uDDS: A Middleware for Real-time Wireless Embedded Systems

[EN] A Real-Time Wireless Distributed Embedded System (RTWDES) is formed by a large quantity of small devices with certain computing power, wireless communication and sensing/actuators capabilities. These types of networks have become popular as they have been developed for applications which can carry out a vast quantity of tasks, including home and building monitoring, object tracking, precision agriculture, military applications, disaster recovery, industry applications, among others. For this type of applications a middleware is used in software systems to bridge the gap between the application and the underlying operating system and networks. As a result, a middleware system can facilitate the development of applications and is designed to provide common services to the applications. The development of a middleware for sensor networks presents several challenges due to the limited computational resources and energy of the different nodes. This work is related with the design, implementation and test of a micro middleware for RTWDES; the proposal incorporates characteristics of a message oriented middleware thus allowing the applications to communicate by employing the publish/subscribe model. Experimental evaluation shows that the proposed middleware provides a stable and timely service to support different Quality of Service (QoS) levels. © 2011 Springer Science+Business Media B.V.This work was developed as a part of the D2ARS Project supported by CYTED. UNESCO code 120325;330417;120314;120305.González, A.; Mata, W.; Villaseñor, L.; Aquino, R.; Simó Ten, JE.; Chávez, M.; Crespo Lorente, A. (2011). uDDS: A Middleware for Real-time Wireless Embedded Systems. Journal of Intelligent and Robotic Systems. 64(3-4):489-503. https://doi.org/10.1007/s10846-011-9550-zS489503643-4Akyildiz, I.F., Su, W., Sankarasubramaniam, Y., Cayirci, E.: A survey on sensor networks. IEEE Commun. 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Energy-efficient node selection algorithms with correlation optimization in wireless sensor networks

The sensing data of nodes is generally correlated in dense wireless sensor networks, and the active node selection problem aims at selecting a minimum number of nodes to provide required data services within error threshold so as to efficiently extend the network lifetime. In this paper, we firstly propose a new Cover Sets Balance (CSB) algorithm to choose a set of active nodes with the partially ordered tuple (data coverage range, residual energy). Then, we introduce a new Correlated Node Set Computing (CNSC) algorithm to find the correlated node set for a given node. Finally, we propose a High Residual Energy First (HREF) node selection algorithm to further reduce the number of active nodes. Extensive experiments demonstrate that HREF significantly reduces the number of active nodes, and CSB and HREF effectively increase the lifetime of wireless sensor networks compared with related works.This work is supported by the National Science Foundation of China under Grand nos. 61370210 and 61103175, Fujian Provincial Natural Science Foundation of China under Grant nos. 2011J01345, 2013J01232, and 2013J01229, and the Development Foundation of Educational Committee of Fujian Province under Grand no. 2012JA12027. It has also 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, though the PAID-15-11 multidisciplinary Projects.Cheng, H.; Su, Z.; Zhang, D.; Lloret, J.; Yu, Z. (2014). Energy-efficient node selection algorithms with correlation optimization in wireless sensor networks. 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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). 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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. 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(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. 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An altruistic cross-layer recovering mechanism for ad hoc wireless networks

