293 research outputs found
Underwater Acoustic Modems
No full text© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Due to the growing interest using underwater acoustic networks, there are more and more research papers about underwater communications. These papers are mainly focused on deployments and studies about the constraints of the underwater medium. The underwater acoustic channel is highly variable and the signal transmission can change according to environmental factors such as the temperature, pressure or salinity of the water. For this reason, it is important to know how these devices are developed and the maximum distance and data transfer rates they can achieve. To this end, this paper presents an exhaustive study of existing underwater acoustic modems where their main features are highlighted. We also review the main features of their hardware. All presented proposals in the research literature are compared with commercial underwater acoustic modems. Finally, we analyze different programs and improvements of existing network simulators that are often used to simulate and estimate the behavior of underwater networks.This work was supported by the Ministerio de Ciencia e Innovacion through the Plan Nacional de I+D+i 2008-2011 within the Subprograma de Proyectos de Investigacion Fundamental under Project TEC2011-27516. The associate editor coordinating the review of this paper and approving it for publication was Dr. Lei Shu. (Corresponding author: Jaime Lloret.)Sendra, Sandra;Lloret, Jaime;Jimenez, Jose M.;Parra-Boronat, L. (2015). Underwater Acoustic Modems. IEEE Sensors Journal. 16(11):4063-4071. https://doi.org/10.1109/JSEN.2015.2434890S40634071161
El Campus de Gandia de la UPV imparte dos charlas de promoción científica en Alzira y Algemesí
No full textLugar: IES Sant Vicent Ferrer d'Algemesí y CIPFP Luís Suñer
Protagonistas: Sandra Sendra
Tipo de evento: charlasSandra Sendra, investigadora de la Universitat Politècnica de València en el Campus de Gandia ha ofrecido esta semana dos charlas de promoción científica, en el IES Sant Vicent Ferrer de Algemesí y en el Centre Integrat Públic de Formació Professional Luis Suñer Sanchis de Alzira, en las que ha acercado al área de estudio e investigación de las Telecomunicaciones a casi 100 estudiantes preuniversitarios, poniendo como ejemplo los contenidos del Grado en Ingeniería de Sistemas de Telecomunicación, Sonido e Imagen que imparte la UPV en Gandia.Barrancos Gregori, S. (2012). El Campus de Gandia de la UPV imparte dos charlas de promoción científica en Alzira y Algemesí. https://riunet.upv.es/handle/10251/14965
Optimizing IoT network lifetime through an enhanced hybrid energy harvesting system
No full text[EN] The growing need for sustainable and renewable energy sources has become critical with the Internet of Things (IoT) advancement. IoT relies on low-power, battery-operated devices, but the limited lifespan of these batteries requires frequent recharging or replacement, which is costly and time-consuming. Researchers have proposed energy harvesting systems that capture sustainable ambient energy from the environment to address this issue. This paper presents a hybrid system for harvesting sustainable energy from solar and wind sources. The system features a boost converter controlled by a novel hybrid method combining the Honey Badger Algorithm (HBA) and Harris Hawks Optimization (HHO). This method maximizes power extraction from solar and wind sources, enhancing overall system efficiency. Additionally, the system includes an innovative energy management algorithm that selects the most powerful input source while protecting the storage battery from overcharging or complete depletion, thereby extending its lifespan. The proposed design is validated through MATLAB/Simulink simulations. The HHO-HBA MPPT is compared with existing MPPT methods, evaluating efficiency, battery charge curves, and IoT network energy status. Simulation results show that the proposed approach significantly increases network longevity, offering a cost-effective and sustainable solution for the energy needs of Wireless Sensor Network (WSN)-IoT devices.Rabah, S.;Zaier, A.;Sendra, Sandra;Lloret, Jaime;Dahman, H. (2025). Optimizing IoT network lifetime through an enhanced hybrid energy harvesting system. Sustainable Computing: Informatics and Systems. 46. https://doi.org/10.1016/j.suscom.2025.101081S4
