-devices/.

23.

Conti, M., & Giordano, S. (2014). Mobile ad hoc networking: Milestones, challenges, and new research directions. IEEE Communications Magazine, 52(1), 85–96. https://doi.org/10.1109/MCOM.2014.6710069.CrossRef

24.

Alexander, R., Brandt, A., Vasseur, J., Hui, J., Pister, K., Thubert, P., Levis, P., Struik, R., Kelsey, R., & Winter, T. (2012). RPL: IPv6 routing protocol for low-power and lossy networks. RFC 6550. https://doi.org/10.17487/RFC6550. https://rfc-editor.org/rfc/rfc6550.txt.

25.

Medjek, F., Tandjaoui, D., Romdhani, I., & Djedjig, N. (2017) Performance evaluation of RPL protocol under mobile sybil attacks. In Proceedings of the IEEE international conference on trust, security and privacy in computing and communications (pp. 1049–1055). https://doi.org/10.1109/Trustcom/BigDataSE/ICESS.2017.351.

26.

Wadhaj, I., Kristof, I., Romdhani, I., & Al-Dubai, A. (2015). Performance evaluation of the RPL protocol in fixed and mobile sink low-power and lossy-networks. In Proceedings of the IEEE international conference on ubiquitous computing and communications (pp. 1600–1605). https://doi.org/10.1109/CIT/IUCC/DASC/PICOM.2015.241.

27.

Sara, G. S., & Sridharan, D. (2014). Routing in mobile wireless sensor network: A survey. Telecommunication Systems, 57(1), 51–79. https://doi.org/10.1007/s11235-013-9766-2.CrossRef

28.

Nuruzzaman, M. T., & Ferng, H. W. (2016) A low energy consumption routing protocol for mobile sensor networks with a path-constrained mobile sink. In Proceedings of the IEEE international conference on communications (ICC) (pp. 1–6). https://doi.org/10.1109/ICC.2016.7511316.

29.

Borsani, L., Guglielmi, S., Redondi, A., & Cesana, M. (2011) Tree-based routing protocol for mobile wireless sensor networks. In Proceedings of the international conference on wireless on-demand network systems and services (pp. 164–170). https://doi.org/10.1109/WONS.2011.5720188.

30.

Lin, T. Y., Santoso, H. A., Wu, K. R., & Wang, G. L. (2017). Enhanced deployment algorithms for heterogeneous directional mobile sensors in a bounded monitoring area. IEEE Transactions on Mobile Computing, 16(3), 744–758. https://doi.org/10.1109/TMC.2016.2563435.CrossRef

31.

Cakici, S., Erturk, I., Atmaca, S., & Karahan, A. (2014). A novel cross-layer routing protocol for increasing packet transfer reliability in mobile sensor networks. Wireless Personal Communications, 77(3), 2235–2254. https://doi.org/10.1007/s11277-014-1635-0.CrossRef

32.

Chang, T. J., Wang, K., & Hsieh, Y. L. (2008). A color-theory-based energy efficient routing algorithm for mobile wireless sensor networks. Computer Networks, 52(3), 531–541. https://doi.org/10.1016/j.comnet.2007.10.004.CrossRefMATH

33.

Choi, L., Jung, J., Cho, B., & Choi, H. (2008). M-Geocast: Robust and energy-efficient geometric routing for mobile sensor networks. In Proceedings of the IFIP international workshop on software technologies for embedded and ubiquitous systems (pp. 304–316). https://doi.org/10.1109/MOBHOC.2007.4428612.

34.

Huo, G., & Wang, X. (2008) An opportunistic routing for mobile wireless sensor networks based on RSSI. In Proceedings of the international conference on wireless communications, networking and mobile computing (pp. 1–4). https://doi.org/10.1109/WiCom.2008.955.

35.

