15,880 research outputs found

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

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

    Power Aware Routing for Sensor Databases

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    Wireless sensor networks offer the potential to span and monitor large geographical areas inexpensively. Sensor network databases like TinyDB are the dominant architectures to extract and manage data in such networks. Since sensors have significant power constraints (battery life), and high communication costs, design of energy efficient communication algorithms is of great importance. The data flow in a sensor database is very different from data flow in an ordinary network and poses novel challenges in designing efficient routing algorithms. In this work we explore the problem of energy efficient routing for various different types of database queries and show that in general, this problem is NP-complete. We give a constant factor approximation algorithm for one class of query, and for other queries give heuristic algorithms. We evaluate the efficiency of the proposed algorithms by simulation and demonstrate their near optimal performance for various network sizes

    A Distributed Geo-Routing Algorithm for Wireless Sensor Networks

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    Geographic wireless sensor networks use position information for greedy routing. Greedy routing works well in dense networks, whereas in sparse networks it may fail and require a recovery algorithm. Recovery algorithms help the packet to get out of the communication void. However, these algorithms are generally costly for resource constrained position-based wireless sensor networks (WSNs). In this paper, we propose a void avoidance algorithm (VAA), a novel idea based on upgrading virtual distance. VAA allows wireless sensor nodes to remove all stuck nodes by transforming the routing graph and forwarding packets using only greedy routing. In VAA, the stuck node upgrades distance unless it finds a next hop node that is closer to the destination than it is. VAA guarantees packet delivery if there is a topologically valid path. Further, it is completely distributed, immediately responds to node failure or topology changes and does not require planarization of the network. NS-2 is used to evaluate the performance and correctness of VAA and we compare its performance to other protocols. Simulations show our proposed algorithm consumes less energy, has an efficient path and substantially less control overheads

    An energy efficient position based Adaptive real-time routing protocol for WSNs

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    Devices for Wireless Sensor Networks (WSN) are limited by power and thus routing protocols should be designed with this constrain in mind. This paper presents and evaluates an Energy Efficient Position Based Adaptive Real-Time Routing protocol (EFPBARP) as a novel, real-time, position based and energy efficient routing protocol. EFPBARP is a lightweight protocol that reduces the number of nodes which receive the RF signal using a novel Parent Forwarding Region (PFR) algorithm. EFPBARP as a Geographical Routing Protocol (GRP) reduces the number of forwarding nodes and thus the traffic and packet collision in the network. A series of performance evaluations through Matlab and Omnet++ simulations show significant improvements in network performance parameters and total energy consumption over CTP and Directed Flooding Routing Protocol (DFRP)

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

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    [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

    High Performance Communication Framework for Mobile Sinks Wireless Sensor Networks

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    A wireless sensor networks typically consist of thousand of nodes and each node has limited power, processing and bandwidth resources. Harvesting advances in the past decade in microelectronics, sensing, wireless communications and networking, sensor networks technology is expected to have a significant impact on our lives in the twenty-first century. Proposed applications of sensor networks include environmental monitoring, natural disaster prediction and relief, homeland security, healthcare, manufacturing, transportation, and home appliances and entertainment. However, Communication is one of the major challenges in wireless sensor networks as it is the main source for energy depletion. Improved network lifetime is a fundamental challenge of wireless sensor networks. Many researchers have proposed using mobile sinks as one possible solution to improve the lifetime of wireless sensor networks. The reason is that the typical manyto- one communication traffic pattern in wireless sensor networks imposes a heavy forwarding load on the nodes close to the sinks. However, it also introduces many research challenges such as the high communication overhead for updating the dynamic routing paths to connect to mobile sinks and packet loss problems while transmitted messages to mobile sinks. Therefore, our goal is to design a robust and efficient routing framework for both non-geographic aware and geographic aware mobile sinks wireless sensor networks. In order to achieve this goal in non-geographic based mobile sinks wireless sensor networks, we proposed a spider-net zone routing protocol to improve network efficiency and lifetime. Our proposed routing protocol utilise spider web topology inspired by the way spiders hunt prey in their web to provide reliable and high performance data delivery to mobile sinks. For routing in geographic aware based mobile sinks wireless sensor networks, we proposed a fault-tolerant magnetic coordinate routing algorithm to allow these network sensors to take advantage of geographic knowledge to build a routing protocol. Our proposed routing algorithm incorporates a coordinated routing algorithm for grid based network topology to improve network performance. Our third contribution is a component level fault diagnosis scheme for wireless sensor networks. The advantage of this scheme, causal model fault diagnosis, is that it can "deeply understand" and express the relationship among failure behaviours and node system components through causal relations. The above contributions constitute a novel routing framework to address the routing challenges in mobile sinks wireless sensor networks, Our framework considers both geographic and non-geographic aware based sensor networks to achieve energy efficient, high performance and network reliability. We have analyzed the proposed protocols and schemes and evaluated their performances using analytical study and simulations. The evaluation was based on the most important metries in wireless sensor networks, such as: power consumption and average delay. The evaluation shows that our solution is more energy efficient, improves the network performance, and provides data reliability in mobile sinks wireless sensor networks