A Secure and Low-Energy Zone-based Wireless Sensor Networks Routing Protocol for Pollution Monitoring
No full text[EN] Sensor networks can be used in many sorts of environments. The increase of pollution and carbon footprint are nowadays an important environmental problem. The use of sensors and sensor networks can help to make an early detection in order to mitigate their effect over the medium. The deployment of wireless sensor networks (WSNs) requires high-energy efficiency and secures mechanisms to ensure the data veracity. Moreover, when WSNs are deployed in harsh environments, it is very difficult to recharge or replace the sensor's batteries. For this reason, the increase of network lifetime is highly desired. WSNs also work in unattended environments, which is vulnerable to different sort of attacks. Therefore, both energy efficiency and security must be considered in the development of routing protocols for WSNs. In this paper, we present a novel Secure and Low-energy Zone-based Routing Protocol (SeLeZoR) where the nodes of the WSN are split into zones and each zone is separated into clusters. Each cluster is controlled by a cluster head. Firstly, the information is securely sent to the zone-head using a secret key; then, the zone-head sends the data to the base station using the secure and energy efficient mechanism. This paper demonstrates that SeLeZoR achieves better energy efficiency and security levels than existing routing protocols for WSNs.Mehmood, A.; Lloret, J.; Sendra, S. (2016). A Secure and Low-Energy Zone-based Wireless Sensor Networks Routing Protocol for Pollution Monitoring. Wireless Communications and Mobile Computing. 16(17):2869-2883. https://doi.org/10.1002/wcm.2734S286928831617Sendra S Deployment of efficient wireless sensor nodes for monitoring in rural, indoor and underwater environments 2013Javaid, N., Qureshi, T. N., Khan, A. H., Iqbal, A., Akhtar, E., & Ishfaq, M. (2013). EDDEEC: Enhanced Developed Distributed Energy-efficient Clustering for Heterogeneous Wireless Sensor Networks. Procedia Computer Science, 19, 914-919. doi:10.1016/j.procs.2013.06.125Garcia, M., Sendra, S., Lloret, J., & Canovas, A. (2011). Saving energy and improving communications using cooperative group-based Wireless Sensor Networks. Telecommunication Systems, 52(4), 2489-2502. doi:10.1007/s11235-011-9568-3Garcia, M., Lloret, J., Sendra, S., & Rodrigues, J. J. P. C. (2011). Taking Cooperative Decisions in Group-Based Wireless Sensor Networks. Cooperative Design, Visualization, and Engineering, 61-65. doi:10.1007/978-3-642-23734-8_9Garcia, M., & Lloret, J. (2009). A Cooperative Group-Based Sensor Network for Environmental Monitoring. Cooperative Design, Visualization, and Engineering, 276-279. doi:10.1007/978-3-642-04265-2_41Jain T Wireless environmental monitoring system (wems) using data aggregation in a bidirectional hybrid protocol In Proc of the 6th International Conference ICISTM 2012 2012Senouci, M. R., Mellouk, A., Senouci, H., & Aissani, A. (2012). Performance evaluation of network lifetime spatial-temporal distribution for WSN routing protocols. Journal of Network and Computer Applications, 35(4), 1317-1328. doi:10.1016/j.jnca.2012.01.016Heinzelman WR Chandrakasan A Balakrishnan H Energy-efficient communication protocol for wireless microsensor networks In proc of the 33rd Annual Hawaii International Conference on System Sciences 2000 2000Xiangning F Yulin S Improvement on LEACH protocol of wireless sensor network In proc of the 2007 International Conference on Sensor Technologies and Applications SensorComm 2007 2007Tong M Tang M LEACH-B: an improved LEACH protocol for wireless sensor network In proc of the 6th