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

An Unsupervised Approach for Automatic Activity Recognition based on Hidden Markov Model Regression

Using supervised machine learning approaches to recognize human activities from on-body wearable accelerometers generally requires a large amount of labelled data. When ground truth information is not available, too expensive, time consuming or difficult to collect, one has to rely on unsupervised approaches. This paper presents a new unsupervised approach for human activity recognition from raw acceleration data measured using inertial wearable sensors. The proposed method is based upon joint segmentation of multidimensional time series using a Hidden Markov Model (HMM) in a multiple regression context. The model is learned in an unsupervised framework using the Expectation-Maximization (EM) algorithm where no activity labels are needed. The proposed method takes into account the sequential appearance of the data. It is therefore adapted for the temporal acceleration data to accurately detect the activities. It allows both segmentation and classification of the human activities. Experimental results are provided to demonstrate the efficiency of the proposed approach with respect to standard supervised and unsupervised classification approache

An event detection framework for the representation of the AGGIR variables

International audienceIn this paper, we propose a framework to study the AGGIR (Autonomy Gerontology Iso-Resources Groups) grid model, in order to evaluate the level of independency of elderly people, according to their capabilities of performing activities and interact with their environments over the time. To model the Activities of Daily Living (ADL), we also extend a previously proposed Domain Specific Language (DSL), in order to employ operators to deal with constraints related to time and location of activities, and event recognition. Our framework aims at providing an analysis tool regarding the performance of elder-ly/handicapped people within a home environment by means of data recovered from sensors using the iCASA simulator. To evaluate our approach, we pick three of the AGGIR variables (i.e., dressing, toileting, and transfers) and evaluate their testability in many scenarios, by means of records representing the occurrence of activities of the elderly. Results demonstrate the accuracy of our framework to manage the obtained records correctly and thus generate the appropriate event information

A Review on Brain-Controlled Home Automation

A "smart home" employs ambient intelligence to keep tabs on things around the house so that the owner may get services tailored to their specific needs and control their home appliances from afar. Home automation for the elderly and handicapped focuses on enabling older persons and those with disabilities to live safely and comfortably at home. Additionally, the integration of this technology with a brain-computer interface (BCI) is perhaps of tremendous usefulness to those who are either old or disabled. These BCI-based brain-controlled home automation (BCHA) systems have emerged as a viable option for people with neuro disorders to remain in their homes rather than move to assisted living facilities. To summarize, BCI-based BCHA for the elderly and handicapped people is transforming people's lives every day. Most individuals prefer a simple approach to save time and effort. Automating the house is the simplest way for individuals to save time and effort. The brain-computer interface, often known as a BCI, is an innovative method of human-computer connection that does not rely on conventional output channels (muscle tissue and peripheral nerve). Over the course of the last three decades, it has attracted the attention of industry experts and developed into a thriving centre for research. Brain-controlled home automation (BCHA), as a typical BCI application, may provide physically challenged people with a new communication route with the outside world. However, the primary challenge that BCHA faces is to rapidly decipher multi-degree-of-freedom control instructions extracted from an electroencephalogram (EEG). The BCHA's research has made significant headway in a short amount of time during the last fifteen years. This study investigates the BCHA from several viewpoints, including the pattern of instructions for the control system, the type of signal acquisition, and the operational mechanism of the control system itself. This paper a concise description of the building blocks of smart homes and how they may be used to construct BCI-controlled home automation to assist disabled individuals. It is a compilation of information pertaining to communication protocols, multimedia devices, sensors, and systems that are often used in the process of putting smart homes into action. A comprehensive strategy for developing a functional and sustainable BCI-controlled home automation system is laid out in this paper as well, which could be useful to researchers in the future

Anticipatory Mobile Computing: A Survey of the State of the Art and Research Challenges

