Uav-assisted data collection in wireless sensor networks: A comprehensive survey

Wireless sensor networks (WSNs) are usually deployed to different areas of interest to sense phenomena, process sensed data, and take actions accordingly. The networks are integrated with many advanced technologies to be able to fulfill their tasks that is becoming more and more complicated. These networks tend to connect to multimedia networks and to process huge data over long distances. Due to the limited resources of static sensor nodes, WSNs need to cooperate with mobile robots such as unmanned ground vehicles (UGVs), or unmanned aerial vehicles (UAVs) in their developments. The mobile devices show their maneuverability, computational and energystorage abilities to support WSNs in multimedia networks. This paper addresses a comprehensive survey of almost scenarios utilizing UAVs and UGVs with strogly emphasising on UAVs for data collection in WSNs. Either UGVs or UAVs can collect data from static sensor nodes in the monitoring fields. UAVs can either work alone to collect data or can cooperate with other UAVs to increase their coverage in their working fields. Different techniques to support the UAVs are addressed in this survey. Communication links, control algorithms, network structures and different mechanisms are provided and compared. Energy consumption or transportation cost for such scenarios are considered. Opening issues and challenges are provided and suggested for the future developments

Smart Computing and Sensing Technologies for Animal Welfare: A Systematic Review

Animals play a profoundly important and intricate role in our lives today. Dogs have been human companions for thousands of years, but they now work closely with us to assist the disabled, and in combat and search and rescue situations. Farm animals are a critical part of the global food supply chain, and there is increasing consumer interest in organically fed and humanely raised livestock, and how it impacts our health and environmental footprint. Wild animals are threatened with extinction by human induced factors, and shrinking and compromised habitat. This review sets the goal to systematically survey the existing literature in smart computing and sensing technologies for domestic, farm and wild animal welfare. We use the notion of \emph{animal welfare} in broad terms, to review the technologies for assessing whether animals are healthy, free of pain and suffering, and also positively stimulated in their environment. Also the notion of \emph{smart computing and sensing} is used in broad terms, to refer to computing and sensing systems that are not isolated but interconnected with communication networks, and capable of remote data collection, processing, exchange and analysis. We review smart technologies for domestic animals, indoor and outdoor animal farming, as well as animals in the wild and zoos. The findings of this review are expected to motivate future research and contribute to data, information and communication management as well as policy for animal welfare

Algorithms design for improving homecare using Electrocardiogram (ECG) signals and Internet of Things (IoT)