International Conference on Wireless Communications Networking and Mobile Computing WiCOM 2010 2010Mohammad El-Basioni, B. M., Abd El-kader, S. M., Eissa, H. S., & Zahra, M. M. (2011). An Optimized Energy-aware Routing Protocol for Wireless Sensor Network. Egyptian Informatics Journal, 12(2), 61-72. doi:10.1016/j.eij.2011.03.001Younis O Fahmy S Distributed clustering in ad-hoc sensor networks: a hybrid, energy-efficient approach In proc of the Twenty-third Annual Joint Conference of the IEEE Computer and Communications Societies INFOCOM 2004 2004Noack, A., & Spitz, S. (2009). Dynamic Threshold Cryptosystem without Group Manager. Network Protocols and Algorithms, 1(1). doi:10.5296/npa.v1i1.161Nasser, N., & Chen, Y. (2007). SEEM: Secure and energy-efficient multipath routing protocol for wireless sensor networks. Computer Communications, 30(11-12), 2401-2412. doi:10.1016/j.comcom.2007.04.014Alippi, C., Camplani, R., Galperti, C., & Roveri, M. (2011). A Robust, Adaptive, Solar-Powered WSN Framework for Aquatic Environmental Monitoring. IEEE Sensors Journal, 11(1), 45-55. doi:10.1109/jsen.2010.2051539Parra L Sendra S Jimenez JM Lloret J Smart system to detect and track pollution in marine environments, in proc. of the 2015 2015 1503 1508Atto, M., & Guy, C. (2014). Routing Protocols and Quality of Services for Security Based Applications Using Wireless Video Sensor Networks. Network Protocols and Algorithms, 6(3), 119. doi:10.5296/npa.v6i3.5802Liu, Z., Zheng, Q., Xue, L., & Guan, X. (2012). A distributed energy-efficient clustering algorithm with improved coverage in wireless sensor networks. Future Generation Computer Systems, 28(5), 780-790. doi:10.1016/j.future.2011.04.019Bri D Sendra S Coll H Lloret J How the atmospheric variables affect to the WLAN datalink layer parameters 2010Ganesh, S., & Amutha, R. (2013). Efficient and secure routing protocol for wireless sensor networks through SNR based dynamic clustering mechanisms. Journal of Communications and Networks, 15(4), 422-429. doi:10.1109/jcn.2013.000073Amjad M 2014 Energy efficient multi level and distance clustering mechanism for wireless sensor networksMeghanathan, N. (2015). A Generic Algorithm to Determine Maximum Bottleneck Node Weight-based Data Gathering Trees for Wireless Sensor Networks. Network Protocols and Algorithms, 7(3), 18. doi:10.5296/npa.v7i3.796
An edge computing wireless sensor network for diagnosing orange fruit disease
No full text[EN] This study introduces an innovative Edge Computing Wireless Sensor Network and Designing a new algorithm for diagnosing orange fruit diseases. The network combines Raspberry Pi using wireless technologies like Zigbee and LoRa with Wireless Mesh Routers using Wireless Technologies like LoRa and Cellular technologies. By using a new system that includes a YOLOv8 model and an image processing algorithm that detects the color spectrum of the diseased part of the fruit, it is possible to quickly identify certain diseases, such as canker, black spot, and melanosis. The system achieves a high accuracy of 92.2% in disease detection. This cost-effective and efficient solution offers farmers a practical tool for early disease detection, enabling timely interventions to protect crops and improve overall agricultural outcomes. In this study, in connection with the proposed algorithm, 97 images of diseased orange fruit, including Canker, melanosis, and black spot, as well as healthy oranges have been tested. It has also been tested in an orange orchard. The proposed new model successfully identified orange black spot disease with 30 correct detections out of 32 images and 2 errors, melanosis disease with 18 correct detections out of 21 images and 3 errors, canker disease with 9 correct detections out of 11 images and 2 errors, and 33 images of healthy oranges fruits with 100% accuracy. The Python codes for the proposed model and the dataset used in this study are available in a GitHub repository and accessible to the public.Ministerio de Ciencia e Innovacion, PID2020114467RRC33/AEI/https://doi.org/10.13039/501100011033,PID2020-114467RRC33/AEI/10.13039/501100011033,PID2020-114467RRC33/AEI/10.13039/501100011033,PID2020-114467RRC33/AEI/10.13039/501100011033, Ministerio de Economia y Competitividad, Spain, TED2021-131040BC31, TED2021-131040BC31, TED2021-131040BC31, TED2021-131040BC31Foroughi, A.;Lloret, Jaime;Jimenez, Jose M.;Sendra, Sandra (2025). An edge computing wireless sensor network for diagnosing orange fruit disease. Cluster Computing. 28(5). https://doi.org/10.1007/s10586-024-04999-yS28