Video streaming services have restrictive delay and bandwidth constraints. Ad hoc networks represent a hostile environment for this kind of real-time data transmission. Emerging mesh networks, where a backbone provides more topological stability, do not even assure a high quality of experience. In such scenario, mobility of terminal nodes causes link breakages until a new route is calculated. In the meanwhile, lost packets cause annoying video interruptions to the receiver. This paper proposes a new mechanism of recovering lost packets by means of caching overheard packets in neighbor nodes and retransmit them to destination. Moreover, an optimization is shown, which involves a video-aware cache in order to recover full frames and prioritize more significant frames. Results show the improvement in reception, increasing the throughput as well as video quality, whereas larger video interruptions are considerably reduced. Copyright © 2014 John Wiley & Sons, Ltd.Arce Vila, P.; Guerri Cebollada, JC. (2015). An altruistic cross-layer recovering mechanism for ad hoc wireless networks. Wireless Communications and Mobile Computing. 15(13):1744-1758. doi:10.1002/wcm.2459S174417581513Li J Blake C De Couto DSJ Lee HI Morris R Capacity of ad hoc wireless networks Proceedings of the 7th Annual International Conference on Mobile Computing and Networks (MobiCom) 2001 61 69Akyildiz, I. F., & Xudong Wang. (2005). A survey on wireless mesh networks. IEEE Communications Magazine, 43(9), S23-S30. doi:10.1109/mcom.2005.1509968Hsu, C.-J., Liu, H.-I., & Seah, W. K. G. (2011). Opportunistic routing – A review and the challenges ahead. Computer Networks, 55(15), 3592-3603. doi:10.1016/j.comnet.2011.06.021Huang, X., Zhai, H., & Fang, Y. (2008). Robust cooperative routing protocol in mobile wireless sensor networks. IEEE Transactions on Wireless Communications, 7(12), 5278-5285. doi:10.1109/t-wc.2008.060680Wieselthier, J. E., Nguyen, G. D., & Ephremides, A. (2001). Mobile Networks and Applications, 6(3), 251-263. doi:10.1023/a:1011478717164Clausen T Jacquet P Optimized Link State Routing Protocol (OLSR), IETF RFC 3626 2003 http://www.rfc-editor.org/rfc/rfc3626.txtMarina, M. K., & Das, S. R. (2006). Ad hoc on-demand multipath distance vector routing. Wireless Communications and Mobile Computing, 6(7), 969-988. doi:10.1002/wcm.432Zhou X Lu Y Ma HG Routing improvement using multiple disjoint paths for ad hoc networks International Conference on Wireless and Optical Communications Networks (IFIP) 2006 1 5Fujisawa H Minami H Yamamoto M Izumi Y Fujita Y Route selection using retransmission packets for video streaming on ad hoc networks IEEE Conference on Radio and Wireless Symposium (RWS) 2006 607 610Badis H Agha KA QOLSR multi-path routing for mobile ad hoc networks based on multiple metrics: bandwidth and delay IEEE 59th Vehicular Technology Conference (VTC) 2004 2181 2184Wu Z Wu J Cross-layer routing optimization for video transmission over wireless ad hoc networks 6th International Conference on Wireless Communications Networks and Mobile Computing (WiCOM) 2010 1 6Schier, M., & Welzl, M. (2012). Optimizing Selective ARQ for H.264 Live Streaming: A Novel Method for Predicting Loss-Impact in Real Time. IEEE Transactions on Multimedia, 14(2), 415-430. doi:10.1109/tmm.2011.2178235Nikoupour M Nikoupour A Dehghan M A cross-layer framework for video streaming over wireless ad-hoc networks 3rd International Conference on Digital Information Management (ICDIM) 2008 340 345Yamamoto R Miyoshi T Distributed retransmission method using neighbor terminals for ad hoc networks Proceedings of the 14th Asia-Pacific Conference on Communications (APCC) 2008 1 5Gravalos I Kokkinos P Varvarigos EA Multi-criteria cooperative energy-aware routing in wireless ad-hoc networks Proceedings of the 9th International Wireless Communications and Mobile Computing Conference (IWCMC) 2013 387 393Abid, R. M., Benbrahim, T., & Biaz, S. (2010). IEEE 802.11s Wireless Mesh Networks for Last-Mile Internet Access: An Open-Source Real-World Indoor Testbed Implementation. Wireless Sensor Network, 02(10), 725-738. doi:10.4236/wsn.2010.210088Yen, Y.-S., Chang, R.-S., & Wu, C.-Y. (2011). A seamless handoff scheme for IEEE 802.11 wireless networks. Wireless Communications and Mobile Computing, 13(2), 157-169. doi:10.1002/wcm.1102Liangzhong Yin, & Guohong Cao. (2006). Supporting cooperative caching in ad hoc networks. IEEE Transactions on Mobile Computing, 5(1), 77-89. doi:10.1109/tmc.2006.15Biswas S Morris R ExOR: opportunistic multi-hop routing for wireless networks Proceedings of ACM SIGCOMM 2005 133 144Chachulski S Jennings M Katti S Katabi D Trading structure for randomness in wireless opportunistic routing Proceedings of ACM SIGCOMM 2007 169 180Kohler E Handley M Floyd S Datagram Congestion Control Protocol (DCCP), IETF RFC 4340 2006 http://www.rfc-editor.org/rfc/rfc4340.txtSchierl, T., Ganger, K., Hellge, C., Wiegand, T., & Stockhammer, T. (2006). SVC-based multisource streaming for robust video transmission in mobile ad hoc networks. IEEE Wireless Communications, 13(5), 96-103. doi:10.1109/wc-m.2006.250365Iera, A., Molinaro, A., Paratore, S. Y., Ruggeri, G., & Zurzolo, A. (2011). Making a mesh router/gateway from a smartphone: Is that a practical solution? Ad Hoc Networks, 9(8), 1414-1429. doi:10.1016/j.adhoc.2011.03.00

