Online Resource Provisioning for Wireless Data Collection

Wireless data collection requires a sequence of resource provisioning decisions due to the limited battery capacity of wireless sensors. The corresponding online resource provisioning problem is challenging. Recently, many prediction methods have been proposed that can be used to benefit the performance of various systems through their incorporation. Therefore, in this article, we focus on online resource provisioning problems with short-term predictions motivated by the wireless data collection problem. Specifically, we design separate online algorithms for systems in which the state evolves in either a stationary manner or an arbitrarily determined manner and prove their performance bounds where their bounds improve as the amount of available predictions increases. Additionally, we design a meta-algorithm that can choose which online algorithm to implement at each point in time, depending on the recent behavior of the system environment. The practical performances of the proposed algorithms are corroborated in trace-driven numerical simulations of data collection of shared bikes. Additionally, we show that the performance of our meta-algorithm in various system environments can be better than that of the single best algorithm chosen in hindsight.

Weigh-in-Motion: Lightweight Real-Time Identification of Gbps Wireless Traffic

Distinguishing between wireless and wired traffic in a network middlebox is an essential ingredient for numerous applications including security monitoring and quality-of-service (QoS) provisioning. The majority of existing approaches have exploited the greater delay statistics, such as round-trip-time and inter-packet arrival time, observed in wireless traffic to infer whether the traffic is originated from Ethernet (i.e., wired) or Wi-Fi (i.e., wireless) based on the assumption that the capacity of the wireless link is much slower than that of the wired link. However, this underlying assumption is no longer valid due to increases in wireless data rates over Gbps enabled by recent Wi-Fi technologies such as 802.11ac/ax. In this paper, we revisit the problem of identifying Wi-Fi traffic in network middleboxes as the wireless link capacity approaches the capacity of the wired. We present Weigh-in-Motion, a lightweight online detection scheme, that analyzes the traffic patterns observed at the middleboxes and infers whether the traffic is originated from high-speed Wi-Fi devices. To this end, we introduce the concept of ACKBunch that captures the unique characteristics of high-speed Wi-Fi, which is further utilized to distinguish whether the observed traffic is originated from a wired or wireless device. The effectiveness of the proposed scheme is evaluated via extensive real experiments, demonstrating its capability of accurately identifying wireless traffic from/to Gigabit 802.11 devices.

Wearable Device for Observation of Physical Activity with the Purpose of Patient Monitoring Due to COVID-19

In late 2019, a new genre of coronavirus (COVID-19) was first identified in humans in Wuhan, China. In addition to this, COVID-19 spreads through droplets, so quarantine is necessary to halt the spread and to recover physically. This modern urgency creates a critical challenge for the latest technologies to detect and monitor potential patients of this new disease. In this vein, the Internet of Things (IoT) contributes to solving such problems. This paper proposed a wearable device that utilizes real-time monitoring to detect body temperature and ambient conditions. Moreover, the system automatically alerts the concerned person using this device. The alert is transmitted when the body exceeds the allowed temperature threshold. To achieve this, we developed an algorithm that detects physical exercise named “Continuous Displacement Algorithm” based on an accelerometer to see whether a potential temperature rise can be attributed to physical activity. The people responsible for the person in quarantine can then connect via nRF Connect or a similar central application to acquire an accurate picture of the person’s condition. This experiment included an Arduino Nano BLE 33 Sense which contains several other sensors like a 9-axis IMU, several types of temperature, and ambient and other sensors equipped. This device successfully managed to measure wrist temperature at all states, ranging from 32 °C initially to 39 °C, providing better battery autonomy than other similar devices, lasting over 12 h, with fast charging capabilities (500 mA), and utilizing the BLE 5.0 protocol for data wireless data transmission and low power consumption. Furthermore, a 1D Convolutional Neural Network (CNN) was employed to classify whether the user is feverish while considering the physical activity status. The results obtained from the 1D CNN illustrated the manner in which it can be leveraged to acquire insight regarding the health of the users in the setting of the COVID-19 pandemic.

Determinants of RFID Adoption Intention in the Healthcare Industry for Patient Monitoring

Radio frequency identification (RFID) technology holds tremendous potential in improving the patient management system in hospitals attaining global importance in the healthcare industry due to the spread of the COVID-19 pandemic at present. RFID assists in wireless data storage and automatic retrieval, making systems efficient, improving patient safety, and decreasing costs. Although RFID is an emerging technology in the healthcare industry, its adoption is yet to gather momentum. This chapter will provide a background for healthcare practitioners and researchers about RFID technologies in the healthcare sector. Moreover, an integrated conceptual framework will be proposed consisting of factors that influence RFID technology adoption intention in the healthcare industry. This study will be the first of its kind to identify and classify various factors of RFID adoption intention and provide a comprehensive model using an exploratory method laying the foundation for academicians and industry practitioners for the future scope of its research.

