End to End Delay and Energy Consumption in a Two Tier Cluster Hierarchical Wireless Sensor Networks

[EN] In this work it is considered a circular Wireless Sensor Networks (WSN) in a planar structure with uniform distribution of the sensors and with a two-level hierarchical topology. At the lower level, a cluster configuration is adopted in which the sensed information is transferred from sensor nodes to a cluster head (CH) using a random access protocol (RAP). At CH level, CHs transfer information, hop-by-hop, ring-by-ring, towards to the sink located at the center of the sensed area using TDMA as MAC protocol. A Markovian model to evaluate the end-to-end (E2E) transfer delay is formulated. In addition to other results such as the well know energy hole problem, the model reveals that for a given radial distance between the CH and the sink, the transfer delay depends on the angular orientation between them. For instance, when two rings of CHs are deployed in the WSN area, the E2E delay of data packets generated at ring 2 and at the ¿west¿ side of the sink, is 20% higher than the corresponding E2E delay of data packets generated at ring 2 and at the ¿east¿ side of the sink. This asymmetry can be alleviated by rotating from time to time the allocation of temporary slots to CHs in the TDMA communication. Also, the energy consumption is evaluated and the numerical results show that for a WSN with a small coverage area, say a radio of 100 m, the energy saving is more significant when a small number of rings are deployed, perhaps none (a single cluster in which the sink acts as a CH). Conversely, topologies with a large number of rings, say 4 or 5, offer a better energy performance when the service WSN covers a large area, say radial distances greater than 400 m.The work of V. Casares-Giner (ITACA research institute) is partly supported by the Spanish national projects TIN2013-47272-C2-1-R and TEC2015-71932-REDT. The work of Tatiana Navas, Dolly Florez, and Tito R. Vargas H., and the collaboration between the two institutions, is supported by the Universidad Santo Tomas under Master Degree's research and academic projects.Casares-Giner, V.; Navas, TI.; Smith Flórez, D.; Vargas Hernández, TR. (2019). End to End Delay and Energy Consumption in a Two Tier Cluster Hierarchical Wireless Sensor Networks. Information. 10(4):1-29. https://doi.org/10.3390/info10040135S129104Sari, A. (2015). Two-Tier Hierarchical Cluster Based Topology in Wireless Sensor Networks for Contention Based Protocol Suite. International Journal of Communications, Network and System Sciences, 08(03), 29-42. doi:10.4236/ijcns.2015.83004Haibo Zhang, & Hong Shen. (2009). Balancing Energy Consumption to Maximize Network Lifetime in Data-Gathering Sensor Networks. IEEE Transactions on Parallel and Distributed Systems, 20(10), 1526-1539. doi:10.1109/tpds.2008.252Wieselthier, J. E., Ephremides, A., & Michaels, L. A. (1989). An exact analysis and performance evaluation of framed ALOHA with capture. IEEE Transactions on Communications, 37(2), 125-137. doi:10.1109/26.20080Liu, W., Zhao, D., & Zhu, G. (2012). End-to-end delay and packet drop rate performance for a wireless sensor network with a cluster-tree topology. Wireless Communications and Mobile Computing, 14(7), 729-744. doi:10.1002/wcm.2230Alabdulmohsin, I., Hyadi, A., Afify, L., & Shihada, B. (2014). End-to-end delay analysis in wireless sensor networks with service vacation. 2014 IEEE Wireless Communications and Networking Conference (WCNC). doi:10.1109/wcnc.2014.6952872Park, J., Lee, S., & Yoo, S. (2015). Time slot assignment for convergecast in wireless sensor networks. Journal of Parallel and Distributed Computing, 83, 70-82. doi:10.1016/j.jpdc.2015.05.004Yang, X., Wang, L., Xie, J., & Zhang, Z. (2018). Energy Efficiency TDMA/CSMA Hybrid Protocol with Power Control for WSN. Wireless Communications and Mobile Computing, 2018, 1-7. doi:10.1155/2018/4168354Sgora, A., Vergados, D. J., & Vergados, D. D. (2015). A Survey of TDMA Scheduling Schemes in Wireless Multihop Networks. ACM Computing Surveys, 47(3), 1-39. doi:10.1145/2677955Martin, E., Liu, L., Covington, M., Pesti, P., & Weber, M. (2010). Positioning Technologies in Location-Based Services. Location-Based Services Handbook, 1-45. doi:10.1201/9781420071986-c1PAL, A. (2010). Localization Algorithms in Wireless Sensor Networks: Current Approaches and Future Challenges. Network Protocols and Algorithms, 2(1). doi:10.5296/npa.v2i1.279Kusdaryono, A., & Lee, K.-O. (2011). A Clustering Protocol with Mode Selection for Wireless Sensor Network. Journal of Information Processing Systems, 7(1), 29-42. doi:10.3745/jips.2011.7.1.029Donald, V. H. M. (1979). Advanced Mobile Phone Service: The Cellular Concept. Bell System Technical Journal, 58(1), 15-41. doi:10.1002/j.1538-7305.1979.tb02209.xCasares-Giner, V., Wuchner, P., Pacheco-Paramo, D., & de Meer, H. (2012). Combined contention and TDMA-based communication in wireless sensor networks. Proceedings of the 8th Euro-NF Conference on Next Generation Internet NGI 2012. doi:10.1109/ngi.2012.6252158Ranganathan, P., & Nygard, K. (2010). Time Synchronization in Wireless Sensor Networks: A Survey. International Journal of UbiComp, 1(2), 92-102. doi:10.5121/iju.2010.1206Sahoo, A., & Chilukuri, S. (2010). DGRAM: A Delay Guaranteed Routing and MAC Protocol for Wireless Sensor Networks. IEEE Transactions on Mobile Computing, 9(10), 1407-1423. doi:10.1109/tmc.2010.107Wu, Y.-C., Chaudhari, Q., & Serpedin, E. (2011). Clock Synchronization of Wireless Sensor Networks. IEEE Signal Processing Magazine, 28(1), 124-138. doi:10.1109/msp.2010.938757Casares-Giner, V., Sempere-Payá, V., & Todolí-Ferrandis, D. (2014). Framed ALOHA Protocol with FIFO-Blocking and LIFO-Push out Discipline. Network Protocols and Algorithms, 6(3), 82. doi:10.5296/npa.v6i3.5557Tello-Oquendo, L., Pla, V., Leyva-Mayorga, I., Martinez-Bauset, J., Casares-Giner, V., & Guijarro, L. (2019). Efficient Random Access Channel Evaluation and Load Estimation in LTE-A With Massive MTC. IEEE Transactions on Vehicular Technology, 68(2), 1998-2002. doi:10.1109/tvt.2018.2885333Adan, I. J. B. F., van Leeuwaarden, J. S. H., & Winands, E. M. M. (2006). On the application of Rouché’s theorem in queueing theory. Operations Research Letters, 34(3), 355-360. doi:10.1016/j.orl.2005.05.012Casares-Giner, V., Martinez-Bauset, J., & Portillo, C. (2019). Performance evaluation of framed slotted ALOHA with reservation packets and succesive interference cancelation for M2M networks. Computer Networks, 155, 15-30. doi:10.1016/j.comnet.2019.02.02

MAC Protocol for Wireless Sensor Networks

Práce se zaměřuje na úvod do problematiky bezdrátových senzorových sítí a přibližuje čtenáři jednotlivé aspekty při řešení problémů v těchto sítích. Práce se blíže zaměřuje na energetickou náročnost při komunikaci mezi zařízeními v síti a popisuje příčiny nadměrné spotřeby energie. Také řeší mechanismy na úsporu energie a dále se zabývá dnešními protokoly, které svým navržením přispívají k řešení problematiky. Autor v práci dále navrhuje adaptovaný protokol, založený na získaných poznatcích z předchozích teoretických pramenů.The work focuses on introduction to the topic of wireless sensor networks and brings readers the various aspects of problem solving in these networks. Work is closely focused on the energy performance when communicating between devices on a network and discusses the causes of excessive energy consumption. It also addresses the mechanisms for saving energy and is also engaged today protocols that its contribution to proposing solutions to problems. The author had also proposes an adapted protocol, based on lessons learned from previous theoretical sources.

Optimized Cluster-Based Dynamic Energy-Aware Routing Protocol for Wireless Sensor Networks in Agriculture Precision

