A critical aspect of applications with wireless sensor networks is network lifetime. Battery-powered sensors are usable as long as they can communicate captured data to a processing node. Sensing and communications consume energy, therefore judicious power management and scheduling can effectively extend the operational time. One important class of wireless sensor applications of deployment of large number of sensors in an area for environmental monitoring. The data collected by the sensors is sent to a central node for processing. In this paper we propose an efficient method to achieve energy savings by organizing the sensor nodes into a maximum number of disjoint dominating sets (DDS) which are activated successively. Only the sensors from the active set are responsible for monitoring the target area and for disseminating the collected data. All other nodes are into a sleep mode, characterized by a low energy consumption. We define the maximum disjoint dominating sets problem and we design a heuristic that computes the sets. Theoretical analysis and performance evaluation results are presented to verify our approach.

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A minimization problem that has arisen from the study of non-unique probe selection with group testing technique is as follows: Given a binary matrix, find a d-disjunct submatrix with the minimum number of rows and the same number of columns. We show that when every probe hybridizes to at most two viruses, i.e., every row contains at most two 1s, this minimization is still MAX SNP-complete, but has a polynomial-time approximation with performance ratio 1 + 2/(d + 1). This approximation is constructed based on an interesting result that the above minimization is polynomial-time solvable when every probe hybridizes to exactly two viruses.

Pooling designs are used in DNA library screening to efficiently distinguish positive from negative clones, which is fundamental for studying gene functions and many other biological applications. One challenge is to design decoding algorithms for determining whether a clone is positive based on the test outcomes and a binary matrix representing the pools. This is more difficult in practice due to errors in biological experiments. More challenging still is a third category of clones called 'inhibitors' whose effect is to neutralise positives. We present a novel decoding algorithm identifying all positive clones in the presence of inhibitors and experimental errors.