669 research outputs found

    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). Dynamic power save techniques for next generation WLAN systems. In Proceedings of the 38th southeastern symposium on system theory (SSST), Cookeville, Tennessee, USA, 5–7 March.Raghunathan, V., Schurgers, C., Park, S., & Srivastava, M. (2002). Energy aware wireless microsensor networks. IEEE Signal Processing Magazine, 19(2), 40–50.Min, R., Bhardwaj, M., Cho, S.-H., Shih, E., Sinha, A., Wang, A., & Chandrakasan, A. (2001). Low power wireless sensor networks. In Proceedings of international conference on VLSI design, India, Bangalore, 3–7 January.Salhieh, A., Weinmann, J., Kochha, M., & Schwiebert, L. (2001). Power efficient topologies for wireless sensor networks. In Proceedings of the IEEE international conference on parallel processing (pp. 156–163), 3–7 September.Jayashree, S., Manoj, B. S., & Murthy, C. S. R. (2004). A battery aware medium access control (BAMAC) protocol for Ad-hoc wireless network. 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? In International symposium on a world of wireless, mobile and multimedia networks, WoWMoM 2006.Garcia, M., & Lloret, J. (2009). A cooperative group-based sensor network for environmental monitoring. In LNCS: Vol. 5738. Cooperative design, visualization, and engineering: CDVE 2009. (pp. 276–279). Berlin: Springer.Garcia, M., Bri, D., Boronat, F., & Lloret, J. (2008). A new neighbour selection strategy for group-based wireless sensor networks. In 4th int. conf. on networking and services, ICNS 2008. 16–21 March (pp. 109–114).Kaplan, E. D. (1996). Understanding GPS: principles and applications. Boston: Artech House.Stojmenovic, I. (2002). Position based routing in ad hoc networks. IEEE Communications Magazine, 40(7), 128–134.Heinzelman, 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.Bhardwaj, M., Garnett, T., & Chandrakasan, A. P. (2001). Upper bounds on the lifetime of sensor networks. In: International conference on communications (ICC’01). June 2001 (pp. 785–790).Gibbons, A. (1985). Algorithmic graph theory. Cambridge: Cambridge University Press.Fraigniaud, P., Pelc, A., Peleg, D., & Perennes, S. (2000). Assigning labels in unknown anonymous networks. In Proceedings of the 19th annual ACM SIGACT-SIGOPS symposium on principles of distributed computing, Portland, OR, USA (Vol. 1, pp. 101–111).OPNET Modeler® Wireless Suite network simulator (2011). Available at http://www.opnet.com/solutions/network_rd/modeler_wireless.html

    An Overview of Own Tracking Wireless Sensors with GSM-GPS Features

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    Wireless Sensors (WS) mobility and pause time have a major impact directly influencing the energy consumption. Lifetime of a WS Network (WSN) depends directly on the energy consumption, thus, the hardware and software components must be optimized for energy management. This study aims to combine a compact hardware architecture with a smart energy management efficiency in order to increase ratio Lifetime/Energy Consumption, to improve the operating time on a portable tracking system with GPS/GSM/GPRS features and own power. In this paper we present the evolution of own WS tracking architecture with GPS/GSM/GPRS features, basic criterion being the lifetime combined with low power consumption. Concern was focused on hardware and software areas: Large number of physical components led to reconsideration of hardware architecture, while for software, we focused on algorithms able to reduce the number of bits in transmitted data packets, which help to reduce energy consumption. The results and conclusions show that the goal was achieved

    A Survey of Green Networking Research

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    Reduction of unnecessary energy consumption is becoming a major concern in wired networking, because of the potential economical benefits and of its expected environmental impact. These issues, usually referred to as "green networking", relate to embedding energy-awareness in the design, in the devices and in the protocols of networks. In this work, we first formulate a more precise definition of the "green" attribute. We furthermore identify a few paradigms that are the key enablers of energy-aware networking research. We then overview the current state of the art and provide a taxonomy of the relevant work, with a special focus on wired networking. At a high level, we identify four branches of green networking research that stem from different observations on the root causes of energy waste, namely (i) Adaptive Link Rate, (ii) Interface proxying, (iii) Energy-aware infrastructures and (iv) Energy-aware applications. In this work, we do not only explore specific proposals pertaining to each of the above branches, but also offer a perspective for research.Comment: Index Terms: Green Networking; Wired Networks; Adaptive Link Rate; Interface Proxying; Energy-aware Infrastructures; Energy-aware Applications. 18 pages, 6 figures, 2 table

    Flexpop: A popularity-based caching strategy for multimedia applications in information-centric networking