Underwater Ad Hoc Wireless Communication for Video Delivery
No full text[EN] Due to the high attenuation of the water at high frequencies, underwater communications in freshwater are not being investigated so much. Many current underwater communication systems are based on acoustic or optical techniques. The use of electromagnetic (EM) waves in this medium, even in short distances, increases the bandwidth, which definitely implies a great advantage for video delivery. Related literature shows that the speed propagation and absorption coefficient in freshwater are independent of the working frequency of the transmitted signals. No work shows any temperature dependence with the electromagnetic waves propagation. In this paper, we study the EM wave's behavior when we vary the temperature at 2.4 GHz in underwater freshwater environments. We are going to study the signal behavior in this medium in order to deliver video images from the marine fish cages with the purpose of monitoring the fish activity. To carry out our study, we fix the water conditions and measure the maximum distance as a function of several network parameters such as the working frequency, data transfer rate, modulations and water temperature. Our results show that some combinations of temperature and working frequency generate better results than others. Finally, we will compare our results with the statements extracted from other works.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), by the postdoctoral Grant "contratacion de doctores para el acceso al sistema espanol de ciencia, tecnologia e innovacion, en estructuras de Investigacion de la UPV (PAID-10-14)" by the "Universitat Politecnica de Valencia" and by the "Programa para la Formacion de Personal Investigador-(FPI-2015-S2-884)" by the "Universitat Politecnica de Valencia".Sendra, S.; Lloret, J.; Jimenez, JM.; Ghafoor, KZ. (2017). Underwater Ad Hoc Wireless Communication for Video Delivery. Wireless Personal Communications. 96(4):5123-5144. https://doi.org/10.1007/s11277-016-3732-8S51235144964Lloret, J. (2013). Underwater sensor nodes and networks. Sensors, 13(9), 11782–11796.Poncela, J., Aguayo, M. C., & Otero, P. (2012). Wireless underwater communications. Wireless Personal Communications, 64(3), 547–560.Men, Shaoyang, Chargé, Pascal, & Pillement, Sébastien. (2015). A robust and energy efficient cooperative spectrum sensing scheme in cognitive wireless sensor networks. Network Protocols and Algorithms, 7(3), 140–156.Garcia, M., Sendra, S., Atenas, M., & Lloret, J. (2011). Underwater wireless ad hoc networks: A survey, book: Mobile ad hoc networks: Current status and future trends (pp. 379–411). Boca Raton: CRC Press.Sendra, S., Lloret, J., García, M., & Toledo, J. F. (2011). Power saving and energy optimization techniques for wireless sensor neworks. Journal of communications, 6(6), 439–459.Smart, J. H. (2005). Underwater optical communications systems part 1: Variability of water optical parameters. In Military communications conference, (MILCOM 2005) Atlantic City, New Jersey (pp. 1140–1146). October 17–20, 2005.Lloret, J., Sendra, S., Ardid, M., & Rodrigues, J. J. (2012). Underwater wireless sensor communications in the 2.4 GHz ISM frequency band. Sensors, 12(4), 4237–4264.Akyildiz, I. F., Pompili, D., & Melodia, T. (2004). Challenges for efficient communication in underwater acoustic sensor networks. ACM Sigbed Review, 1(2), 3–8.Che, X., Wells, I., Dickers, G., Kear, P., & Gong, X. (2010). Re-evaluation of RF electromagnetic communication in underwater sensor