[EN] Wireless sensor networks (WSNs) are becoming one of the demanding platforms, where sensor nodes are sensing and monitoring the physical or environmental conditions and transmit the data to the base station via multihop routing. Agriculture sector also adopted these networks to promote innovations for environmental friendly farming methods, lower the management cost, and achieve scientific cultivation. Due to limited capabilities, the sensor nodes have suffered with energy issues and complex routing processes and lead to data transmission failure and delay in the sensor-based agriculture fields. Due to these limitations, the sensor nodes near the base station are always relaying on it and cause extra burden on base station or going into useless state. To address these issues, this study proposes a Gateway Clustering Energy-Efficient Centroid- (GCEEC-) based routing protocol where cluster head is selected from the centroid position and gateway nodes are selected from each cluster. Gateway node reduces the data load from cluster head nodes and forwards the data towards the base station. Simulation has performed to evaluate the proposed protocol with state-of-the-art protocols. The experimental results indicated the better performance of proposed protocol and provide more feasible WSN-based monitoring for temperature, humidity, and illumination in agriculture sector.This work has also been partially supported by the European Union through the ERANETMED (Euromediterranean Cooperation through ERANET joint activities and beyond) project ERANETMED3-227 SMARTWATIR.Qureshi, KN.; Bashir, MU.; Lloret, J.; León Fernández, A. (2020). Optimized Cluster-Based Dynamic Energy-Aware Routing Protocol for Wireless Sensor Networks in Agriculture Precision. Journal of Sensors. 2020:1-19. https://doi.org/10.1155/2020/9040395S1192020Sneha, K., Kamath, R., Balachandra, M., & Prabhu, S. (2019). New Gossiping Protocol for Routing Data in Sensor Networks for Precision Agriculture. Soft Computing and Signal Processing, 139-152. doi:10.1007/978-981-13-3393-4_15Qureshi, K. N., Abdullah, A. H., Bashir, F., Iqbal, S., & Awan, K. M. (2018). Cluster-based data dissemination, cluster head formation under sparse, and dense traffic conditions for vehicular ad hoc networks. International Journal of Communication Systems, 31(8), e3533. doi:10.1002/dac.3533Rault, T., Bouabdallah, A., & Challal, Y. (2014). Energy efficiency in wireless sensor networks: A top-down survey. Computer Networks, 67, 104-122. doi:10.1016/j.comnet.2014.03.027Feng, X., Zhang, J., Ren, C., & Guan, T. (2018). An Unequal Clustering Algorithm Concerned With Time-Delay for Internet of Things. IEEE Access, 6, 33895-33909. doi:10.1109/access.2018.2847036Savaglio, C., Pace, P., Aloi, G., Liotta, A., & Fortino, G. (2019). Lightweight Reinforcement Learning for Energy Efficient Communications in Wireless Sensor Networks. IEEE Access, 7, 29355-29364. doi:10.1109/access.2019.2902371Srbinovska, M., Gavrovski, C., Dimcev, V., Krkoleva, A., & Borozan, V. (2015). Environmental parameters monitoring in precision agriculture using wireless sensor networks. Journal of Cleaner Production, 88, 297-307. doi:10.1016/j.jclepro.2014.04.036Lloret, J., Garcia, M., Bri, D., & Diaz, J. (2009). A Cluster-Based Architecture to Structure the Topology of Parallel Wireless Sensor Networks. Sensors, 9(12), 10513-10544. doi:10.3390/s91210513Qureshi, K. N., Din, S., Jeon, G., & Piccialli, F. (2020). Link quality and energy utilization based preferable next hop selection routing for wireless body area networks. Computer Communications, 149, 382-392. doi:10.1016/j.comcom.2019.10.030Kumar, S. A., & Ilango, P. (2017). The Impact of Wireless Sensor Network in the Field of Precision Agriculture: A Review. Wireless Personal Communications, 98(1), 685-698. doi:10.1007/s11277-017-4890-zAnisi, M. H., Abdul-Salaam, G., & Abdullah, A. H. (2014). A survey of wireless sensor network approaches and their energy consumption for monitoring farm fields in precision agriculture. Precision Agriculture, 16(2), 216-238. doi:10.1007/s11119-014-9371-8Long, D. S., & McCallum, J. D. (2015). On-combine, multi-sensor data collection for post-harvest assessment of environmental stress in wheat. Precision Agriculture, 16(5), 492-504. doi:10.1007/s11119-015-9391-zFu, X., Fortino, G., Li, W., Pace, P., & Yang, Y. (2019). WSNs-assisted opportunistic network for low-latency message forwarding in sparse settings. Future Generation Computer Systems, 91, 223-237. doi:10.1016/j.future.2018.08.031Mehmood, A., Khan, S., Shams, B., & Lloret, J. (2013). Energy-efficient multi-level and distance-aware clustering mechanism for WSNs. International Journal of Communication Systems, 28(5), 972-989. doi:10.1002/dac.2720Pantazis, N. A., Nikolidakis, S. A., & Vergados, D. D. (2013). Energy-Efficient Routing Protocols in Wireless Sensor Networks: A Survey. IEEE Communications Surveys & Tutorials, 15(2), 551-591. doi:10.1109/surv.2012.062612.00084De Farias, C. M., Pirmez, L., Fortino, G., & Guerrieri, A. (2019). A multi-sensor data fusion technique using data correlations among multiple applications. Future Generation Computer Systems, 92, 109-118. doi:10.1016/j.future.2018.09.034Rao, P. C. S., Jana, P. K., & Banka, H. (2016). A particle swarm optimization based energy efficient cluster head selection algorithm for wireless sensor networks. Wireless Networks, 23(7), 2005-2020. doi:10.1007/s11276-016-1270-7Fu, X., Fortino, G., Pace, P., Aloi, G., & Li, W. (2020). Environment-fusion multipath routing protocol for wireless sensor networks. Information Fusion, 53, 4-19. doi:10.1016/j.inffus.2019.06.001Liu, X. (2015). Atypical Hierarchical Routing Protocols for Wireless Sensor Networks: A Review. IEEE Sensors Journal, 15(10), 5372-5383. doi:10.1109/jsen.2015.2445796Jan, N., Javaid, N., Javaid, Q., Alrajeh, N., Alam, M., Khan, Z. A., & Niaz, I. A. (2017). A Balanced Energy-Consuming and Hole-Alleviating Algorithm for Wireless Sensor Networks. IEEE Access, 5, 6134-6150. doi:10.1109/access.2017.2676004Gupta, G. P., Misra, M., & Garg, K. (2014). Energy and trust aware mobile agent migration protocol for data aggregation in wireless sensor networks. Journal of Network and Computer Applications, 41, 300-311. doi:10.1016/j.jnca.2014.01.003Safa, H., Karam, M., & Moussa, B. (2014). PHAODV: Power aware heterogeneous routing protocol for MANETs. Journal of Network and Computer Applications, 46, 60-71. doi:10.1016/j.jnca.2014.07.035Liu, X. (2015). An Optimal-Distance-Based Transmission Strategy for Lifetime Maximization of Wireless Sensor Networks. IEEE Sensors Journal, 15(6), 3484-3491. doi:10.1109/jsen.2014.2372340Brar, G. S., Rani, S., Chopra, V., Malhotra, R., Song, H., & Ahmed, S. H. (2016). Energy Efficient Direction-Based PDORP Routing Protocol for WSN. IEEE Access, 4, 3182-3194. doi:10.1109/access.2016.2576475Abo-Zahhad, M., Ahmed, S. M., Sabor, N., & Sasaki, S. (2015). Mobile Sink-Based Adaptive Immune Energy-Efficient Clustering Protocol for Improving the Lifetime and Stability Period of Wireless Sensor Networks. IEEE Sensors Journal, 15(8), 4576-4586. doi:10.1109/jsen.2015.2424296Huynh, T.-T., Dinh-Duc, A.-V., & Tran, C.-H. (2016). Delay-constrained energy-efficient cluster-based multi-hop routing in wireless sensor networks. Journal of Communications and Networks, 18(4), 580-588. doi:10.1109/jcn.2016.000081Shen, J., Wang, A., Wang, C., Hung, P. C. K., & Lai, C.-F. (2017). An Efficient Centroid-Based Routing Protocol for Energy Management in WSN-Assisted IoT. IEEE Access, 5, 18469-18479. doi:10.1109/access.2017.2749606Awan, K. M., Shah, P. A., Iqbal, K., Gillani, S., Ahmad, W., & Nam, Y. (2019). Underwater Wireless Sensor Networks: A Review of Recent Issues and Challenges. Wireless Communications and Mobile Computing, 2019, 1-20. doi:10.1155/2019/6470359Sajwan, M., Gosain, D., & Sharma, A. K. (2018). CAMP: cluster aided multi-path routing protocol for wireless sensor networks. Wireless Networks, 25(5), 2603-2620. doi:10.1007/s11276-018-1689-0Varga, A. (2010). OMNeT++. Modeling and Tools for Network Simulation, 35-59. doi:10.1007/978-3-642-12331-3_3Lartillot, O., Toiviainen, P., & Eerola, T. (2008). A Matlab Toolbox for Music Information Retrieval. Studies in Classification, Data Analysis, and Knowledge Organization, 261-268. doi:10.1007/978-3-540-78246-9_31Mathur, P., Nielsen, R. H., Prasad, N. R., & Prasad, R. (2016). Data collection using miniature aerial vehicles in wireless sensor networks. IET Wireless Sensor Systems, 6(1), 17-25. doi:10.1049/iet-wss.2014.0120Zou, T., Lin, S., Feng, Q., & Chen, Y. (2016). Energy-Efficient Control with Harvesting Predictions for Solar-Powered Wireless Sensor Networks. Sensors, 16(1), 53. doi:10.3390/s16010053Song, Y., Ma, J., Zhang, X., & Feng, Y. (2012). Design of Wireless Sensor Network-Based Greenhouse Environment Monitoring and Automatic Control System. Journal of Networks, 7(5). doi:10.4304/jnw.7.5.838-844Nikolidakis, S., Kandris, D., Vergados, D., & Douligeris, C. (2013). Energy Efficient Routing in Wireless Sensor Networks Through Balanced Clustering. Algorithms, 6(1), 29-42. doi:10.3390/a6010029Ndzi, D. L., Harun, A., Ramli, F. M., Kamarudin, M. L., Zakaria, A., Shakaff, A. Y. M., … Farook, R. S. (2014). Wireless sensor network coverage measurement and planning in mixed crop farming. Computers and Electronics in Agriculture, 105, 83-94. doi:10.1016/j.compag.2014.04.01

