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

A survey of outlier detection methodologies

Outlier detection has been used for centuries to detect and, where appropriate, remove anomalous observations from data. Outliers arise due to mechanical faults, changes in system behaviour, fraudulent behaviour, human error, instrument error or simply through natural deviations in populations. Their detection can identify system faults and fraud before they escalate with potentially catastrophic consequences. It can identify errors and remove their contaminating effect on the data set and as such to purify the data for processing. The original outlier detection methods were arbitrary but now, principled and systematic techniques are used, drawn from the full gamut of Computer Science and Statistics. In this paper, we introduce a survey of contemporary techniques for outlier detection. We identify their respective motivations and distinguish their advantages and disadvantages in a comparative review

Anomaly Detection in Ethernet Networks Using Self Organising Maps

The network is a highly vulnerable venture for any organization that needs to have a set of computers for their work and needs to communicate among them. Any large organization that sets up a network needs a basic Ethernet or wireless framework for transferring data. Nevertheless the security concern of the organization creeps in and the computers storing the highly sensitive data need to be safeguarded. The threat to the network comes from the internal network as well as the external network. The amount of monitoring data generated in computer networks is enormous. Tools are needed to ease the work of system operators. Anomaly detection attempts to recognize abnormal behavior to detect intrusions. We have concentrated to design a prototype UNIX Anomaly Detection System. Neural Networks are tolerant of imprecise data and uncertain information. We worked to devise a tool for detecting such intrusions into the network. The tool uses the machine learning approaches ad clustering techniques like Self Organizing Map and compares it with the k-means approach. Our system is described for applying hierarchical unsupervised neural network to intrusion detection system. The network connection is characterized by six parameters and specified as a six dimensional vectors. The self organizing map creates a two dimensional lattice of neurons for network for each network service. During real time analysis, network features are fed to the neural network approaches and a winner is selected by finding a neuron that is closest in distance to it. The network is then classified as an intrusion if the distance is more than a preset threshold. The evaluation of this approach will be based on data sets provided by the Defense Advanced Research Projects Agency (DARPA) IDS evaluation in 1999

Data analytics for smart parking applications

We consider real-life smart parking systems where parking lot occupancy data are collected from field sensor devices and sent to backend servers for further processing and usage for applications. Our objective is to make these data useful to end users, such as parking managers, and, ultimately, to citizens. To this end, we concoct and validate an automated classification algorithm having two objectives: (1) outlier detection: to detect sensors with anomalous behavioral patterns, i.e., outliers; and (2) clustering: to group the parking sensors exhibiting similar patterns into distinct clusters. We first analyze the statistics of real parking data, obtaining suitable simulation models for parking traces. We then consider a simple classification algorithm based on the empirical complementary distribution function of occupancy times and show its limitations. Hence, we design a more sophisticated algorithm exploiting unsupervised learning techniques (self-organizing maps). These are tuned following a supervised approach using our trace generator and are compared against other clustering schemes, namely expectation maximization, k-means clustering and DBSCAN, considering six months of data from a real sensor deployment. Our approach is found to be superior in terms of classification accuracy, while also being capable of identifying all of the outliers in the dataset

Novelty Detection And Cluster Analysis In Time Series Data Using Variational Autoencoder Feature Maps

The identification of atypical events and anomalies in complex data systems is an essential yet challenging task. The dynamic nature of these systems produces huge volumes of data that is often heterogeneous, and the failure to account for this will impede the detection of anomalies. Time series data encompass these issues and its high dimensional nature intensifies these challenges. This research presents a framework for the identification of anomalies in temporal data. A comparative analysis of Centroid, Density and Neural Network-based clustering techniques was performed and their scalability was assessed. This facilitated the development of a new algorithm called the Variational Autoencoder Feature Map (VAEFM) which is an ensemble method that is based on Kohonen’s Self-Organizing Maps (SOM) and Variational Autoencoders. The VAEFM is an unsupervised learning algorithm that models the distribution of temporal data without making a priori assumptions. It incorporates principles of novelty detection to enhance the representational capacity of SOMs neurons, which improves their ability to generalize with novel data. The VAEFM technique was demonstrated on a dataset of accumulated aircraft sensor recordings, to detect atypical events that transpired in the approach phase of flight. This is a proactive means of accident prevention and is therefore advantageous to the Aviation industry. Furthermore, accumulated aircraft data presents big data challenges, which requires scalable analytical solutions. The results indicated that VAEFM successfully identified temporal dependencies in the flight data and produced several clusters and outliers. It analyzed over 2500 flights in under 5 minutes and identified 12 clusters, two of which contained stabilized approaches. The remaining comprised of aborted approaches, excessively high/fast descent patterns and other contributory factors for unstabilized approaches. Outliers were detected which revealed oscillations in aircraft trajectories; some of which would have a lower detection rate using traditional flight safety analytical techniques. The results further indicated that VAEFM facilitates large-scale analysis and its scaling efficiency was demonstrated on a High Performance Computing System, by using an increased number of processors, where it achieved an average speedup of 70%

A survey of machine learning techniques applied to self organizing cellular networks

In this paper, a survey of the literature of the past fifteen years involving Machine Learning (ML) algorithms applied to self organizing cellular networks is performed. In order for future networks to overcome the current limitations and address the issues of current cellular systems, it is clear that more intelligence needs to be deployed, so that a fully autonomous and flexible network can be enabled. This paper focuses on the learning perspective of Self Organizing Networks (SON) solutions and provides, not only an overview of the most common ML techniques encountered in cellular networks, but also manages to classify each paper in terms of its learning solution, while also giving some examples. The authors also classify each paper in terms of its self-organizing use-case and discuss how each proposed solution performed. In addition, a comparison between the most commonly found ML algorithms in terms of certain SON metrics is performed and general guidelines on when to choose each ML algorithm for each SON function are proposed. Lastly, this work also provides future research directions and new paradigms that the use of more robust and intelligent algorithms, together with data gathered by operators, can bring to the cellular networks domain and fully enable the concept of SON in the near future