Search

When using a Wireless Sensor Network (WSN) for target tracking applications, optimum selection of right functioning nodes can reduce the number of active nodes and also ensuring tracking reliability requirement. Due to the limitations of the WSN's sensing range, it is crucial to create a mechanism that allows nodes to coordinate in order to follow the target reliably and with a high probability. By doing this, the network's overall energy consumption can be decreased, resulting in a longer network lifetime. Target tracking (TT) is a well observed and significant application of WSNs. In simple words, it maintains a proper trade-off between tracking quality and energy consumption. In the proposed work, Particle Filter (PF) with a machine learning technique called Support Vector Machine (SVM) based energy efficient target tracking used in WSN's. PF is considered to be the most accepted filtering algorithm in various tracking and localization problems. Simulation results show greater performance in determining the target location and maintain lower energy consumption.

Wireless Sensor Networks (WSN) has a wide range of opportunities and can be applied to almost every part of our life. Intrusion detection and monitoring across disputed areas and on army bases are two of the most important uses of WSN. An innovative method that precisely calculates many barriers involved in the process for quick identification and prevention of intrusions. The relevance and risk level of the chosen characteristics were assessed in this paper using feature importance and feature sensitivity metrics. This paper develops a deep learning-based feed-forward artificial neural network model that enables precise estimates of the k-barrier count for effective intrusion detection and mitigation. The area of the region of interest, sensor sensing area, sensing transmission area, and many sensors are the four potential characteristics that were used to learn and assess the feed-forward ANN model. The various attributes are extracted via Monte Carlo simulation. This research found that the method correctly forecasts the number of barriers with Root Mean Square Error and correlation coefficient. The proposed methodology is more efficient than the other standard methods.