Modeling of Duty-Cycled MAC Protocols for Heterogeneous WSN with Priorities

[EN] Wireless Sensor Networks (WSN) have experienced an important revitalization, particularly with the arrival of Internet of Things applications. In a general sense, a WSN can be composed of different classes of nodes, having different characteristics or requirements (heterogeneity). Duty-cycling is a popular technique used in WSN, that allows nodes to sleep and wake up periodically in order to save energy. We believe that the modeling and performance evaluation of heterogeneous WSN with priorities operating in duty-cycling, being of capital importance for their correct design and successful deployment, have not been sufficiently explored. The present work presents a performance evaluation study of a WSN with these features. For a scenario with two classes of nodes composing the network, each with a different channel access priority, an approximate analytical model is developed with a pair of two-dimensional discrete-time Markov chains. Note that the same modeling approach can be used to analyze networks with a larger number of classes. Performance parameters such as average packet delay, throughput and average energy consumption are obtained. Analytical results are validated by simulation, showing accurate results. Furthermore, a new procedure to determine the energy consumption of nodes is proposed that significantly improves the accuracy of previous proposals. We provide quantitative evidence showing that the energy consumption accuracy improvement can be up to two orders of magnitudeThis work is part of the project PGC2018-094151-B-I00, which is financed by the Ministerio de Ciencia, Innovacion y Universidades (MCIU), Agencia Estatal de Investigacion (AEI) and Fondo Europeo de Desarrollo Regional (FEDER) (MCIU/AEI/FEDER.UE). C. Portillo acknowledges the funding received from the European Union under the program Erasmus Mundus Partnerships, project EuroinkaNet, GRANT AGREEMENT NUMBER -2014 -0870/001/001, and the support received from SEP-SES (DSA/103.5/15/6629)Portillo, C.; Martínez Bauset, J.; Pla, V.; Casares-Giner, V. (2020). Modeling of Duty-Cycled MAC Protocols for Heterogeneous WSN with Priorities. Electronics. 9(3):1-16. https://doi.org/10.3390/electronics9030467S11693Gomes, D. A., & Bianchini, D. (2016). Interconnecting Wireless Sensor Networks with the Internet Using Web Services. IEEE Latin America Transactions, 14(4), 1937-1942. doi:10.1109/tla.2016.7483537Libo, Z., Tian, H., & Chunyun, G. (2019). Wireless multimedia sensor network for rape disease detections. EURASIP Journal on Wireless Communications and Networking, 2019(1). doi:10.1186/s13638-019-1468-3Shi, X., An, X., Zhao, Q., Liu, H., Xia, L., Sun, X., & Guo, Y. (2019). State-of-the-Art Internet of Things in Protected Agriculture. Sensors, 19(8), 1833. doi:10.3390/s19081833Rajandekar, A., & Sikdar, B. (2015). A Survey of MAC Layer Issues and Protocols for Machine-to-Machine Communications. IEEE Internet of Things Journal, 2(2), 175-186. doi:10.1109/jiot.2015.2394438Dai, H.-N., Ng, K.-W., & Wu, M.-Y. (2013). On Busy-Tone Based MAC Protocol for Wireless Networks with Directional Antennas. Wireless Personal Communications, 73(3), 611-636. doi:10.1007/s11277-013-1206-9Padilla, P., Padilla, J. L., Valenzuela-Valdés, J. F., Serrán-González, J.-V., & López-Gordo, M. A. (2015). Performance Analysis of Different Link Layer Protocols in Wireless Sensor Networks (WSN). Wireless Personal Communications, 84(4), 3075-3089. doi:10.1007/s11277-015-2783-6Ye, W., Heidemann, J., & Estrin, D. (2004). Medium Access Control With Coordinated Adaptive Sleeping for Wireless Sensor Networks. IEEE/ACM Transactions on Networking, 12(3), 493-506. doi:10.1109/tnet.2004.828953Kuo, Y.-W., Li, C.-L., Jhang, J.-H., & Lin, S. (2018). Design of a Wireless Sensor Network-Based IoT Platform for Wide Area and Heterogeneous Applications. IEEE Sensors Journal, 18(12), 5187-5197. doi:10.1109/jsen.2018.2832664He, X., Liu, S., Yang, G., & Xiong, N. (2018). Achieving Efficient Data Collection in Heterogeneous Sensing WSNs. IEEE Access, 6, 63187-63199. doi:10.1109/access.2018.2876552Ortin, J., Cesana, M., Redondi, A. E. C., Canales, M., & Gallego, J. R. (2019). Analysis of Unslotted IEEE 802.15.4 Networks With Heterogeneous Traffic Classes. IEEE Wireless Communications Letters, 8(2), 380-383. doi:10.1109/lwc.2018.2873347Bianchi, G. (2000). Performance analysis of the IEEE 802.11 distributed coordination function. IEEE Journal on Selected Areas in Communications, 18(3), 535-547. doi:10.1109/49.840210Liu, R. P., Sutton, G. J., & Collings, I. B. (2010). A New Queueing Model for QoS Analysis of IEEE 802.11 DCF with Finite Buffer and Load. IEEE Transactions on Wireless Communications, 9(8), 2664-2675. doi:10.1109/twc.2010.061010.091803Ou Yang, & Heinzelman, W. (2012). Modeling and Performance Analysis for Duty-Cycled MAC Protocols with Applications to S-MAC and X-MAC. IEEE Transactions on Mobile Computing, 11(6), 905-921. doi:10.1109/tmc.2011.121Martinez-Bauset, J., Guntupalli, L., & Li, F. Y. (2015). Performance Analysis of Synchronous Duty-Cycled MAC Protocols. IEEE Wireless Communications Letters, 4(5), 469-472. doi:10.1109/lwc.2015.2439267Guntupalli, L., Martinez-Bauset, J., Li, F. Y., & Weitnauer, M. A. (2017). Aggregated Packet Transmission in Duty-Cycled WSNs: Modeling and Performance Evaluation. IEEE Transactions on Vehicular Technology, 66(1), 563-579. doi:10.1109/tvt.2016.2536686Zhang, R., Moungla, H., Yu, J., & Mehaoua, A. (2017). Medium Access for Concurrent Traffic in Wireless Body Area Networks: Protocol Design and Analysis. IEEE Transactions on Vehicular Technology, 66(3), 2586-2599. doi:10.1109/tvt.2016.2573718Guntupalli, L., Martinez-Bauset, J., & Li, F. Y. (2018). Performance of frame transmissions and event-triggered sleeping in duty-cycled WSNs with error-prone wireless links. Computer Networks, 134, 215-227. doi:10.1016/j.comnet.2018.01.047(July, 2019). The State Transition Probabilities of the Two 2D-DTMC. Technical Report http://personales.upv.es/jmartine/public/2DDTMC.pdfCrossbow Technology Incorporated, San Jose, CA, USA http://www.openautomation.net/uploadsproductos/micaz-datasheet.pd

