A Secure and Low-Energy Zone-based Wireless Sensor Networks Routing Protocol for Pollution Monitoring

[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

Advances in Green Communications and Networking

Lloret, 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

Comunicaciones inalámbricas subacuáticas a 2,4 GHz para la transmisión de datos con altas tasas de transferencia

Underwater communication networks have become an important field of research for many research groups in the recent years. These types of networks are envisioned to enable applications for oceanographic data collection, ocean sampling, environmental and pollution monitoring, in other. In order to communicate us through the underwater networks, we can use acoustic links, links based on electromagnetic waves and optical signals. Currently, the applications are based in acoustic systems because the sound waves are better adapted to the aquatic environment, although the characteristics of these waves have important limitations. The main problem when using low frequencies is the lack of bandwidth to transmit high data rate. Sometimes wireless sensors must be placed quite close in order to obtain accurate measures from the water, so higher frequency bands could be used in special cases. In this Project of final grade, we measure the maximum coverage distance of underwater wireless sensors when they are placed at about 15 cm underneath the water surface, without having wireless coverage from the air side. In order to characterize the behavior of this communication method, we measure, the number of lost packets and round trip time of pings, for transfer rates of 1 Mbps, 2, 5.5 and 11, working with different modulations at different frequencies for different distances. Las redes subacuáticas de comunicación se han convertido, en los últimos años, en un importante campo de investigación para muchos grupos de investigación. Este tipo de redes se prevén que sean utilizadas, para posibilitar el desarrollo de aplicaciones para la recolección de datos oceanográficos, toma de muestras del océano, la monitorización de contaminación ambiental, en otros. Para poder comunicarnos a través de las redes subacuáticas, podemos utilizar, enlaces acústicos, enlaces, basados en ondas electromagnéticas y señales ópticas. Actualmente, las aplicaciones se basan en sistemas acústicos debido a que las ondas acústicas se adaptan mejor al medio acuático, aunque por las características de este tipo de ondas, presenta limitaciones importantes. El principal problema al utilizar las frecuencias bajas es la falta de ancho de banda para transmitir datos a alta velocidad. Pero, existen casos, donde los sensores inalámbricos ser colocado muy cerca con el fin de obtener medidas exactas del agua, por lo que se podría utilizar las bandas de frecuencia más alta, en casos especiales. En este trabajo final de grado, se mide la distancia de cobertura máxima de sensores inalámbricos subacuáticos cuando se colocan a unos 15 cm por debajo de la superficie del agua, sin la necesidad de tener cobertura inalámbrica desde el lado del aire. Caracterizaremos en comportamiento de este método de comunicación, midiendo, número de paquetes perdidos y el tiempo de ida y vuelta de los pings, instantáneos y medios), para tasas de transferencia de 1 Mbps, 2, 5,5 y 11, que trabajan con distintas modulaciones, en diferentes frecuencias para diferentes distancias.Sendra Compte, S. (2011). Comunicaciones inalámbricas subacuáticas a 2,4 GHz para la transmisión de datos con altas tasas de transferencia. Universitat Politècnica de València. http://hdl.handle.net/10251/14605Archivo delegad

A Smart Architecture for Diabetic Patient Monitoring Using Machine Learning Algorithms

[EN] Continuous monitoring of diabetic patients improves their quality of life. The use of multiple technologies such as the Internet of Things (IoT), embedded systems, communication technologies, artificial intelligence, and smart devices can reduce the economic costs of the healthcare system. Different communication technologies have made it possible to provide personalized and remote health services. In order to respond to the needs of future intelligent e-health applications, we are called to develop intelligent healthcare systems and expand the number of applications connected to the network. Therefore, the 5G network should support intelligent healthcare applications, to meet some important requirements such as high bandwidth and high energy efficiency. This article presents an intelligent architecture for monitoring diabetic patients by using machine learning algorithms. The architecture elements included smart devices, sensors, and smartphones to collect measurements from the body. The intelligent system collected the data received from the patient, and performed data classification using machine learning in order to make a diagnosis. The proposed prediction system was evaluated by several machine learning algorithms, and the simulation results demonstrated that the sequential minimal optimization (SMO) algorithm gives superior classification accuracy, sensitivity, and precision compared to other algorithms.Rghioui, A.; Lloret, J.; Sendra, S.; Oumnad, A. (2020). A Smart Architecture for Diabetic Patient Monitoring Using Machine Learning Algorithms. Healthcare. 8(3):1-16. https://doi.org/10.3390/healthcare80303481168

