A New Reliable and Self-Stabilizing Data Fusion Scheme in Unsafe Wireless Sensor Networks

Abstract

In this paper, we deal with the problem of distributed data fusion in unsafe large-scale sensor networks. Data fusion application is the phase of processing the collected data by sensor nodes before sending it the end user. During this phase, resource failures are more likely to occur and can have an adverse effect on the application. Hence, we introduce first an efficient self-stabilizing algorithm to achieve/ensure the convergence of node states to the average of the initial measurements of the network. Next, we present a fault tolerant scheme to resist to frequent and unexpected not concomitant fail-silent/fail-stop node failures. The major contribution of this paper is the design of an analytical expression (an upper bound) of the actual number of moves/iterations required by the algorithm. We provide a comprehensive set of experimental results, that fully demonstrate the usefulness of the proposed schemes.

Experiments with Self-Stabilizing Distributed Data Fusion

“…Instead of working with measurements (intervals of R), a simple frame of discernment Ω = {rainy, cloudy, sunny} is used, where rainy corresponds to low athmospheric pressure and sunny to high pressure. Each node computes its direct confidence as a mass function on Ω using sigmoid functions ( Figure 2) so that their sum is equal to 1 − α, leaving α for the Ω component representing the uncertainty 2 .…”

“…However in all these applications, the network is supposed to be reliable. In [2], a self-stabilizing algorithm is studied for computing the average of sensors values. However it does not rely on the belief function framework and does not take uncertainties into account.…”