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

Intelligent Devices for IoT Applications

Internet of Things (IoT) devices refer to a vast network of physical devices that are connected to the internet and can communicate with each other through sensors and software. These devices range from simple household appliances, like smart thermostats and security cameras, to more complex industrial equipment, such as sensors used in manufacturing and logistics. Specially, IoT enabled wireless gas sensing systems which can withstand harsh environments without compromising the performance are getting popular day by day, which necessitates adequate developments in this field. By being the essential components of a wireless gas sensing system, both the sensor and the elements for communication should be agile and resilient when it comes to tackle unfavorable scenario. Moreover, gas sensors are prone to drift, which can lead to inaccurate readings and decreased reliability over time. Again, recent advancements in antenna design, such as fractal antennas and metamaterial structures, have shown promises in improving the bandwidth and gain parameters of the antennas built on top of high temperature tackling substrates. This piece of research targets three fundamental sections: demonstration of recent advances in data driven techniques for gas sensing system optimization, designing of antennas for different applications, and device design as well as fabrication. The Dimatix DMP-2831 inkjet printer has been optimized to operate with six different inks and two different substrates including PET and 3 mol yttria-stabilized zirconia (3YSZ) based ceramic substrate. Later, the feature oriented gas sensor data analysis to investigate correlations among stability, selectivity and long term drift is illustrated, which should significant relations among those parameters that can be considered while designing different intelligent data driven models to compensate drift. Moreover, a subspace transfer based approach is proposed to classify drifted gas sensor response to detect particular gas with higher accuracy. The model achieved an average accuracy greater than 87% while using only 40% of the total dataset to be trained. In the field of antenna technology, a co-planar waveguide (CPW) fed super wideband antenna is proposed which can cover C, X, Ku, K, Ka, Q, V, and W bands according to the simulated performance with high gain and radiation efficiency. Again, a high temperature tolerant antenna based on 3YSZ substrate is proposed which achieved good alignment between the simulated and fabricated device performance

An overview on structural health monitoring: From the current state-of-the-art to new bio-inspired sensing paradigms

In the last decades, the field of structural health monitoring (SHM) has grown exponentially. Yet, several technical constraints persist, which are preventing full realization of its potential. To upgrade current state-of-the-art technologies, researchers have started to look at nature’s creations giving rise to a new field called ‘biomimetics’, which operates across the border between living and non-living systems. The highly optimised and time-tested performance of biological assemblies keeps on inspiring the development of bio-inspired artificial counterparts that can potentially outperform conventional systems. After a critical appraisal on the current status of SHM, this paper presents a review of selected works related to neural, cochlea and immune-inspired algorithms implemented in the field of SHM, including a brief survey of the advancements of bio-inspired sensor technology for the purpose of SHM. In parallel to this engineering progress, a more in-depth understanding of the most suitable biological patterns to be transferred into multimodal SHM systems is fundamental to foster new scientific breakthroughs. Hence, grounded in the dissection of three selected human biological systems, a framework for new bio-inspired sensing paradigms aimed at guiding the identification of tailored attributes to transplant from nature to SHM is outlined.info:eu-repo/semantics/acceptedVersio

Advancements in Forest Fire Prevention: A Comprehensive Survey

Nowadays, the challenges related to technological and environmental development are becoming increasingly complex. Among the environmentally significant issues, wildfires pose a serious threat to the global ecosystem. The damages inflicted upon forests are manifold, leading not only to the destruction of terrestrial ecosystems but also to climate changes. Consequently, reducing their impact on both people and nature requires the adoption of effective approaches for prevention, early warning, and well-coordinated interventions. This document presents an analysis of the evolution of various technologies used in the detection, monitoring, and prevention of forest fires from past years to the present. It highlights the strengths, limitations, and future developments in this field. Forest fires have emerged as a critical environmental concern due to their devastating effects on ecosystems and the potential repercussions on the climate. Understanding the evolution of technology in addressing this issue is essential to formulate more effective strategies for mitigating and preventing wildfires

Machine learning for Internet of Things data analysis: A survey

Rapid developments in hardware, software, and communication technologies have allowed the emergence of Internet-connected sensory devices that provide observation and data measurement from the physical world. By 2020, it is estimated that the total number of Internet-connected devices being used will be between 25 and 50 billion. As the numbers grow and technologies become more mature, the volume of data published will increase. Internet-connected devices technology, referred to as Internet of Things (IoT), continues to extend the current Internet by providing connectivity and interaction between the physical and cyber worlds. In addition to increased volume, the IoT generates Big Data characterized by velocity in terms of time and location dependency, with a variety of multiple modalities and varying data quality. Intelligent processing and analysis of this Big Data is the key to developing smart IoT applications. This article assesses the different machine learning methods that deal with the challenges in IoT data by considering smart cities as the main use case. The key contribution of this study is presentation of a taxonomy of machine learning algorithms explaining how different techniques are applied to the data in order to extract higher level information. The potential and challenges of machine learning for IoT data analytics will also be discussed. A use case of applying Support Vector Machine (SVM) on Aarhus Smart City traffic data is presented for a more detailed exploration.Comment: Digital Communications and Networks (2017