Comparison of Online Platforms for the Review Process of Conference Paper

[EN] Organizing conferences requires the consideration of several aspects, such as the choice of the most appropriate platform to manage the received papers or the conference location, among others. To this goal, we are going to compare some of the most important review platforms, which allow us to host our conferences. In recent years,new systems based on software applications have emerged. This software can be downloaded from the developer websites. These give us more options to choose from. Keeping in mind some of the most important review platforms, we are going to compare the services that each one offers, as well as their advantages and disadvantages. In addition, we are going to show several statistics about the use of these platforms during recent years. This work can help the conference organizers choose the most appropriate platform to manage their conference.Parra, L.; Sendra, S.; Ficarelli, S.; Lloret, J. (2013). Comparison of Online Platforms for the Review Process of Conference Paper. IARIA XPS Press. 16-22. http://hdl.handle.net/10251/191354162

Evaluation of CupCarbon Network Simulator for Wireless Sensor Networks

[EN] Wireless sensor networks (WSNs) are a technology in continuous evolution with great future and a huge quantity of applications. The implementation and deployment of a WSN imply important expenses, so it is interesting to simulate the operation of our design before deploying it. In addition, WSNs are limited by a set of parameters such as the low processing capacity, low storing capacity or limited energy. Energy consumption is the most limiting parameter since the network stability and availability depends on the survival of the nodes. To check the correct operation of a network, currently, there are several network simulators and day by day new proposals are launched. This paper presents the evaluation of a new network simulator called CupCarbon. Along the document, we present the main characteristics of this simulator and check its operation by an example. To evaluate the ease of use of this new network simulator, we propose a modified version of Dijkstra algorithm. In addition of considering the cost route to calculate the best route, it considers the remaining energy in nodes as an additional parameter to evaluate the best route. CupCarbon allows implementing our proposal and the results show that our proposal is able to offer a more stable network with an increase of the network lifetime of the 20%. Finally, to extract some conclusions from our experiences, we compare the characteristics and results of CupCarbon with the most common network simulators currently used by researchers. Our conclusions point out that CupCarbon can be used as a complementary tool for those simulators that are not able to monitor the energy consumption in nodes. However, it needs some improvements to reach the level of functionality of the most used simulators. CupCarbon could be an interesting option for academic environments.López-Pavón, C.; Sendra, S.; Valenzuela-Valdés, JF. (2018). Evaluation of CupCarbon Network Simulator for Wireless Sensor Networks. Network Protocols and Algorithms. 10(2):1-27. https://doi.org/10.5296/npa.v10i2.13201S12710

Smart System for Bicarbonate Control in Irrigation for Hydroponic Precision Farming

