Personalized ambient parameters monitoring: design and implementing of a wrist-worn prototype for hazardous gases and sound level detection

The concentration is on “3D space utilization” as the concept and infrastructure of designing of a wearable in ambient parameters monitoring. This strategy is implemented according to “multi-layer” approach. In this approach, each group of parameters from the same category is monitored by a modular physical layer enriched with the respected sensors. Depending on the number of parameters and layers, each physical layer is located on top of another. The intention is to implement a device for “everyone in everywhere for everything”

Innovative energy-efficient wireless sensor network applications and MAC sub-layer protocols employing RTS-CTS with packet concatenation

of energy-efficiency as well as the number of available applications. As a consequence there are challenges that need to be tackled for the future generation of WSNs. The research work from this Ph.D. thesis has involved the actual development of innovative WSN applications contributing to different research projects. In the Smart-Clothing project contributions have been given in the development of a Wireless Body Area Network (WBAN) to monitor the foetal movements of a pregnant woman in the last four weeks of pregnancy. The creation of an automatic wireless measurement system for remotely monitoring concrete structures was an contribution for the INSYSM project. This was accomplished by using an IEEE 802.15.4 network enabling for remotely monitoring the temperature and humidity within civil engineering structures. In the framework of the PROENEGY-WSN project contributions have been given in the identification the spectrum opportunities for Radio Frequency (RF) energy harvesting through power density measurements from 350 MHz to 3 GHz. The design of the circuits to harvest RF energy and the requirements needed for creating a WBAN with electromagnetic energy harvesting and Cognitive Radio (CR) capabilities have also been addressed. A performance evaluation of the state-of-the art of the hardware WSN platforms has also been addressed. This is explained by the fact that, even by using optimized Medium Access Control (MAC) protocols, if the WSNs platforms do not allow for minimizing the energy consumption in the idle and sleeping states, energy efficiency and long network lifetime will not be achieved. The research also involved the development of new innovative mechanisms that tries and solves overhead, one of the fundamental reasons for the IEEE 802.15.4 standard MAC inefficiency. In particular, this Ph.D. thesis proposes an IEEE 802.15.4 MAC layer performance enhancement by employing RTS/CTS combined with packet concatenation. The results have shown that the use of the RTS/CTS mechanism improves channel efficiency by decreasing the deferral time before transmitting a data packet. In addition, the Sensor Block Acknowledgment MAC (SBACK-MAC) protocol has been proposed that allows the aggregation of several acknowledgment responses in one special Block Acknowledgment (BACK) Response packet. Two different solutions are considered. The first one considers the SBACK-MAC protocol in the presence of BACK Request (concatenation) while the second one considers the SBACK-MAC in the absence of BACK Request (piggyback). The proposed solutions address a distributed scenario with single-destination and single-rate frame aggregation. The throughput and delay performance is mathematically derived under both ideal conditions (a channel environment with no transmission errors) and non ideal conditions (a channel environment with transmission errors). An analytical model is proposed, capable of taking into account the retransmission delays and the maximum number of backoff stages. The simulation results successfully validate our analytical model. For more than 7 TX (aggregated packets) all the MAC sub-layer protocols employing RTS/CTS with packet concatenation allows for the optimization of channel use in WSNs, v8-48 % improvement in the maximum average throughput and minimum average delay, and decrease energy consumption

Usable Security for Wireless Body-Area Networks

We expect wireless body-area networks of pervasive wearable devices will enable in situ health monitoring, personal assistance, entertainment personalization, and home automation. As these devices become ubiquitous, we also expect them to interoperate. That is, instead of closed, end-to-end body-worn sensing systems, we envision standardized sensors that wirelessly communicate their data to a device many people already carry today, the smart phone. However, this ubiquity of wireless sensors combined with the characteristics they sense present many security and privacy problems. In this thesis we describe solutions to two of these problems. First, we evaluate the use of bioimpedance for recognizing who is wearing these wireless sensors and show that bioimpedance is a feasible biometric. Second, we investigate the use of accelerometers for verifying whether two of these wireless sensors are on the same person and show that our method is successful as distinguishing between sensors on the same body and on different bodies. We stress that any solution to these problems must be usable, meaning the user should not have to do anything but attach the sensor to their body and have them just work. These methods solve interesting problems in their own right, but it is the combination of these methods that shows their true power. Combined together they allow a network of wireless sensors to cooperate and determine whom they are sensing even though only one of the wireless sensors might be able to determine this fact. If all the wireless sensors know they are on the same body as each other and one of them knows which person it is on, then they can each exploit the transitive relationship to know that they must all be on that person’s body. We show how these methods can work together in a prototype system. This ability to operate unobtrusively, collecting in situ data and labeling it properly without interrupting the wearer’s activities of daily life, will be vital to the success of these wireless sensors