Karp, B., & Kung, H.T. (2000) GPSR: Greedy perimeter stateless routing for wireless networks. In Proceedings of the international conference on mobile computing and networking (MobiCom), MobiCom ’00 (pp. 243–254). ACM, New York, NY, USA. https://doi.org/10.1145/345910.345953.

36.

Komai, Y., Sasaki, Y., Hara, T., & Nishio, S. (2014). KNN query processing methods in mobile ad hoc networks. IEEE Transactions on Mobile Computing, 13(5), 1090–1103. https://doi.org/10.1109/TMC.2013.133.CrossRef

37.

Ruhrup, S., & Stojmenovic, I. (2013). Optimizing communication overhead while reducing path length in beaconless georouting with guaranteed delivery for wireless sensor networks. IEEE Transactions on Computers, 62(12), 2440–2453. https://doi.org/10.1109/TC.2012.148.MathSciNetCrossRefMATH

38.

Keally, M., Zhou, G., & Xing, G. (2009) Sidewinder: A predictive data forwarding protocol for mobile wireless sensor networks. In Proceedings of the IEEE conference on sensor, mesh and ad hoc communications and networks (pp. 1–9). https://doi.org/10.1109/SAHCN.2009.5168972.

39.

Goto, K., Sasaki, Y., Hara, T., & Nishio, S. (2013). Data gathering using mobile agents for reducing traffic in dense mobile wireless sensor networks. Mobile Information Systems, 9(4), 295–314. https://doi.org/10.3233/MIS-130164.CrossRef

40.

Khalid, S., Masood, A., Hussain, F. B., Abbas, H., & Ghafoor, A. (2014) Load balanced routing for lifetime maximization in mobile wireless sensor networks. International Journal of Distributed Sensor Networks. https://doi.org/10.1155/2014/979086.CrossRef

41.

Huang, X., Zhai, H., & Fang, Y. (2008). Robust cooperative routing protocol in mobile wireless sensor networks. IEEE Transactions on Wireless Communications, 7(12), 5278–5285. https://doi.org/10.1109/T-WC.2008.060680.CrossRef

42.

Hayes, T., & Ali, F. (2015). Proactive highly ambulatory sensor routing (PHASeR) protocol for mobile wireless sensor networks. Pervasive and Mobile Computing, 21, 47–61. https://doi.org/10.1016/j.pmcj.2015.04.005.CrossRef

43.

Hayes, T., & Ali, F. (2016). Robust ad-hoc sensor routing (RASeR) protocol for mobile wireless sensor networks. Ad Hoc Networks, 50, 128–144. https://doi.org/10.1016/j.adhoc.2016.07.013.CrossRef

44.

Raju, M., Oliveira, T., & Agrawal, D. P. (2012) A practical distance estimator through distributed RSSI/LQI processing: An experimental study. In Proceedings of the IEEE international conference on communications (ICC) (pp. 6575–6579). https://doi.org/10.1109/ICC.2012.6364794.

45.

Xiang, Y., Li, J., & Wang, W. (2013) Research on distance measurement based on LQI. In Procedings of the international conference on communications, signal processing, and systems (pp. 1159–1171). https://doi.org/10.1007/978-3-319-00536-2_132.

46.

Wang, Y. C. (2014). Mobile sensor networks. ACM Computing Surveys, 47(1), 1–36. https://doi.org/10.1145/2617662.CrossRef

47.

IEEE: Wireless medium access control (MAC) and physical layer (PHY) specifications for low-rate wireless personal area networks (WPANs) (2006). https://standards.ieee.org/standard/802_15_4-2006.html.

48.

De, P., Liu, Y., & Das, S. K. (2010). Energy-efficient reprogramming of a swarm of mobile sensors. IEEE Transactions on Mobile Computing, 9(5), 703–718. https://doi.org/10.1109/TMC.2009.159.CrossRef

49.

Alliance, Z. (2012). Zigbee specification. http://www.zigbee.org/wp-content/uploads/2014/11/docs-05-3474-20-0csg-zigbee-specification.pdf.