networks. IEEE Communications Magazine, 48(12), 143–151.Chakraborty, U., Tewary, T., & Chatterjee, R. P. (2009). Exploiting the loss-frequency relationship using RF communication in underwater communication networks, In The 4th international conference on computers and devices for communication, (CODEC 2009) Kolkata, India, December 14–16, 2009.Balanis, C. A. (1989). Advanced engineering electromagnetics. New York, NY: Wiley.Somaraju, R., & Trumpf, J. (2006). Frequency, temperature and salinity variation of the permittivity of seawater. IEEE Transactions on Antennas and Propagation, 54(11), 3441–3448.Zahedi, Y. K., Ghafghazi, H., Ariffin, S. H. S., and Kassim, N. M. (2011). Feasibility of electromagnetic communication in underwater wireless sensor networks. In Informatics engineering and information science (pp. 614–623). Berlin: Springer.McEachen, J. C., & Casias, J. (2008). Performance of a wireless unattended sensor network in a freshwater environment. In Proceedings of the IEEE 41st annual Hawaii international conference on system sciences 2008, Waikoloa, Big Island, Hawaii (pp. 496–496). January 7–10, 2008.Sendra, S., Lamparero, J. V., Lloret, J.,& Ardid, M. (2012). Study of the optimum frequency at 2.4 GHz ISM band for underwater wireless ad hoc communications. In Ad hoc, mobile, and wireless networks, (Vol. 7363, pp. 260–273). Berlin: Springer.Sendra, S., Lamparero, J. V., Lloret, J., & Ardid, M. (2011).Underwater communications in wireless sensor networks using WLAN at 2.4 Ghz. In The 8th IEEE international conference on mobile ad hoc and sensor systems (IEEE MASS 2011), Valencia (Spain) October 17–22, 2011.Atenas, M., Sendra, S., Garcia, M., & Lloret, J., (2010), IPTV performance in IEEE 802.11n WLANs. In Proceedings of the IEEE global communications conference (IEEE Globecom 2010), Miami (USA) (pp. 929–933). December 6–10, 2010.Jimenez, J. M., Diaz, J. R., Sendra, S., & Lloret, J. (2014). Choosing the best video compression codec depending on the recorded environment. In Globecom 2014—communications software, services and multimedia symposium, Austin, Texas (USA), December 8–12, 2014.Partan, J., Kurose, J., & Levine, B. N. (2007). A survey of practical issues in underwater networks. ACM SIGMOBILE Mobile Computing and Communications Review, 11(4), 23–33.Jiang, S., & Georgakopoulos, S. (2011). Electromagnetic wave propagation into fresh water. Journal of Electromagnetic Analysis and Applications, 3(07), 261.Abdou, A. A., Shaw, A., Mason, A., Al-Shamma’a, A., Cullen, J., & Wylie, S. (2011). Electromagnetic (EM) wave propagation for the development of an underwater wireless sensor network (WSN). In IEEE sensors Limerick, Ireland October 28–31, 2011.Wang Z., Zeitoun A., & Jamin S., (2003). Challenges and lessons learned in measuring path RTT for proximity-based applications. In Proceedings of the 6th workshop on passive and active measurement 2003 San Diego, CA, USA.Chaitanya, D. E., Sridevi, C. V., & Rao, G. S. B. (2011). Path loss analysis of underwater communication systems, IEEE Students’ technology symposium (TechSym 2011) Kharagpur, India, January 14–16, 2011.Kim, B. C., & Lu, I. T. (2000). Parameter study of OFDM underwater communications system. In OCEANS 2000 MTS/IEEE conference and exhibition providence, Rhode Island–The Ocean State, September 11–14, 2000.Wells, I., Davies, A., Che, X., Kear, P., Dickers, G., Gong, X., & Rhodes, M. (2009). Node pattern simulation of an undersea sensor network using RF electromagnetic communications. In Ultra modern telecommunications & workshops, St. Petersburg, Russia, October 12–14, 2009.Al-Shamma’a, A., Shaw, A., & Saman, S. (2004). Propagation of electromagnetic waves at MHz frequencies through seawater. Transactions on IEEE Antennas and Propagation, 52(11), 2843–2849.Shaw, A., Wylie, S. R., & Toal, D. (2006). Experimental investigations of electromagnetic wave propagation in seawater. In 36th European microwave conference, Manchester, UK (pp. 572–575). September 10–15, 2006.Cella, U. M., Johnstone, R., & Shuley, N. (2009). Electromagnetic