1. DISTRIBUTED COMPRESSIVE SAMPLING FOR LIFETIME
OPTIMIZATION IN DENSE WIRELESS SENSOR NETWORKS

2. Abstract:
• In this paper, we consider a scenario in which a large WSN, based on ZigBee protocol, is
used for monitoring (e.g., building, industry, etc.).
• We propose a new algorithm for in-network compression aiming at longer network
lifetime.
• Our approach is fully distributed: each node autonomously takes a decision about the
compression and forwarding scheme to minimize the number of packets to transmit.
• Performance is investigated with respect to network size using datasets gathered by a
real-life deployment.
• An enhanced version of the algorithm is also introduced to take into account the energy
spent in compression.
• Experiments demonstrate that the approach helps finding an optimal tradeoff between
the energy spent in transmission and data compression.

3. Existing system:
• Data gathering in large-scale wireless sensor networks (WSNs) relies on small
and inexpensive devices with severe energy constraints .Network lifetime in
this context is a critical concern.
• In large network nodes may run out energy as a consequence of the high
number of communications required to forward packets produced by nodes
toward a data-gathering sink.
• Increasing network size poses significant data collection challenges, for what
concerns sampling and transmission coordination as well as network lifetime.

4. Disadvantages:
• High power consumption
• Network lifetime become critical in large network
• Data sampling is critical in collected in large wireless sensor network

5. Proposed system:
• The proposed solution successfully minimizes the power consumption and the number of
packets transmitted in the network according to nodes status, extending the system
lifetime.
• Our algorithm performs better than the two previous schemes, presenting a number of
sent packets that is always smaller than both PF and DCS.
• For small-sized networks, the proposed solution approaches DCS. This is why the
number of packets sent with PF or DCS is the same. Therefore, according to the
algorithm proposed, the node compresses data using CS.

6. Continues…
• The proposed modified algorithm is able to prolong the lifetime of the network
achieving a trade-off between traffic in the network and energy spent in
compression.
• The simulations performed, carefully calibrated on values for power consumption
extracted from real sensor nodes, have shown that one of the main source of energy
expenditure is the compression phase.

7. Advantages:
• Low power consumption.
• To secure network lifetime.
• Data is compress and then sampled. So there is no loss of data.

8. Software requirements:
• Simulation---ns2

9. Reference:
• [1] G. Anastasi, M. Conti, and M. Di Francesco, “Extending the lifetime
of wireless sensor networks through adaptive sleep,” IEEE Trans. Ind.
Informat., vol. 5, no. 3, pp. 351–365, Aug. 2009.
• [2] M. Jongerden, A. Mereacre, H. Bohnenkamp, B. Haverkort, and J. P.
Katoen, “Computing optimal schedules of battery usage in embedded
systems,” IEEE Trans. Ind. Informat., vol. 6, no. 3, pp. 276–286, Aug.
2010.
• [3] A. Willig, “Recent and emerging topics in wireless industrial communications:
A selection,” IEEE Trans. Ind. Informat., vol. 4, no. 2, pp.
102–124, May 2008.

10. Thank you…