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    Information-Centric Networking (ICN) is the dominant architecture for the future Internet. In ICN, the content items are stored temporarily in network nodes such as routers. When the memory of routers becomes full and there is no room for a new arriving content, the stored contents are evicted to cope with the limited cache size of the routers. Therefore, it is crucial to develop an effective caching strategy for keeping popular contents for a longer period of time. This study proposes a new caching strategy, named Flexible Popularity-based Caching (FlexPop) for storing popular contents. The FlexPop comprises two mechanisms, i.e., Content Placement Mechanism (CPM), which is responsible for content caching, and Content Eviction Mechanism (CEM) that deals with content eviction when the router cache is full and there is no space for the new incoming content. Both mechanisms are validated using Fuzzy Set Theory, following the Design Research Methodology (DRM) to manifest that the research is rigorous and repeatable under comparable conditions. The performance of FlexPop is evaluated through simulations and the results are compared with those of the Leave Copy Everywhere (LCE), ProbCache, and Most Popular Content (MPC) strategies. The results show that the FlexPop strategy outperforms LCE, ProbCache, and MPC with respect to cache hit rate, redundancy, content retrieval delay, memory utilization, and stretch ratio, which are regarded as extremely important metrics (in various studies) for the evaluation of ICN caching. The outcomes exhibited in this study are noteworthy in terms of making FlexPop acceptable to users as they can verify the performance of ICN before selecting the right caching strategy. Thus FlexPop has potential in the use of ICN for the future Internet such as in deployment of the IoT technology

    Power saving and energy optimization techniques for Wireless Sensor Networks

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    Wireless sensor networks have become increasingly popular due to their wide range of applications. Energy consumption is one of the biggest constraints of the wireless sensor node and this limitation combined with a typical deployment of large number of nodes have added many challenges to the design and management of wireless sensor networks. They are typically used for remote environment monitoring in areas where providing electrical power is difficult. Therefore, the devices need to be powered by batteries and alternative energy sources. Because battery energy is limited, the use of different techniques for energy saving is one of the hottest topics in WSNs. In this work, we present a survey of power saving and energy optimization techniques for wireless sensor networks, which enhances the ones in existence and introduces the reader to the most well known available methods that can be used to save energy. They are analyzed from several points of view: Device hardware, transmission, MAC and routing protocols.Sendra Compte, S.; Lloret, J.; GarcĂ­a Pineda, M.; Toledo AlarcĂłn, JF. (2011). Power saving and energy optimization techniques for Wireless Sensor Networks. Journal of Communications. 6(6):439-459. doi:10.4304/jcm.6.6.439-459S4394596

    A scheme for efficient peer-to-peer live video streaming over wireless mesh networks

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    Peers in a Peer-to-Peer (P2P) live video streaming system over hybrid wireless mesh networks (WMNs) enjoy high video quality when both random network coding (RNC) and an efficient hybrid routing protocol are employed. Although RNC is the most recently used method of efficient video streaming, it imposes high transmission overhead and decoding computational complexity on the network which reduces the perceived video quality. Besides that, RNC cannot guaranty a non-existence of linear dependency in the generated coefficients matrix. In WMNs, node mobility has not been efficiently addressed by current hybrid routing protocols that increase video distortion which would lead to low video quality. In addition, these protocols cannot efficiently support nodes which operate in infrastructure mode. Therefore, the purpose of this research is to propose a P2P live video streaming scheme which consists of two phases followed by the integration of these two phases known as the third phase to provide high video quality in hybrid WMNs. In the first phase, a novel coefficients matrix generation and inversion method has been proposed to address the mentioned limitations of RNC. In the second phase, the proposed enhanced hybrid routing protocol was used to efficiently route video streams among nodes using the most stable path with low routing overhead. Moreover, this protocol effectively supports mobility and nodes which operate in infrastructure mode by exploiting the advantages of the designed locator service. Results of simulations from the first phase showed that video distortion as the most important performance metric in live video streaming, had improved by 36 percent in comparison with current RNC method which employs the Gauss-Jordan Elimination (RNC-GJE) method in decoding. Other metrics including frame dependency distortion, initial start-up delay and end-to-end delay have also improved using the proposed method. Based on previous studies, although Reactive (DYMO) routing protocol provides better performance than other existing routing protocols in a hybrid WMN, the proposed protocol in the second phase had average improvements in video distortion of l86% for hybrid wireless mesh protocol (HWMP), 49% for Reactive (Dynamic MANET On-Demand-DYMO), 75% for Proactive (Optimized Link State Routing-OLSR), and 60% for Ad-hoc on-demand Distance Vector Spanning-Tree (AODV-ST). Other metrics including end-to-end delay, packet delay variation, routing overhead and number of delivered video frames have also improved using the proposed protocol. Finally, the third phase, an integration of the first two phases has proven to be an efficient scheme for high quality P2P live video streaming over hybrid WMNs. This video streaming scheme had averagely improved video distortion by 41%, frame dependency distortion by 50%, initial start-up delay by 15% and end-to-end delay by 33% in comparison with the average introduced values by three other considered integration cases which are Reactive and RNC-GJE, Reactive and the first phase, the second phase and RNC-GJE

    Monitoring of Wireless Sensor Networks

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    Wireless Mesh Networks to Support Video Surveillance: Architecture, Protocol, and Implementation Issues

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    Current video-surveillance systems typically consist of many video sources distributed over a wide area, transmitting live video streams to a central location for processing and monitoring. The target of this paper is to present an experience of implementation of a large-scale video-surveillance system based on a wireless mesh network infrastructure, discussing architecture, protocol, and implementation issues. More specifically, the paper proposes an architecture for a video-surveillance system, and mainly centers its focus on the routing protocol to be used in the wireless mesh network, evaluating its impact on performance at the receiver side. A wireless mesh network was chosen to support a video-surveillance application in order to reduce the overall system costs and increase scalability and performance. The paper analyzes the performance of the network in order to choose design parameters that will achieve the best trade-off between video encoding quality and the network traffic generated
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