wave wireless communication in shallow water coastal environment: Theoretical analysis and experimental results. In Proceedings of the fourth ACM international workshop on underwater networks Berkeley, California, USA. November 3, 2009.Sendra, S., Lloret, J., Rodrigues, J. J., & Aguiar, J. M. (2013). Underwater wireless communications in freshwater at 2.4 GHz. IEEE Communications Letters, 17(9), 1794–1797.Eureqa Formulize web site. (2012). http://formulize.nutonian.com (Last Access: November 28, 2015).Lloret, J., Garcia, M., Sendra, S., & Lloret, G. (2014). An underwater wireless group-based sensor network for marine fish farms sustainability monitoring. Telecommunication Systems, 60(1), 67–84.Garcia, M., Sendra, S., Lloret, G., & Lloret, J. (2011). Monitoring and control sensor system for fish feeding in marine fish farms. IET Communications, 5(12), 1682–1690.Heidemann, J., Ye, W., Wills, J., Syed, A., & Li, Y. (2006). Research challenges and applications for underwater sensor networking. In IEEE wireless communications and networking conference (WCNC 2006), Las Vegas, NV USA (pp. 228–235). April 3–6, 2006.Liu, L., Zhou, S., & Cui, J. H. (2008). Prospects and problems of wireless communication for underwater sensor networks. Wireless Communications and Mobile Computing, 8(8), 977–994.Parra, L., Sendra, S., Vincent-Vela, M. C., Garcia-Gabaldón, M., & Lloret, J. (2015). Improving the signal propagation at 2.4 GHz using conductive membranes. IEEE Systems Journal. doi: 10.1109/JSYST.2015.2496204 .Lloret, J., Sendra, S., Garcia, M., Lloret, G., Group-based underwater wireless sensor network for marine fish farms, In Proceedings of the 2011 IEEE GLOBECOM workshops Houston, Texas, USA (pp. 115–119). December 5–9, 2011.Lombardo, A., Panarello, C., & Schembra, G. (2013). EE-ARQ: A Green ARQ-based algorithm for the transmission of video streams on noise wireless channels. Network Protocols and Algorithms, 5(1), 41–70.He, D., Zhang, Y., & Chen, J. (2014). Cryptanalysis and improvement of an anonymous authentication protocol for wireless access networks. Wireless Personal Communications, 74(2), 229–243
Advances in Green Communications and Networking
No full textLloret, J.; Sendra, S.; Macias-Lopez, E. (2019). Advances in Green Communications and Networking. Mobile Networks and Applications. 24(2):653-656. https://doi.org/10.1007/s11036-019-01212-yS65365624
Authentication protocol for the Internet of Drones with fog computing based on aggregate signatures for forest inventory
No full text[EN] The Internet of Drones (IoD) has become increasingly important in applications such as forest inventory, leveraging advanced sensors and internet connectivity to enable efficient data collection. Compared to traditional methods, IoD offers superior cost-effectiveness. However, its reliance on public channels, unreliable connectivity, and dynamic environments poses significant security and privacy challenges. Safeguarding forest inventory data is essential to maintaining accuracy, preventing unauthorized access, and mitigating the risk of data manipulation, which can lead to suboptimal management decisions. To address these concerns, it is essential to design a lightweight authentication protocol that secures IoD communication, accounts for network bandwidth limitations and scalability, and supports integration with emerging technologies. This manuscript introduces a new Authentication and Key Agreement (AKA) protocol specifically designed for the Internet of Drones (IoD), leveraging asymmetric cryptography and aggregate signatures to enhance security and privacy in forest inventories with fog computing. Its robustness was confirmed through informal and formal security analyses by the AVISPA tool and the ROR model, demonstrating resistance to known attacks and superior communication, computational, and energy performance compared to existing protocols.This work was supported by the National Council for Scientific and Technological Development (CNPq) , Brazil.Sousa, MDJ.;Gondim, PRL.;Sendra, Sandra;Lloret, Jaime (2026). Authentication protocol for the Internet of Drones with fog computing based on aggregate signatures for forest inventory. Ad Hoc Networks. 181. https://doi.org/10.1016/j.adhoc.2025.104034S18
