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 framework for automated anomaly detection in high frequency water-quality data from in situ sensors

River water-quality monitoring is increasingly conducted using automated in situ sensors, enabling timelier identification of unexpected values. However, anomalies caused by technical issues confound these data, while the volume and velocity of data prevent manual detection. We present a framework for automated anomaly detection in high-frequency water-quality data from in situ sensors, using turbidity, conductivity and river level data. After identifying end-user needs and defining anomalies, we ranked their importance and selected suitable detection methods. High priority anomalies included sudden isolated spikes and level shifts, most of which were classified correctly by regression-based methods such as autoregressive integrated moving average models. However, using other water-quality variables as covariates reduced performance due to complex relationships among variables. Classification of drift and periods of anomalously low or high variability improved when we applied replaced anomalous measurements with forecasts, but this inflated false positive rates. Feature-based methods also performed well on high priority anomalies, but were also less proficient at detecting lower priority anomalies, resulting in high false negative rates. Unlike regression-based methods, all feature-based methods produced low false positive rates, but did not and require training or optimization. Rule-based methods successfully detected impossible values and missing observations. Thus, we recommend using a combination of methods to improve anomaly detection performance, whilst minimizing false detection rates. Furthermore, our framework emphasizes the importance of communication between end-users and analysts for optimal outcomes with respect to both detection performance and end-user needs. Our framework is applicable to other types of high frequency time-series data and anomaly detection applications

Predictive intelligence to the edge through approximate collaborative context reasoning

We focus on Internet of Things (IoT) environments where a network of sensing and computing devices are responsible to locally process contextual data, reason and collaboratively infer the appearance of a specific phenomenon (event). Pushing processing and knowledge inference to the edge of the IoT network allows the complexity of the event reasoning process to be distributed into many manageable pieces and to be physically located at the source of the contextual information. This enables a huge amount of rich data streams to be processed in real time that would be prohibitively complex and costly to deliver on a traditional centralized Cloud system. We propose a lightweight, energy-efficient, distributed, adaptive, multiple-context perspective event reasoning model under uncertainty on each IoT device (sensor/actuator). Each device senses and processes context data and infers events based on different local context perspectives: (i) expert knowledge on event representation, (ii) outliers inference, and (iii) deviation from locally predicted context. Such novel approximate reasoning paradigm is achieved through a contextualized, collaborative belief-driven clustering process, where clusters of devices are formed according to their belief on the presence of events. Our distributed and federated intelligence model efficiently identifies any localized abnormality on the contextual data in light of event reasoning through aggregating local degrees of belief, updates, and adjusts its knowledge to contextual data outliers and novelty detection. We provide comprehensive experimental and comparison assessment of our model over real contextual data with other localized and centralized event detection models and show the benefits stemmed from its adoption by achieving up to three orders of magnitude less energy consumption and high quality of inference

Data Analytics and Performance Enhancement in Edge-Cloud Collaborative Internet of Things Systems

Based on the evolving communications, computing and embedded systems technologies, Internet of Things (IoT) systems can interconnect not only physical users and devices but also virtual services and objects, which have already been applied to many different application scenarios, such as smart home, smart healthcare, and intelligent transportation. With the rapid development, the number of involving devices increases tremendously. The huge number of devices and correspondingly generated data bring critical challenges to the IoT systems. To enhance the overall performance, this thesis aims to address the related technical issues on IoT data processing and physical topology discovery of the subnets self-organized by IoT devices. First of all, the issues on outlier detection and data aggregation are addressed through the development of recursive principal component analysis (R-PCA) based data analysis framework. The framework is developed in a cluster-based structure to fully exploit the spatial correlation of IoT data. Specifically, the sensing devices are gathered into clusters based on spatial data correlation. Edge devices are assigned to the clusters for the R-PCA based outlier detection and data aggregation. The outlier-free and aggregated data are forwarded to the remote cloud server for data reconstruction and storage. Moreover, a data reduction scheme is further proposed to relieve the burden on the trunk link for data uploading by utilizing the temporal data correlation. Kalman filters (KFs) with identical parameters are maintained at the edge and cloud for data prediction. The amount of data uploading is reduced by using the data predicted by the KF in the cloud instead of uploading all the practically measured data. Furthermore, an unmanned aerial vehicle (UAV) assisted IoT system is particularly designed for large-scale monitoring. Wireless sensor nodes are flexibly deployed for environmental sensing and self-organized into wireless sensor networks (WSNs). A physical topology discovery scheme is proposed to construct the physical topology of WSNs in the cloud server to facilitate performance optimization, where the physical topology indicates both the logical connectivity statuses of WSNs and the physical locations of WSN nodes. The physical topology discovery scheme is implemented through the newly developed parallel Metropolis-Hastings random walk based information sampling and network-wide 3D localization algorithms, where UAVs are served as the mobile edge devices and anchor nodes. Based on the physical topology constructed in the cloud, a UAV-enabled spatial data sampling scheme is further proposed to efficiently sample data from the monitoring area by using denoising autoencoder (DAE). By deploying the encoder of DAE at the UAV and decoder in the cloud, the data can be partially sampled from the sensing field and accurately reconstructed in the cloud. In the final part of the thesis, a novel autoencoder (AE) neural network based data outlier detection algorithm is proposed, where both encoder and decoder of AE are deployed at the edge devices. Data outliers can be accurately detected by the large fluctuations in the squared error generated by the data passing through the encoder and decoder of the AE

