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

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

Lightweight Anomaly Detection Scheme Using Incremental Principal Component Analysis and Support Vector Machine

Wireless Sensors Networks have been the focus of significant attention from research and development due to their applications of collecting data from various fields such as smart cities, power grids, transportation systems, medical sectors, military, and rural areas. Accurate and reliable measurements for insightful data analysis and decision-making are the ultimate goals of sensor networks for critical domains. However, the raw data collected by WSNs usually are not reliable and inaccurate due to the imperfect nature of WSNs. Identifying misbehaviours or anomalies in the network is important for providing reliable and secure functioning of the network. However, due to resource constraints, a lightweight detection scheme is a major design challenge in sensor networks. This paper aims at designing and developing a lightweight anomaly detection scheme to improve efficiency in terms of reducing the computational complexity and communication and improving memory utilization overhead while maintaining high accuracy. To achieve this aim, oneclass learning and dimension reduction concepts were used in the design. The One-Class Support Vector Machine (OCSVM) with hyper-ellipsoid variance was used for anomaly detection due to its advantage in classifying unlabelled and multivariate data. Various One-Class Support Vector Machine formulations have been investigated and Centred-Ellipsoid has been adopted in this study due to its effectiveness. Centred-Ellipsoid is the most effective kernel among studies formulations. To decrease the computational complexity and improve memory utilization, the dimensions of the data were reduced using the Candid Covariance-Free Incremental Principal Component Analysis (CCIPCA) algorithm. Extensive experiments were conducted to evaluate the proposed lightweight anomaly detection scheme. Results in terms of detection accuracy, memory utilization, computational complexity, and communication overhead show that the proposed scheme is effective and efficient compared few existing schemes evaluated. The proposed anomaly detection scheme achieved the accuracy higher than 98%, with O(nd) memory utilization and no communication overhead

FEATURE SELECTION FOR INTRUSION DETECTION SYSTEM IN A CLUSTER-BASED HETEROGENEOUS WIRELESS SENSOR NETWORK

Wireless sensor network (WSN) has become one of the most promising networking solutions with exciting new applications for the near future. Notwithstanding the resource constrain of WSNs, it has continued to enjoy widespread deployment.  Security in WSN, however, remains an ongoing research trend as the deployed sensor nodes (SNs) are susceptible to various security challenges due to its architecture, hostile deployment environment and insecure routing protocols. In this work, we propose a feature selection method by combining three filter methods; Gain ratio, Chi-squared and ReliefF (triple-filter) in a cluster-based heterogeneous WSN prior to classification. This will increase the classification accuracy and reduce system complexity by extracting 14 important features from the 41 original features in the dataset. An intrusion detection benchmark dataset, NSL-KDD, is used for performance evaluation by considering detection rate, accuracy and the false alarm rate. Results obtained show that our proposed method can effectively reduce the number of features with a high classification accuracy and detection rate in comparison with other filter methods. In addition, this proposed feature selection method tends to reduce the total energy consumed by SNs during intrusion detection as compared with other filter selection methods, thereby extending the network lifetime and functionality for a reasonable period

Distributed CESVM-DR anomaly detection for wireless sensor network

Nowadays, the advancement of the sensor technology, has introduced the smart living community where the sensor is communicating with each other or to other entities. This has introduced the new term called internet-of-things (IoT). The data collected from sensor nodes will be analyzed at the endpoint called based station or sink for decision making. Unfortunately, accurate data is not usually accurate and reliable which will affect the decision making at the base station. There are many reasons constituted to the inaccurate and unreliable data like the malicious attack, harsh environment as well as the sensor node failure itself. In a worse case scenario, the node failure will also lead to the dysfunctional of the entire network. Therefore, in this paper, an unsupervised one-class SVM (OCSVM) is used to build the anomaly detection schemes in recourse constraint Wireless Sensor Networks (WSNs). Distributed network topology will be used to minimize the data communication in the network which can prolong the network lifetime. Meanwhile, the dimension reduction has been providing the lightweight of the anomaly detection schemes. In this paper Distributed Centered Hyperellipsoidal Support Vector Machine (DCESVM-DR) anomaly detection schemes is proposed to provide the efficiency and effectiveness of the anomaly detection schemes

Raamistik mobiilsete asjade veebile

Internet on oma arengus läbi aastate jõudnud järgmisse evolutsioonietappi - asjade internetti (ingl Internet of Things, lüh IoT). IoT ei tähista ühtainsat tehnoloogiat, see võimaldab eri seadmeil - arvutid, mobiiltelefonid, autod, kodumasinad, loomad, virtuaalsensorid, jne - omavahel üle Interneti suhelda, vajamata seejuures pidevat inimesepoolset seadistamist ja juhtimist. Mobiilseadmetest nagu näiteks nutitelefon ja tahvelarvuti on saanud meie igapäevased kaaslased ning oma mitmekülgse võimekusega on nad motiveerinud teadustegevust mobiilse IoT vallas. Nutitelefonid kätkevad endas võimekaid protsessoreid ja 3G/4G tehnoloogiatel põhinevaid internetiühendusi. Kuid kui kasutada seadmeid järjepanu täisvõimekusel, tühjeneb mobiili aku kiirelt. Doktoritöö esitleb energiasäästlikku, kergekaalulist mobiilsete veebiteenuste raamistikku anduriandmete kogumiseks, kasutades kergemaid, energiasäästlikumaid suhtlustprotokolle, mis on IoT keskkonnale sobilikumad. Doktoritöö käsitleb põhjalikult energia kokkuhoidu mobiilteenuste majutamisel. Töö käigus loodud raamistikud on kontseptsiooni tõestamiseks katsetatud mitmetes juhtumiuuringutes päris seadmetega.The Internet has evolved, over the years, from just being the Internet to become the Internet of Things (IoT), the next step in its evolution. IoT is not a single technology and it enables about everything from computers, mobile phones, cars, appliances, animals, virtual sensors, etc. that connect and interact with each other over the Internet to function free from human interaction. Mobile devices like the Smartphone and tablet PC have now become essential to everyday life and with extended capabilities have motivated research related to the mobile Internet of Things. Although, the recently developed Smartphones enjoy the high performance and high speed 3G/4G mobile Internet data transmission services, such high speed performances quickly drain the battery power of the mobile device. This thesis presents an energy efficient lightweight mobile Web service provisioning framework for mobile sensing utilizing the protocols that were designed for the constrained IoT environment. Lightweight protocols provide an energy efficient way of communication. Finally, this thesis highlights the energy conservation of the mobile Web service provisioning, the developed framework, extensively. Several case studies with the use of the proposed framework were implemented on real devices and has been thoroughly tested as a proof-of-concept.https://www.ester.ee/record=b522498