[EN] Improving the sustainability in agriculture is nowadays an important challenge. The automation of irrigation processes via low-cost sensors can to spread technological advances in a sector very influenced by economical costs. This article presents an auto-calibrated pH sensor able to detect and adjust the imbalances in the pH levels of the nutrient solution used in hydroponic agriculture. The sensor is composed by a pH probe and a set of micropumps that sequentially pour the different liquid solutions to maintain the sensor calibration and the water samples from the channels that contain the nutrient solution. To implement our architecture, we use an auto-calibrated pH sensor connected to a wireless node. Several nodes compose our wireless sensor networks (WSN) to control our greenhouse. The sensors periodically measure the pH level of each hydroponic support and send the information to a data base (DB) which stores and analyzes the data to warn farmers about the measures. The data can then be accessed through a user-friendly, web-based interface that can be accessed through the Internet by using desktop or mobile devices. This paper also shows the design and test bench for both the auto-calibrated pH sensor and the wireless network to check their correct operation.The research leading to these results has received funding from "la Caixa" Foundation and Triptolemos Foundation. This work has also been partially supported by European Union through the ERANETMED (Euromediterranean Cooperation through ERANET joint activities and beyond) project ERANETMED3-227 SMARTWATIRCambra-Baseca, C.; Sendra, S.; Lloret, J.; Lacuesta, R. (2018). Smart System for Bicarbonate Control in Irrigation for Hydroponic Precision Farming. Sensors. 18(5):1-16. https://doi.org/10.3390/s18051333S116185Salley, S. W., Sleezer, R. O., Bergstrom, R. M., Martin, P. H., & Kelly, E. F. (2016). A long-term analysis of the historical dry boundary for the Great Plains of North America: Implications of climatic variability and climatic change on temporal and spatial patterns in soil moisture. Geoderma, 274, 104-113. doi:10.1016/j.geoderma.2016.03.020Yang, H., Du, T., Qiu, R., Chen, J., Wang, F., Li, Y., … Kang, S. (2017). Improved water use efficiency and fruit quality of greenhouse crops under regulated deficit irrigation in northwest China. Agricultural Water Management, 179, 193-204. doi:10.1016/j.agwat.2016.05.029Ferentinos, K. P., Katsoulas, N., Tzounis, A., Bartzanas, T., & Kittas, C. (2017). Wireless sensor networks for greenhouse climate and plant condition assessment. Biosystems Engineering, 153, 70-81. doi:10.1016/j.biosystemseng.2016.11.005Ibayashi, H., Kaneda, Y., Imahara, J., Oishi, N., Kuroda, M., & Mineno, H. (2016). A Reliable Wireless Control System for Tomato Hydroponics. Sensors, 16(5), 644. doi:10.3390/s16050644Ntinas, G. K., Neumair, M., Tsadilas, C. D., & Meyer, J. (2017). Carbon footprint and cumulative energy demand of greenhouse and open-field tomato cultivation systems under Southern and Central European climatic conditions. Journal of Cleaner Production, 142, 3617-3626. doi:10.1016/j.jclepro.2016.10.106Europapress Newshttp://www.europapress.es/andalucia/almeria-00350/noticia-superficie-invernaderos-crece-105-ultimos-cuatro-anos-llegar-29596-hectareas-20150213102204.htmlTreftz, C., & Omaye, S. T. (2016). Hydroponics: potential for augmenting sustainable food production in non-arable regions. Nutrition & Food Science, 46(5), 672-684. doi:10.1108/nfs-10-2015-0118De Anda, J., & Shear, H. (2017). Potential of Vertical Hydroponic Agriculture in Mexico. Sustainability, 9(1), 140. doi:10.3390/su9010140Croft, M. M., Hallett, S. G., & Marshall, M. I. (2017). Hydroponic production of vegetable Amaranth (Amaranthus cruentus) for improving nutritional security and economic viability in Kenya. Renewable Agriculture and Food Systems, 32(6), 552-561. doi:10.1017/s1742170516000478Ferrarezi, R. S., & Testezlaf, R. (2014). Performance of wick irrigation system using self-compensating troughs with substrates for lettuce production. Journal of Plant Nutrition, 39(1), 147-161. doi:10.1080/01904167.2014.983127Understanding Irrigation Water Test Results and Their Implications on Nursery and Greenhouse Crophttps://uknowledge.uky.edu/cgi/viewcontent.cgi?article=1160&context=anr_reportsKim, H.-J., Kim, D.-W., Kim, W. K., Cho, W.-J., & Kang, C. I. (2017). PVC membrane-based portable ion analyzer for hydroponic and water monitoring. Computers and Electronics in Agriculture, 140, 374-385. doi:10.1016/j.compag.2017.06.015(2017). Remote Sensing for Irrigation of Horticultural Crops. Horticulturae, 3(2), 40. doi:10.3390/horticulturae3020040Suárez-Albela, M., Fraga-Lamas, P., Fernández-Caramés, T., Dapena, A., & González-López, M. (2016). Home Automation System Based on Intelligent Transducer Enablers. Sensors, 16(10), 1595. doi:10.3390/s16101595Zhang, Q., Yang, X., Zhou, Y., Wang, L., & Guo, X. (2007). A wireless solution for greenhouse monitoring and control system based on ZigBee technology. Journal of Zhejiang University-SCIENCE A, 8(10), 1584-1587. doi:10.1631/jzus.2007.a1584Gill, S. S., Chana, I., & Buyya, R. (2017). IoT Based Agriculture as a Cloud and Big Data Service. Journal of Organizational and End User Computing, 29(4), 1-23. doi:10.4018/joeuc.2017100101Nordic Semiconductor, RF Specialist in Ultra-Low Power Wireless Communicationshttp://www.nordicsemi.com/eng/Products/2.4GHzRF/nRF24L01Pawlowski, A., Guzman, J., Rodríguez, F., Berenguel, M., Sánchez, J., & Dormido, S. (2009). Simulation of Greenhouse Climate Monitoring and Control with Wireless Sensor Network and Event-Based Control. Sensors, 9(1), 232-252. doi:10.3390/s90100232Li, X., Cheng, X., Yan, K., & Gong, P. (2010). A Monitoring System for Vegetable Greenhouses based on a Wireless Sensor Network. Sensors, 10(10), 8963-8980. doi:10.3390/s10100896