Web spider defense technique in wireless sensor networks

Wireless sensor networks (WSNs) are currently widely used in many environments. Some of them gather many critical data, which should be protected from intruders. Generally, when an intruder is detected in the WSN, its connection is immediately stopped. But this way does not let the network administrator gather information about the attacker and/or its purposes. In this paper, we present a bioinspired system that uses the procedure taken by the web spider when it wants to catch its prey. We will explain how all steps performed by the web spider are included in our system and we will detail the algorithm and protocol procedure. A real test bench has been implemented in order to validate our system. It shows the performance for different response times, the CPU and RAM consumption, and the average and maximum values for ping and tracert time responses using constant delay and exponential jitter.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.Cánovas Solbes, A.; Lloret, J.; Macias Lopez, EM.; Suarez Sarmiento, A. (2014). Web spider defense technique in wireless sensor networks. International Journal of Distributed Sensor Networks. 2014:1-7. https://doi.org/10.1155/2014/348606S172014Bri, D., Garcia, M., Lloret, J., & Dini, P. (2009). Real Deployments of Wireless Sensor Networks. 2009 Third International Conference on Sensor Technologies and Applications. doi:10.1109/sensorcomm.2009.69Sendra, 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.6.6.439-459Xie, M., Han, S., Tian, B., & Parvin, S. (2011). Anomaly detection in wireless sensor networks: A survey. Journal of Network and Computer Applications, 34(4), 1302-1325. doi:10.1016/j.jnca.2011.03.004Yu, Y., Li, K., Zhou, W., & Li, P. (2012). Trust mechanisms in wireless sensor networks: Attack analysis and countermeasures. Journal of Network and Computer Applications, 35(3), 867-880. doi:10.1016/j.jnca.2011.03.005Zhu, W. T., Zhou, J., Deng, R. H., & Bao, F. (2012). Detecting node replication attacks in wireless sensor networks: A survey. Journal of Network and Computer Applications, 35(3), 1022-1034. doi:10.1016/j.jnca.2012.01.002Maleh, Y., & Ezzati, A. (2013). A Review of Security Attacks and Intrusion Detection Schemes in Wireless Sensor Network. International Journal of Wireless & Mobile Networks, 5(6), 79-90. doi:10.5121/ijwmn.2013.5606Alrajeh, N. A., Khan, S., & Shams, B. (2013). Intrusion Detection Systems in Wireless Sensor Networks: A Review. International Journal of Distributed Sensor Networks, 9(5), 167575. doi:10.1155/2013/167575Sun, B., Osborne, L., Xiao, Y., & Guizani, S. (2007). Intrusion detection techniques in mobile ad hoc and wireless sensor networks. IEEE Wireless Communications, 14(5), 56-63. doi:10.1109/mwc.2007.4396943Fatema, N., & Brad, R. (2013). Attacks and Counterattacks on Wireless Sensor Networks. International Journal of Ad hoc, Sensor & Ubiquitous Computing, 4(6), 1-15. doi:10.5121/ijasuc.2013.4601Ankala, R. P., Kavitha, D., & Haritha, D. (2011). MOBILE AGENT BASED ROUTING in MANETS –ATTACKS & DEFENCES. Network Protocols and Algorithms, 3(4). doi:10.5296/npa.v3i4.1351Hylsberg Jacobsen, R., Zhang, Q., & Skjødeberg Toftegaard, T. (2011). Bioinspired Principles for Large-Scale Networked Sensor Systems: An Overview. Sensors, 11(4), 4137-4151. doi:10.3390/s110404137Kofahi, N. (2013). An Empirical Study to Compare the Performance of some Symmetric and Asymmetric Ciphers. International Journal of Security and Its Applications, 7(5), 1-16. doi:10.14257/ijsia.2013.7.5.01Sisodia, M. S., & Raghuwanshi, V. (2011). Anomaly Base Network Intrusion Detection by Using Random Decision Tree and Random Projection: A Fast Network Intrusion Detection Technique. Network Protocols and Algorithms, 3(4). doi:10.5296/npa.v3i4.1342Zhijie, H., & Ruchuang, W. (2012). Intrusion Detection for Wireless Sensor Network Based on