Abstract
In this work we will develop an extension of one of existing routing algorithm in wireless sensor network. This new adaptation will permit the sensor node to save more energy and transmit images in wireless mode. This situation will be strategic and helpful especially in disaster scenario, where groups of rescuers must be on site to accomplish emergency tasks; therefore it’s very important and necessary to establish a wireless communication in real time between individuals or groups. The nature of wireless video sensor network makes it suitable to be used in the context of emergencies because introducing a video give more information in precise time and this is very advantageous when the existing infrastructure is down or severely overloaded. In emergencies the network topology may change rapidly and randomly. The increasing mobility of terminals makes them progressively dependent on their autonomy from the power source. This is illustrated by introducing many mobility models and using many scenarios of mobility in emergency situation, where image transmission via sensor node is used. Low complexity algorithm in image processing in order to reduce time transfer of selected data by this way allows saving energy. Efficiency in emergency scenario is the main objective of this work, achieved by the combination of three strategies: low-power mode algorithm, a power-aware routing strategy and compression technique in image processing used in sensor node. A selected set of simulations studies and real test bed on sensor node platform (Telos-B) indicate a reduction in energy consumption and a significant increase in node lifetime whereas network performance is not affected significantly. This is the big interest of our work in emergency situation, by increasing life time of node, individual can communicate longer and give more chance to rescuers to find them.
Similar content being viewed by others
References
Sakar, S. K., Basavaraju, T. G., & Puttamadappa, C. (2008). Principles protocols, and applications, ad hoc mobile wireless networks. London: Taylor & Francis Group.
Wan, Z., Xiong, N., & Yang, L. T. (2015). Cross-layer video transmission over IEEE 802.11e multihop networks. Multimedia Tools and Applications, 74(1), 5–23.
Perkins, C. E., & Royer, E. M. (1999). Ad-hoc on-demand distance vector routing. In Proceeding of IEEE Workshop on Mobile Computing Systems and Applications (WMCSA) (pp.1–11).
Ramrekha, A., & Talooki, V. C. (2010). Energy efficient and scalable routing protocol for extreme emergency ad hoc communications. Mobile Networks and Applications, 17(2), 312–324. doi:10.1007/s11036-011-0336-0.
Jiang, M., Li, J., & Tay, Y. (1999). Cluster based routing protocol (CBRP) functional specication, internet draft. MANET working group.
Han, K., et al. (2013). Algorithm design for data communications in duty-cycled wireless sensor networks: A survey. IEEE Communications Magazine, 51(7), 107–113.
Li, M., et al. (2013). A survey on topology control in wireless sensor networks: Taxonomy, comparative study, and open issues. Proceedings of the IEEE, 101(12), 2538–2557.
Xiang, L., et al. (2011). Compressed data aggregation for energy efficient wireless sensor networks. In SECON-IEEE communication society (pp. 46–54).
Chilamkurti, N., et al. (2009). Cross-layer support for energy efficient routing in wireless sensor networks. Journal of Sensors, 2009, 134165. http://dx.doi.org/10.1155/2009/134165.
Cheng, H., et al. (2012). Nodes organization for channel assignment with topology preservation in multi-radio wireless mesh networks. Ad Hoc Networks, 10(5), 760–773.
Zeng, Y., et al. (2013). Directional routing and scheduling for green vehicular delay tolerant networks. Wireless Networks, 19(2), 161–173.
Awwad, S. A. B., Ng, C. K., Noordin, N. K., & Rasid, M. F. A. (2011). Cluster based routing protocol for mobile nodes wireless sensor network. Wireless Personal Communications, 61, 251–281.
Ramrekha, T. A., & Politis, C. (2009). An adaptive QoS routing solution for Manet based multimedia communications in emergency cases. In First international ICST conference, MOBILIGHT (pp. 74–84).
Panaousis, E. A., Ramrekha, T. A., Millar, G. P., & Politis, C. (2010). Adaptive and secure routing protocol for emergency mobile ad hoc networks. International Journal of Wireless and Mobile Networks, 2(2), 62–78.
Ramrekha, T. A., Talooki, V. N., Rodriguez, J., & Politis, C. (2011). Energy efficient and scalable routing protocol for extreme emergency ad hoc communications. Berlin: Springer Science+Business Media LLC.
Ramrekha, T. A., Millar, G. P., Politis, C. (2011). A model for designing scalable and efficient adaptive routing approaches in emergency adhoc communications. In IEEE Symposium on computers and communications(ISCC) (pp. 916–923).
Cucurull, J., Asplund, M., & Nadjm-Tehrani, S. (2012). Anomaly detection and mitigation for disaster area networks. In 13th International Symposium on recent advances in intrusion detection, MONET (Vol. 17, No 2, pp. 312–324).
Vergara, E. J., Nadjm-Tehrani, S., Asplund et, M., & Zurutuza, U. (2011). Resource footprint of a manycast protocol implementation on multiple mobile platforms. In The fifth international conference on next generation mobile applications, services and technologies (NGMAST), IEEE (pp. 154–160). doi:10.1109/NGMAST.2011.36.
Ferrigno, L., Marano, S., Paciello, V., & Pietrosanto, A. (2005). Balancing computational and transmission power consumption in wireless image sensor networks. In IEEE29 international conference on virtual environments, human computer interfaces, and measures systems (VECIMS), VECIMS (p. 6).
