A Study of Machine Learning in Wireless Sensor Network

Khan, Z.; Samad, Abdus

doi:10.22247/ijcna/2017/49122

Cited by 38 publications

(17 citation statements)

References 27 publications

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“…The k-Nearest Neighbor approach is a good choice in our scenario because of its simplicity [14]. Basically, the method classifies a new sample based on the labels of the k instances in the dataset that most resembles the sample given a distance metric.…”

Section: K-nearest Neighbormentioning

confidence: 99%

Activity recognition through interactive machine learning in a dynamic sensor setting

Tegen

Davidsson

Persson

2020

Pers Ubiquit Comput

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The advances in Internet of things lead to an increased number of devices generating and streaming data. These devices can be useful data sources for activity recognition by using machine learning. However, the set of available sensors may vary over time, e.g. due to mobility of the sensors and technical failures. Since the machine learning model uses the data streams from the sensors as input, it must be able to handle a varying number of input variables, i.e. that the feature space might change over time. Moreover, the labelled data necessary for the training is often costly to acquire. In active learning, the model is given a budget for requesting labels from an oracle, and aims to maximize accuracy by careful selection of what data instances to label. It is generally assumed that the role of the oracle only is to respond to queries and that it will always do so. In many real-world scenarios however, the oracle is a human user and the assumptions are simplifications that might not give a proper depiction of the setting. In this work we investigate different interactive machine learning strategies, out of which active learning is one, which explore the effects of an oracle that can be more proactive and factors that might influence a user to provide or withhold labels. We implement five interactive machine learning strategies as well as hybrid versions of them and evaluate them on two datasets. The results show that a more proactive user can improve the performance, especially when the user is influenced by the accuracy of earlier predictions. The experiments also highlight challenges related to evaluating performance when the set of classes is changing over time.

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Section: K-nearest Neighbormentioning

confidence: 99%

Activity recognition through interactive machine learning in a dynamic sensor setting

Tegen

Davidsson

Persson

2020

Pers Ubiquit Comput

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“…Naïve Bayes classifier has several advantages given the type of setting we are looking at. It requires fewer training examples compared to many other machine learning algorithms [24,25] and is suitable for online learning, i.e., when one instance of data is processed at a time. The classifier is not computationally complex, which is of importance when producing real-time predictions from streaming data.…”

Section: Resultsmentioning

confidence: 99%

Collaborative Sensing with Interactive Learning using Dynamic Intelligent Virtual Sensors

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Davidsson

Mihailescu

et al. 2019

Sensors

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Although the availability of sensor data is becoming prevalent across many domains, it still remains a challenge to make sense of the sensor data in an efficient and effective manner in order to provide users with relevant services. The concept of virtual sensors provides a step towards this goal, however they are often used to denote homogeneous types of data, generally retrieved from a predetermined group of sensors. The DIVS (Dynamic Intelligent Virtual Sensors) concept was introduced in previous work to extend and generalize the notion of a virtual sensor to a dynamic setting with heterogenous sensors. This paper introduces a refined version of the DIVS concept by integrating an interactive machine learning mechanism, which enables the system to take input from both the user and the physical world. The paper empirically validates some of the properties of the DIVS concept. In particular, we are concerned with the distribution of different budget allocations for labelled data, as well as proactive labelling user strategies. We report on results suggesting that a relatively good accuracy can be achieved despite a limited budget in an environment with dynamic sensor availability, while proactive labeling ensures further improvements in performance.

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“…Machine learning was originally proposed as a unique method for Artificial Intelligence (AI) in the late 1950s. It has been gradually developed and has been applied in many applications such as bioinformatics, spam detection, speech recognition and data analysis [67]. Algorithms of machine learning are used as powerful predictors.…”

Section: Machine Learningmentioning

confidence: 99%

A Survey of Voice Pathology Surveillance Systems Based on Internet of Things and Machine Learning Algorithms

et al. 2020

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The incorporation of the cloud technology with the Internet of Things (IoT) is significant in order to obtain better performance for a seamless, continuous, and ubiquitous framework. IoT has many applications in the healthcare sector, one of these applications is voice pathology monitoring. Unfortunately, voice pathology has not gained much attention, where there is an urgent need in this area due to the shortage of research and diagnosis of lethal diseases. Most of the researchers are focusing on the voice pathology and their finding is only to differentiating either the voice is normal (healthy) or pathological voice, where there is a lack of the current studies for detecting a certain disease such as laryngeal cancer. In this paper, we present an extensive review of the state-of-the-art techniques and studies of IoT frameworks and machine learning algorithms used in the healthcare in general and in the voice pathology surveillance systems in particular. Furthermore, this paper also presents applications, challenges and key issues of both IoT and machine learning algorithms in the healthcare. Finally, this paper highlights some open issues of IoT in healthcare that warrant further research and investigation in order to present an easy, comfortable and effective diagnosis and treatment of disease for both patients and doctors.

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A Study of Machine Learning in Wireless Sensor Network

Cited by 38 publications

References 27 publications

Activity recognition through interactive machine learning in a dynamic sensor setting

Activity recognition through interactive machine learning in a dynamic sensor setting

Collaborative Sensing with Interactive Learning using Dynamic Intelligent Virtual Sensors

A Survey of Voice Pathology Surveillance Systems Based on Internet of Things and Machine Learning Algorithms

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