Secured node detection technique based on artificial neural network for wireless sensor network

The wireless sensor network is becoming the most popular network in the last recent years as it can measure the environmental conditions and send them to process purposes. Many vital challenges face the deployment of WSNs such as energy consumption and security issues. Various attacks could be subjects against WSNs and cause damage either in the stability of communication or in the destruction of the sensitive data. Thus, the demands of intrusion detection-based energy-efficient techniques rise dramatically as the network deployment becomes vast and complicated. Qualnet simulation is used to measure the performance of the networks. This paper aims to optimize the energy-based intrusion detection technique using the artificial neural network by using MATLAB Simulink. The results show how the optimized method based on the biological nervous systems improves intrusion detection in WSN. In addition to that, the unsecured nodes are affected the network performance negatively and trouble its behavior. The regress analysis for both methods detects the variations when all nodes are secured and when some are unsecured. Thus, Node detection based on packet delivery ratio and energy consumption could efficiently be implemented in an artificial neural network

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. 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Artificial neural network based detection of energy exhaustion attacks in wireless sensor networks capable of energy harvesting

[EN] Energy consumption is the important factor when designing any mechanism for wireless sensor network (WSN). Research community is trying to enable energy harvesting mechanisms to provide long term energy source to WSN. However, energy consumption is generally greater than energy harvesting in WSN. Furthermore, if nodes are under any kind of energy exhaustion security attack, then energy harvesting mechanism cannot extend the lifetime of the WSN. In this paper, we propose a detection mechanism of energy exhaustion attacks that uses an artificial neural network (ANN). It has been developed for cluster-based WSN and takes into account the energy harvesting system. Simulation results show that our mechanism can detect and prevent such kind of attacks, even having lower percentage of false positives than other systems, and thus enlarge the wireless sensor node lifetime.The authors extend their appreciation to the Research Centre, College of Applied Medical Sciences and the Deanship of Scientific Research at King Saud University for funding this research.Alrajeh, 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. Adhoc and Sensor Wireless Networks. 22(1-2):109-133. http://hdl.handle.net/10251/51078S109133221-

Cooperative Monitoring of the Delivery of Fresh Products

12th International Conference on Cooperative Design, Visualization, and Engineering, CDVE 2015; Mallorca; Spain; 20 September 2015 through 23 September 2015The proper transport of hit sensitive products, such as fish and fruit, is very important because their deterioration may cause the value lost and even the product rejection by the buyer. For this reason, in this paper we present a cooperative monitoring system for the delivery of fresh products. The system consists of fixed wireless nodes and a mobile wireless node that is installed in the packet. This mobile node is able to take data of the internal temperature, external temperature and the 3 axis movement. With the information stored in the network, a vendor can know the optimal conditions of the transport. Finally, we test the maximum distance to the fixed nodes, as well as the data collected by the sensor.Sendra, S.; Lloret, J.; Lacuesta, R.; Jimenez, JM. (2015). Cooperative Monitoring of the Delivery of Fresh Products. Lecture Notes in Computer Science. 9320:76-86. doi:10.1007/978-3-319-24132-6_10S76869320Derks, H.G., Buehler, W.S., Hall, M.B.: Real-time method and system for locating a mobile object or person in a tracking environment. US Patent 8514071 B2, 20 August 2013Witmond, R., Dutta, R., Charroppin, P.: Method for tracking a mail piece. US Patent 7003376 B2, 21 February 2006Lu, L., Liu, Y., Han, J.: ACTION: breaking the privacy barrier for RFID systems. Ad Hoc Sens. Wirel. Netw. 24(1–2), 135–159 (2015)Dhakal, S., Shin, S.: Precise time system efficiency of a frame slotted aloha based anti-collision algorithm in a RFID system. Netw. Protoc. Algorithms 5(2), 16–27 (2013)Garcia, M., Bri, D., Sendra, S., Lloret, J.: Practical deployments of wireless sensor networks: a survey. Int. J. Adv. Netw. Serv. 3(1&2), 163–178 (2010)Bri, D., Garcia, M., Lloret, J., Dini, P.: Real deployments of wireless sensor networks. In: Third International Conference on Sensor Technologies and Applications (SENSORCOMM 2009), Athens, Greece, 18–23 June 2009, pp. 415–423Karim, L., Anpalagan, A., Nasser, N., Almhana, J.: Sensor-based M2M agriculture monitoring systems for developing countries: state and challenges. Netw. Protoc. Algorithms 5(3), 68–86 (2013)Garcia, M., Lloret, J., Sendra, S., Rodrigues, J.J.: Taking cooperative decisions in group-based wireless sensor networks. In: Luo, Y. (ed.) CDVE 2011. LNCS, vol. 6874, pp. 61–65. Springer, Heidelberg (2011)Garcia-Sabater, J.P., Lloret, J., Marin-Garcia, J.A., Puig-Bernabeu, X.: Coordinating a cooperative automotive manufacturing network – an agent-based model. In: Luo, Y. (ed.) CDVE 2010. LNCS, vol. 6240, pp. 231–238. Springer, Heidelberg (2010)Li, J., Cao, J.: Survey of object tracking in wireless sensor networks. Netw. Protoc. Algorithms 25(1–2), 89–120 (2015)Vock, C.A., Larkin, A.F., Amsbury, B.W., Youngs, P.: Device for monitoring movement of shipped goods. U.S. Patent 8,280,682, 2 October 2012Jedermann, R., Schouten, R., Sklorz, A., Lang, W., Van Kooten, O.: Linking keeping quality models and sensor systems to an autonomous transport supervision system. In: The 2nd International Workshop Cold Chain Management, Bonn, Germany, 8–9 May 2006, pp. 3–18Ko, D., Kwak, Y., Song, S.: Real time traceability and monitoring system for agricultural products based on wireless sensor network. Int. J. Distrib. Sens. Netw. 2014, 1–7 (2014). Article ID 832510Ruiz-Garcia, L., Barreiro, P., Robla, J.I.: Performance of ZigBee-based wireless sensor nodes for real-time monitoring of fruit logistics. J. Food Eng. 87(3), 405–415 (2008)Shamsuzzoha, A., Addo-Tenkorang, R., Phuong, D., Helo, P.: Logistics tracking: an implementation issue for delivery network. In: PICMET 2011Conference Technology Management in the Energy Smart World, Portland, Oregon, USA, July 31–August 4 2011, pp. 1–10Torres, R.V., Sanchez, J.C., Galan, L.M.: Unmarked point and adjacency vertex, mobility models for the generation of emergency and rescue scenarios in urban areas. Ad Hoc Sens. Wirel. Netw. 23(3–4), 211–233 (2014)Waspmote features. In Digi Web Site. http://www.digi.com/products/wireless-wired-embedded-solutions/zigbee-rf-modules/point-multipoint-rfmodules/xbee-series1-module#specs . Accessed 18 April 2015Meghanathan, N., Mumford, P.: Centralized and distributed algorithms for stability-based data gathering in mobile sensor networks. Netw. Protoc. Algorithms 5(4), 84–116 (2013)Alrajeh, N.A., Khan, S., Lloret, J., Loo, J.: Artificial neural network based detection of energy exhaustion attacks in wireless sensor networks capable of energy harvesting. Ad Hoc Sens. Wirel. Netw. 22(3–4), 109–133 (2014)Garcia, M., Sendra, S., Lloret, J., Canovas, A.: Saving energy and improving communications using cooperative group-based wireless sensor networks. Telecommun. Syst. 52(4), 2489–2502 (2013