Energy-Efficient Multi-Level and Distance-Aware Clustering Mechanism for WSNs

[EN] Most sensor networks are deployed at hostile environments to sense and gather specific information. As sensor nodes have battery constraints, therefore, the research community is trying to propose energyefficient solutions for wireless sensor networks (WSNs) to prolong the lifetime of the network. In this paper, we propose an energy-efficient multi-level and distance-aware clustering (EEMDC) mechanism for WSNs. In this mechanism, the area of the network is divided into three logical layers, which depends upon the hop-count-based distance from the base station. The simulation outcomes show that EEMDC is more energy efficient than other existing conventional approaches.This work has been partially supported by the 'Ministerio de Ciencia e Innovacion', through the 'Plan Nacional de I+D+i 2008-2011' in the 'Subprograma de Proyectos de Investigacion Fundamental', project TEC2011-27516, and by the Polytechnic University of Valencia, through the PAID-15-11 multidisciplinary projectsMehmood, A.; Khan, S.; Shams, B.; Lloret, J. (2015). Energy-Efficient Multi-Level and Distance-Aware Clustering Mechanism for WSNs. International Journal of Communication Systems. 28(5):972-989. https://doi.org/10.1002/dac.2720S972989285Sendra, S., Lloret, J., Garcia, M., & Toledo, J. F. (2011). Power Saving and Energy Optimization Techniques for Wireless Sensor Neworks (Invited Paper). Journal of Communications, 6(6). doi:10.4304/jcm.6.6.439-459Bri D Garcia M Lloret J Dini P Real deployments of wireless sensor networks Third International Conference on Sensor Technologies and Applications (SENSORCOMM 2009) 2009 8 23GUI, L., VAL, T., & WEI, A. (2011). A Novel Two-Class Localization Algorithm in Wireless Sensor Networks. Network Protocols and Algorithms, 3(3). doi:10.5296/npa.v3i3.863Rajeswari, A., & P.T, K. (2011). A Novel Energy Efficient Routing Protocols for Wireless Sensor Networks Using Spatial Correlation Based Collaborative Medium Access Control Combined with Hybrid MAC. Network Protocols and Algorithms, 3(4). doi:10.5296/npa.v3i4.1296Lloret, J., Garcia, M., Tomás, J., & Boronat, F. (2008). GBP-WAHSN: A Group-Based Protocol for Large Wireless Ad Hoc and Sensor Networks. Journal of Computer Science and Technology, 23(3), 461-480. doi:10.1007/s11390-008-9147-6Lloret, J., Garcia, M., Bri, D., & Diaz, J. (2009). A Cluster-Based Architecture to Structure the Topology of Parallel Wireless Sensor Networks. Sensors, 9(12), 10513-10544. doi:10.3390/s91210513LEHSAINI, M., GUYENNET, H., & FEHAM, M. (2010). Cluster-based Energy-efficient k-Coverage for Wireless Sensor Networks. Network Protocols and Algorithms, 2(2). doi:10.5296/npa.v2i2.325Liu, G., Xu, B., & Chen, H. (2011). Decentralized estimation over noisy channels in cluster-based wireless sensor networks. International Journal of Communication Systems, 25(10), 1313-1329. doi:10.1002/dac.1308Cheng, L., Chen, C., Ma, J., & Shu, L. (2011). Contention-based geographic forwarding in asynchronous duty-cycled wireless sensor networks. International Journal of Communication Systems, 25(12), 1585-1602. doi:10.1002/dac.1325Wang, X., & Qian, H. (2011). Hierarchical and low-power IPv6 address configuration for wireless sensor networks. International Journal of Communication Systems, 25(12), 1513-1529. doi:10.1002/dac.1318Zhang, D., Yang, Z., Raychoudhury, V., Chen, Z., & Lloret, J. (2013). An Energy-Efficient Routing Protocol Using Movement Trends in Vehicular Ad hoc Networks. The Computer Journal, 56(8), 938-946. doi:10.1093/comjnl/bxt028Chen, J.-S., Hong, Z.-W., Wang, N.-C., & Jhuang, S.-H. (2010). Efficient Cluster Head Selection Methods for Wireless Sensor Networks. Journal of Networks, 5(8). doi:10.4304/jnw.5.8.964-970Peiravi, A., Mashhadi, H. R., & Hamed Javadi, S. (2011). An optimal energy-efficient clustering method in wireless sensor networks using multi-objective genetic algorithm. International Journal of Communication Systems, 26(1), 114-126. doi:10.1002/dac.1336Zeynali, M., Mollanejad, A., & Khanli, L. M. (2011). Novel hierarchical routing protocol in wireless sensor network. Procedia Computer Science, 3, 292-300. doi:10.1016/j.procs.2010.12.050Heinzelman W Chandrakasan A Balakrishnan H Energy-efficient communication protocol for wireless microsensor networks 33rd Hawaii International Conference on System Sciences (HICSS) 2000 3005 3014Wang, A., Yang, D., & Sun, D. (2012). A clustering algorithm based on energy information and cluster heads expectation for wireless sensor networks. Computers & Electrical Engineering, 38(3), 662-671. doi:10.1016/j.compeleceng.2011.11.017Gou H Yoo Y An energy balancing LEACH algorithm for wireless sensor networks Proceedings of the 7th International Conference on Information Technology: New Generations (ITNG) 2010Ding, P., Holliday, J., & Celik, A. (2005). Distributed Energy-Efficient Hierarchical Clustering for Wireless Sensor Networks. Lecture Notes in Computer Science, 322-339. doi:10.1007/11502593_25Bandyopadhyay S Coyle E An energy-efficient hierarchical clustering algorithm for wireless sensor networks The 32nd IEEE International Conference on Computer Communication (INFOCOM 2003) 2003Jarry, A., Leone, P., Nikoletseas, S., & Rolim, J. (2011). Optimal data gathering paths and energy-balance mechanisms in wireless networks. Ad Hoc Networks, 9(6), 1036-1048. doi:10.1016/j.adhoc.2010.11.003Zhu, Y., Wu, W., Pan, J., & Tang, Y. (2010). An energy-efficient data gathering algorithm to prolong lifetime of wireless sensor networks. Computer Communications, 33(5), 639-647. doi:10.1016/j.comcom.2009.11.008Khamfroush H Saadat R Khademzadeh A Khamfroush K Lifetime increase for wireless sensor networks using cluster-based routing International Association of Computer Science and Information Technology-Spring Conference (IACSIT-SC 2009) 2009Li, H., Liu, Y., Chen, W., Jia, W., Li, B., & Xiong, J. (2013). COCA: Constructing optimal clustering architecture to maximize sensor network lifetime. Computer Communications, 36(3), 256-268. doi:10.1016/j.comcom.2012.10.006Aslam N Phillips W Robertson W Sivakumar S A multi-criterion optimization technique for energy efficient cluster formation in wireless sensor networks 4th IEEE Consumer Communications and Networking Conference, (CCNC 2007) 2007 650 654Yi, S., Heo, J., Cho, Y., & Hong, J. (2007). PEACH: Power-efficient and adaptive clustering hierarchy protocol for wireless sensor networks. Computer Communications, 30(14-15), 2842-2852. doi:10.1016/j.comcom.2007.05.034Yong, Z., & Pei, Q. (2012). A Energy-Efficient Clustering Routing Algorithm Based on Distance and Residual Energy for Wireless Sensor Networks. Procedia Engineering, 29, 1882-1888. doi:10.1016/j.proeng.2012.01.231Chuan-Chi W A minimum transmission energy consumption routing protocol for user-centric wireless networks 2011 1143 1148Kumar, D., Aseri, T. C., & Patel, R. B. (2009). EEHC: Energy efficient heterogeneous clustered scheme for wireless sensor networks. Computer Communications, 32(4), 662-667. doi:10.1016/j.comcom.2008.11.025Kim KT Moon SS Tree-Based Clustering (TBC) for energy efficient wireless sensor networks IEEE 24th International Conference on Advanced Information Networking and Applications Workshops (WAINA) 2010 680 685Yu, J., Qi, Y., Wang, G., & Gu, X. (2012). A cluster-based routing protocol for wireless sensor networks with nonuniform node distribution. AEU - International Journal of Electronics and Communications, 66(1), 54-61. doi:10.1016/j.aeue.2011.05.002Ye M Li C Wu J EECS: an Energy Efficient Clustering Scheme in wireless sensor networks 24th IEEE International Performance on Computing, and Communications Conference 2005 535 540Gautama N Lee W Pyun J Dynamic clustering and distance aware routing protocol for wireless sensor networks PE-WASUN'09 2009Heinzelman, W. B., Chandrakasan, A. P., & Balakrishnan, H. (2002). An application-specific protocol architecture for wireless microsensor networks. IEEE Transactions on Wireless Communications, 1(4), 660-670. doi:10.1109/twc.2002.804190Lai, W. K., Fan, C. S., & Lin, L. Y. (2012). Arranging cluster sizes and transmission ranges for wireless sensor networks. Information Sciences, 183(1), 117-131. doi:10.1016/j.ins.2011.08.029Pantazis, N. A., Vergados, D. J., Vergados, D. D., & Douligeris, C. (2009). Energy efficiency in wireless sensor networks using sleep mode TDMA scheduling. Ad Hoc Networks, 7(2), 322-343. doi:10.1016/j.adhoc.2008.03.006OMNeT++ Community Documentation and Tutorials of omnet++ http://www.omnetpp.org/Castallia Documentation and Tutorials of Castalia Simulator for WSN and BAN http://castalia.research.nicta.com.au/index.php/en/Research Group on Computer Networks and Multimedia Communication UFPA - Brazil Download-Leach-v2-for-Castalia http://www.gercom.ufpa.br/index.php?option=com_filecabinet&view=files&id=1&Itemid=31&lang=p

