Conference Paper

Efficient Aggregate Computations in Large-Scale Dense WSN.

IPP-HURRAY Res. Group CISTER/ISEP, Polytech. Inst. of Porto, Porto
DOI: 10.1109/RTAS.2009.22 Conference: 15th IEEE Real-Time and Embedded Technology and Applications Symposium, RTAS 2009, San Francisco, CA, USA, 13-16 April 2009
Source: IEEE Xplore


We focus on large-scale and dense deeply embedded systems where, due to the large amount of information generated by all nodes, even simple aggregate computations such as the minimum value (MIN) of the sensor readings become notoriously expensive to obtain. Recent research has exploited a dominance-based medium access control(MAC) protocol, the CAN bus, for computing aggregated quantities in wired systems. For example, MIN can be computed efficiently and an interpolation function which approximates sensor data in an area can be obtained efficiently as well. Dominance-based MAC protocols have recently been proposed for wireless channels and these protocols can be expected to be used for achieving highly scalable aggregate computations in wireless systems. But no experimental demonstration is currently available in the research literature. In this paper, we demonstrate that highly scalable aggregate computations in wireless networks are possible. We do so by (i) building a new wireless hardware platform with appropriate characteristics for making dominance-based MAC protocols efficient, (ii) implementing dominance-based MAC protocols on this platform, (iii) implementing distributed algorithms for aggregate computations (MIN, MAX, Interpolation) using the new implementation of the dominance-based MAC protocol and (iv) performing experiments to prove that such highly scalable aggregate computations in wireless networks are possible.

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    • "Instead, the WiDom implementation [6] guarantees a constant time (10 ms) for calculating the MIN value regardless of network density. The process of finding the MIN has been leveraged in the past to find an approximate interpolation and other aggregate quantities [5], [6]. Interpolated values are computed through an iterative process by integration of local information available in each sensor node (its own location information and measured value plus the location information and the measured value of the winner received after each tournament). "
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    ABSTRACT: With the reduction in size and cost of sensor nodes, dense sensor networks are becoming more popular in a wide-range of applications. Many such applications with dense deployments are geared towards finding various patterns or features such as peaks, boundaries and shapes in the spread of sensed physical quantities over an area. However, collecting all the data from individual sensor nodes can be impractical both in terms of timing requirements and the overall resource consumption. Hence, it is imperative to devise distributed information processing techniques that can help in identifying such features with a high accuracy and within certain time constraints. In this paper, we exploit the prioritized channel-access mechanism of dominance-based Medium Access Control (MAC) protocols to efficiently obtain exterma of the sensed quantities. We show how by the use of simple transforms that sensor nodes employ on local data it is also possible to efficiently extract certain features such as local extrema and boundaries of events. Using these transformations, we show through extensive evaluations that our proposed technique is fast and efficient at retrieving only sensor data point with the most constructive information, independent of the number of sensor nodes in the network.
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    • "Various interesting features of dominance-based protocols [9] (CAN [10] and WiDom [11] [12] are examples) can be exploited to obtain aggregate quantities in large scale dense networks, with a time-complexity that is very low and independent of the number of nodes. Such mechanisms are being used as a key building block in densely instrumented Cyber-Physical Systems as is discussed next. "
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    • "Note also that each line can execute very quickly. In particular, note that line 36, send_and_rcv, can be executed within 3 milliseconds if the hardware platform from [2] is used. Because the execution of the lines 17-44 can be performed at such a high speed we see that it is possible to build a sensor network that monitors its environment by obtaining an interpolation as a representation of the physical world and obtain an update of that interpolation very quickly. "
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