Exploitation of Data Correlation and Performance Enhancement in Wireless Sensor Networks

With the combination of wireless communications and embedded system, lots of progress has been made in the area of wireless sensor networks (WSNs). The networks have already been widely deployed, due to their self-organization capacity and low-cost advantage. However, there are still some technical challenges needed to be addressed. In the thesis, three algorithms are proposed in improving network energy efficiency, detecting data fault and reducing data redundancy. The basic principle behind the proposed algorithms is correlation in the data collected by WSNs. The first sensor scheduling algorithm is based on the spatial correlation between neighbor sensor readings. Given the spatial correlation, sensor nodes are clustered into groups. At each time instance, only one node within each group works as group representative, namely, sensing and transmitting sensor data. Sensor nodes take turns to be group representative. Therefore, the energy consumed by other sensor nodes within the same group can be saved. Due to the continuous nature of the data to be collected, temporal and spatial correlation of sensor data has been exploited to detect the faulty data. By exploitation of temporal correlation, the normal range of upcoming sensor data can be predicted by the historical observations. Based on spatial correlation, weighted neighbor voting can be used to diagnose whether the value of sensor data is reliable. The status of the sensor data, normal or faulty, is decided by the combination of these two proposed detection procedures. Similar to the sensor scheduling algorithm, the recursive principal component analysis (RPCA) based algorithm has been studied to detect faulty data and aggregate redundant data by exploitation of spatial correlation as well. The R-PCA model is used to process the sensor data, with the help of squared prediction error (SPE) score and cumulative percentage formula. When SPE score of a collected datum is distinctly larger than that of normal data, faults can be detected. The data dimension is reduced according to the calculation result of cumulative percentage formula. All the algorithms are simulated in OPNET or MATLAB based on practical and synthetic datasets. Performances of the proposed algorithms are evaluated in each chapter

Micro Smart Micro-grid and Its Cyber Security Aspects in a Port Infrastructure

Maritime ports are intensive energy areas with a plenty of electrical systems that require an average power of many tens of megawatts (MW). Competitiveness, profits, reduction of pollution, reliability of operations, carbon emission trading are important energy related considerations for any port authority. Current technology allows the deployment of a local micro-grid of the size of tenths of MW, capable of islanded operation in case of emergency and to grant an increasing energy independency. Ownership of the grid permits a large flexibility on prices of energy sold inside the port, trading on local electric market and reduction of pollution. Renewable energy generation has a large impact on costs since features a low marginal cost. Unfortunately the smart grid is a critical asset within the port infrastructure and its intelligence is a high-level target for cyberattacks. Such attacks are often based on malicious software (malware), which makes use of a controlling entity on the network to coordinate and propagate. In this document, we will outline some features of a port smart grid and typical characteristics of cyber-attacks including potential ways to recognize it and suggestion for effective countermeasures

Automatic outlier detection in automated water quality measurement stations

Des stations de mesure de la qualité de l’eau sont utilisées pour mesurer la qualité de l'eau à haute fréquence. Pour une gestion efficace de ces mesures, la qualité des données doit être vérifiée. Dans une méthode univariée précédemment développée, des points aberrants et des fautes étaient détectés dans les données mesurées par ces stations en employant des modèles à lissage exponentiel pour prédire les données au moment suivant avec l’intervalle de confiance. Dans la présente étude, ne considérant que le cas univarié, la détection de points aberrants est améliorée par l’identification d’un modèle autorégressif à moyenne mobile sur une fenêtre mobile de données pour prédire la donnée au moment suivant. Les données de turbidité mesurées à l'entrée d'une station d'épuration municipale au Danemark sont utilisées comme étude de cas pour comparer la performance de l’utilisation des deux modèles. Les résultats montrent que le nouveau modèle permet de prédire la donnée au moment suivant avec plus de précision. De plus, l’inclusion du nouveau modèle dans la méthode univariée présente une performance satisfaisante pour la détection de points aberrants et des fautes dans les données de l'étude de cas.Water quality monitoring stations are used to measure water quality at high frequency. For effective data management, the quality of the data must be evaluated. In a previously developed univariate method both outliers and faults were detected in the data measured by these stations by using exponential smoothing models that give one-step ahead forecasts and their confidence intervals. In the present study, the outlier detection step of the univariate method is improved by identifying an auto-regressive moving average model for a moving window of data and forecasting one-step ahead. The turbidity data measured at the inlet of a municipal treatment plant in Denmark is used as case study to compare the performance of the use of the two models. The results show that the forecasts made by the new model are more accurate. Also, inclusion of the new forecasting model in the univariate method shows satisfactory performance for detecting outliers and faults in the case study data