## 1 Introduction  
In typical wireless sensor networks, the role of particular sensors is to register a given signal from the environment, perform an initial data preprocessing and then to transfer it to a base station where an artificial neural network (ANN) may be used to carry out a more detailed analysis. Signal processing performed at the sensor level typically includes an anti-aliasing filtering, analog-to-digital conversion, and optionally data compression to reduce the amount of data transferred over the wireless network. One of the problems associated with WSN development is high energy required to transfer data. Radio-frequency (RF) communication block, used of this purpose, consumes even 80-90% of total energy consumed by the overall sensor [1; 2]. To reduce the described problem, we propose a solution, in which to a much greater extent than now, data processing will take place directly at the sensor level. The proposed scenario is as follows. After the analog-to-digital conversion, a data preprocessing takes place inside the sensor to prepare a training file for an ANN integrated together with other sensor components in a single device. Thus, subsequent data analysis would be performed inside the sensor. In this situation, only the results provided by the ANN would be transferred to the base station, which usually means a much smaller amount of data than in the case of raw data. An important question here is the one about the profitability of this approach. The problem can be defined, for example, as follows: Does the energy consumed by the ANN implemented in the sensor will be smaller than the energy saved by the radio-frequency (RF) transmission block in case of reduced operation time? This problem has been studied in more detail in previous works on hardware implemented ANNs, which may be found in the literature [3]. The answer to some extent depends on the type of the processed data, and thus the data rate. The investigation presented in [3] shows that optimized parallel ANN, working at data rates at the level of 1000-2000 samples/s dissipates the power in-between 2 and 30 \(\upmu\)W, for 64 and 1000 neurons, respectively, for 10 inputs of the network. In many real applications, the number of neurons is closer to the lower value. Such data rates are sufficient, for example, while processing various biomedical data. Even at data rates two orders of magnitude higher, the ANN would dissipate power not exceeding 200-500 \(\upmu\)W. For the comparison, the RF communication block may dissipate power up to 15 mW, as reported in [1; 2]. For the described problem, however, we can look not only from the point of view of the energy consumption. When the ANN is implemented inside the wireless sensor, then in the overall WSN we have \(N\) distinct ANNs operating in parallel (distributed data processing), where \(N\) is the number of sensors in a given WSN. If data collected by all these sensor would have to be analyzed by the ANN implemented in a base station, a throughput problem may appear. Such ANN would have to be multiplexed and switched between data streams coming from particular sensors. An additional problem appears when there is no synchronization between particular sensors. In other words, the distributed data processing may be more convenient at all. One of the problems here is how to implement the ANN, so that it features a small silicon area and very low energy consumption. Such sensors can be described as intelligent wireless sensors. A substantial limitation of data transmitted over the wireless network will, in turn, strongly reduce the energy consumed by the overall sensor. As a result, the sensor to higher degree would be able to operate with the energy scavenged from the environment. This approach may greatly simplify the assembly, as well as the operation of such devices over a longer period.