Saving Energy and Improving Communications using Cooperative Group-based Wireless Sensor Networks
No full textWireless Sensor Networks (WSNs) can be used in many real applications (environmental monitoring, habitat monitoring, health, etc.). The energy consumption of each sensor should be as lower as possible, and methods for grouping nodes can improve the network performance. In this work, we show how organizing sensors in cooperative groups can reduce the global energy consumption of the WSN. We will also show that a cooperative group-based network reduces the number of the messages transmitted inside the WSNs, which implieasa reduction of energy consumed by the whole network, and, consequently, an increase of the network lifetime. The simulations will show how the number of groups improves the network performance. © 2011 Springer Science+Business Media, LLC.García Pineda, M.; Sendra Compte, S.; Lloret, J.; Canovas Solbes, A. (2013). Saving Energy and Improving Communications using Cooperative Group-based Wireless Sensor Networks. Telecommunication Systems. 52(4):2489-2502. doi:10.1007/s11235-011-9568-3S24892502524Akyildiz, I. F., Su, W., Sankarasubramaniam, Y., & Cayirci, E. (2002). Wireless sensor networks: a survey. Journal of Computer Networks, 38(4), 393–422.Garcia, M., Bri, D., Sendra, S., & Lloret, J. (2010). Practical deployments of wireless sensor networks: a survey. Journal on Advances in Networks and Services, 3(1&2), 1–16.Lloret, J., Garcia, M., Bri, D., & Sendra, S. (2009). A wireless sensor network deployment for rural and forest fire detection and verification. Sensors, 9(11), 8722–8747.Mainwaring, A., Polastre, J., Szewczyk, R., & Culler, D. (2002). Wireless sensor networks for habitat monitoring. In ACM workshop on sensor networks and applications (WSNA’02), Atlanta, GA, USA, September.Garcia, M., Sendra, S., Lloret, G., & Lloret, J. (2010, in press). Monitoring and control sensor system for fish feeding in marine fish farms. IET Communications, pp. 1–9. doi: 10.1049/iet-com.2010.0654 .Sinha, A., & Chandrakasan, A. (2001). Dynamic power management in wireless sensor networks. IEEE Design & Test of Computers, 18(2), 62–74.Garcia, M., Coll, H., Bri, D., & Lloret, J. (2008). Using MANET protocols in wireless sensor and actor networks. In The second international conference on sensor technologies and applications (SENSORCOMM 2008), Cap Esterel, Costa Azul, France, 25–31 August.Lloret, J., García, M., Boronat, F., & Tomás, J. (2008). MANET protocols performance in group-based networks. In Wireless and mobile networking: Vol. 284 (Chap. 13, pp. 161–172). Berlin, Heidelberg, Boston: Springer.Lloret, J., García, M., & Tomás, J. (2008). Improving mobile and ad-hoc networks performance using group-based topologies. In Wireless sensor and actor networks 2008 (WSAN 2008), Ottawa, Canada, 14–15 July. Berlin, Heidelberg, New York: Springer.Lloret, J., Palau, C., Boronat, F., & Tomas, J. (2008). Improving networks using group-based topologies. Journal of Computer Communications, 31(14), 3438–3450.Lloret, 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.Lloret, J., García, M., Boronat, F., & Tomás, J. (2008). MANET protocols performance in group-based networks. In 10th IFIP international conference on mobile and wireless communications networks (MWCN 2008), Toulouse, France, 30 September–2 October.Garcia, M., Sendra, S., Lloret, J., & Lacuesta, R. (2010). Saving energy with cooperative group-based wireless sensor networks. In LNCS: Vol. 6240. Cooperative design, visualization, and engineering: CDVE 2010 (pp. 231–238), September. Berlin: Springer.Lloret, J., Sendra, S., Coll, H., & García, M. (2010). Saving energy in wireless local area sensor networks. Computer Journal, 53(10), 1658–1673.Meiyappan, S. S., Frederiks, G., & Hahn, S. (2006). 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