Due to the fast growing of population, a lot of hospitals get crowded from the huge amount of patients visits. Moreover, during COVID-19 a lot of patients prefer staying at home to minimize the spread of the virus. The need for providing care to patients at home is essential. Internet of Things (IoT) is widely known and used by different fields. IoT based homecare will help in reducing the burden upon hospitals. IoT with homecare bring up several benefits such as minimizing human exertions, economical savings and improved efficiency and effectiveness. One of the important requirement on homecare system is the accuracy because those systems are dealing with human health which is sensitive and need high amount of accuracy. Moreover, those systems deal with huge amount of data due to the continues sensing that need to be processed well to provide fast response regarding the diagnosis with minimum cost requirements. Heart is one of the most important organ in the human body that requires high level of caring. Monitoring heart status can diagnose disease from the early stage and find the best medication plan by health experts. Continues monitoring and diagnosis of heart could exhaust caregivers efforts. Having an IoT heart monitoring model at home is the solution to this problem. Electrocardiogram (ECG) signals are used to track heart condition using waves and peaks. Accurate and efficient IoT ECG monitoring at home can detect heart diseases and save human lives. As a consequence, an IoT ECG homecare monitoring model is designed in this thesis for detecting Cardiac Arrhythmia and diagnosing heart diseases. Two databases of ECG signals are used; one online which is old and limited, and another huge, unique and special from real patients in hospital. The raw ECG signal for each patient is passed through the implemented Low Pass filter and Savitzky Golay filter signal processing techniques to remove the noise and any external interference. The clear signal in this model is passed through feature extraction stage to extract number of features based on some metrics and medical information along with feature extraction algorithm to find peaks and waves. Those features are saved in the local database to apply classification on them. For the diagnosis purpose a classification stage is made using three classification ways; threshold values, machine learning and deep learning to increase the accuracy. Threshold values classification technique worked based on medical values and boarder lines. In case any feature goes above or beyond these ranges, a warning message appeared with expected heart disease. The second type of classification is by using machine learning to minimize the human efforts. A Support Vector Machine (SVM) algorithm is proposed by running the algorithm on the features extracted from both databases. The classification accuracy for online and hospital databases was 91.67% and 94% respectively. Due to the non-linearity of the decision boundary, a third way of classification using deep learning is presented. A full Multilayer Perceptron (MLP) Neural Network is implemented to improve the accuracy and reduce the errors. The number of errors reduced to 0.019 and 0.006 using online and hospital databases. While using hospital database which is huge, there is a need for a technique to reduce the amount of data. Furthermore, a novel adaptive amplitude threshold compression algorithm is proposed. This algorithm is able to make diagnosis of heart disease from the reduced size using compressed ECG signals with high level of accuracy and low cost. The extracted features from compressed and original are similar with only slight differences of 1%, 2% and 3% with no effects on machine learning and deep learning classification accuracy without the need for any reconstructions. The throughput is improved by 43% with reduced storage space of 57% when using data compression. Moreover, to achieve fast response, the amount of data should be reduced further to provide fast data transmission. A compressive sensing based cardiac homecare system is presented. It gives the channel between sender and receiver the ability to carry small amount of data. Experiment results reveal that the proposed models are more accurate in the classification of Cardiac Arrhythmia and in the diagnosis of heart diseases. The proposed models ensure fast diagnosis and minimum cost requirements. Based on the experiments on classification accuracy, number of errors and false alarms, the dictionary of the compressive sensing selected to be 900. As a result, this thesis provided three different scenarios that achieved IoT homecare Cardiac monitoring to assist in further research for designing homecare Cardiac monitoring systems. The experiment results reveal that those scenarios produced better results with high level of accuracy in addition to minimizing data and cost requirements

IoT pohjainen betonin kuivumisolosuhteiden hallinta uusien asuinrakennuskohteiden tuotantovaiheessa

