165 research outputs found

    A survey of localization in wireless sensor network

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    Localization is one of the key techniques in wireless sensor network. The location estimation methods can be classified into target/source localization and node self-localization. In target localization, we mainly introduce the energy-based method. Then we investigate the node self-localization methods. Since the widespread adoption of the wireless sensor network, the localization methods are different in various applications. And there are several challenges in some special scenarios. In this paper, we present a comprehensive survey of these challenges: localization in non-line-of-sight, node selection criteria for localization in energy-constrained network, scheduling the sensor node to optimize the tradeoff between localization performance and energy consumption, cooperative node localization, and localization algorithm in heterogeneous network. Finally, we introduce the evaluation criteria for localization in wireless sensor network

    Topographical Automation of MANET using Reactive Routing Protocols

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    Wireless mobile ad-hoc networks (MANET) are characterized as infrastructure less networks. Topologies are formed with movement of regular nodes which has multi radio links and these regular nodes under demand behaves as backbone node (router) to forward packets across the network. These networks suffer frequent topology changes due to the dynamic stochastic process behavior of incoming nodes. Mobile ad-hoc networks lack load balancing that causes unnecessary packet loss and route break up in real-time data transmission. Area of operation, interference, and communication link range and path loss are the factors to affect the throughput of MANET. In this paper we evaluated the performance of AODV and DSR routing protocols which are enhanced by an Automation Topography, In our proposed Topographical Automation the location of incoming nodes are completely random and those will be confined themselves within a certain communication range such that the throughput is enhanced to meet better QoS level. As location of the nodes are system defined and quite automatic, nodes before being forwarded with the full assurance of successful session flows. It is often advantageous to position stable and capable relay nodes, including unmanned ground vehicles (UGVs) or unmanned aerial vehicles (UAVs), and unmanned under sea vehicles (UUVs) used by Defense to save cost as well as life

    Multiservice Vertical Handoff Decision Algorithms

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    An energy-efficient adaptive modulation suitable for wireless sensor networks with SER and throughput constraints

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    We consider the problem of minimizing transmission energy in wireless sensor networks by taking into account that every sensor may require a different bit rate and reliability according to its particular application. We propose a cross-layer approach to tackle such a minimization in centralized networks for the total transmission energy consumption of the network: in the physical layer, for each sensor the sink estimates the channel gain and adaptively selects a modulation scheme; in the MAC layer, each sensor is correspondingly assigned a number of time slots. The modulation level and the number of allocated time slots for every sensor are constrained to attain their applications bit rates in a global energy-efficient manner. The signal-to-noise ratio gap approximation is used in our exposition in order to jointly handle required bit rates, transmission energies, and symbol error rates.This work has been partially funded by CRUISE NoE (IST-4-027738), MAMBO2 (CCG06-UC3M/TIC-0698) and MACAWI (TEC- 2005-07477-C02-02) projects.Publicad

    Survey of millimeter-wave propagation measurements and models in indoor environments

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    The millimeter-wave (mmWave) is expected to deliver a huge bandwidth to address the future demands for higher data rate transmissions. However, one of the major challenges in the mmWave band is the increase in signal loss as the operating frequency increases. This has attracted several research interests both from academia and the industry for indoor and outdoor mmWave operations. This paper focuses on the works that have been carried out in the study of the mmWave channel measurement in indoor environments. A survey of the measurement techniques, prominent path loss models, analysis of path loss and delay spread for mmWave in different indoor environments is presented. This covers the mmWave frequencies from 28 GHz to 100 GHz that have been considered in the last two decades. In addition, the possible future trends for the mmWave indoor propagation studies and measurements have been discussed. These include the critical indoor environment, the roles of artificial intelligence, channel characterization for indoor devices, reconfigurable intelligent surfaces, and mmWave for 6G systems. This survey can help engineers and researchers to plan, design, and optimize reliable 5G wireless indoor networks. It will also motivate the researchers and engineering communities towards finding a better outcome in the future trends of the mmWave indoor wireless network for 6G systems and beyond