Security in Vehicles With IoT by Prioritization Rules, Vehicle Certificates, and Trust Management

[EN] The Internet of Vehicles (IoV) provides new opportunities for the coordination of vehicles for enhancing safety and transportation performance. Vehicles can be coordinated for avoiding collisions by communicating their positions when near to each other, in which the information flow is indexed by their geographical positions or the ones in road maps. Vehicles can also be coordinated to ameliorate traffic jams by sharing their locations and destinations. Vehicles can apply optimization algorithms to reduce the overuse of certain streets without excessively enlarging the paths. In this way, traveling time can be reduced. However, IoV also brings security challenges, such as keeping safe from virtual hijacking. In particular, vehicles should detect and isolate the hijacked vehicles ignoring their communications. The current work presents a technique for enhancing security by applying certain prioritization rules, using digital certificates, and applying trust and reputation policies for detecting hijacked vehicles. We tested the proposed approach with a novel agent-based simulator about security in Internet of Things (IoT) for vehicle-to-vehicle communications. The experiments focused on the scenario of avoidance of collisions with hijacked vehicles misinforming other vehicles. The results showed that the current approach increased the average speed of vehicles with a 64.2% when these are giving way to other vehicles in a crossing by means of IoT.This work was supported by Harvard University (stay funded by T49_17R), University of Zaragoza (JIUZ-2017-TEC-03), Foundation Bancaria Ibercaja, Foundation CAI (IT1/18), University Foundation Antonio Gargallo (call 2017), and "Ministerio de Economia y Competitividad" in the "Programa Estatal de Fomento de la Investigacion Cientifica y Tecnica de Excelencia, Subprograma Estatal de Generacion de Conocimiento" (TIN2017-84802-C2-1-P).García-Magariño, I.; Sendra, S.; Lacuesta, R.; Lloret, J. (2019). Security in Vehicles With IoT by Prioritization Rules, Vehicle Certificates, and Trust Management. IEEE Internet of Things. 6(4):5927-5934. https://doi.org/10.1109/JIOT.2018.2871255S592759346

