The number of sensors that collect information about the environment is constantly increasing. The amount of information obtained from the sensors is constantly increasing, also. For example, information obtained is ambient conditions, health status of the patient, video sequences from surveillance systems, aero and satellite images, and the like. Wireless sensors networks play an important role in the collection of large amounts of data. The sensors network is made up of a large number of sensor nodes, which are distributed in an environment, which is monitored. Sensor nodes frequently sense ambient conditions such as humidity, light, pressure, sound pressure level, presence of natural barriers etc. Recently, we have witnessed the expansion of the use of body sensor networks, which monitor the health status of patients and elderly people (temperature, pressure, ECG, blood sugar, bio-impedance) and prevent deterioration of health condition.

These examples demonstrate a wide range of applications of sensor networks. Recent developments in electronics, digital signal processing and computer hardware allow for capture of large amounts of information in the real time. Almost daily new techniques of signal acquisition appear. Therefore, we are constantly looking for effective methods for signal processing, analysis and interpretation of the information obtained.

The purpose of this research is to find new and effective methods of digital signal processing and information analysis and interpretation for signals and data obtained in sensor networks. Also, we will search for the appropriate hardware platform for their implementation. The effectiveness of the proposed algorithms will be mainly reflected in the ability to extract useful information from a large amount of data obtained from the sensor network and to determine the actual state of the object, which is monitored, and eventually to take appropriate decisions. Algorithms should be time, space (memory) and energy efficient. Moreover, given the large amount of information acquired from the sensor networks we will also focus on the effective use of distributed and parallel computation (e.g. GPGPU). Research for this project will be highly multidisciplinary as it covers the area of digital signal processing, computer hardware, electronics, telecommunications, system software, bioengineering, medicine, biology, etc.