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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.
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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:
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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
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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
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[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.
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[8] Crossbow Technology, iMote2 Datasheet, 2006. [Online]. Available: www.xbow.com
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[10] G.Mainland, D. C. Parkes, andM.Welsh, “Decentralized, adaptive resource allocation for sensor networks,”
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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–
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[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