Today's mobile phones are far from mere communication devices they were ten years ago. Equipped with sophisticated sensors and advanced computing hardware, phones can be used to infer users' location, activity, social setting and more. As devices become increasingly intelligent, their capabilities evolve beyond inferring context to predicting it, and then reasoning and acting upon the predicted context. This article provides an overview of the current state of the art in mobile sensing and context prediction paving the way for full-fledged anticipatory mobile computing. We present a survey of phenomena that mobile phones can infer and predict, and offer a description of machine learning techniques used for such predictions. We then discuss proactive decision making and decision delivery via the user-device feedback loop. Finally, we discuss the challenges and opportunities of anticipatory mobile computing.Comment: 29 pages, 5 figure

Exploring the Landscape of Ubiquitous In-home Health Monitoring: A Comprehensive Survey

Ubiquitous in-home health monitoring systems have become popular in recent years due to the rise of digital health technologies and the growing demand for remote health monitoring. These systems enable individuals to increase their independence by allowing them to monitor their health from the home and by allowing more control over their well-being. In this study, we perform a comprehensive survey on this topic by reviewing a large number of literature in the area. We investigate these systems from various aspects, namely sensing technologies, communication technologies, intelligent and computing systems, and application areas. Specifically, we provide an overview of in-home health monitoring systems and identify their main components. We then present each component and discuss its role within in-home health monitoring systems. In addition, we provide an overview of the practical use of ubiquitous technologies in the home for health monitoring. Finally, we identify the main challenges and limitations based on the existing literature and provide eight recommendations for potential future research directions toward the development of in-home health monitoring systems. We conclude that despite extensive research on various components needed for the development of effective in-home health monitoring systems, the development of effective in-home health monitoring systems still requires further investigation.Comment: 35 pages, 5 figure

Biodegradable dual semicircular patch antenna tile for smart floors

A dual semicircular microstrip patch antenna implemented on a biodegradable substrate is presented for operation in the [863-873] MHz and [2.4-2.5] GHz frequency bands. To cover these frequency bands, two semicircular patches are compactly integrated onto a biodegradable cork tile, commonly found as support in laminate flooring, serving as a substrate. Thereby, the antenna tile may be seamlessly embedded as a sublayer of the floor structure. A higher-order mode is generated by applying via pins in the antenna topology to produce a conical radiation pattern with a null at broadside and sectoral coverage in the vertical plane. As such, the concealed floor antenna covers all azimuth angles of arrival in smart houses. The antenna performance is fully validated, also when the tile is covered by different polyvinyl chloride sheets. Owing to the supplementary design margins, the antenna impedance bandwidth remains covered. Moreover, the radiation patterns are measured in various elevation planes. Under standalone conditions, a radiation efficiency and a maximum gain of 74.3% and 5.8 dBi at 2.45 GHz and 48.1% and 2 dBi at 868 MHz are, respectively, obtained. Its omnidirectional coverage in the horizontal plane, stable performance on the inhomogeneous and biocompatible cork substrate and for various inhomogeneous superstrates, and its low-profile integration make the proposed antenna an excellent candidate for smart floors and smart houses

Activity recognition in a home setting using off the shelf smart watch technology

Being able to detect in real-time the activity per- formed by a user in a home setting provides highly valuable context. It can allow more effective use of novel technologies in a large variety of applications, from comfort and safety to energy efficiency, remote health monitoring and assisted living. In a home setting, activity recognition has been traditionally studied based on either a large sensor network infrastructure already set up in a home, or a network of wearable sensors attached to various parts of the user’s body. We argue that both approaches suffer considerably in terms of practicality and propose instead the use of commercial off-the-shelf smart watches, already owned by the users. We test the feasibility of this approach with two different smart watches of very different capabilities, on a variety of activities performed daily in a domestic environment, from brushing teeth to preparing food. Our experimental results are encouraging, as using stan- dard Support Vector Machine based classification, the accuracy rates range between 88% and 100%, depending on the type of smart watch and the window size chosen for data segmentation