Artificial intelligence based event detection in wireless sensor networks

Wireless sensor networks (WSNs) are composed of large number of small, inexpensive devices, called sensor nodes, which are equipped with sensing, processing, and communication capabilities. While traditional applications of wireless sensor networks focused on periodic monitoring, the focus of more recent applications is on fast and reliable identification of out-of-ordinary situations and events. This new functionality of wireless sensor networks is known as event detection. Due to the fact that collecting all sensor data centrally to perform event detection is inefficient in many occasions, the new trend in event detection in wireless sensor networks is to perform detection in the network. Design of in-network event detection methods for wireless sensor networks is by no means straightforward, as it needs to efficiently cope with various challenges and concerns including unreliability, heterogeneity, adaptability, and resource constraints. In this thesis, we tackle this problem by proposing fast, accurate, in-network, and intelligent event detection methods using artificial intelligence (AI) and machine learning (ML) approaches. To this end, the main objective of this thesis is to analyze, investigate applicability, and optimize artificial intelligence (AI) and machine learning (ML) methods for efficient, distributed, local and in-network event detection in wireless sensor networks (WSNs)

Online Unsupervised Event Detection in Wireless Sensor Networks

Event detection applications of wireless sensor networks (WSNs) highly rely on accurate and timely detection of out of ordinary situations. Majority of the existing event detection techniques designed for WSNs have focused on detection of events with known patterns requiring a priori knowledge about events being detected. In this paper, however, we propose an online unsupervised event detection technique for detection of unknown events. Traditional unsupervised learning techniques cannot directly be applied in WSNs due to their high computational and memory complexities. To this end, by considering specific resource limitations of the WSNs we modify the standard K-means algorithm in this paper and explore its applicability for online and fast event detection in WSNs. For performance evaluation, we investigate event detection accuracy, false alarm, similarity calculation (using the Rand Index), computational and memory complexity of the proposed approach on two real datasets

Fast and Accurate Residential Fire Detection Using Wireless Sensor Networks

Prompt and accurate residential fire detection is important for on-time fire extinguishing and consequently reducing damages and life losses. To detect fire sensors are needed to measure the environmental parameters and algorithms are required to decide about occurrence of fire. Recently, wireless sensor networks (WSNs) have been used for environmental monitoring and real-time event detection because of their low implementation costs and their capability of distributed sensing and processing. Although there are several works on fire detection using WSNs, they have rarely paid sufficient attention to investigate the optimal sensor sets and usage of suitable artificial intelligence (AI) methods. Therefore, by aiming at residential fire detection, this paper investigates proper sensor sets and proposes AI-based techniques for fire detection in WSNs. The proposed methods are evaluated in terms of detection accuracy rate and computational complexity

Aportaciones de la prospección geofísica al estudio del subsuelo del Berguedà y Solsonés, (pre-pirineo Catalán, N.E. de la Península Ibérica)

En este trabajo se presenta la interpretaciónpor métodos geofísicos combinados de un sector del contacto entre el Pre-Pirineo y la La zona objeto del presente estudio se halla a Depresión Central Catalana..

How Wireless Sensor Networks Can Benefit from Brain Emotional Learning Based Intelligent Controller (BELBIC)

Wireless sensor networks (WSNs) are composed of small sensing and actuating devices that collaboratively monitor a phenomena, process and reason about sensor measurements, and provide adequate feedback or take actions. One of WSNs tasks is event detection, in which occurrence of events of interest is detected in situ whenever and wherever they occur. Some examples of these events include environmental (e.g. fire), personal (e.g. activities), and data-related (e.g. outlier) events. Simply speaking, event detection is a classification process, in which membership of data measurements to each event class is determined. Neural network is one of the classifiers that have often been used for detecting events with known patterns. One of the techniques to maximise the neural network performance during classification process is enabling a learning process. Through this learning process, neural network can learn from errors generated in each round of classification to gradually improve its performance. In this paper we investigate applicability of Brain Emotional Based Intelligent Controller (BELBIC) to improve neural network performance. Empirical results show that incorporating the BELBIC with neural networks improves the accuracy of event detection in many circumstances

On the Effects of Input Unreliability on Classifion Algorithms

The abundance of data available on Wireless Sensor Networks makes online processing necessary. In industrial applications, for example, the correct operation of equipment can be the point of interest. The raw sampled data is of minor importance. Classification algorithms can be used to make state classifications based on the available data for devices such as industrial refrigerators. The reliability through redundancy approach used in Wireless Sensor Networks complicates practical realizations of classification algorithms. Individual inputs are susceptible to multiple disturbances like hardware failure, communication failure and battery depletion. In order to demonstrate the effects of input failure on classification algorithms, we have compared three widely used algorithms in multiple error scenarios. The compared algorithms are Feed Forward Neural Networks, naive Bayes classifiers and decision trees. Using a new experimental data-set, we show that the performance under error scenarios degrades less for the naive Bayes classifier than for the two other algorithms

SUPER-SAPSO: A New SA-Based PSO Algorithm

Swarm Optimisation (PSO) has been received increasing attention due to its simplicity and reasonable convergence speed surpassing genetic algorithm in some circumstances. In order to improve convergence speed or to augment the exploration area within the solution space to find a better optimum point, many modifications have been proposed. One of such modifications is to fuse PSO with other search strategies such as Simulated Annealing (SA) in order to make a new hybrid algorithm – so called SAPSO. To the best of the authors’ knowledge, in the earlier studies in terms of SAPSO, the researchers either assigned an inertia factor or a global temperature to particles decreasing in the each iteration globally. In this study the authors proposed a local temperature, to be assigned to the each particle, and execute SAPSO with locally allocated temperature. The proposed model is called SUPERSAPSO because it often surpasses the previous SAPSO model and standard PSO appropriately. Simulation results on different benchmark functions demonstrate superiority of the proposed model in terms of convergence speed as well as optimisation accuracy