Suffix arrays are a simple and powerful data structure for text processing that can be used for full text indexes, data compression, and many other applications in particular in bioinformatics. However, so far it has looked prohibitive to build suffix arrays for huge inputs that do not fit into main memory. This paper presents design, analysis, implementation, and experimental evaluation of several new and improved algorithms for suffix array construction. The algorithms are asymptotically optimal in the worst case or on the average. Our implementation can construct suffix arrays for inputs of up to 4GBytes in hours on a low cost machine.As a tool of possible independent interest we present a systematic way to design, analyze, and implement pipelined algorithms.

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Object localization using wireless sensor networks (WSN) often requires data from many sensor nodes and different types of sensors for position estimation. This incurs a heavy communication load, which can cause packet loss, communication delay and much energy consumption, deteriorating the performance of object localization. Here we employ an event-driven Gaussian process in order to learn the position of an unknown object using WSN with multiple types of sensors. In the event-driven framework, each sensor node transmits data only when decision criteria are satisfied. We consider the error-bounded algorithm as the decision criteria based on the measurement history of each sensor node. The overall communication between sensor nodes is reduced, thus increasing energy-efficiency of the network and relieving the concentration of communication traffic at the base node. Experiments to track the position of a mobile robot are conducted using a multisensor WSN, and the comparison is made between the eventdriven framework and the conventional approach in which sensors transmit data at a constant sampling rate. Experimental results demonstrate the efficiency and accuracy of the proposed event-driven Gaussian process approach.