Technology Implications of UWB on Wireless Sensor Network-A detailed Survey

In today’s high tech “SMART” world sensor based networks are widely used. The main challenge with wireless-based sensor networks is the underneath physical layer. In this survey, we have identified core obstacles of wireless sensor network when UWB is used at PHY layer. This research was done using a systematic approach to assess UWB’s effectiveness (for WSN) based on information taken from various research papers, books, technical surveys and articles. Our aim is to measure the UWB’s effectiveness for WSN and analyze the different obstacles allied with its implementation. Starting from existing solutions to proposed theories. Here we have focused only on the core concerns, e.g. spectrum, interference, synchronization etc.Our research concludes that despite all the bottlenecks and challenges, UWB’s efficient capabilities makes it an attractive PHY layer scheme for the WSN, provided we can control interference and energy problems. This survey gives a fresh start to the researchers and prototype designers to understand the technological concerns associated with UWB’s implementatio

Towards reliable communication in low-power wireless body area networks

Es wird zunehmend die Ansicht vertreten, dass tragbare Computer und Sensoren neue Anwendungen in den Bereichen Gesundheitswesen, personalisierte Fitness oder erweiterte Realität ermöglichen werden. Die am Körper getragenen Geräte sind dabei mithilfe eines Wireless Body Area Network (WBAN) verbunden, d.h. es wird drahtlose Kommunikation statt eines drahtgebundenen Kanals eingesetzt. Der drahtlose Kanal ist jedoch typischerweise ein eher instabiles Kommunikationsmedium und die Einsatzbedingungen von WBANs sind besonders schwierig: Einerseits wird die Kanalqualität stark von den physischen Bewegungen der Person beeinflusst, andererseits werden WBANs häufig in lizenzfreien Funkbändern eingesetzt und sind daher Störungen von anderen drahtlosen Geräten ausgesetzt. Oft benötigen WBAN Anwendungen aber eine zuverlässige Datenübertragung. Das erste Ziel dieser Arbeit ist es, ein besseres Verständnis dafür zu schaffen, wie sich die spezifischen Einsatzbedingungen von WBANs auf die intra-WBAN Kommunikation auswirken. So wird zum Beispiel analysiert, welchen Einfluss die Platzierung der Geräte auf der Oberfläche des menschlichen Körpers und die Mobilität des Benutzers haben. Es wird nachgewiesen, dass während regelmäßiger Aktivitäten wie Laufen die empfangene Signalstärke stark schwankt, gleichzeitig aber Signalstärke-Spitzen oft einem regulären Muster folgen. Außerdem wird gezeigt, dass in urbanen Umgebungen die Effekte von 2.4 GHz Radio Frequency (RF) Interferenz im Vergleich zu den Auswirkungen von fading (Schwankungen der empfangenen Signalstärke) eher gering sind. Allerdings führt RF Interferenz dazu, dass häufiger Bündelfehler auftreten, d.h. Fehler zeitlich korrelieren. Dies kann insbesondere in Anwendungen, die eine geringe Übertragungslatenz benötigen, problematisch sein. Der zweite Teil dieser Arbeit beschäftigt sich mit der Analyse von Verfahren, die potentiell die Zuverlässigkeit der Kommunikation in WBANs erhöhen, ohne dass wesentlich mehr Energie verbraucht wird. Zunächst wird der Trade-off zwischen Übertragungslatenz und der Zuverlässigkeit der Kommunikation analysiert. Diese Analyse basiert auf einem neuen Paket-Scheduling Algorithmus, der einen Beschleunigungssensor nutzt, um die WBAN Kommunikation auf die physischen Bewegungen der Person abzustimmen. Die Analyse zeigt, dass unzuverlässige Kommunikationsverbindungen oft zuverlässig werden, wenn Pakete während vorhergesagter Signalstärke-Spitzen gesendet werden. Ferner wird analysiert, inwiefern die Robustheit gegen 2.4 GHz RF Interferenz verbessert werden kann. Dazu werden zwei Verfahren betrachtet: Ein bereits existierendes Verfahren, das periodisch einen Wechsel der Übertragungsfrequenz durchführt (channel hopping) und ein neues Verfahren, das durch RF Interferenz entstandene Bitfehler reparieren kann, indem der Inhalt mehrerer fehlerhafter Pakete kombiniert wird (packet combining). Eine