Wireless sensor networks with energy harvesting: Modeling and simulation based on a practical architecture using real radiation levels

This paper presents a new energy-harvesting model for a network simulator that implements super-capacitor energy storage with solar energy-harvesting recharge. The model is easily extensible, and other energyharvesting systems, or different energy storages, can be further developed. Moreover, code can be conveniently reused as the implementation is entirely uncoupled from the radio and node models. Real radiation data are obtained from available online databases in order to dynamically calculate super-capacitor charge and discharge. Such novelty enables the evaluation of energy evolution on a network of sensor nodes at various physical world locations and during different seasons. The model is validated against a real and fully working prototype, and good result correlation is shown. Furthermore, various experiments using the ns-3 simulator were conducted, demonstrating the utility of the model in assisting the research and development of the deployment of everlasting wireless sensor networks.This work was supported by the CICYT (research projects CTM2011-29691-C02-01 and TIN2011-28435-C03-01) and UPV research project SP20120889.Climent, S.; Sánchez Matías, AM.; Blanc Clavero, S.; Capella Hernández, JV.; Ors Carot, R. (2013). Wireless sensor networks with energy harvesting: Modeling and simulation based on a practical architecture using real radiation levels. Concurrency and Computation: Practice and Experience. 1-19. https://doi.org/10.1002/cpe.3151S119Akyildiz, I. F., & Vuran, M. C. (2010). Wireless Sensor Networks. doi:10.1002/9780470515181Seah, W. K. G., Tan, Y. K., & Chan, A. T. S. (2012). Research in Energy Harvesting Wireless Sensor Networks and the Challenges Ahead. Autonomous Sensor Networks, 73-93. doi:10.1007/5346_2012_27Vullers, R., Schaijk, R., Visser, H., Penders, J., & Hoof, C. (2010). Energy Harvesting for Autonomous Wireless Sensor Networks. IEEE Solid-State Circuits Magazine, 2(2), 29-38. doi:10.1109/mssc.2010.936667Ammar, Y., Buhrig, A., Marzencki, M., Charlot, B., Basrour, S., Matou, K., & Renaudin, M. (2005). Wireless sensor network node with asynchronous architecture and vibration harvesting micro power generator. Proceedings of the 2005 joint conference on Smart objects and ambient intelligence innovative context-aware services: usages and technologies - sOc-EUSAI ’05. doi:10.1145/1107548.1107618Vijayaraghavan, K., & Rajamani, R. (2007). Active Control Based Energy Harvesting for Battery-Less Wireless Traffic Sensors. 2007 American Control Conference. doi:10.1109/acc.2007.4282842Bottner, H., Nurnus, J., Gavrikov, A., Kuhner, G., Jagle, M., Kunzel, C., … Schlereth, K.-H. (2004). New thermoelectric components using microsystem technologies. Journal of Microelectromechanical Systems, 13(3), 414-420. doi:10.1109/jmems.2004.828740Mateu L Codrea C Lucas N Pollak M Spies P Energy harvesting for wireless communication systems using thermogenerators Conference on Design of Circuits and Integrated Systems (DCIS) 2006AEMet Agencia Estatal de Meteorolgía 2013 http//www.aemet.esPANGAEA Data Publisher for Earth & Environmental Science 2013 http://www.pangaea.de/Zeng, K., Ren, K., Lou, W., & Moran, P. J. (2007). Energy aware efficient geographic routing in lossy wireless sensor networks with environmental energy supply. Wireless Networks, 15(1), 39-51. doi:10.1007/s11276-007-0022-0Hasenfratz, D., Meier, A., Moser, C., Chen, J.-J., & Thiele, L. (2010). Analysis, Comparison, and Optimization of Routing Protocols for Energy Harvesting Wireless Sensor Networks. 2010 IEEE International Conference on Sensor Networks, Ubiquitous, and Trustworthy Computing. doi:10.1109/sutc.2010.35Noh, D. K., & Hur, J. (2012). Using a dynamic backbone for efficient data delivery in solar-powered WSNs. Journal of Network and Computer Applications, 35(4), 1277-1284. doi:10.1016/j.jnca.2012.01.012Lin, L., Shroff, N. B., & Srikant, R. (2007). Asymptotically Optimal Energy-Aware Routing for Multihop Wireless Networks With Renewable Energy Sources. IEEE/ACM Transactions on Networking, 15(5), 1021-1034. doi:10.1109/tnet.2007.896173Ferry, N., Ducloyer, S., Julien, N., & Jutel, D. (2011). Power/Energy Estimator for Designing WSN Nodes with Ambient Energy Harvesting Feature. EURASIP Journal on Embedded Systems, 2011(1), 242386. doi:10.1155/2011/242386Glaser, J., Weber, D., Madani, S., & Mahlknecht, S. (2008). Power Aware Simulation Framework for Wireless Sensor Networks and Nodes. EURASIP Journal on Embedded Systems, 2008(1), 369178. doi:10.1155/2008/369178De Mil, P., Jooris, B., Tytgat, L., Catteeuw, R., Moerman, I., Demeester, P., & Kamerman, A. (2010). Design and Implementation of a Generic Energy-Harvesting Framework Applied to the Evaluation of a Large-Scale Electronic Shelf-Labeling Wireless Sensor Network. EURASIP Journal on Wireless Communications and Networking, 2010(1). doi:10.1155/2010/343690Castagnetti, A., Pegatoquet, A., Belleudy, C., & Auguin, M. (2012). A framework for modeling and simulating energy harvesting WSN nodes with efficient power management policies. EURASIP Journal on Embedded Systems, 2012(1). doi:10.1186/1687-3963-2012-8Alippi, C., & Galperti, C. (2008). An Adaptive System for Optimal Solar Energy Harvesting in Wireless Sensor Network Nodes. IEEE Transactions on Circuits and Systems I: Regular Papers, 55(6), 1742-1750. doi:10.1109/tcsi.2008.922023Xiaofan Jiang, Polastre, J., & Culler, D. (s. f.). Perpetual environmentally powered sensor networks. IPSN 2005. Fourth International Symposium on Information Processing in Sensor Networks, 2005. doi:10.1109/ipsn.2005.1440974Simjee, F., & Chou, P. H. (2006). Everlast. Proceedings of the 2006 international symposium on Low power electronics and design - ISLPED ’06. doi:10.1145/1165573.1165619Sánchez, A., Climent, S., Blanc, S., Capella, J. V., & Piqueras, I. (2011). WSN with energy-harvesting. Proceedings of the 6th ACM workshop on Performance monitoring and measurement of heterogeneous wireless and wired networks - PM2HW2N ’11. doi:10.1145/2069087.2069091Renner C Jessen J Turau V Lifetime prediction for supercapacitor-powered wireless sensor nodes Proc. of the 8th GI/ITG KuVS Fachgesprächİ Drahtlose Sensornetze(FGSN09) 2009TI Analog, Embedded Processing, Semiconductor Company, Texas Instruments 2013 http//www.ti.comWSNVAL Wireless Sensor Networks Valencia 2013 www.wsnval.comSanchez, A., Blanc, S., Yuste, P., & Serrano, J. J. (2011). RFID Based Acoustic Wake-Up System for Underwater Sensor Networks. 2011 IEEE Eighth International Conference on Mobile Ad-Hoc and Sensor Systems. doi:10.1109/mass.2011.103Fan, K.-W., Zheng, Z., & Sinha, P. (2008). Steady and fair rate allocation for rechargeable sensors in perpetual sensor networks. Proceedings of the 6th ACM conference on Embedded network sensor systems - SenSys ’08. doi:10.1145/1460412.1460436Moser, C., Thiele, L., Brunelli, D., & Benini, L. (2010). Adaptive Power Management for Environmentally Powered Systems. IEEE Transactions on Computers, 59(4), 478-491. doi:10.1109/tc.2009.15