Energy Efficiency in Cooperative Wireless Sensor Networks

[EN] The transport of sensitive products is very important because their deterioration may cause the value lost and even the product rejection by the buyer. In addition, it is important to choose the optimal way to achieve this end. In a data network, the task of calculating the best routes is performed by routers. We can consider the optimal path as the one that provides a shortest route. However, if a real transport network is considered the shortest path can sometimes be affected by incidents and traffic jams that would make it inadvisable. On the other hand, when we need to come back, due to features that symmetry provides, it would be interesting to follow the same path in reverse sense. For this reason, in this paper we present a symmetric routing mechanism for cooperative monitoring system for the delivery of fresh products. The systems is based on a combination of fixed nodes and a mobile node that stores the path followed to be able of coming back following the same route in reverse sense. If this path is no longer available, the system will try to maintain the symmetry principle searching the route that provide the shortest time to the used in the initial trip. The paper shows the algorithm used by the systems to calculate the symmetric routes. Finally, the system is tested in a real scenario which combines different kind of roads. As the results shows, the energy consumption of this kind of nodes is highly influenced by the activity of sensors.This work has been supported by the "Ministerio de Economia y Competitividad", through the "Convocatoria 2014. Proyectos I+D -Programa Estatal de Investigacion Cientifica y Tecnica de Excelencia" in the "Subprograma Estatal de Generacion de Conocimiento", (project TIN2014-57991-C3-1- P) and the "programa para la Formacion de Personal Investigador - (FPI-2015-S2-884)" by the "Universitat Politecnica de Valencia".Sendra, S.; Lloret, J.; Lacuesta, R.; Jimenez, JM. (2019). Energy Efficiency in Cooperative Wireless Sensor Networks. Mobile Networks and Applications. 24(2):678-687. https://doi.org/10.1007/s11036-016-0788-3S678687242Derks HG, Buehler WS, Hall MB (2013) Real-time method and system for locating a mobile object or person in a tracking environment. US Patent 8514071 B2. Aug 20, 2013Witmond R, Dutta R, Charroppin P (2006) Method for tracking a mail piece. US Patent 7003376 B2, Feb 21, 2006Lu L, Liu Y, Han J (2015) ACTION: breaking the privacy barrier for RFID systems. Ad Hoc and Sensor Wireless Networks 24(1–2):135–159Dhakal S, Shin S (2013) Precise time system efficiency of a frame slotted aloha based anti-collision algorithm in a RFID system. Network Protocols and Algorithms 5(2):16–27. doi: 10.5296/npa.v5i2.3373Ghafoor KZ, Bakar KA, Lloret J, Khokhar RH, Lee KC (2013) Intelligent beaconless geographical forwarding for urban vehicular environments. Wirel Netw 19(3):345–362. doi: 10.1007/s11276-012-0470-zWeinsberg U, Shavitt Y, Schwartz Y (2009) Stability and symmetry of internet routing. In proc of the 2009 I.E. INFOCOM Workshops 2009, April 19–25, Rio de Janeiro, Brazil, p 1–2 doi: 10.1109/INFCOMW.2009.5072192Garcia M, Bri D, Sendra S, Lloret J (2010) Practical deployments of wireless sensor networks: a survey. Int Journal on Advances in Networks and Services 3(1&2):163–178Bri D, Garcia M, Lloret J, Dini P (2009) Real deployments of wireless sensor networks. in Proc of the third International Conference on Sensor Technologies and Applications (SENSORCOMM’09), June 18–23. Athens (Greece), p 415–423 doi: 10.1109/SENSORCOMM.2009.69Karim L, Anpalagan A, Nasser N, Almhana J (2013) Sensor-based M2 M agriculture monitor-ing Systems for Developing Countries: state and challenges. Network Protocols and Algorithms 5(3):68–86. doi: 10.5296/npa.v5i3.3787Garcia M, Lloret J, Sendra S, Rodrigues JJPC (2011) Taking cooperative decisions in group-based wireless sensor networks. Lect Notes Comput Sci 6874:61–65. doi: 10.1007/978-3-642-23734-8_9Garcia M, Sendra S, Lloret J, Lacuesta R (2010) Saving energy with cooperative group-based wireless sensor networks. Lect Notes Comput Sci 