Efficient Aggregate Computations in Large-Scale Dense WSN

Abstract

We focus on large-scale and dense deeply embedded systemswhere, due to the large amount of information generatedby all nodes, even simple aggregate computations suchas the minimum value (MIN) of the sensor readings becomenotoriously expensive to obtain. Recent research has exploiteda dominance-based medium access control (MAC)protocol, the CAN bus, for computing aggregated quantitiesin wired systems. For example, MIN can be computedefficiently and an interpolation function which approximatessensor data in an area can be obtained efficiently aswell. Dominance-based MAC protocols have recently beenproposed for wireless channels and these protocols can beexpected to be used for achieving highly scalable aggregatecomputations in wireless systems. But no experimentaldemonstration of that is currently available in the researchliterature.In this paper, we demonstrate that highly scalable aggregatecomputations in wireless networks are possible. We doso by (i) building a new wireless hardware platform with appropriatecharacteristics for making dominance-based MACprotocols efficient, (ii) implementing dominance-based MACprotocols on this platform, (iii) implementing distributed algorithmsfor aggregate computations (MIN,MAX, Interpolation)using the new implementation of the dominance-basedMAC protocol and (iv) performing experiments to prove thatsuch highly scalable aggregate computations in wireless networksare possible. ! AbstractWe 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.

Response time analysis of slotted WiDOM in noisy wireless channels

“…The wireless dominance-based protocol (WiDOM) [5], [7] is the adaptation of the CAN bus [3] into the wireless domain and supports a very large number of priority levels. Moreover, assuming to know the main sources of interference, WiDOM allows to check in advance whether all the deadlines for the time-critical messages can be met.…”

“…Another issue is that this protocol imposes a large overhead (missing R5). On this account, a new version of WiDOM, called Slotted WiDOM [7], has been proposed to offer low overhead (fulfilling R1, R2 and R5), but no schedulability analysis is available for it. Hence, the development of a schedulability analysis for Slotted WiDOM, capable to predict the timing of those message streams suffering from jitter or experiencing noise on the channel, would help in fulfilling all the above requirements.…”

Feature Extraction in Densely Sensed Environments: Extensions to Multiple Broadcast Domains

“…With carrier-sense or time-division based MAC protocols, computing MIN depends on the number of sensor nodes. Studies show that the computation of MIN with standard IEEE 802.15.4 MAC linearly increases with the growth of network density (e.g., 80 ms for a 40-node network size) [7,14]. Instead, the WiDom implementation [7] guarantees a constant time (10 ms) for calculating MIN regardless of network density.…”

“…← a fraction of max ; // termination condition setting (5) while new-peak > do (6) if ̸ = 1 then // not filtered out (7) new-peak ← M(− ); // find the new peak (8) C o m p u t e based on (2); (9) ⟨ adj-valley , adj-valley ⟩ ← M( ); (10) ← adj-valley ; (11) ← distance between new-peak and node ; (12) if…”

“…Computing even simple aggregate quantities such as extrema (minimum or maximum) is not trivial for a dense network as it may require collecting data, in the worst case, from all nodes [7] (even if some sort of spatial subsampling is employed [8]). Dominance-based or binarycountdown MAC protocols help in finding the minimum value in constant time [9][10][11], provided that all nodes reside in a single broadcast domain (SBD).…”