A survey of IEEE 802.15.4 effective system parameters for wireless body sensor networks

This is the peer reviewed version of the following article: Moravejosharieh, Amirhossein, Lloret, Jaime. (2016). A survey of IEEE 802.15.4 effective system parameters for wireless body sensor networks.International Journal of Communication Systems, 29, 7, 1269-1292. DOI: 10.1002/dac.3098, which has been published in final form at http://doi.org/10.1002/dac.3098. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving[EN] Wireless body sensor networks are offered to meet the requirements of a diverse set of applications such as health-related and well-being applications. For instance, they are deployed to measure, fetch and collect human body vital signs. Such information could be further used for diagnosis and monitoring of medical conditions. IEEE 802.15.4 is arguably considered as a well-designed standard protocol to address the need for low-rate, low-power and low-cost wireless body sensor networks. Apart from the vast deployment of this technology, there are still some challenges and issues related to the performance of the medium access control (MAC) protocol of this standard that are required to be addressed. This paper comprises two main parts. In the first part, the survey has provided a thorough assessment of IEEE 802.15.4 MAC protocol performance where its functionality is evaluated considering a range of effective system parameters, that is, some of the MAC and application parameters and the impact of mutual interference. The second part of this paper is about conducting a simulation study to determine the influence of varying values of the system parameters on IEEE 802.15.4 performance gains. More specifically, we explore the dependability level of IEEE 802.5.4 performance gains on a candidate set of system parameters. Finally, this paper highlights the tangible needs to conduct more investigations on particular aspect(s) of IEEE 802.15.4 MAC protocol. Copyright (c) 2015 John Wiley & Sons, Ltd.Moravejosharieh, A.; Lloret, J. (2016). A survey of IEEE 802.15.4 effective system parameters for wireless body sensor networks. International Journal of Communication Systems. 29(7):1269-1292. https://doi.org/10.1002/dac.3098S12691292297Alrajeh, N. A., Lloret, J., & Canovas, A. (2014). A Framework for Obesity Control Using a Wireless Body Sensor Network. International Journal of Distributed Sensor Networks, 10(7), 534760. doi:10.1155/2014/534760Lopes I Silva B Rodrigues J Lloret J Proenca M A mobile health monitoring solution for weight control International Conference on Wireless Communications and Signal Processing (WCSP) Nanjing / China 2011 1 5Singh, N., Singh, A. K., & Singh, V. K. (2015). Design and performance of wearable ultrawide band textile antenna for medical applications. Microwave and Optical Technology Letters, 57(7), 1553-1557. doi:10.1002/mop.29131Lan, K., Chou, C.-M., Wang, T., & Li, M.-W. (2012). Using body sensor networks for motion detection: a cluster-based approach for green radio. Transactions on Emerging Telecommunications Technologies, 25(2), 199-216. doi:10.1002/ett.2559Lloret, J., Garcia, M., Catala, A., & Rodrigues, J. J. P. C. (2016). A group-based wireless body sensors network using energy harvesting for soccer team monitoring. International Journal of Sensor Networks, 21(4), 208. doi:10.1504/ijsnet.2016.079172Garcia M Catala A Lloret J Rodrigues J A wireless sensor network for soccer team monitoring International Conference on Distributed Computing in Sensor Systems and Workshops (DCOSS) Barcelona / Spain 2011 1 6Penders J Gyselinckx B Vullers R De Nil M Nimmala V van de Molengraft J Yazicioglu F Torfs T Leonov V Merken P Van Hoof C Human++: from technology to emerging health monitoring concepts 5th International Summer School and Symposium ISSS-MDBS on Medical Devices and Biosensors Hong Kong 2008 94 98Penders J Van de Molengraft J. Brown L Grundlehner B Gyselinckx B Van Hoof C Potential and challenges of body area networks for personal health Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC Minneapolis, U.S. 2009 6569 6572Ullah, S., Higgins, H., Braem, B., Latre, B., Blondia, C., Moerman, I., … Kwak, K. S. (2010). A Comprehensive Survey of Wireless Body Area Networks. Journal of Medical Systems, 36(3), 1065-1094. doi:10.1007/s10916-010-9571-3Cao, H., Leung, V., Chow, C., & Chan, H. (2009). Enabling technologies for wireless body area networks: A survey and outlook. IEEE Communications Magazine, 47(12), 84-93. doi:10.1109/mcom.2009.5350373Hall, P. S., Yang Hao, Nechayev, Y. I., Alomainy, A., Constantinou, C. C., Parini, C., … Bozzetti, M. (2007). Antennas and propagation for on-body communication systems. IEEE Antennas and Propagation Magazine, 49(3), 41-58. doi:10.1109/map.2007.4293935Mamaghanian, H., Khaled, N., Atienza, D., & Vandergheynst, P. (2011). Compressed Sensing for Real-Time Energy-Efficient ECG Compression on Wireless Body Sensor Nodes. IEEE Transactions on Biomedical Engineering, 58(9), 2456-2466. doi:10.1109/tbme.2011.2156795LAN-MAN Standards Committee the IEEE Computer Society IEEE standard for local and metropolitan area networks - part 15.4: low rate wireless personal area networks (LR-WPANs) 2011Petrova M Riihijarvi J Mahonen P Labella S Performance study of IEEE 802.15.4 using measurements and simulations IEEE Wireless Communications and Networking Conference (WCNC) Las Vegas, U.S. 2006 487 492Vaithiyanathan, J., Raju, R. K., & Sadayan, G. (2011). Performance Evaluation of IEEE 802.15.4 Using Association Process and Channel Measurement. Communications in Computer and Information Science, 409-417. doi:10.1007/978-3-642-22555-0_42Yazdi E Moravejosharieh A Willig A Pawlikowski K Coupling power and frequency adaptation for interference mitigation in IEEE 802.15.4-based mobile body sensor networks: part II 2014 Australasian Telecommunication Networks and Applications Conference (ATNAC) Melbourne, Australia 2014 105 110Pelegris P Banitsas K Investigating the efficiency of IEEE 802.15.4 for medical monitoring applications 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC Boston, U.S. 2011 8215 8218Ranjit, J. S., & Shin, S. (2013). A Modified IEEE 802.15.4 Superframe Structure for Guaranteed Emergency Handling in Wireless Body Area Network. Network Protocols and Algorithms, 5(2), 1. doi:10.5296/npa.v5i2.3375Jianliang Zheng, & Lee, M. J. (2004). Will IEEE 802.15.4 make ubiquitous networking a reality?: a discussion on a potential low power, low bit rate standard. IEEE Communications Magazine, 42(6), 140-146. doi:10.1109/mcom.2004.1304251Toscano E Lo Bello L Cross-channel interference in IEEE 802.15.4 networks IEEE International Workshop on Factory Communication Systems, 2008. WFCS 2008 Dresden, Germany 2008 139 148Bashir F Baek WS Sthapit P Pandey D young Pyun J Coordinator assisted passive discovery for mobile end devices in IEEE 802.15.4 2013 IEEE Consumer Communications and Networking Conference (CCNC) Las Vegas, U.S. 2013 601 604Tabatabaei Yazdi E Willig A Pawlikowski K Shortening orphan time in IEEE 802.15.4: what can be gained 2013 19th IEEE International Conference on Networks (ICON) Singapore 2013 1 6Park, T. R., Kim, T. H., Choi, J. Y., Choi, S., & Kwon, W. H. (2005). Throughput and energy consumption analysis of IEEE 802.15.4 slotted CSMA∕CA. Electronics Letters, 41(18), 1017. doi:10.1049/el:20051662Bianchi, G. (2000). Performance analysis of the IEEE 802.11 distributed coordination function. IEEE Journal on Selected Areas in Communications, 18(3), 535-547. doi:10.1109/49.840210IEEE Computer Society LAN MAN Standards Committee Wireless LAN medium access control (MAC) and physical layer (PHY) specifications 1997Pollin, S., Ergen, M., Ergen, S. C., Bougard, B., Der Perre, L. V., Moerman, I., … Catthoor, F. (2008). Performance Analysis of Slotted Carrier Sense IEEE 802.15.4 Medium Access Layer. IEEE Transactions on Wireless Communications, 7(9), 3359-3371. doi:10.1109/twc.2008.060057Xinhua Ling, Yu Cheng, Mark, J. W., & Xuemin Shen. (2008). A Renewal Theory Based Analytical Model for the Contention Access Period of IEEE 802.15.4 MAC. IEEE Transactions on Wireless Communications, 7(6), 2340-2349. doi:10.1109/twc.2008.070048Lee, C. Y., Cho, H. I., Hwang, G. U., Doh, Y., & Park, N. (2011). Performance modeling and analysis of IEEE 802.15.4 slotted CSMA/CA protocol with ACK mode. AEU - International Journal of Electronics and Communications, 65(2), 123-131. doi:10.1016/j.aeue.2010.02.007Wang, F., Zhao, Y., & Li, D. (2011). Analysis of CSMA/CA in IEEE 802.15.4. IET Communications, 5(15), 2187-2195. doi:10.1049/iet-com.2010.1007Zhu, J., Tao, Z., & Lv, C. (2011). Performance Evaluation of IEEE 802.15.4 CSMA/CA Scheme Adopting a Modified LIB Model. Wireless Personal Communications, 65(1), 25-51. doi:10.1007/s11277-011-0226-6Shu F Sakurai T Analysis of an energy conserving CSMA-CA GLOBECOM Washington DC, U.S. 2007 2536 2540Shu, F., & Sakurai, T. (2011). A new analytical model for the IEEE 802.15.4 CSMA-CA protocol. Computer Networks, 55(11), 2576-2591. doi:10.1016/j.comnet.2011.04.017Cano-Garcia, J. M., & Casilari, E. (2011). An empirical evaluation of the consumption of 802.15.4/ZigBee sensor motes in noisy environments. 2011 International Conference on Networking, Sensing and Control. doi:10.1109/icnsc.2011.5874886Baz, M., Mitchell, P. D., & Pearce, D. A. J. (2013). Versatile Analytical Model for Delay and Energy Evaluation in WPANs: A Case Study for IEEE 802.15.4 CSMA-CA. Wireless Personal Communications, 75(1), 415-445. doi:10.1007/s11277-013-1370-yLiu Q Czylwik A A priority-based adaptive service differentiation scheme for IEEE 802.15.4 sensor networks Proceedings of European Wireless 2014; 20th European Wireless Conference Barcelona, Spain 2014 1 6Golmie, N., Cypher, D., & Rebala, O. (s. f.). Performance evaluation of low rate WPANs for medical applications. IEEE MILCOM 2004. Military Communications Conference, 2004. doi:10.1109/milcom.2004.1494952Misic, J., Misic, V. B., & Shafi, S. (s. f.). Performance of IEEE 802.15.4 beacon enabled PAN with uplink transmissions in non-saturation mode - access delay for finite buffers. First International Conference on Broadband Networks. doi:10.1109/broadnets.2004.61Mišić, J., Shafi, S., & Mišić, V. B. (2005). The impact of MAC parameters on the performance of 802.15.4 PAN. Ad Hoc Networks, 3(5), 509-528. doi:10.1016/j.adhoc.2004.08.002Anastasi, G., Conti, M., & Di Francesco, M. (2011). A Comprehensive Analysis of the MAC Unreliability Problem in IEEE 802.15.4 Wireless Sensor Networks. IEEE Transactions on Industrial Informatics, 7(1), 52-65. doi:10.1109/tii.2010.2085440Lee, B.-H., Al Rasyid, M. U. H., & Wu, H.