50.

Braun, T., Heissenbüttel, M., & Roth, T. (2010). Performance of the beacon-less routing protocol in realistic scenarios. Ad Hoc Networks, 8(1), 96–107. https://doi.org/10.1016/j.adhoc.2009.04.014.CrossRef

51.

Noureddine, H., Ni, Q., & Al-Raweshidy, H. (2010) SS-CBF: Sender-based suppression algorithm for contention-based forwarding in mobile ad-hoc networks. In Proceedings of the IEEE international symposium on personal, indoor and mobile radio communications (PIMRC) (pp. 1810–1813). https://doi.org/10.1109/PIMRC.2010.5671638.

52.

Sinha, A., & Chandrakasan, A. (2001). Dynamic power management in wireless sensor networks. IEEE Design Test of Computers, 18(2), 62–74. https://doi.org/10.1109/54.914626.CrossRef

53.

Fallahi, A., & Hossain, E. (2007). Qos provisioning in wireless video sensor networks: a dynamic power management framework. IEEE Wireless Communications, 14(6), 40–49. https://doi.org/10.1109/MWC.2007.4407226.CrossRef

54.

Dargie, W. (2012). Dynamic power management in wireless sensor networks: State-of-the-art. IEEE Sensors Journal, 12(5), 1518–1528. https://doi.org/10.1109/JSEN.2011.2174149.CrossRef

55.

Sausen, P. S., de Brito Sousa, J. R., Spohn, M. A., Perkusich, A., & Lima, A. M. N. (2008). Dynamic power management with scheduled switching modes. Computer Communications, 31(15), 3625–3637. https://doi.org/10.1016/j.comcom.2008.06.019.CrossRef

56.

Salvadori, F., de Campos, M., Sausen, P. S., de Camargo, R. F., Gehrke, C., Rech, C., et al. (2009). Monitoring in industrial systems using wireless sensor network with dynamic power management. IEEE Transactions on Instrumentation and Measurement, 58(9), 3104–3111. https://doi.org/10.1109/TIM.2009.2016882.CrossRef

57.

Hsu, R. C., Liu, C., & Wang, H. (2014). A reinforcement learning-based tod provisioning dynamic power management for sustainable operation of energy harvesting wireless sensor node. IEEE Transactions on Emerging Topics in Computing, 2(2), 181–191. https://doi.org/10.1109/TETC.2014.2316518.CrossRef

58.

Chen, X., Ma, M., & Liu, A. (2018). Dynamic power management and adaptive packet size selection for iot in e-healthcare. Computers and Electrical Engineering, 65, 357–375. https://doi.org/10.1016/j.compeleceng.2017.06.010.CrossRef

59.

Yoo, H., Shim, M., & Kim, D. (2012). Dynamic duty-cycle scheduling schemes for energy-harvesting wireless sensor networks. IEEE Communications Letters, 16(2), 202–204. https://doi.org/10.1109/LCOMM.2011.120211.111501.CrossRef

60.

Zhang, J., Li, Z., & Tang, S. (2016). Value of information aware opportunistic duty cycling in solar harvesting sensor networks. IEEE Transactions on Industrial Informatics, 12(1), 348–360. https://doi.org/10.1109/TII.2015.2508745.CrossRef

61.

Sharma, H., Haque, A., & Jaffery, Z. A. (2019). Maximization of wireless sensor network lifetime using solar energy harvesting for smart agriculture monitoring. Ad Hoc Networks, 94, 101966. https://doi.org/10.1016/j.adhoc.2019.101966.CrossRef

62.

Lee, S. H., & Choi, L. (2015). Speed-mac: speedy and energy efficient data delivery mac protocol for real-time sensor network applications. Wireless Networks, 21(3), 883–898. https://doi.org/10.1007/s11276-014-0827-6.CrossRef

63.

