DISTRIBUTED COMPRESSIVE SAMPLING FOR LIFETIMEOPTIMIZATION IN DENSE WIRELESS SENSOR NETWORKS
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.
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.
Disadvantages: • High power consumption • Network lifetime become critical in large network • Data sampling is critical in collected in large wireless sensor network
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.
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.
Advantages:• Low power consumption.• To secure network lifetime.• Data is compress and then sampled. So there is no loss of data.
Reference:•  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.•  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.•  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.