Recently, moisture related problems, particularly in relatively new residential buildings, have drawn a significant amount of media attention. Consequently, numerous researches and studies have been conducted in order to detect, analyse, and prevent future moisture problems from occurring. Unwanted moisture in the structures has been found to cause both considerable financial losses and severe health issues due to potential growth of mold, dust mite presence and VOC emissions. There are many causes to the above-mentioned problems throughout the building life cycle, including inadequate design, negligent construction and inappropriate use and maintenance. One of the major single causes is construction moisture that is not properly controlled and removed during the construction phase. The aim of this Master’s thesis is to determine whether it is technically possible and financially feasible to actively steer the construction site management to maintain at all times indoor conditions that are optimal for drying of concrete with the help of real-time IoT-based monitoring technology, and additionally support or even replace current humidity measurement process. Secondary aim is to determine which wireless area network (WAN) is best suited for achieving the former aim, both from technical and financial point of view, taking into account the constantly changing construction environment conditions. The theory section is compiled as a literature survey containing previous studies and literature on concrete including the Finnish building code, the Building Information Ltd and the Confederation of Finnish Construction Industries (CFCI) the as the main source, due to the fact that the concreting conditions in Finland and other Nordic countries are rather challenging, also implying that the quality of Finnish concrete technology is very advanced. The empirical part consists of building, testing and installing the IoT-architecture on the pilot construction site, collecting, analysing and storing sensor data, and developing a web based application designed for the construction site management. The thesis proves that real-time control of drying conditions provides significant financial benefits by cutting down construction time, by optimizing the use of heating energy and by reducing the need for traditional moisture measurements. The case study also shows that the construction management is benefitting greatly from a supplementary mobile web application highlighting the importance of building physics in the drying process.Viime aikoina etenkin uudehkojen asuinrakennusten kosteus- ja homeongelmat ovat saaneet huomattavaa näkyvyyttä mediassa, minkä seurauksena lukuisia uusia tutkimuksia on tehty selvittääkseen ongelmien perimmäiset syyt sekä pystyäkseen ehkäisemään tulevia ongelmia. Kosteusvaurioiden syiksi on todettu mm. uusien energiamääräysten mukaiset erityisen haasteelliset arkkitehti-, rakenne- ja talotekniikkasuunnitelmat, rakennustuotannon ylikuumentuminen ja sitä kautta kasvanut riski työmaalla tapahtuviin virheisiin sekä vääränlainen käyttö ja ylläpito. Yksi isoimmista yksittäisistä syistä on kuitenkin rakennuskosteus eli rakennusmateriaalien kuten betonielementtien valmistuskosteus ja rakenteisiin rakennusaikana päässyt kosteus, jota pitäisi pystyä hallitsemaan nykyistä paremmin. Diplomityön tavoitteena on selvittää, voiko reaaliaikaisella, IoT-teknologiaan perustuvalla työmaan rakennusaikaisten ulko- ja sisäolosuhteiden seurannalla ja betonin kosteuden mittauksella aktiivisesti ohjata työmaan johtoa betonin kuivumiselle optimaalisten olosuhteiden ylläpitämisessä sekä täydentämään tai jopa mahdollisesti korvaamaan nykyistä kosteudenmittausprosessia. Toissijaisena tavoitteena on selvittää, mikä langaton tiedonsiirtoverkko sopii kyseisten tavoitteiden saavuttamisessa parhaiten, sekä tekniseltä toteutukseltaan että kustannustehokkuuden näkökulmasta, ottaen huomioon haastavat ja muuttuvat työmaaolosuhteet. Tutkimuksen teoreettinen osuus on toteutettu kirjallisuuskatsauksena. Tämän tutkimuksen lähteinä on käytetty pääasiassa Suomen rakentamismääräykokoelman, Rakennustiedon (RT), Betoniteollisuus Ry:n ja Betoniyhdistys Ry:n (BY) betonirakenteiden ohjeita ja määräyksiä, johtuen siitä, että Suomessa ja muissa pohjoismaissa rakennusolosuhteet ovat hyvin haastavat, mikä tarkoittaa myös sitä, että suomalainen betonitekniikka on laadullisesti hyvin korkealla tasolla. Tutkimuksen empiirinen osuus koostuu IoT-arkkitehtuurin rakentamisesta ja asentamisesta pilottityömaalle, sensorien datan keräämisestä, analyysistä ja talletuksesta sekä työmaahenkilöstölle tarkoitetun verkkopohjaisen applikaation kehittämisestä. Tutkimus osoittaa, että reaaliaikaisella kuivumisolosuhteiden seurannalla voidaan saavuttaa projektista riippuen hyvinkin merkittävää taloudellista hyötyä lyhentämällä rakennusvaiheen kestoa, tehostamalla lämmitysenergian käyttöä ja vähentämällä perinteisten kosteusmittausten määrää. Pilottiprojekti osoittaa, että työnjohto hyötyy selvästi applikaation käytöstä ja se auttaa ennen kaikkea nuoria ja vähemmän kokeneita työnjohtajia ymmärtämään työmaan rakennusfysiikan merkityksen betonin kuivumisprosessissa

Marshall Space Flight Center Research and Technology Report 2019

Today, our calling to explore is greater than ever before, and here at Marshall Space Flight Centerwe make human deep space exploration possible. A key goal for Artemis is demonstrating and perfecting capabilities on the Moon for technologies needed for humans to get to Mars. This years report features 10 of the Agencys 16 Technology Areas, and I am proud of Marshalls role in creating solutions for so many of these daunting technical challenges. Many of these projects will lead to sustainable in-space architecture for human space exploration that will allow us to travel to the Moon, on to Mars, and beyond. Others are developing new scientific instruments capable of providing an unprecedented glimpse into our universe. NASA has led the charge in space exploration for more than six decades, and through the Artemis program we will help build on our work in low Earth orbit and pave the way to the Moon and Mars. At Marshall, we leverage the skills and interest of the international community to conduct scientific research, develop and demonstrate technology, and train international crews to operate further from Earth for longer periods of time than ever before first at the lunar surface, then on to our next giant leap, human exploration of Mars. While each project in this report seeks to advance new technology and challenge conventions, it is important to recognize the diversity of activities and people supporting our mission. This report not only showcases the Centers capabilities and our partnerships, it also highlights the progress our people have achieved in the past year. These scientists, researchers and innovators are why Marshall and NASA will continue to be a leader in innovation, exploration, and discovery for years to come