-K. (2012). Analysis of superframe adjustment and beacon transmission for IEEE 802.15.4 cluster tree networks. EURASIP Journal on Wireless Communications and Networking, 2012(1). doi:10.1186/1687-1499-2012-219Zimmerling, M., Ferrari, F., Mottola, L., Voigt, T., & Thiele, L. (2012). pTunes. Proceedings of the 11th international conference on Information Processing in Sensor Networks - IPSN ’12. doi:10.1145/2185677.2185730Rohm, D., Goyal, M., Hosseini, H., Divjak, A., & Bashir, Y. (2009). Configuring Beaconless IEEE 802.15.4 Networks Under Different Traffic Loads. 2009 International Conference on Advanced Information Networking and Applications. doi:10.1109/aina.2009.84Jin-Shyan Lee. (2006). Performance evaluation of IEEE 802.15.4 for low-rate wireless personal area networks. IEEE Transactions on Consumer Electronics, 52(3), 742-749. doi:10.1109/tce.2006.1706465De Paz Alberola, R., & Pesch, D. (2012). Duty cycle learning algorithm (DCLA) for IEEE 802.15.4 beacon-enabled wireless sensor networks. Ad Hoc Networks, 10(4), 664-679. doi:10.1016/j.adhoc.2011.06.006Barbieri, A., Chiti, F., & Fantacci, R. (2006). WSN17-2: Proposal of an Adaptive MAC Protocol for Efficient IEEE 802.15.4 Low Power Communications. IEEE Globecom 2006. doi:10.1109/glocom.2006.989Jeon, J., Lee, J. W., Ha, J. Y., & Kwon, W. H. (2007). DCA: Duty-Cycle Adaptation Algorithm for IEEE 802.15.4 Beacon-Enabled Networks. 2007 IEEE 65th Vehicular Technology Conference - VTC2007-Spring. doi:10.1109/vetecs.2007.35Kang, M., Chong, J., Hyun, H., Kim, S., Jung, B., & Sung, D. (2007). Adaptive Interference-Aware Multi-Channel Clustering Algorithm in a ZigBee Network in the Presence of WLAN Interference. 2007 2nd International Symposium on Wireless Pervasive Computing. doi:10.1109/iswpc.2007.342601Yi, P., Iwayemi, A., & Zhou, C. (2011). Developing ZigBee Deployment Guideline Under WiFi Interference for Smart Grid Applications. IEEE Transactions on Smart Grid, 2(1), 110-120. doi:10.1109/tsg.2010.2091655Tang, L., Wang, K.-C., Huang, Y., & Gu, F. (2007). Channel Characterization and Link Quality Assessment of IEEE 802.15.4-Compliant Radio for Factory Environments. IEEE Transactions on Industrial Informatics, 3(2), 99-110. doi:10.1109/tii.2007.898414Sha M Xing G Zhou G Liu S Wang X C-MAC: model-driven concurrent medium access control for wireless sensor networks IEEE INFOCOM 2009 Rio de Janeiro, Brazil 2009 1845 1853 10.1109/INFCOM.2009.5062105Peizhong Yi, Iwayemi, A., & Chi Zhou. (2010). Frequency agility in a ZigBee network for smart grid application. 2010 Innovative Smart Grid Technologies (ISGT). doi:10.1109/isgt.2010.5434747Torabi N Wong W Leung VCM A robust coexistence scheme for IEEE 802.15.4 wireless personal area networks IEEE Consumer Communications and Networking Conference (CCNC) Las Vegas, U.S. 2011 1031 1035 10.1109/CCNC.2011.5766322IEEE standard for local and metropolitan area networks - part 15.6: wireless body area networks IEEE Std 802.15.6-2012 2012 1 271 10.1109/IEEESTD.2012.6161600Kim, S., Kim, S., Kim, J.-W., & Eom, D.-S. (2012). Flexible beacon scheduling scheme for interference mitigation in body sensor networks. 2012 9th Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks (SECON). doi:10.1109/secon.2012.6275772Bradai N Fourati LC Kamoun L Performance analysis of medium access control protocol for wireless body area networks 27th International Conference on Advanced Information Networking and Applications Workshops (WAINA) Barcelona, Spain 2013 916 921Moravejosharieh A Yazdi ET Study of resource utilization in IEEE 802.15.4 wireless body sensor network, part I: the need for enhancement IEEE 16th International Conference on Computational Science and Engineering (CSE) Sydney, Australia 2013 1226 1231Moravejosharieh A Yazdi ET Willig A Study of resource utilization in IEEE 802.15.4 wireless body sensor network, part II: greedy channel utilization 19th IEEE International Conference on Networks (ICON) Singapore 2013 1 6Moravejosharieh A Yazdi E Willig A Pawlikowski K Adaptive channel utilisation in IEEE 802.15.4 wireless body sensor networks: continuous hopping approach Australasian Telecommunication Networks and Applications Conference (ATNAC) Melbourne, Australia 2014 93 98 10.1109/ATNAC.2014.7020880Moravejosharieh, A. H. (2015). Frequency-Adaptive Approach In IEEE 802.15.4 Wireless Body Sensor Networks: Continuous-Assessment or Periodic-Assessment? International Journal of Information, Communication Technology and Applications, 1(1), 19. doi:10.17972/ajicta2015113Moravejosharieh A Yazdi E Pawlikowski K Sirisena H Adaptive channel utilisation in IEEE 802.15.4 wireless body sensor networks: adaptive phase-shifting approach International Telecommunication Networks and Applications Conference (ITNAC) Sydney, Australia 2015 93 98Bian, K., Park, J.-M., & Gao, B. (2014). Channel Assignment for Multi-hop Cognitive Radio Networks. Cognitive Radio Networks, 101-116. doi:10.1007/978-3-319-07329-3_6Bian, K., Park, J.-M., & Gao, B. (2014). Coexistence-Aware Spectrum Sharing for Homogeneous Cognitive Radio Networks. Cognitive Radio Networks, 61-75. doi:10.1007/978-3-319-07329-3_4Wu C Yan H Huo H A multi-channel MAC protocol design based on IEEE 802.15.4 standard in industry 2012 10th IEEE International Conference on Industrial Informatics (INDIN) Beijing, China 2012 1206 1211 10.1109/INDIN.2012.6300916Incel, O. D. (2011). A survey on multi-channel communication in wireless sensor networks. Computer Networks, 55(13), 3081-3099. doi:10.1016/j.comnet.2011.05.020Kim Y Shin H Cha H Y-MAC: an energy-efficient multi-channel MAC protocol for dense wireless sensor networks Proceedings of the 7th International Conference on Information Processing in Sensor Networks IPSN '08 St. Louis MO, U.S. 2008 53 63Demirkol, I., Ersoy, C., & Alagoz, F. (2006). MAC protocols for wireless sensor networks: a survey. IEEE Communications Magazine, 44(4), 115-121. doi:10.1109/mcom.2006.1632658Wykret T Correia L Macedo D Giacomin J Andrade L Evaluation and avoidance of interference in WSN: a multi-radio node prototype using dynamic spectrum allocation IFIP Wireless Days (WD) Valencia, Spain 2013 1 3 10.1109/WD.2013.6686533Doyle L Sutton P Nolan K Lotze J Ozgul B Rondeau T Fahmy S Lahlou H DaSilva L Experiences from the IRIS testbed in dynamic spectrum access and cognitive radio experimentation IEEE Symposium on New Frontiers in Dynamic Spectrum Singapore 2010 1 8 10.1109/DYSPAN.2010.5457835Ansari, J., Zhang, X., & Mahonen, P. (2010). Multi-radio medium access control protocol for wireless sensor networks. International Journal of Sensor Networks, 8(1), 47. doi:10.1504/ijsnet.2010.034066Liu Z Wu W A dynamic multi-radio multi-channel MAC protocol for wireless sensor networks 2nd International Conference on Communication Software and Networks (ICCSN) Singapore 2010 105 109Xu, W., Trappe, W., & Zhang, Y. (2008). Defending wireless sensor networks from radio interference through channel adaptation. ACM Transactions on Sensor Networks, 4(4), 1-34. doi:10.1145/1387663.1387664Kim Y Shin H Cha H Y-MAC: an energy-efficient multi-channel MAC protocol for dense wireless sensor networks Proceedings of the 7th IEEE Computer Society International Conference on Information Processing in Sensor Networks IPSN '08 Washington, DC, USA 2008 53 63Tae Hyun Kim, Jae Yeol Ha, & Sunghyun Choi. (2009). Improving Spectral and Temporal Efficiency of Collocated IEEE 802.15.4 LR-WPANs. IEEE Transactions on Mobile Computing, 8(12), 1596-1609. doi:10.1109/tmc.2009.85Chowdhury, K. R., Nandiraju, N., Chanda, P., Agrawal, D. P., & Zeng, Q.-A. (2009). Channel allocation and medium access control for wireless sensor networks. Ad Hoc Networks, 7(2), 307-321. doi:10.1016/j.adhoc.2008.03.004Deylami, M., & Jovanov, E. (2012). A distributed and collaborative scheme for mitigating coexistence in IEEE 802.15.4 based WBANs. Proceedings of the 50th Annual Southeast Regional Conference on - ACM-SE ’12. doi:10.1145/2184512.2184514Deylami, M. N., & Jovanov, E. (2014). A Distributed Scheme to Manage The Dynamic Coexistence of IEEE 802.15.4-Based Health-Monitoring WBANs. IEEE Journal of Biomedical and Health Informatics, 18(1), 327-334. doi:10.1109/jbhi.2013.2278217Deylami M Jovanov E An implementation of a distributed scheme for managing the dynamic coexistence of wireless body area networks Southeastcon, 2013 Proceedings of IEEE Jacksonville, U.S. 2013 1 6 10.1109/SECON.2013.6567446Cavallari, R., Martelli, F., Rosini, R., Buratti, C., & Verdone, R. (2014). A Survey on Wireless Body Area Networks: Technologies and Design Challenges. IEEE Communications Surveys & Tutorials, 16(3), 1635-1657. doi:10.1109/surv.2014.012214.00007Chen, M., Gonzalez, S., Vasilakos, A., Cao, H., & Leung, V. C. M. (2010). Body Area Networks: A Survey. Mobile Networks and Applications, 16(2), 171-193. doi:10.1007/s11036-010-0260-8Movassaghi, S., Abolhasan, M., Lipman, J., Smith, D., & Jamalipour, A. (2014). Wireless Body Area Networks: A Survey. IEEE Communications Surveys & Tutorials, 16(3), 1658-1686. doi:10.1109/surv.2013.121313.00064Patel, M., & Wang, J. (2010). Applications, challenges, and prospective in emerging body area networking technologies. IEEE Wireless Communications, 17(1), 80-88. doi:10.1109/mwc.2010.5416354ULLAH, S., KHAN, P., ULLAH, N., SALEEM, S., HIGGINS, H., & Sup KWAK, K. (2009). A Review of Wireless Body Area Networks for Medical Applications. International Journal of Communications, Network and System Sciences, 02(08), 797-803. doi:10.4236/ijcns.2009.28093Boulis, A., Smith, D., Miniutti, D., Libman, L., & Tselishchev, Y. (2012). Challenges in body area networks for healthcare: the MAC. IEEE Communications Magazine, 50(5), 100-106. doi:10.1109/mcom.2012.6194389Pantelopoulos A Bourbakis N A survey on wearable biosensor systems for health monitoring 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society Vancouver, Canada 2008 4887 4890 10.1109/IEMBS.2008.4650309Takei, K., Honda, W., Harada, S., Arie, T., & Akita, S. (2014). Toward Flexible and Wearable Human-Interactive Health-Monitoring Devices. Advanced Healthcare Materials, 4(4), 487-500. doi:10.1002/adhm.201400546Caldeira, J. M. L. P., Rodrigues, J. J. P. C., & Lorenz, P. (2013). Intra-Mobility Support Solutions for Healthcare Wireless Sensor Networks–Handover Issues. IEEE Sensors Journal, 13(11), 4339-4348. doi:10.1109/jsen.2013.2267729Carrano, R. C., Passos, D., Magalhaes, L. C. S., & Albuquerque, C. V. N. (2014). Survey and Taxonomy of Duty Cycling Mechanisms in Wireless Sensor Networks. IEEE Communications Surveys & Tutorials, 16(1), 181-194. doi:10.1109/surv.2013.052213.00116Sudevalayam, S., & Kulkarni, P. (2011). Energy Harvesting Sensor Nodes: Survey and Implications. IEEE Communications Surveys & Tutorials, 13(3), 443-461. doi:10.1109/surv.2011.060710.00094Khanafer, M., Guennoun, M., & Mouftah, H. T. (2014). A Survey of Beacon-Enabled IEEE 802.15.4 MAC Protocols in Wireless Sensor Networks. IEEE Communications Surveys & Tutorials, 16(2), 856-876. doi:10.1

