JAVA 2013 IEEE NETWORKING PROJECT Harvesting aware energy management for time-critical wireless sensor networks

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To Get any Project for CSE, IT ECE, EEE Contact Me @ 09849539085, 09966235788 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org

To Get any Project for CSE, IT ECE, EEE Contact Me @ 09849539085, 09966235788 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org

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  • 1. HARVESTING-AWARE ENERGY MANAGEMENT FOR TIME- CRITICAL WIRELESS SENSOR NETWORKS ABSTRACT: Our paper proposes a general purpose, multihop WSN architecture capable of supporting time-critical CPS systems using energy harvesting. We then present a set of Harvesting Aware Speed Selection (HASS) algorithms. Our technique maximizes the minimum energy reserve for all the nodes in the network, thus ensuring highly resilient performance under emergency or fault- driven situations. We present an optimal centralized solution, along with an efficient, distributed solution. We propose a CPS- specific experimental methodology, enabling us to evaluate our approach. Our experiments show that our algorithms yield significantly higher energy reserves than baseline methods. We conducted extensive simulations to evaluate our HASS solutions under a variety of data processing, communication and performance requirements. We propose an experimental methodology to simulate a WSN system utilizing energy harvested from water flow in a water distribution system. Our results show that both the centralized and distributed solutions significantly improve the capacity of time- critical WSN systems to deal with emergency situations, in addition to meeting performance requirements. GLOBALSOFT TECHNOLOGIES IEEE PROJECTS & SOFTWARE DEVELOPMENTS IEEE FINAL YEAR PROJECTS|IEEE ENGINEERING PROJECTS|IEEE STUDENTS PROJECTS|IEEE BULK PROJECTS|BE/BTECH/ME/MTECH/MS/MCA PROJECTS|CSE/IT/ECE/EEE PROJECTS CELL: +91 98495 39085, +91 99662 35788, +91 98495 57908, +91 97014 40401 Visit: www.finalyearprojects.org Mail to:ieeefinalsemprojects@gmail.com
  • 2. SCOPE OF THE PROJECT: The focus of this paper is a coordinated energy management policy for time-critical WSN applications that use energy harvesting and that must maintain required performance under emergency or fault-driven situations.
  • 3. EXISTING SYSTEM: Existing tasks to reduce CPU energy consumption in hard real-time systems through dynamic voltage scaling scheduling solution includes three components: 1) a static (offline) solution to compute the optimal speed, assuming worst-case workload for each arrival, 2) an online speed reduction mechanism to reclaim energy by adapting to the actual workload, and 3) an online, adaptive and speculative speed adjustment mechanism to anticipate early completions of future executions by using the average-case workload information. All these solutions still guarantee that all deadlines are met results show that our reclaiming algorithm alone outperforms other recently proposed intertask voltage scheduling schemes. Existing techniques are shown to provide additional gains, approaching the theoretical lower-bound by a margin of 10 percent.
  • 4. PROPOSED SYSTEM: We propose a set of Harvesting Aware Speed Selection (HASS) algorithms that use both DVS and DMS in conjunction with energy harvesting modules. The purpose of the HASS framework is to maximize energy reserves while meeting application performance requirements, therefore maximizing the system’s resilience in the face of emergency situations. One difficulty in managing energy for these systems is that nodes may have quite different workload requirements and available energy sources. This may arise from natural factors such as the differences in nodes’ energy harvesting opportunities, unbalanced distribution of processing workloads, or network traffic among nodes. Because of these conflicting design considerations, the HASS approach attempts to maximize the energy reserve levels of nodes in the network, while guaranteeing the required system performance levels. Our ultimate goal is to maximize system resilience to network-wide workload traffic in the amount of energy harvested. We conducted extensive simulations to evaluate our HASS solutions under a variety of data processing, communication and performance requirements. We propose an experimental methodology to simulate a WSN system utilizing energy harvested from water flow in a water distribution system. Our results show that both the centralized and distributed solutions significantly improve the capacity of time-critical WSN systems to deal with emergency situations, in addition to meeting performance requirements.