Wu, H., & Abouzeid, A. A. (2004). Energy efficient distributed JPEG2000 image compression in multihop wireless networks. In 4th workshop on applications and services in wireless networks (pp. 152–160).
Wagner, R., Nowak, R., & Baraniuk, R. (2003). Distributed image compression for sensor networks using correspondence analysis and super-resolution. Proceedings of IEEE International Conference on Image Processing (ICIP), 1, 597–600.
Misra, S., Reisslein, M., & Xue, G. (2008). A survey of multimedia streaming in wireless sensor networks. IEEE Communications Surveys and Tutorials, 10(4), 18–39.
Sarisaray-Boluk, P. (2013). Performance comparisons of the image quality evaluation techniques in Wireless Multimedia Sensor Networks. Wireless Networks, 19, 443–460.
Boluk, P. S., & Baydere, S. (2011). Robust image transmission over wireless sensor networks. Mobile Networks and Applications, 16, 149–170.
Wan, Z., Xiong, N., Ghani, N., Vasilakos, A. V., & Zhou, L. (2014). Adaptive unequal protection for wireless video transmission over IEEE 802.11e networks. Multimedia Tools and Applications, 2014, 72(1), 541–571.
Wei, G., et al. (2011). Prediction-based data aggregation in wireless sensor networks: Combining grey model and Kalman Filter. Computer Communications, 34(6), 793–802.
Liu, XY, Zhu, Y, Kong, L, Liu, C, Gu, Y., Vasilakos, A. V., & Wu MY. CDC: Compressive data collection for wireless sensor networks. IEEE Transactions on Parallel and Distributed Systems. doi:10.1109/TPDS.2014.2345257.
Li, P., et al. (2012). CodePipe: An opportunistic feeding and routing protocol for reliable multicast with pipelined network coding. In INFOCOM 2012 (pp. 100–108).
Li, P., et al. (2014). Reliable multicast with pipelined network coding using opportunistic feeding and routing. IEEE Transactions on Parallel and Distributed Systems, 25(12), 3264–3273.
Yen, Y.-S., et al. (2011). Flooding-limited and multi-constrained QoS multicast routing based on the genetic algorithm for MANETs. Mathematical and Computer Modelling, 53(11–12), 2238–2250.
Yao, Y., et al. (2013). EDAL: An energy-efficient, delay-aware, and lifetime-balancing data collection protocol for wireless sensor networks. In MASS 2013 (pp. 182–190).
Yao, Y., et al. (2014). EDAL: An energy-efficient, delay-aware, and lifetime-balancing data collection protocol for heterogeneous wireless sensor networks. In IEEE/ACM Transactions on Networking. doi:10.1109/TNET.2014.2306592.
Meng, T., et al. (2015). Spatial reusability-aware routing in multi-hop wireless networks. In IEEE TMC. doi:10.1109/TC.2015.2417543.
Song, Y., et al. (2014). A biology-based algorithm to minimal exposure problem of wireless sensor networks. IEEE Transactions on Network and Service Management, 11(3), 417–430.
Liu, L., et al. (2015). Physarum optimization: A biology-inspired algorithm for the Steiner tree problem in networks. IEEE Transactions on Computers, 64(3), 819–832.
Zhang, E., & Cai, W. (2010). Vision mesh: A novel video sensor networks platform for water conservancy engineering. In Proceedings IEEE
Aschenbruck, N., Frank, M., Martini, P., & Tlle, J. (2004) Human mobility in MANET disaster area simulation: A realistic approach. In 29th annual IEEE international conference on local computer network (LCN04) (pp. 668–675).
Aschenbruck, N., Ernst, R., Gerhards-Padilla, E., Schwamborn, M. (2010). Bonnmotion: A mobility scenario generation and analysis tool. In Simutool (p. 51).
Achenbruck, N., Gerhaps-Padilla, E., Gerharz, M., Frank, M., & Martini, P. (2007). Modeling mobility in disaster area scenarios. In The 10-th ACM international Symposium on modeling, analysis and simulation of wireless and mobile systems.
Feeney, L. M. (2011). Energy effcient communication in ad hoc wireless networks. In Computer and network architectures laboratory, SE 164 29, Sweden.
Hong, X., Gerla, M., Pei, G. & Chiang, C. (1999). A group mobility model for ad hoc wireless networks. In Proceedings of the 2nd ACM international workshop on modeling, analysis and simulation of wireless and mobile systems (MSWiM) (pp. 53–60). Seattle, WA, USA.
Zeng, Y., et al. (2013). Real-time data report and task execution in wireless sensor and actuator networks using self-aware mobile actuators. Computer Communications, 36(9), 988–997.
Sanchez, M., & Manzoni, P. (2001). Anejos: A Java based simulator for ad-hoc networks. Future Generation Computer System, 17(5), 573–583.
Sum, F. W., Gerla, M. (1999). IPv6 flow handoff in ad-hoc wireless networks using mobility prediction. In Proceedings of IEEE GLOBECOM (pp. 271–275)
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Tahar Abbes, M., Mohamed, B. & Mohamed, S. Adaptation of a routing algorithm in wireless video sensor network for disaster scenarios using JPEG 2000. Wireless Netw 22, 453–465 (2016). https://doi.org/10.1007/s11276-015-0979-z
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11276-015-0979-z