Performance model for two-tier mobile wireless networks with macrocells and small cells

[EN] A new analytical model is proposed to evaluate the performance of two-tier cellular networks composed of macrocells (MCs) and small cells (SCs), where terminals roam across the service area. Calls being serviced by MCs may retain their channel when entering a SC service area, if no free SC channels are available. Also, newly offered SC calls can overflow to the MC. However, in both situations channels may be repacked to vacate MC channels. The cardinality of the state space of the continuous-time Markov chain (CTMC) that models the system dynamics makes the exact system analysis unfeasible. We propose an approximation based on constructing an equivalent CTMC for which a product-form solution exist that can be obtained with very low computational complexity. We determine performance parameters such as the call blocking probabilities for the MC and SCs, the probability of forced termination, and the carried traffic. We validate the analytical model by simulation. Numerical results show that the proposed analytical model achieves very good precision in scenarios with diverse mobility rates and MCs and SCs loads, as well as when MCs overlay a large number of SCs.Authors would like to thank you the anonymous reviewers for the review comments provided to our work that have decisively contributed to improve the paper. Most of the contribution of V. Casares-Giner was done while visiting the Huazhong University of Science and Technolgy (HUST), Whuhan, China. This visit was supported by the European Commission, 7FP, S2EuNet project. The authors from the Universitat Politecnica de Valencia are partially supported by the Ministry of Economy and Competitiveness of Spain under grant TIN2013-47272-C2-1-R and TEC2015-71932-REDT. The research of Xiaohu Ge was supported by the National Natural Science Foundation of China (NSFC) grant 61210002, the Fundamental Research Funds for the Central Universities grant 2015XJGH011, and China International Joint Research Center of Green Communications and Networking grant 2015B01008.Casares-Giner, V.; Martínez Bauset, J.; Ge, X. (2018). Performance model for two-tier mobile wireless networks with macrocells and small cells. Wireless Networks. 24(4):1327-1342. https://doi.org/10.1007/s11276-016-1407-8S13271342244ABIresearch. (2016). In-building mobile data traffic forecast. ABIreseach, Technical Report.NGMN Alliance. (2015). Recommendations for small cell development and deployment. NGMN Alliance, Technical Report.Chandrasekhar, V., Andrews, J., & Gatherer, A. (2008). Femtocell networks: A survey. IEEE Communications Magazine, 46(9), 59–67.Yamamoto, T., & Konishi, S. (2013). Impact of small cell deployments on mobility performance in LTE-Advanced systems. In IEEE PIMRC workshops (pp. 189–193).Balakrishnan, R., & Akyildiz, I. (2016). Local anchor schemes for seamless and low-cost handover in coordinated small cells. IEEE Transactions on Mobile Computing, 15(5), 1182–1196.Zahir, T., Arshad, K., Nakata, A., & Moessner, K. (2013). Interference management in femtocells. IEEE Communications Surveys & Tutorials, 15(1), 293–311.Yassin, M., AboulHassan, M. A., Lahoud, S., Ibrahim, M., Mezher, D., Cousin, B., & Sourour, E. A. (2015). Survey of ICIC techniques in LTE networks under various mobile environment parameters. Wireless Networks, 1–16.Andrews, M., & Zhang, L. (2015). Utility optimization in heterogeneous networks via CSMA-based algorithms. Wireless Networks, 1–14.El-atty, S. M. A., & Gharsseldien, Z. M. (2016). Performance analysis of an advanced heterogeneous mobile network architecture with multiple small cell layers. Wireless Networks, 1–22.Huang, Q., Huang, Y.-C., Ko, K.-T., & Iversen, V. B. (2011). Loss performance modeling for hierarchical heterogeneous wireless networks with speed-sensitive call admission control. IEEE Transactions on Vehicular Technology, 60(5), 2209–2223.Bonald, T., & Roberts, J. W. (2003). Congestion at flow level and the impact of user behaviour. Computer Networks, 42, 521–536.Lee, Y. L., Chuah, T. C., Loo, J., & Vinel, A. (2014). Recent advances in radio resource management for heterogeneous LTE/LTE-A networks. IEEE Communications Surveys & Tutorials, 16(4), 2142–2180.Rappaport, S. S., & Hu, L.-R. (1994). Microcellular communication systems with hierarchical macrocell overlays: Traffic performance models and analysis. Proceedings of the IEEE, 82(9), 1383–1397.Ge, X., Han, T., Zhang, Y., Mao, G., Wang, C.-X., Zhang, J., et al. (2014). Spectrum and energy efficiency evaluation of two-tier femtocell networks with partially open channels. IEEE Transactions on Vehicular Technology, 63(3), 1306–1319.Song, W., Jiang, H., & Zhuang, W. (2007). Performance analysis of the WLAN-first scheme in cellular/WLAN interworking. IEEE Transactions on Wireless Communications, 6(5), 1932–1952.Ge, X., Martinez-Bauset, J., Gasares-Giner, V., Yang, B., Ye, J., & Chen, M. (2013). Modeling and performance analysis of different access schemes in two-tier wireless networks. In IEEE Globecom (pp. 4402–4407).Tsai, H.-M., Pang, A.-C., Lin, Y.-C., & Lin, Y.-B. (2005). Repacking on demand for hierarchical cellular networks. Wireless Networks, 11(6), 719–728.Maheshwari, K., & Kumar, A. (2000). Performance analysis of microcellization for supporting two mobility classes in cellular wireless networks. IEEE Transactions on Vehicular Technology, 49(2), 321–333.Whiting, P., & McMillan, D. (1990). Modeling for repacking in cellular radio. In 7th UK Teletraffic Symposium, IEE, Durham.Kelly, F. (1989). Fixed point models of loss networks. The Journal of the Australian Mathematical Society. Series B. Applied Mathematics, 31(02), 204–218.McMillan, D. (1991). Traffic modelling and analysis for cellular mobile networks. In A. Jensen & V. Iversen (Eds.), Proceedigs of ITC-13 (pp. 627–632). IAC. Copenhaguen: Elsevier Science.Fu, H.-L., Lin, P., & Lin, Y.-B. (2012). Reducing signaling overhead for femtocell/macrocell networks. IEEE Transactions on Mobile Computing, 12(8), 1587–1597.Eklundh, B. (1986). Channel utilization and blocking probability in a cellular mobile telephone system with directed retry. IEEE Transactions on Communications, 37, 329–337.Karlsson, J., & Eklundh, B. (1989). A cellular telephone system with load sharing—An enhancement of directed retry. IEEE Transactions on Communications, 37(5), 530–535.Nelson, R. (1995). Probability, stochastic processes and queueing theory. New York: Springer.Iversen, V.B. (Aug. 1987). The exact evaluation of multi-service loss systems with access control. In Proceedings of the Seventh Nordic Teletraffic Seminar (NTS-7) (Vol. 31, pp. 56–61) Lund, (Sweden).Ross, K. W. (1995). Multiservice loss models for broadband telecommunication networks. New York: Springer.Lin, Y.-B., & Mak, V. W. (1994). Eliminating the boundary effect of a large-scale personal communication service network simulation. ACM Transactions on Modeling and Computer Simulation (TOMACS), 4(2), 165–190.Karray, M.K. (2010). Evaluation of the blocking probability and the throughput in the uplink of wireless cellular networks. In IEEE ComNet (pp. 1–8)

QoS Analysis for a Non-Preemptive Continuous Monitoring and Event Driven WSN Protocol in Mobile Environments

Evolution in wireless sensor networks (WSNs) has allowed the introduction of new applications with increased complexity regarding communication protocols, which have to ensure that certain QoS parameters are met. Specifically, mobile applications require the system to respond in a certain manner in order to adequately track the target object. Hybrid algorithms that perform Continuous Monitoring (CntM) and Event-Driven (ED) duties have proven their ability to enhance performance in different environments, where emergency alarms are required. In this paper, several types of environments are studied using mathematical models and simulations, for evaluating the performance of WALTER, a priority-based nonpreemptive hybrid WSN protocol that aims to reduce delay and packet loss probability in time-critical packets. First, randomly distributed events are considered. This environment can be used to model a wide variety of physical phenomena, for which report delay and energy consumption are analyzed by means of Markov models. Then, mobile-only environments are studied for object tracking purposes. Here, some of the parameters that determine the performance of the system are identified. Finally, an environment containing mobile objects and randomly distributed events is considered. It is shown that by assigning high priority to time-critical packets, report delay is reduced and network performance is enhanced.This work was partially supported by CONACyT under Project 183370. The research of Vicent Pla has been supported in part by the Ministry of Economy and Competitiveness of Spain under Grant TIN2013-47272-C2-1-R.Leyva Mayorga, I.; Rivero-Angeles, ME.; Carreto-Arellano, C.; Pla, V. (2015). QoS Analysis for a Non-Preemptive Continuous Monitoring and Event Driven WSN Protocol in Mobile Environments. International Journal of Distributed Sensor Networks. 2015:1-16. https://doi.org/10.1155/2015/471307S1162015Arampatzis, T., Lygeros, J., & Manesis, S. (s. f.). A Survey of Applications of Wireless Sensors and Wireless Sensor Networks. Proceedings of the 2005 IEEE International Symposium on, Mediterrean Conference on Control and Automation Intelligent Control, 2005. doi:10.1109/.2005.1467103Ramachandran, C., Misra, S., & Obaidat, M. S. (2008). A probabilistic zonal approach for swarm-inspired wildfire detection using sensor networks. International Journal of Communication Systems, 21(10), 1047-1073. doi:10.1002/dac.937Misra, S., Singh, S., Khatua, M., & Obaidat, M. S. (2013). Extracting mobility pattern from target trajectory in wireless sensor networks. International Journal of Communication Systems, 28(2), 213-230. doi:10.1002/dac.2649Heinzelman, W. B., Chandrakasan, A. P., & Balakrishnan, H. (2002). An application-specific protocol architecture for wireless microsensor networks. IEEE Transactions on Wireless Communications, 1(4), 660-670. doi:10.1109/twc.2002.804190Younis, O., & Fahmy, S. (s. f.). Distributed clustering in ad-hoc sensor networks: a hybrid, energy-efficient approach. IEEE INFOCOM 2004. doi:10.1109/infcom.2004.1354534Manjeshwar, A., & Agrawal, D. P. (s. f.). TEEN: a routing protocol for enhanced efficiency in wireless sensor networks. Proceedings 15th International Parallel and Distributed Processing Symposium. IPDPS 2001. doi:10.1109/ipdps.2001.925197Manjeshwar, A., & Agrawal, D. P. (2002). APTEEN: a hybrid protocol for efficient routing and comprehensive information retrieval in wireless. Proceedings 16th International Parallel and Distributed Processing Symposium. doi:10.1109/ipdps.2002.1016600Sharif, A., Potdar, V., & Rathnayaka, A. J. D. (2010). Prioritizing Information for Achieving QoS Control in WSN. 2010 24th IEEE International Conference on Advanced Information Networking and Applications. doi:10.1109/aina.2010.166Alappat, V. J., Khanna, N., & Krishna, A. K. (2011). Advanced Sensor MAC protocol to support applications having different priority levels in Wireless Sensor Networks. 2011 6th International ICST Conference on Communications and Networking in China (CHINACOM). doi:10.1109/chinacom.2011.6158175Alam, K. M., Kamruzzaman, J., Karmakar, G., & Murshed, M. (2012). Priority Sensitive Event Detection in Hybrid Wireless Sensor Networks. 2012 21st International Conference on Computer Communications and Networks (ICCCN). doi:10.1109/icccn.2012.6289220Raja, A., & Su, X. (2008). A Mobility Adaptive Hybrid Protocol for Wireless Sensor Networks. 2008 5th IEEE Consumer Communications and Networking Conference. doi:10.1109/ccnc08.2007.159Srikanth, B., Harish, M., & Bhattacharjee, R. (2011). An energy efficient hybrid MAC protocol for WSN containing mobile nodes. 2011 8th International Conference on Information, Communications & Signal Processing. doi:10.1109/icics.2011.6173629Lee, Y.-D., Jeong, D.-U., & Lee, H.-J. (2011). Empirical analysis of the reliability of low-rate wireless u-healthcare monitoring applications. International Journal of Communication Systems, 26(4), 505-514. doi:10.1002/dac.1360Deepak, K. S., & Babu, A. V. (2013). Improving energy efficiency of incremental relay based cooperative communications in wireless body area networks. International Journal of Communication Systems, 28(1), 91-111. doi:10.1002/dac.2641Yuan Li, Wei Ye, & Heidemann, J. (s. f.). Energy and latency control in low duty cycle MAC protocols. IEEE Wireless Communications and Networking Conference, 2005. doi:10.1109/wcnc.2005.1424589Bianchi, G. (2000). Performance analysis of the IEEE 802.11 distributed coordination function. IEEE Journal on Selected Areas in Communications, 18(3), 535-547. doi:10.1109/49.840210Wei Ye, Heidemann, J., & Estrin, D. (s. f.). An energy-efficient MAC protocol for wireless sensor networks. Proceedings.Twenty-First Annual Joint Conference of the IEEE Computer and Communications Societies. doi:10.1109/infcom.2002.101940