Transmission Power Adjustment Scheme for Mobile Beacon-Assisted Sensor Localization

© 2005-2012 IEEE. Localization, as a crucial service for sensor networks, is an energy-demanding process for both indoor and outdoor scenarios. GPS-based localization schemes are infeasible in remote, indoor areas, and it is not a cost-effective solution for large-scale networks. Single mobile-beacon architecture is recently considered to localize sensor networks with the aim of removing numerous GPS-equipped nodes. The critical issue for the mobile beacon-Assisted localization is to preserve the consumed power to increase the lifetime. This paper presents a novel power control scheme, namely 'Z-power,' for mobile beacon traveling along a predefined path. The proposed scheme takes the advantage of deterministic path traveled by the single beacon to efficiently adjust the transmission power. Based on the extensive results, the proposed power control scheme could successfully improve the beacon and sensors energy consumption about 25.37% and 34.09%, respectively. A significant energy-Accuracy tradeoff was achieved using Z-power, which could successfully keep the same level of accuracy while providing lower energy consumption. Another group of results collected when obstacle-handling algorithm was applied at the presence of obstacles. In this scenario, Z-power improves energy consumption and localization accuracy with the same level of success

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

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

Service Provisioning in Mobile Networks Through Distributed Coordinated Resource Management

The pervasiveness of personal computing platforms offers an unprecedented opportunity to deploy large-scale services that are distributed over wide physical spaces. Two major challenges face the deployment of such services: the often resource-limited nature of these platforms, and the necessity of preserving the autonomy of the owner of these devices. These challenges preclude using centralized control and preclude considering services that are subject to performance guarantees. To that end, this thesis advances a number of new distributed resource management techniques that are shown to be effective in such settings, focusing on two application domains: distributed Field Monitoring Applications (FMAs), and Message Delivery Applications (MDAs). In the context of FMA, this thesis presents two techniques that are well-suited to the fairly limited storage and power resources of autonomously mobile sensor nodes. The first technique relies on amorphous placement of sensory data through the use of novel storage management and sample diffusion techniques. The second approach relies on an information-theoretic framework to optimize local resource management decisions. Both approaches are proactive in that they aim to provide nodes with a view of the monitored field that reflects the characteristics of queries over that field, enabling them to handle more queries locally, and thus reduce communication overheads. Then, this thesis recognizes node mobility as a resource to be leveraged, and in that respect proposes novel mobility coordination techniques for FMAs and MDAs. Assuming that node mobility is governed by a spatio-temporal schedule featuring some slack, this thesis presents novel algorithms of various computational complexities to orchestrate the use of this slack to improve the performance of supported applications. The findings in this thesis, which are supported by analysis and extensive simulations, highlight the importance of two general design principles for distributed systems. First, a-priori knowledge (e.g., about the target phenomena of FMAs and/or the workload of either FMAs or DMAs) could be used effectively for local resource management. Second, judicious leverage and coordination of node mobility could lead to significant performance gains for distributed applications deployed over resource-impoverished infrastructures

Online Learning of Energy Consumption for Navigation of Electric Vehicles

Energy efficient navigation constitutes an important challenge in electric vehicles, due to their limited battery capacity. We employ a Bayesian approach to model the energy consumption at road segments for efficient navigation. In order to learn the model parameters, we develop an online learning framework and investigate several exploration strategies such as Thompson Sampling and Upper Confidence Bound. We then extend our online learning framework to the multi-agent setting, where multiple vehicles adaptively navigate and learn the parameters of the energy model. We analyze Thompson Sampling and establish rigorous regret bounds on its performance in the single-agent and multi-agent settings, through an analysis of the algorithm under batched feedback. Finally, we demonstrate the performance of our methods via experiments on several real-world city road networks

Reliable load-balancing routing for resource-constrained wireless sensor networks

Wireless sensor networks (WSNs) are energy and resource constrained. Energy limitations make it advantageous to balance radio transmissions across multiple sensor nodes. Thus, load balanced routing is highly desirable and has motivated a significant volume of research. Multihop sensor network architecture can also provide greater coverage, but requires a highly reliable and adaptive routing scheme to accommodate frequent topology changes. Current reliability-oriented protocols degrade energy efficiency and increase network latency. This thesis develops and evaluates a novel solution to provide energy-efficient routing while enhancing packet delivery reliability. This solution, a reliable load-balancing routing (RLBR), makes four contributions in the area of reliability, resiliency and load balancing in support of the primary objective of network lifetime maximisation. The results are captured using real world testbeds as well as simulations. The first contribution uses sensor node emulation, at the instruction cycle level, to characterise the additional processing and computation overhead required by the routing scheme. The second contribution is based on real world testbeds which comprises two different TinyOS-enabled senor platforms under different scenarios. The third contribution extends and evaluates RLBR using large-scale simulations. It is shown that RLBR consumes less energy while reducing topology repair latency and supports various aggregation weights by redistributing packet relaying loads. It also shows a balanced energy usage and a significant lifetime gain. Finally, the forth contribution is a novel variable transmission power control scheme which is created based on the experience gained from prior practical and simulated studies. This power control scheme operates at the data link layer to dynamically reduce unnecessarily high transmission power while maintaining acceptable link reliability

PREDICTING THE ADOPTION OF CONNECTED AUTONOMOUS VEHICLES BY TRANSPORTATION ORGANIZATIONS USING PEER EFFECTS

PREDICTING THE ADOPTION OF CONNECTED AUTONOMOUS VEHICLES BY TRANSPORTATION ORGANIZATIONS USING PEER EFFECT

Applications

Volume 3 describes how resource-aware machine learning methods and techniques are used to successfully solve real-world problems. The book provides numerous specific application examples: in health and medicine for risk modelling, diagnosis, and treatment selection for diseases in electronics, steel production and milling for quality control during manufacturing processes in traffic, logistics for smart cities and for mobile communications

HUC-HISF: A Hybrid Intelligent Security Framework for Human-centric Ubiquitous Computing

制度:新 ; 報告番号:乙2336号 ; 学位の種類:博士(人間科学) ; 授与年月日:2012/1/18 ; 早大学位記番号:新584