Wireless sensor networks consist of large number of sensor nodes that have an ability to interact with the surrounding environment. The importance of finding the suitable protocol to estimate the trajectory for any moving target in a certain environment is a case of study for researchers. In this paper, new and accurate protocol for target tracking with minimum energy consumption is proposed. The proposed approach uses the static cluster and dynamic cluster head mechanism (SCDCH) to collect data from the active nodes, and then forwards it to the cluster head (CH), which in turn will send the collected data to the base station, without changing cluster border during the network's lifetime. Simulation results show that the proposed protocol has higher accuracy and save more energy compared with static clustering protocols which in turn increases the network lifetime. Keywords: Dynamic source routing (DSR), energy efficient, localization, static cluster and dynamic cluster head mechanism (SCDCH), target tracking.I.

In a wireless sensor network the sensor outputs are required to be quantized because of energy and bandwidth requirements. We propose such a distributed detection scheme for a point source which is based on Neyman-Pearson criterion where sensor outputs are quantized maximizing the average output entropy of the sensors under both hypotheses. The quantized local outputs are transmitted to a fusion center (FC) where they are used to make a global decision. The performance of the proposed maximum average entropy (MAE) method in quantizing sensor outputs was tested for binary, ternary and quarternary quantization. The effects of the channel from the sensors to the FC is also addressed by simplified channel models. The simulation studies show the success of the MAE method.

Since, alpha-stable noise signals similarity can only be gauged by Fractional Lower-Order Auto-Covariance (FLOAC), therefore, the role of impulsiveness and skewness parameters, in generation and detection of the skewed alphastable (SkαS) noise signals, has been analyzed several times in the past. However, in this paper, a thorough analysis on the role of fractional powers in changing the FLOAC of SkαS noise signals has been carried out. The two associated fractional powers of FLOAC has been maneuvered in three possible ways to observe the probable trend of SkαS noise signals in the presence and absence of Gaussian noise. According to the observed results, the fractional powers largely and solely affect the FLOAC when they are manipulated collaboratively or even individually where the analyzed results can be handy in enhancing many SkαS noise signal processing techniques, especially, in the detection of SkαS noise carrier signals in Random Communication Systems.

In a wireless sensor network, multilevel quantization is necessary in order to find a compromise between the smallest possible power consumption of the sensors and the detection performance at the fusion center (FC). The general methodology is using distance measures such as J-divergence and Bhattacharyya distance in this quantization. This work proposes a different approach which is based on maximizing the average output entropy of the sensors under both hypotheses and utilizes it in a Neyman-Pearson criterion based distributed detection scheme in order to detect a point source. The receiver operating characteristics of the proposed maximum average entropy (MAE) method in quantizing sensor outputs was obtained for multilevel quantization both when the sensor outputs are available error-free at the FC and when non-coherent M-ary frequency shift keying communication is used for transmitting MAE based multilevel quantized sensor outputs over a Rayleigh fading channel. The simulation studies show the success of the MAE both in the cases of isolated errorfree fusion and in the case where the effect of the wireless channel is incorporated. As expected the performance gets better as the level of quantization increases and with six-level quantization it approaches the performance of non-quantized data transmission.

Since, Fractional Lower-Order Auto-Covariance (FLOAC) remains the only technique to quantify the similarity between alpha-stable (α-stable) signals, therefore, the effects of impulsiveness and skewness parameters has also been analyzed before for better generation and detection in various applications. This paper includes the detailed analysis of the FLOAC of symmetric alpha-stable (SαS) noise signals in order to observe the possible involvement of the associated fractional powers. The two associated fractional powers of FLOAC has been maneuvered in three possible ways to observe the probable trend of SαS noise signals in the presence and absence of Gaussian noise. The observation depicts that the fractional powers largely and solely affect the FLOAC when they are maneuvered collaboratively or even individually where the obtained results can be useful in improving many SαS noise signal processing techniques, especially, in the detection of SαS noise carrier signals in Random Communication Systems.