Traffic Prediction Model. Physics Procedia, 25, 2072-2080. doi:10.1016/j.phpro.2012.03.352Al-Gharabally, N., El-Sayed, N., Al-Mulla, S., & Ahmad, I. (2009). Wireless honeypots. Proceedings of the 2009 conference on Information Science, Technology and Applications - ISTA ’09. doi:10.1145/1551950.1551969Gopinath V.Success analysis of deception in wireless sensor networks [M.S. thesis]2010Oklahoma State UniversityZhongshan Zhang, Keping Long, Jianping Wang, & Dressler, F. (2014). On Swarm Intelligence Inspired Self-Organized Networking: Its Bionic Mechanisms, Designing Principles and Optimization Approaches. IEEE Communications Surveys & Tutorials, 16(1), 513-537. doi:10.1109/surv.2013.062613.00014Rathore, H., & Jha, S. (2013). Bio-inspired machine learning based Wireless Sensor Network security. 2013 World Congress on Nature and Biologically Inspired Computing. doi:10.1109/nabic.2013.6617852Alrajeh, N. A., & Lloret, J. (2013). Intrusion Detection Systems Based on Artificial Intelligence Techniques in Wireless Sensor Networks. International Journal of Distributed Sensor Networks, 9(10), 351047. doi:10.1155/2013/351047Amirkolaei M. K.Enhancing bio-inspired intrusion response in Ad-hoc networks [Ph.D. thesis]August 2013Edinburgh, UKEdinburgh Napier Universityhttp://researchrepository.napier.ac.uk/6533/Muraleedharan, R., & Osadciw, L. A. (2009). An intrusion detection framework for Sensor Networks using Honeypot and Swarm Intelligence. Proceedings of the 6th Annual International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services. doi:10.4108/icst.mobiquitous2009.7084Hortos, W. S. (2012). Bio-inspired, cross-layer protocol design for intrusion detection and identification in wireless sensor networks. 37th Annual IEEE Conference on Local Computer Networks -- Workshops. doi:10.1109/lcnw.2012.6424040Benahmed, K., Merabti, M., & Haffaf, H. (2012). Inspired Social Spider Behavior for Secure Wireless Sensor Networks. International Journal of Mobile Computing and Multimedia Communications, 4(4), 1-10. doi:10.4018/jmcmc.2012100101Herberstein, M. E. (Ed.). (2009). Spider Behaviour. doi:10.1017/cbo9780511974496Ficco, M. (2010). Achieving Security by Intrusion-Tolerance Based on Event Correlation. Network Protocols and Algorithms, 2(3). doi:10.5296/npa.v2i3.42

A critical analysis of research potential, challenges and future directives in industrial wireless sensor networks

In recent years, Industrial Wireless Sensor Networks (IWSNs) have emerged as an important research theme with applications spanning a wide range of industries including automation, monitoring, process control, feedback systems and automotive. Wide scope of IWSNs applications ranging from small production units, large oil and gas industries to nuclear fission control, enables a fast-paced research in this field. Though IWSNs offer advantages of low cost, flexibility, scalability, self-healing, easy deployment and reformation, yet they pose certain limitations on available potential and introduce challenges on multiple fronts due to their susceptibility to highly complex and uncertain industrial environments. In this paper a detailed discussion on design objectives, challenges and solutions, for IWSNs, are presented. A careful evaluation of industrial systems, deadlines and possible hazards in industrial atmosphere are discussed. The paper also presents a thorough review of the existing standards and industrial protocols and gives a critical evaluation of potential of these standards and protocols along with a detailed discussion on available hardware platforms, specific industrial energy harvesting techniques and their capabilities. The paper lists main service providers for IWSNs solutions and gives insight of future trends and research gaps in the field of IWSNs

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. 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