Subramanian, A. K., & Paramasivam, I. (2017). Prin: A priority-based energy efficient mac protocol for wireless sensor networks varying the sample inter-arrival time. Wireless Personal Communications, 92(3), 863–881. https://doi.org/10.1007/s11277-016-3581-5.CrossRef

64.

Ryoo, I., Sun, K., Lee, J., & Kim, S. (2018). A 3-dimensional group management mac scheme for mobile iot devices in wireless sensor networks. Journal of Ambient Intelligence and Humanized Computing, 9(4), 1223–1234. https://doi.org/10.1007/s12652-017-0557-6.CrossRef

65.

Wong, Y. S., Chen, Y. S., Deng, D. J., & Huang, D. C. (2013). Nonpreemptive priority scheme for the s-mac protocol in multimedia mobile sensor networks. Telecommunication Systems, 52(4), 2533–2540. https://doi.org/10.1007/s11235-011-9571-8.CrossRef

66.

Yang, X., Wang, L., Su, J., & Gong, Y. (2018). Hybrid mac protocol design for mobile wireless sensors networks. IEEE Sensors Letters, 2(2), 1–4. https://doi.org/10.1109/LSENS.2018.2828339.CrossRef

67.

Armaghani, F. R., Jamuar, S. S., Khatun, S., & Rasid, M. F. A. (2011). Performance analysis of tcp with delayed acknowledgments in multi-hop ad-hoc networks. Wireless Personal Communications, 56(4), 791–811. https://doi.org/10.1007/s11277-009-9848-3.CrossRef

68.

Al-Jubari, A. M., Othman, M., Mohd Ali, B., & Abdul Hamid, N. A. W. (2013). An adaptive delayed acknowledgment strategy to improve tcp performance in multi-hop wireless networks. Wireless Personal Communications, 69(1), 307–333. https://doi.org/10.1007/s11277-012-0575-9.CrossRef

69.

NSNAM: ns-3 (2016). https://www.nsnam.org/ns-3-25/.

70.

Jiang, D., Huo, L., & Li, Y. (2018). Fine-granularity inference and estimations to network traffic for SDN. Plos One, 13(5), 1–23. https://doi.org/10.1371/journal.pone.0194302.CrossRef

71.

Jiang, D., Nie, L., Lv, Z., & Song, H. (2016). Spatio-temporal kronecker compressive sensing for traffic matrix recovery. IEEE Access, 4(5), 3046–3053. https://doi.org/10.1109/ACCESS.2016.2573264.CrossRef

72.

Jiang, D., Wang, W., Shi, L., & Song, H.: A compressive sensing-based approach to end-to-end network traffic reconstruction. IEEE Transactions on Network Science and Engineering (in press). https://doi.org/10.1109/TNSE.2018.2877597.

73.

Jiang, D., Xu, Z., Chen, Z., Han, Y., & Xu, H. (2011). Joint time-frequency sparse estimation of large-scale network traffic. Computer Networks, 55(15), 3533–3547. https://doi.org/10.1016/j.comnet.2011.06.027.CrossRef

Title: Design and evaluation of an LQI-based beaconless routing protocol for a heterogeneous MSN
Authors: Muhammad Taufiq Nuruzzaman
Huei-Wen Ferng
Publication date: 01-11-2019
Publisher: Springer US
Published in: Wireless Networks / Issue 1/2020
Print ISSN: 1022-0038
Electronic ISSN: 1572-8196
DOI: https://doi.org/10.1007/s11276-019-02177-2

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+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Other articles of this Issue 1/2020

FTR: features tree based routing in mobile social networks

Efficient receiver-based flooding in mobile ad hoc networks

Mono-bit millimeter-wave channel estimation: Bayesian and adaptive quantization frameworks

Energy efficient scheduling in local area networks

A Time Quanta Bit coding method

A Bayesian game model and network availability model for small cells under denial of service (DoS) attack in 5G wireless communication network