Schlussfolgerung ist, dass Frequenzdiversität zwar das Auftreten von Bündelfehlern reduzieren kann, dass jedoch die statische Auswahl eines Kanals am oberen Ende des 2.4 GHz Bandes häufig schon eine akzeptable Abhilfe gegen RF Interferenz darstellt.There is a growing belief that wearable computers and sensors will enable new applications in areas such as healthcare, personal fitness or augmented reality. The devices are attached to a person and connected through a Wireless Body Area Network (WBAN), which replaces the wires of traditional monitoring systems by wireless communication. This comes, however, at the cost of turning a reliable communication channel into an unreliable one. The wireless channel is typically a rather unstable medium for communication and the conditions under which WBANs have to operate are particularly harsh: not only is the channel strongly influenced by the movements of the person, but WBANs also often operate in unlicensed frequency bands and may therefore be exposed to a significant amount of interference from other wireless devices. Yet, many envisioned WBAN applications require reliable data transmission. The goals of this thesis are twofold: first, we aim at establishing a better understanding of how the specific WBAN operating conditions, such as node placement on the human body surface and user mobility, impact intra-WBAN communication. We show that during periodic activities like walking the received signal strength on an on-body communication link fluctuates strongly, but signal strength peaks often follow a regular pattern. Furthermore, we find that in comparison to the effects of fading 2.4 GHz Radio Frequency (RF) interference causes relatively little packet loss - however, urban 2.4 GHz RF noise is bursty (correlated in time), which may be problematic for applications with low latency bounds. The second goal of this thesis is to analyze how communication reliability in WBANs can be improved without sacrificing a significant amount of additional energy. To this end, we first explore the trade-off between communication latency and communication reliability. This analysis is based on a novel packet scheduling algorithm, which makes use of an accelerometer to couple WBAN communication with the movement patterns of the user. The analysis shows that unreliable links can often be made reliable if packets are transmitted at predicted signal strength peaks. In addition, we analyze to what extent two mechanisms can improve robustness against 2.4 GHz RF interference when adopted in a WBAN context: we analyze the benefits of channel hopping, and we examine how the packet retransmission process can be made more efficient by using a novel packet combining algorithm that allows to repair packets corrupted by RF interference. One of the conclusions is that while frequency agility may decrease "burstiness" of errors the static selection of a channel at the upper end of the 2.4 GHz band often already represents a good remedy against RF interference

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig

Intelligent Circuits and Systems

ICICS-2020 is the third conference initiated by the School of Electronics and Electrical Engineering at Lovely Professional University that explored recent innovations of researchers working for the development of smart and green technologies in the fields of Energy, Electronics, Communications, Computers, and Control. ICICS provides innovators to identify new opportunities for the social and economic benefits of society.　 This conference bridges the gap between academics and R&D institutions, social visionaries, and experts from all strata of society to present their ongoing research activities and foster research relations between them. It provides opportunities for the exchange of new ideas, applications, and experiences in the field of smart technologies and finding global partners for future collaboration. The ICICS-2020 was conducted in two broad categories, Intelligent Circuits & Intelligent Systems and Emerging Technologies in Electrical Engineering

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