6240:231–238. doi: 10.1007/978-3-642-16066-0_11Silva FN, Comin CH, Peron TKDM, Rodrigues FA, Ye C, Wilson RC, Hancock ER, Costa LF (2016) Concentric network symmetry. Inf Sci 333:61–80. doi: 10.1016/j.ins.2015.11.014Jedermann R, Schouten R, Sklorz A, Lang W, Van Kooten O (2006) Linking keeping quality models and sensor systems to an autonomous transport supervision system. In proc of the 2nd Int Workshop Cold Chain Management, May 8–9, Bonn, Germany, p 3–18Li J, Cao J (2015) Survey of object tracking in wireless sensor networks. Ad Hoc and Sensor Wireless Networks 25(1–2):89–120Shamsuzzoha A, Addo-Tenkorang R, Phuong D, Helo P. (2011). Logistics tracking: An implementation issue for delivery network. In proc of the PICMET’11: Conference Technology Management in the Energy Smart World, July 31–August 4, Portland, (Oregon-USA) p 1–10Torres RV, Sanchez JC, Galan LM (2014) Unmarked point and adjacency vertex, mobility models for the generation of emergency and rescue scenarios in urban areas. Ad Hoc and Sensor Wireless Networks 23(3–4):211–233Paxson V (1997) Measurements and Analysis of End-to-End Internet Dynamics. (Ph.D. Thesis). University of California, Berkeley. April, 1997. Available at: ftp://ftp.ee.lbl.gov/papers/vp-thesis/ Last access: 18 Oct 2016Codish M, Frank M, Itzhakov A, Miller A. (2014). Solving Graph Coloring Problems with Abstraction and Symmetry. AarXiv preprint arXiv:1409.5189. Available at: http://arxiv.org/abs/1409.5189 Last access: 18 Oct 2016Chambers D, Flapan E (2014) Topological symmetry groups of small complete graphs. Symmetry 6(2):189–209. doi: 10.3390/sym6020189Gong Y, Zhang W, Zhang Z, Li Y (2016) Research and implementation of traffic sign recognition system. Wireless Communications, Networking and Applications 348:553–560. doi: 10.1007/978-81-322-2580-5_50Waspmote features (2016) In Digi Web Site. Available at: http://www.digi.com/products/wireless-wired-embedded-solutions/zigbee-rf-modules/point-multipoint-rfmodules/xbee-series1-module#specs , Last access: 18 Oct 2016Wang Z, Lu M, Yuan X, Zhang J, Van De Wetering H (2013) Visual traffic jam analysis based on trajectory data. IEEE Trans Vis Comput Graph 19(12):2159–2168. doi: 10.1109/TVCG.2013.228Meghanathan N, Mumford P (2013) Centralized and distributed algorithms for stability-based data gathering in mobile sensor networks. Network Protocols and Algorithms 5(4):84–116. doi: 10.5296/npa.v5i4.4208Alrajeh NA, Khan S, Lloret J, Loo J (2014) Artificial neural network based detection of energy exhaustion attacks in wireless sensor networks capable of energy harvesting. Ad Hoc & Sensor Wireless Networks 22(3–4):109–133Garcia M, Sendra S, Lloret J, Canovas A (2013) Saving energy and improving communications using cooperative group-based wireless sensor networks. Telecommun Syst 52(4):2489–2502. doi: 10.1007/s11235-011-9568-

Systems and WBANs for Controlling Obesity

According to World Health Organization (WHO) estimations, one out of five adults worldwide will be obese by 2025. Worldwide obesity has doubled since 1980. In fact, more than 1.9 billion adults (39%) of 18 years and older were overweight and over 600 million (13%) of these were obese in 2014. 42 million children under the age of five were overweight or obese in 2014. Obesity is a top public health problem due to its associated morbidity and mortality. This paper reviews the main techniques to measure the level of obesity and body fat percentage, and explains the complications that can carry to the individual's quality of life, longevity and the significant cost of healthcare systems. Researchers and developers are adapting the existing technology, as intelligent phones or some wearable gadgets to be used for controlling obesity. They include the promoting of healthy eating culture and adopting the physical activity lifestyle. The paper also shows a comprehensive study of the most used mobile applications and Wireless Body Area Networks focused on controlling the obesity and overweight. Finally, this paper proposes an intelligent architecture that takes into account both, physiological and cognitive aspects to reduce the degree of obesity and overweight