Ary Mazharuddin Shiddiqi

Adaptive data stream mining for wireless sensor networks

Data stream mining in wireless sensor networks has many important applications. Realizing these applications is faced by resource constraints of the sensor nodes that form the network. Adaptation to availability of resources is crucial to the success of these applications. In this paper, we propose a distributed data stream classification technique that has been tested on a real sensor network platform, namely, Sun SPOT. Experimental results evidenced the applicability of our technique to operate in such an environment of scarce resources.

Sensor placement strategy for locating leaks using lean graphs

Undetected leaks in water distribution networks are a significant economic and environmental problem worldwide. Small leaks are hard to detect yet they contribute significantly to water losses. We propose a method to detect small leaks using a leak signature, called a lean graph, which is a sub-graph of the distribution network. Since it is not feasible to install flow sensors on every pipe, we utilize lean graphs to develop a sensor placement strategy that maximizes leak detection accuracy given a fixed number of sensors. Experimental results show the promising prospect of our approach.

Distributed Classification of Data Streams: An Adaptive Technique

Mining data streams is a critical task of actual Big Data applications. Usually, data stream mining algorithms work on resource-constrained environments, which call for novel requirements like availability of resources and adaptivity. Following this main trend, in this paper we propose a distributed data stream classification technique that has been tested on a real sensor network platform, namely, Sun SPOT. The proposed technique shows several points of research innovation, with are also confirmed by its effectiveness and efficiency assessed in our experimental campaign.