    Energy-Efficient Load Balancing Ant Based Routing Algorithm for Wireless Sensor Networks

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    Wireless Sensor Networks (WSNs) are a type of self-organizing networks with limited energy supply and communication ability. One of the most crucial issues in WSNs is to use an energy-efficient routing protocol to prolong the network lifetime. We therefore propose the novel Energy-Efficient Load Balancing Ant-based Routing Algorithm (EBAR) for WSNs. EBAR adopts a pseudo-random route discovery algorithm and an improved pheromone trail update scheme to balance the energy consumption of the sensor nodes. It uses an efficient heuristic update algorithm based on a greedy expected energy cost metric to optimize the route establishment. Finally, in order to reduce the energy consumption caused by the control overhead, EBAR utilizes an energy-based opportunistic broadcast scheme. We simulate WSNs in different application scenarios to evaluate EBAR with respect to performance metrics such as energy consumption, energy efficiency, and predicted network lifetime. The results of this comprehensive study show that EBAR provides a significant improvement in comparison to the state-of-the-art approaches EEABR, SensorAnt, and IACO

    Energy Efficient Multipath Ant Colony Based Routing Algorithm for Mobile Ad hoc Networks

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    This paper describes the novel wireless routing protocol made for mobile ad hoc networks or wireless sensor networks using the bio-inspired technique. Bio-inspired algorithms include the routing capabilities taken from the social behavior of ant colonies, bird flocking, honey bee dancing, etc and promises to be capable of catering to the challenges posed by wireless sensors. Some of the challenges of wireless sensor networks are limited bandwidth, limited battery life, low memory, etc. An energy-efficient multipath routing algorithm based on the foraging nature of ants is proposed including many meta-heuristic impact factors to provide good robust paths from source to destination to overcome the challenges faced by resource-constrained sensors. Analysis of individual impact factor is represented which justifies their importance in routing performance. The multi-path routing feature is claimed by showing energy analysis as well as statistical analysis in-depth to the readers. The proposed routing algorithm is analyzed by considering various performance metrics such as throughput, delay, packet loss, network lifetime, etc. Finally, the comparison is done against AODV routing protocol by considering performance metrics where the proposed routing algorithm shows a 49% improvement in network lifetime

    From carbon nanotubes and silicate layers to graphene platelets for polymer nanocomposites

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    In spite of extensive studies conducted on carbon nanotubes and silicate layers for their polymer-based nanocomposites, the rise of graphene now provides a more promising candidate due to its exceptionally high mechanical performance and electrical and thermal conductivities. The present study developed a facile approach to fabricate epoxy–graphene nanocomposites by thermally expanding a commercial product followed by ultrasonication and solution-compounding with epoxy, and investigated their morphologies, mechanical properties, electrical conductivity and thermal mechanical behaviour. Graphene platelets (GnPs) of 3.5
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