Conference Paper

RSSI-Based Machine Learning with Pre-and Post-Processing for Cell-Localization in IWSNs

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Conference Paper
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We propose to add a monitoring system consisting of so-called path- and guard nodes to industrial wireless sensor network (IWSN), to increase the security level by using RSSI measurements. Via these measurements, the monitoring system determines the presence of a mobile sensor node in a predefined area, which can be used to handle access rights and to increase automation capabilities in industrial applications. We add this monitoring system to an IWSN based on the EPhESOS protocol, which has a high degree of flexibility to meet industrial requirements in different applications throughout the lifetime of a sensor node while enabling energy-autonomous operation. Two practical machine learning algorithms for RSSI-based presence detection are presented, namely a support vector machine and a neural network algorithm. They are evaluated in an automotive example and tested for their robustness against malicious attacks. Additionally, a method to find the best node locations of the monitoring system is presented.
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The vision to connect everyday physical objects to the Internet promises to create the Internet of Things (IoT), which is expected to integrate the diverse technologies such as sensors, actuators, radio frequency identification, communication technologies, and Internet protocols. Thus, IoT promises to transfer traditional industry to advance digital industry known as the Industry 4.0. At the core of the Industry 4.0 are the wireless sensor networks (WSNs) and wireless sensor and actuator networks (WSANs) that led to the development of industrial wireless sensor networks (IWSNs) and industrial wireless sensor and actuator networks (IWSANs). These networks play a central role of connecting machines, parts, products, and humans and create a diverse set of new applications to support intelligent and autonomous decision making. The IWSAN is a promising technology for numerous industrial applications because of their several potential benefits such as simple deployment, low cost, less complexity, and mobility support. However, despite such benefits, they impose several unique challenges at different layers of the protocol stack when deploying them for various monitoring and control applications in the Industry 4.0. In this article, we explore IWSAN, its applications, requirements, challenges, and solutions in the context of industrial control applications. Our main focus is on the medium access control (MAC) layer that can be exploited to satisfy such requirements. Our discussion presents extensive background study of the MAC schemes and it reviews the MAC protocols of the existing wireless standards and technologies. A number of application‐specific MAC protocols developed to support industrial applications, which are not part of these standards, are also elaborated. We rationalize to what extent the existing standards and protocols help in solving such requirements as laid down by the Industry 4.0. In the end, we emphasize on existing challenges and present important future directions.
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We present the design of a suite of protocols for wireless sensor networks (WSNs) with respect to a complete life cycle of a WSN node from warehouse to the end of operation. While there are numerous publications on various, usually isolated, aspects of WSNs, the whole life cycle of a node from registration in an automation system via warehouse, calibration, mounting, performing measurements to finally unmounting, has not yet been sufficiently addressed as compound survey. Our application example is a WSN to be used in automotive test beds in which a large amount of testing with many different sensors is performed in controlled environments. While there is published work on WSNs for performing the measurements focusing on node hardware and MAC protocol, we now extend this work by accounting for the whole life cycle of operation of such a WSN and its nodes. This is mainly achieved by introducing optimized MAC protocols for wireless communication in all life cycle phases. Right from beginning of the life cycle the nodes are synchronized with a base node. Even during long offline periods nodes stay synchronized. The life cycle is modeled via a set of states, instantiated in state machines, which control operation in the base station and the nodes. Besides, considering the whole life cycle of the sensor nodes, our design minimizes energy consumption, largely avoids collisions due to suitable multiple access protocols, and allows tight synchronization even during long sleep periods. A demonstrator concludes the presentation and shows functionality and benefits of the concept.
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Time series data are ubiquitous and being generated at an unprecedented speed and volume in many fields including finance, medicine, oil and gas industry and other business domains. Many techniques have been developed to analyze time series and understand the system that produces them. In this paper we propose a hybrid approach to improve the accuracy of time series classifiers by using Hidden Markov Models (HMM). The proposed approach is based on the principle of learning by mistakes. A HMM model is trained using the confusion matrices which are normally used to measure the classification accuracy. Misclassified samples are the basis of learning process. Our approach improves the classification accuracy by executing a second cycle of classification taking into account the temporal relations in the data. The objective of the proposed approach is to utilize the strengths of Hidden Markov Models (dealing with temporal data) to complement the weaknesses of other classification techniques. Consequently, instead of finding single isolated patterns, we focus on understanding the relationships between these patterns. The proposed approach was evaluated with a case study. The target of the case study was to classify real drilling data generated by rig sensors. Experimental evaluation proves the feasibility and effectiveness of the approach.
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Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems. This package focuses on bringing machine learning to non-specialists using a general-purpose high-level language. Emphasis is put on ease of use, performance, documentation, and API consistency. It has minimal dependencies and is distributed under the simplified BSD license, encouraging its use in both academic and commercial settings. Source code, binaries, and documentation can be downloaded from http://scikit-learn.sourceforge.net.
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Wireless Sensor Networks (WSNs) are applicable in numerous domains, including industrial automation where WSNs may be used for monitoring and control of industrial plants and equipment. However, the requirements in the industrial systems differ from the general WSN requirements. In recent years, standards have been defined by several industrial alliances. These standards are specified as frameworks with modifiable parts that can be defined based on the particular application of WSN. However, limited work has been done on defining industry-specific protocols that could be used as a part of these standards. In this survey, we discuss representative protocols that meet some of the requirements of the industrial applications. Since the industrial applications domain in itself is a vast area, we divide them into classes with similar requirements. We discuss these industrial classes, set of common requirements and various state-of-the-art WSN standards proposed to satisfy these requirements. We then present a broader view towards the WSN solution by discussing important functions like medium access control, routing, and transport in detail to give some insight into specific requirements and the classification of protocols based on certain factors. We list and discuss representative protocols for each of these functions that address requirements defined in the industrial classes. Security function is discussed in brief, mainly in relation to industrial standards. Finally, we identify unsolved challenges that are encountered during design of protocols and standards. In addition some new challenges are introduced and discussed.
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The task of estimating the location of a mobile transceiver using the Received Signal Strength Indication (RSSI) values of radio transmissions is an inference problem. Contextual information, i.e., if the target is in a specific region, is sufficient for most applications. Therefore, instead of estimating position coordinates, we take a slightly different approach and look at localization as a classification problem. We perform a comparison between the K-Nearest Neighbor (KNN), the Support Vector Machine (SVM) and the Simple Gaussian Classifier (SGC), three classifiers proposed previously under different contexts. Using experimental results, we demonstrate that the SGC achieves a competitive performance despite its simplicity. Furthermore, we consider the extension of the SGC to a Hidden Markov Model (HMM) and demonstrate the performance gains. The derivative of the HMM filter allows us to do online parameter tracking, realizing an adaptive scheme. To our knowledge, this adaptive scheme has not been used for the SGC before. Considering the advantages of the SGC, we advocate the SGC as a competitive solution for estimating contextual location information.
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