  • 5. HARDWARE & SOFTWARE REQUIREMENTS: HARDWARE REQUIREMENT:  Processor - Pentium –IV  Speed - 1.1 GHz  RAM - 256 MB (min)  Hard Disk - 20 GB  Floppy Drive - 1.44 MB  Key Board - Standard Windows Keyboard  Mouse - Two or Three Button Mouse  Monitor - SVGA SOFTWARE REQUIREMENTS:  Operating System : Windows XP  Front End : Visual Studio 2008 .NET  Scripts : C# Script.
  • 6. CONCLUSION: This paper presented an epoch-based approach for energy management in performance-constrained WSNs that utilize energy harvesting. We adjust radio modulation levels and CPU frequencies in order to satisfy performance requirement. The goal of our approach is to maximize the minimum energy reserve over any node in the network. Through this objective, we ensure highly resilient performance under both normal and emergency situations. We formulated our problem as an optimization problem, and solved it with centralized and distributed algorithms. Through simulation we show our algorithms achieve significantly higher performance than a baseline approach under both normal and emergency situations.
  • 7. REFERENCES: [1] Z. Hanzalek and P. Jurcik, “Energy efficient scheduling for cluster-tree wireless sensor networks with time- bounded data flows: Application to ieee 802.15.4/zigbee,” IEEE Trans. Ind. Informat., vol. 6, no. 3, pp. 438– 450, Aug. 2010. [2] G. Anastasi, M. Conti, and M. D. Francesco, “Extending the lifetime of wireless sensor networks through adaptive sleep,” IEEE Trans. Ind. Informat., vol. 56, no. 3, pp. 351–365, Jul. 2009. [3] Y. K. Tan and S. K. Panda, “Energy harvesting from hybrid indoor ambient light and thermal energy sources for enhanced performance of wireless sensor nodes,” IEEE Trans. Ind. Electron., vol. 58, no. 9, pp. 4424–4435, 2011. [4] S. E. Yoo, P. K. Chong, D. Kim, Y. Doh, M. L. Pham, E. Choi, and J. Huh, “Guaranteeing real-time services for industrial wireless sensor networks with IEEE 802.15.4,” IEEE Trans. Ind. Electron., vol. 57, no. 11, pp. 3868–3876, Nov. 2010. [5] G. W. Allen, S. D. Haggerty, and M. welsh, “Lance: Optimizing highresolution signal collection in wireless sensor networks,” in Proc. 6th ACM Conf. Embedded Network Sensor Syst., Raleigh, NC, 2008, pp. 169–182. [6] H. Aydin et al., “Power-aware scheduling for periodic real-time tasks,” IEEE Trans. Comput., vol. 53, pp. 584–600, 2004. [7] G. W. Challen, J. Waterman, andM. Welsh, “Integrated distributed energy awareness for wireless sensor networks,” in Proc. 7th ACM Conf. Embedded Networked Sensor Syst. (Sensys’09), Berkeley, CA, 2009, pp. 381–382. [8] Crossbow Technology, iMote2 Datasheet, 2006. [Online]. Available: www.xbow.com [9] R. S. Liu, K. W. Fan, Z. Z. Zheng, and P. Sinha, “Perpetual and fair data collection for environmental energy harvesting sensor networks,” IEEE/ACM Trans. Networking, vol. 19, no. 4, pp. 947–960, Aug. 2011. [10] G.Mainland, D. C. Parkes, andM.Welsh, “Decentralized, adaptive resource allocation for sensor networks,” in Proc. 2nd Conf. Symp. Networked Syst. Design Implementation (NSDI’05), Boston, MA, 2005, pp. 315–328.
  • 8. [11] J. Heo, J. Hong, and Y. Cho, “EARQ: Energy aware routing for real-time and reliable communication in wireless industrial sensor networks,” IEEE Trans. Ind. Informat., vol. 5, no. 1, pp. 3–11, Feb. 2009. [12] W. L. Hwang, S. Fei, and K. Chakrabarty, “Automated design of pinconstrained digital microfluidic arrays for lab-on-a-chip applications,” in Proc. ACM Design Autom. Conf., San Francisco, CA, 2006. [13] A. Kansal et al., “Power management in energy harvesting sensor networks,” ACM Trans. Embedded Comput. Syst., vol. 6, no. 4, Sep. 2007. [14] S. Liu, Q. Qiu, and Q. Wu, “Energy aware dynamic voltage and frequency selection for real-time systems with energy harvesting,” in Proc. Design Automation and Test in Europe, Munich, Germany, 2008, pp. 236– 241. [15] S. Madden et al., “TAG: A tiny aggregation service for ad-hoc sensor networks,” in Proc. USENIX Symp. Oper. Syst. Design Implementation (OSDI’02), Boston, MA, 2002, pp. 131–146.
  • 9. CLOUING DOMAIN: WIRELESS NETWORK PROJECTS