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 Hierarchical Framework Using Approximated Local Outlier Factor for Efficient Anomaly Detection

AbstractAnomaly detection aims to identify rare events that deviate remarkably from existing data. To satisfy real-world appli- cations, various anomaly detection technologies have been proposed. Due to the resource constraints, such as limited energy, computation ability and memory storage, most of them cannot be directly used in wireless sensor networks (WSNs). In this work, we proposed a hierarchical anomaly detection framework to overcome the challenges of anomaly detection in WSNs. We aim to detect anomalies by the accurate model and the approximated model learned at the re- mote server and sink nodes, respectively. Besides the framework, we also proposed an approximated local outlier factor algorithm, which can be learned at the sink nodes. The proposed algorithm is more efficient in computation and storage by comparing with the standard one. Experimental results verify the feasibility of our proposed method in terms of both accuracy and efficiency

Wireless sensor network as a distribute database

Wireless sensor networks (WSN) have played a role in various fields. In-network data processing is one of the most important and challenging techniques as it affects the key features of WSNs, which are energy consumption, nodes life circles and network performance. In the form of in-network processing, an intermediate node or aggregator will fuse or aggregate sensor data, which are collected from a group of sensors before transferring to the base station. The advantage of this approach is to minimize the amount of information transferred due to lack of computational resources. This thesis introduces the development of a hybrid in-network data processing for WSNs to fulfil the WSNs constraints. An architecture for in-network data processing were proposed in clustering level, data compression level and data mining level. The Neighbour-aware Multipath Cluster Aggregation (NMCA) is designed in the clustering level, which combines cluster-based and multipath approaches to process different packet loss rates. The data compression schemes and Optimal Dynamic Huffman (ODH) algorithm compressed data in the cluster head for the compressed level. A semantic data mining for fire detection was designed for extracting information from the raw data by the semantic data-mining model is developed to improve data accuracy and extract the fire event in the simulation. A demo in-door location system with in-network data processing approach is built to test the performance of the energy reduction of our designed strategy. In conclusion, the added benefits that the technical work can provide for in-network data processing is discussed and specific contributions and future work are highlighted

Anomaly Recognition in Wireless Ad-hoc Network by using Ant Colony Optimization and Deep Learning

As a result of lower initial investment, greater portability, and lower operational expenses, wireless networks are rapidly replacing their wired counterparts. The new technology that is on the rise is the Mobile Ad-Hoc Network (MANET), which operates without a fixed network infrastructure, can change its topology on the fly, and requires no centralised administration to manage its individual nodes. As a result, MANETs must focus on network efficiency and safety. It is crucial in MANET to pay attention to outliers that may affect QoS settings. Nonetheless, despite the numerous studies devoted to anomaly detection in MANET, security breaches and performance difficulties keep coming back. There is an increased need to provide strategies and approaches that help networks be more safe and robust due to the wide variety of security and performance challenges in MANET. This study presents outlier detection strategies for addressing security and performance challenges in MANET, with a special focus on network anomaly identification. The suggested work utilises a dynamic threshold and outlier detection to tackle the security and performance challenges in MANETs, taking into account metrics such as end-to-end delay, jitter, throughput, packet drop, and energy usage

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

A New MAC Address Spoofing Detection Technique Based on Random Forests

Media access control (MAC) addresses in wireless networks can be trivially spoofed using off-the-shelf devices. The aim of this research is to detect MAC address spoofing in wireless networks using a hard-to-spoof measurement that is correlated to the location of the wireless device, namely the received signal strength (RSS). We developed a passive solution that does not require modification for standards or protocols. The solution was tested in a live test-bed (i.e., a wireless local area network with the aid of two air monitors acting as sensors) and achieved 99.77%, 93.16% and 88.38% accuracy when the attacker is 8–13 m, 4–8 m and less than 4 m away from the victim device, respectively. We implemented three previous methods on the same test-bed and found that our solution outperforms existing solutions. Our solution is based on an ensemble method known as random forests.https://doi.org/10.3390/s1603028

Anomaly detection on data streams from vehicular networks

As redes veiculares são compostas por nós com elevada mobilidade que apenas estão ativos quando o veículo se encontra em movimento, tornando a rede imprevisível e em constante mudança. Num cenário tão dinâmico, detetar anomalias na rede torna-se uma tarefa exigente, mas crucial. A Veniam opera uma rede veicular que garante conexão fiável através de redes heterogéneas como LTE, Wi-Fi e DSRC, conectando os veículos à Internet e a outros dispositivos espalhados pela cidade. Ao longo do tempo, os nós enviam dados para a Cloud tanto por tecnologias em tempo real como por tecnologias tolerantes a atraso, aumentando a dinâmica da rede. O objetivo desta dissertação é propor e implementar um método para detetar anomalias numa rede veicular real, através de uma análise online dos fluxos de dados enviados dos veículos para a Cloud. Os fluxos da rede foram explorados de forma a caracterizar os dados disponíveis e selecionar casos de uso. Os datasets escolhidos foram submetidos a diferentes técnicas de deteção de anomalias, como previsão de séries temporais e deteção de outliers baseados na densidade da vizinhança, seguido da análise dos trade-offs para selecionar os algoritmos que melhor se ajustam às características dos dados. A solução proposta engloba duas etapas: uma primeira fase de triagem seguida de uma classificação baseada no método dos vizinhos mais próximos. O sistema desenvolvido foi implementado no cluster distribuído da Veniam, que executa Apache Spark, permitindo uma solução rápida e escalável que classifica os dados assim que chegam à Cloud. A performance do método foi avaliada pela sua precisão, i.e., a percentagem de verdadeiras anomalias dentro das anomalias detetadas, quando foi submetido a datasets com anomalias artificiais provenientes de fontes de dados diferentes, recebidas tanto por tecnologias em tempo real como por tecnologias tolerantes a atraso.Vehicular networks are characterized by high mobility nodes that are only active when the vehicle is moving, thus making the network unpredictable and in constant change. In such a dynamic scenario, detecting anomalies in the network is a challenging but crucial task. Veniam operates a vehicular network that ensures reliable connectivity through heterogeneous networks such as LTE, Wi-Fi and DSRC, connecting the vehicles to the Internet and to other devices spread throughout the city. Over time, nodes send data to the cloud either by real time technologies or by delay tolerant ones, increasing the network's dynamics. The aim of this dissertation is to propose and implement a method for detecting anomalies in a real-world vehicular network through means of an online analysis of the data streams that come from the vehicles to the cloud. The network's streams were explored in order to characterize the available data and select target use cases. The chosen datasets were submitted to different anomaly detection techniques, such as time series forecasting and density-based outlier detection, followed by the trade-offs' analysis to select the algorithms that best modeled the data characteristics. The proposed solution comprises two stages: a lightweight screening step, followed by a Nearest Neighbor classification. The developed system was implemented on Veniam's distributed cluster running Apache Spark, allowing a fast and scalable solution that classifies the data as soon as it reaches the Cloud. The performance of the method was evaluated by its precision, i.e., the percentage of true anomalies within the detected outliers, when it was submitted to datasets presenting artificial anomalies from different data sources, received either by real-time or delay tolerant technologies