    A Primer on Software Defined Radios

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    The commercial success of cellular phone systems during the late 1980s and early 1990 years heralded the wireless revolution that became apparent at the turn of the 21st century and has led the modern society to a highly interconnected world where ubiquitous connectivity and mobility are enabled by powerful wireless terminals. Software defined radio (SDR) technology has played a major role in accelerating the pace at which wireless capabilities have advanced, in particular over the past 15 years, and SDRs are now at the core of modern wireless communication systems. In this paper we give an overview of SDRs that includes a discussion of drivers and technologies that have contributed to their continuous advancement, and presents the theory needed to understand the architecture and operation of current SDRs. We also review the choices for SDR platforms and the programming options that are currently available for SDR research, development, and teaching, and present case studies illustrating SDR use. Our hope is that the paper will be useful as a reference to wireless researchers and developers working in the industry or in academic settings on further advancing and refining the capabilities of wireless systems

    Saving Energy and Improving Communications using Cooperative Group-based Wireless Sensor Networks

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    Wireless Sensor Networks (WSNs) can be used in many real applications (environmental monitoring, habitat monitoring, health, etc.). The energy consumption of each sensor should be as lower as possible, and methods for grouping nodes can improve the network performance. In this work, we show how organizing sensors in cooperative groups can reduce the global energy consumption of the WSN. We will also show that a cooperative group-based network reduces the number of the messages transmitted inside the WSNs, which implieasa reduction of energy consumed by the whole network, and, consequently, an increase of the network lifetime. The simulations will show how the number of groups improves the network performance. © 2011 Springer Science+Business Media, LLC.García Pineda, M.; Sendra Compte, S.; Lloret, J.; Canovas Solbes, A. (2013). Saving Energy and Improving Communications using Cooperative Group-based Wireless Sensor Networks. Telecommunication Systems. 52(4):2489-2502. doi:10.1007/s11235-011-9568-3S24892502524Akyildiz, I. F., Su, W., Sankarasubramaniam, Y., & Cayirci, E. (2002). Wireless sensor networks: a survey. Journal of Computer Networks, 38(4), 393–422.Garcia, M., Bri, D., Sendra, S., & Lloret, J. (2010). Practical deployments of wireless sensor networks: a survey. Journal on Advances in Networks and Services, 3(1&2), 1–16.Lloret, J., Garcia, M., Bri, D., & Sendra, S. (2009). A wireless sensor network deployment for rural and forest fire detection and verification. Sensors, 9(11), 8722–8747.Mainwaring, A., Polastre, J., Szewczyk, R., & Culler, D. (2002). Wireless sensor networks for habitat monitoring. In ACM workshop on sensor networks and applications (WSNA’02), Atlanta, GA, USA, September.Garcia, M., Sendra, S., Lloret, G., & Lloret, J. (2010, in press). Monitoring and control sensor system for fish feeding in marine fish farms. IET Communications, pp. 1–9. doi: 10.1049/iet-com.2010.0654 .Sinha, A., & Chandrakasan, A. (2001). Dynamic power management in wireless sensor networks. IEEE Design & Test of Computers, 18(2), 62–74.Garcia, M., Coll, H., Bri, D., & Lloret, J. (2008). Using MANET protocols in wireless sensor and actor networks. In The second international conference on sensor technologies and applications (SENSORCOMM 2008), Cap Esterel, Costa Azul, France, 25–31 August.Lloret, J., García, M., Boronat, F., & Tomás, J. (2008). MANET protocols performance in group-based networks. In Wireless and mobile networking: Vol. 284 (Chap. 13, pp. 161–172). Berlin, Heidelberg, Boston: Springer.Lloret, J., García, M., & Tomás, J. (2008). Improving mobile and ad-hoc networks performance using group-based topologies. In Wireless sensor and actor networks 2008 (WSAN 2008), Ottawa, Canada, 14–15 July. Berlin, Heidelberg, New York: Springer.Lloret, J., Palau, C., Boronat, F., & Tomas, J. (2008). Improving networks using group-based topologies. Journal of Computer Communications, 31(14), 