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Sensors: Theory, Algorithms, and Applications (Springer Optimization and Its Applications)

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In recent years, technological advances have resulted in the rapid development of a new exciting research direction – the interdisciplinary use of sensors for data collection, systems analysis, and monitoring. Application areas include military surveillance, environmental screening, computational neuroscience, seismic detection, transportation, along with many other important fields.

Broadly speaking, a sensor is a device that responds to a physical stimulus (e.g., heat, light, sound, pressure, magnetism, or motion) and collects and measures data regarding some property of a phenomenon, object, or material. Typical types of sensors include cameras, scanners, radiometers, radio frequency receivers, radars, sonars, thermal devices, etc.

The amount of data collected by sensors is enormous; moreover, this data is heterogeneous by nature. The fundamental problems of utilizing the collected data for efficient system operation and decision making encompass multiple research areas, including applied mathematics, optimization, and signal/image processing, to name a few. Therefore, the task of crucial importance is not only developing the knowledge in each particular research field, but also bringing together the expertise from many diverse areas in order to unify the process of collecting, processing, and analyzing sensor data. This process includes theoretical, algorithmic, and application-related aspects, all of which constitute essential steps in advancing the interdisciplinary knowledge in this area.

Besides individual sensors, interconnected systems of sensors, referred to as sensor networks, are receiving increased attention nowadays. The importance of rigorous studies of sensor networks stems from the fact that these systems of multiple sensors not only acquire individual (possibly complimentary) pieces of information, but also effectively exchange the obtained information. Sensor networks may operate in static (the locations of individual sensor nodes are fixed) or dynamic (sensor nodes may be mobile) settings.
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