Data Acquisition: Towards Optimal Use of Sensors

Abstract

The main source of knowledge is processing data. Data comes from sensors. Within a limited budget, it is extremely important to make sure that the use of the sensors is optimized so that we get the largest possible amount of useful data from these sensors. Traditionally, most data comes from stationary sensors, i.e., sensors which we place at fixed locations . For such sensors, it is important to come up with the optimal placement , the placement which would lead to the largest amount of useful data. We analyze this problem in Sect. 2.1, on the example of placing bio-weapon detectors, and in Sect. 2.2, on the example of placing environmental sensors. The problem of optimal use becomes more technically challenging if we take into account the possibility of using mobile sensors , i.e., sensors which we can move along different trajectories. In this case, it is important to come up with optimal trajectories, i.e., the trajectories which would lead to the largest amount of useful data. We analyze this problem in Sect. 2.3, on the example of Unmanned Aerial Vehicles (UAVs) patrolling the border . In all these cases, it is important to make sure that not only we have an algorithm producing the optimal placement or optimal trajectory: we also need to make sure that the corresponding algorithms are computationally efficient, i.e., that the corresponding optimization algorithms can produce the resulting optimal setting in reasonable time. The more sensors we need to place, the more computations we need and therefore, the more important it is for the computation time to be reasonable. This is especially important in situations of big data , when the amount of data is so huge that the traditional numerical methods are not applicable [1,2,3,4]. We analyze this problem in Sect. 2.4, again on the example of security problems.