Nội dung được dịch bởi AI, chỉ mang tính chất tham khảo
Thiết kế và đánh giá một giao thức định tuyến không phát sóng dựa trên LQI cho mạng cảm biến di động không đồng nhất
Tóm tắt
Trong một mạng cảm biến di động với điểm đích di động, việc chọn bước nhảy tiếp theo phụ thuộc vào vị trí hiện tại của điểm đích. Điều này đòi hỏi việc cập nhật thường xuyên các đường dẫn định tuyến trong mạng.
Trong bài báo này, chỉ số chất lượng liên kết (LQI) được đo bởi cảm biến khi nhận một gói POLLING trực tiếp từ điểm đích được sử dụng để xác định vị trí tương đối của cảm biến đối với điểm đích. Bằng cách này, cảm biến chọn bước nhảy tiếp theo với giá trị LQI cao hơn (hoặc nói cách khác, gần hơn với điểm đích di động). Do tính không đồng nhất của công suất phát, và để đảm bảo khả năng tiếp cận của bước nhảy tiếp theo đã chọn, một giao thức định tuyến không phát sóng dựa trên LQI tiết kiệm năng lượng và đáng tin cậy (LQI-BLR) được đề xuất trong bài báo này. Để tránh việc tràn ngập các gói REPOLLING, chỉ cho những cảm biến có giá trị LQI thấp được phép phát gói REPOLLING để tạo ra một đường dẫn định tuyến cho các cảm biến bên ngoài vùng phủ sóng của điểm đích. Thông qua các phương pháp phân tích và mô phỏng, hiệu suất của LQI-BLR và giao thức định tuyến dựa trên lãnh đạo (LBR) Burgos et al. (Sensors 17(7):1587, 2017. https://doi.org/10.3390/s17071587) được so sánh. Với các mô phỏng trong kịch bản thực tế rộng lớn, chúng tôi đã thành công trong việc chứng minh rằng LQI-BLR vượt trội hơn LBR Burgos et al. (Sensors 17(7):1587, 2017. https://doi.org/10.3390/s17071587) và giao thức định tuyến dựa trên dữ liệu (DDRP) Shi et al. (Int J Commun Syst 26(10):1341–1355, 2013. https://doi.org/10.3390/s17071587) về tỷ lệ giao gói, tiêu thụ năng lượng và độ trễ giao gói.
Từ khóa
#định tuyến không phát sóng #mạng cảm biến di động #chỉ số chất lượng liên kết #LQI #giao thức định tuyến tiết kiệm năng lượngTài liệu tham khảo
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Moteiv Corporation: TMote Sky: Ultra low power ieee 802.15.4 compliant wireless sensor module (2006). http://www.snm.ethz.ch/Projects/TmoteSky.
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.
Texas Instruments: CC2640 SimpleLink\(^{{tm}}\) Bluetooth\(\textregistered \) Wireless MCU (2016). http://www.ti.com/lit/ds/symlink/cc2640.pdf.
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.
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.
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.
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.
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.
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).
PackStatus: GPS tracking and sensoring devices (2019). https://www.packstatus.com/gps-tracking-sensoring-devices/.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Wang, Y. C. (2014). Mobile sensor networks. ACM Computing Surveys, 47(1), 1–36. https://doi.org/10.1145/2617662.
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.
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.
Alliance, Z. (2012). Zigbee specification. http://www.zigbee.org/wp-content/uploads/2014/11/docs-05-3474-20-0csg-zigbee-specification.pdf.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
NSNAM: ns-3 (2016). https://www.nsnam.org/ns-3-25/.
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.
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.
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.
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.