Diagnosis of Scoliosis in Juvenile Patients

In this paper, a wearable device used to monitor the posture variations. This device is useful in early detection and monitoring of patient having spine related disease such as scoliosis, kyphosis. Scoliosis is a 3-dimensional deformation of spine. The most common characteristics are bending of backbone in coronal plane and rotation of vertebrae, which results in various deformations of human postures. It mostly occurs in juvenile stage (3-10 years). The existing system consists of wearable sensor network for posture data acquisition, wireless data transmission and conventional smartphone for data processing. The biofeedback device helps to improve the self-awareness in natural environment, but it is not suitable in case of severe deformity. The flex sensor used because of its High level of reliability, consistency, repeatability and harsh temperature resistance. Keywords: scoliosis, microcontroller, flex sensor

Irriman Platform: Enhancing Farming Sustainability through Cloud Computing Techniques for Irrigation Management

Crop sustainability is essential for balancing economic development and environmental care, mainly in strong and very competitive regions in the agri-food sector, such as the Region of Murcia in Spain, considered to be the orchard of Europe, despite being a semi-arid area with an important scarcity of fresh water. In this region, farmers apply efficient techniques to minimize supplies and maximize quality and productivity; however, the effects of climate change and the degradation of significant natural environments, such as, the “Mar Menor”, the most extent saltwater lagoon of Europe, threatened by resources overexploitation, lead to the search of even better irrigation management techniques to avoid certain effects which could damage the quaternary aquifer connected to such lagoon. This paper describes the Irriman Platform, a system based on Cloud Computing techniques, which includes low-cost wireless data loggers, capable of acquiring data from a wide range of agronomic sensors, and a novel software architecture for safely storing and processing such information, making crop monitoring and irrigation management easier. The proposed platform helps agronomists to optimize irrigation procedures through a usable web-based tool which allows them to elaborate irrigation plans and to evaluate their effectiveness over crops. The system has been deployed in a large number of representative crops, located along near 50000 ha of the surface, during several phenological cycles. Results demonstrate that the system enables crop monitoring and irrigation optimization, and makes interaction between farmers and agronomists easier.

Rancang Bangun Sistem Monitoring Listrik Tiga Fasa Berbasis Wireless Sensor Network Menggunakan LoRa Ra-02 SX1278

Pada sistem kelistrikan, monitoring perlu dilakukan untuk mengatasi terjadinya gangguan dan pemanfaatan energi listrik yang kurang efektif. Monitoring listrik saat ini masih dilakukan menggunakan alat ukur sederhana dan pencatatan nilai terukur secara manual. Kekurangan tersebut dapat diatasi dengan merancang sebuah sistem monitoring listrik tiga fasa jarak jauh berbasis wireless sensor network (WSN) menggunakan komunikasi LoRa. Sistem yang dibangun terdiri dari node transmitter sebagai alat ukur tegangan, arus listrik, frekuensi, faktor daya, dan daya aktif dengan memanfaatkan modul PZEM- 004T. Node receiver berfungsi sebagai gateway yang dapat menerima data dari node transmitter secara wireless. Data yang diterima receiver kemudian disimpan ke database melalui server menggunakan jaringan internet. Ketika node receiver tidak terkoneksi dengan jaringan WiFi, data akan secara otomatis disimpan pada micro SD card. Web monitoring dirancang untuk memudahkan pengguna mengakses data monitoring listrik. Web monitoring akan menampilkan data tegangan, arus listrik, frekuensi, dan faktor daya dalam sistem satu fasa serta daya aktif dalam bentuk tiga fasa ke dalam bentuk tabel dan grafik.

Bio-Compatible Sensor for Middle Ear Pressure Monitoring on a Bio-Degradable Substrate

Hypotension in the middle ear can cause serious diseases and hearing disorders. Until now, pressure in the middle ear is measured indirectly by using the impedance of the tympanic membrane (tympanometry). Direct methods are just described in scientific studies and would be harmful in clinical routine. Here, we demonstrate a bio-compatible pressure sensor, which can resolve pressure changes in the range of −7.5 kPa up to +7.5 kPa, and due to its compact design (area of 2 × 4 mm2), can be directly implanted in the human middle ear. Furthermore, the read-out of the pressure sensor can be conveniently done using wireless data communication technologies employing a plate capacitor with an elastic dielectric for pressure monitoring and a planar coil. Thus, our sensor allows for direct pressure measurements in the middle ear, avoiding additional surgeries after device implantation.

Visible Light Wireless Data Center Links with Distinct Beam Configurations

Visible light communication (VLC) is being explored as one promising approach to enable wireless data centers (WDC). Up to now, the visible light wireless data center links are still limited to the conventional Lambertian beam paradigm. The potential coverage gain relevant to the optical beam space is waiting for sufficient investigation. For addressing this issue, in this paper, the dynamic optical beam based WDC coverage enhancement scheme is introduced, and for each transmitter, the best candidate asymmetrical optical beam is selected to load the data signal. Numerical evaluation shows that, compared with the conventional static beam configuration, up to 6.76 dB peak signal to noise ratio (SNR) gain and 4.46 dB average SNR gain could be provided by the proposed dynamic beam scheme. Moreover, this SNR dynamic range is reduced to 36.65 dB while the counterpart of the static non-Lambertian beam configuration is up to 44.78 dB.