Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications

Wireless sensor networks monitor dynamic environments that change rapidly over time. This dynamic behavior is either caused by external factors or initiated by the system designers themselves. To adapt to such conditions, sensor networks often adopt machine learning techniques to eliminate the need for unnecessary redesign. Machine learning also inspires many practical solutions that maximize resource utilization and prolong the lifespan of the network. In this paper, we present an extensive literature review over the period 2002-2013 of machine learning methods that were used to address common issues in wireless sensor networks (WSNs). The advantages and disadvantages of each proposed algorithm are evaluated against the corresponding problem. We also provide a comparative guide to aid WSN designers in developing suitable machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial

Incremental document map formation: multi-stage approach

The paper presents methodology for the incremental map formation in a multi-stage process of a search engine with the map based user interface1. The architecture of the experimental system allows for comparative evaluation of different constituent technologies for various stages of the process. The quality of the map generation process has been investigated based on a number of clustering and classification measures. Some conclusions concerning the impact of various technological solutions on map quality are presented

A Survey on Personalized Recommendation Techniques

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Evolving Neural Networks through a Reverse Encoding Tree

NeuroEvolution is one of the most competitive evolutionary learning frameworks for designing novel neural networks for use in specific tasks, such as logic circuit design and digital gaming. However, the application of benchmark methods such as the NeuroEvolution of Augmenting Topologies (NEAT) remains a challenge, in terms of their computational cost and search time inefficiency. This paper advances a method which incorporates a type of topological edge coding, named Reverse Encoding Tree (RET), for evolving scalable neural networks efficiently. Using RET, two types of approaches -- NEAT with Binary search encoding (Bi-NEAT) and NEAT with Golden-Section search encoding (GS-NEAT) -- have been designed to solve problems in benchmark continuous learning environments such as logic gates, Cartpole, and Lunar Lander, and tested against classical NEAT and FS-NEAT as baselines. Additionally, we conduct a robustness test to evaluate the resilience of the proposed NEAT algorithms. The results show that the two proposed strategies deliver improved performance, characterized by (1) a higher accumulated reward within a finite number of time steps; (2) using fewer episodes to solve problems in targeted environments, and (3) maintaining adaptive robustness under noisy perturbations, which outperform the baselines in all tested cases. Our analysis also demonstrates that RET expends potential future research directions in dynamic environments. Code is available from https://github.com/HaolingZHANG/ReverseEncodingTree.Comment: Accepted to IEEE Congress on Evolutionary Computation (IEEE CEC) 2020. Lecture Presentatio

Unsupervised Intrusion Detection with Cross-Domain Artificial Intelligence Methods

Cybercrime is a major concern for corporations, business owners, governments and citizens, and it continues to grow in spite of increasing investments in security and fraud prevention. The main challenges in this research field are: being able to detect unknown attacks, and reducing the false positive ratio. The aim of this research work was to target both problems by leveraging four artificial intelligence techniques. The first technique is a novel unsupervised learning method based on skip-gram modeling. It was designed, developed and tested against a public dataset with popular intrusion patterns. A high accuracy and a low false positive rate were achieved without prior knowledge of attack patterns. The second technique is a novel unsupervised learning method based on topic modeling. It was applied to three related domains (network attacks, payments fraud, IoT malware traffic). A high accuracy was achieved in the three scenarios, even though the malicious activity significantly differs from one domain to the other. The third technique is a novel unsupervised learning method based on deep autoencoders, with feature selection performed by a supervised method, random forest. Obtained results showed that this technique can outperform other similar techniques. The fourth technique is based on an MLP neural network, and is applied to alert reduction in fraud prevention. This method automates manual reviews previously done by human experts, without significantly impacting accuracy

Median topographic maps for biomedical data sets

Median clustering extends popular neural data analysis methods such as the self-organizing map or neural gas to general data structures given by a dissimilarity matrix only. This offers flexible and robust global data inspection methods which are particularly suited for a variety of data as occurs in biomedical domains. In this chapter, we give an overview about median clustering and its properties and extensions, with a particular focus on efficient implementations adapted to large scale data analysis

Data Stream Mining: an Evolutionary Approach

Este trabajo presenta un algoritmo para agrupar flujos de datos, llamado ESCALIER. Este algoritmo es una extensión del algoritmo de agrupamiento evolutivo ECSAGO Evolutionary Clustering with Self Adaptive Genetic Operators. ESCALIER toma el proceso evolutivo propuesto por ECSAGO para encontrar grupos en los flujos de datos, los cuales son definidos por la técnica Sliding Window. Para el mantenimiento y olvido de los grupos detectados a través de la evolución de los datos, ESCALIER incluye un mecanismo de memoria inspirado en la teoría de redes inmunológicas artificiales. Para probar la efectividad del algoritmo, se realizaron experimentos utilizando datos sintéticos simulando un ambiente de flujos de datos, y un conjunto de datos reales.Abstract. This work presents a data stream clustering algorithm called ESCALIER. This algorithm is an extension of the evolutionary clustering ECSAGO - Evolutionary Clustering with Self Adaptive Genetic Operators. ESCALIER takes the advantage of the evolutionary process proposed by ECSAGO to find the clusters in the data streams. They are defined by sliding window technique. To maintain and forget clusters through the evolution of the data, ESCALIER includes a memory mechanism inspired by the artificial immune network theory. To test the performance of the algorithm, experiments using synthetic data, simulating the data stream environment, and a real dataset are carried out.Maestrí