3438–3450.Lloret, J., Garcia, M., Tomás, J., & Boronat, F. (2008). GBP-WAHSN: a group-based protocol for large wireless ad hoc and sensor networks. Journal of Computer Science and Technology, 23(3), 461–480.Lloret, J., García, M., Boronat, F., & Tomás, J. (2008). MANET protocols performance in group-based networks. In 10th IFIP international conference on mobile and wireless communications networks (MWCN 2008), Toulouse, France, 30 September–2 October.Garcia, M., Sendra, S., Lloret, J., & Lacuesta, R. (2010). Saving energy with cooperative group-based wireless sensor networks. In LNCS: Vol. 6240. Cooperative design, visualization, and engineering: CDVE 2010 (pp. 231–238), September. Berlin: Springer.Lloret, J., Sendra, S., Coll, H., & García, M. (2010). Saving energy in wireless local area sensor networks. Computer Journal, 53(10), 1658–1673.Meiyappan, S. S., Frederiks, G., & Hahn, S. (2006). 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In Proceedings of the 15th IEEE international symposium on personal, indoor and mobile radio communications (PIMRC 2004), Barcelona, Spain, 5–8 September (Vol. 2, pp. 995–999).Ye, W., Heidemann, J., & Estrin, D. (2002). An energy-efficient MAC protocol for wireless sensor networks. In Proceedings IEEE INFOCOM 2002, the 21st annual joint conference of the IEEE computer and communications societies, New York, USA, 23–27 June.Ching, C., & Schindelhauer, C. (2010). Utilizing detours for energy conservation in mobile wireless networks. Journal of Telecommunication Systems. doi: 10.1007/s11235-009-9188-3 .Gao, Q., Blow, K., Holding, D., Marshall, I., & Peng, X. (2004). Radio range adjustment for energy efficient wireless sensor networks. Journal of Ad Hoc Networks, 4(1), 75–82.Li, D., Jia, X., & Liu, H. (2004). Energy efficient broadcast routing in static ad hoc wireless networks. IEEE Transactions on Mobile Computing, 3(1), 1–8.Camilo, T., Carreto, C., Silva, J., & Boavida, F. (2006). An energy-efficient ant-based routing algorithm for wireless sensor networks. In Lecture notes in computer science: Vol. 4150. Ant colony optimization and swarm intelligence (pp. 49–59). Berlin: Springer.Younis, M., Youssef, M., & Arisha, K. (2002). Energy-aware routing in cluster-based sensor networks. In Proceedings of the 10th IEEE international symposium on modeling, analysis, and simulation of computer and telecommunications systems (MASCOTS ’02) (pp. 129–136). Washington: IEEE Computer Society.Cheng, Z., Perillo, M., & Heinzelman, W. B. (2008). General network lifetime and cost models for evaluating sensor network deployment strategies. IEEE Transactions on Mobile Computing, 7(4), 484–497.Heo, N., & Varshney, P. K. (2005). Energy-efficient deployment of intelligent mobile sensor networks. IEEE Transactions on Systems, Man and Cybernetics Part A Systems and Humans, 35(1), 78–92.Vlajic, N., & Xia, D. (2006). Wireless sensor networks: to cluster or not to cluster? 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    Multiradio Resource Management: Parallel Transmission for Higher Throughput?

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    Mobile communication systems beyond the third generation will see the interconnection of heterogeneous radio access networks (UMTS, WiMax, wireless local area networks, etc.) in order to always provide the best quality of service (QoS) to users with multimode terminals. This scenario poses a number of critical issues, which have to be faced in order to get the best from the integrated access network. In this paper, we will investigate the issue of parallel transmission over multiple radio access technologies (RATs), focusing the attention on the QoS perceived by final users. We will show that the achievement of a real benefit from parallel transmission over multiple RATs is conditioned to the fulfilment of some requirements related to the kind of RATs, the multiradio resource management (MRRM) strategy, and the transport-level protocol behaviour. All these aspects will be carefully considered in our investigation, which will be carried out partly adopting an analytical approach and partly by means of simulations. In this paper, in particular, we will propose a simple but effective MRRM algorithm, whose performance will be investigated in IEEE802.11a-UMTS and IEEE802.11a-IEEE802.16e heterogeneous networks (adopted as case studies)
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