Remote monitoring applications in urban vehicular ad-hoc networks (VANETs) enable authorities to monitor data related to various activities of a moving vehicle from a static infrastructure. However, urban environment constraints along with various characteristics of remote monitoring applications give rise to significant hurdles while developing routing solutions in urban VANETs. Since the urban environment comprises several road intersections, using their geographic information can greatly assist in achieving efficient and reliable routing. With an aim to leverage this information, this article presents a receiver-based data forwarding protocol, termed Intersection-based Link-adaptive Beaconless Forwarding for City scenarios (ILBFC). ILBFC uses the position information of road intersections to effectively limit the duration for which a relay vehicle can stay as a default forwarder. In addition, a winner relay management scheme is employed to consider the drastic speed decay in vehicles. Furthermore, ILBFC is simulated in realistic urban traffic conditions, and its performance is compared with other existing state-of-the-art routing protocols in terms of packet delivery ratio, average end-to-end delay and packet redundancy coefficient. In particular, the results highlight the superior performance of ILBFC, thereby offering an efficient and reliable routing solution for remote monitoring applications.
Flow measurement is a critical element for liquid resources monitoring for various applications in many industrial systems. The purposes of the study are to determine the flow rate of liquid system in flow rig using radiotracer techniques and to compare the result with that obtained by the conventional flow meters. The flow rig consists of 58.7m long and 20cm diameter pipeline that can accommodate about 0.296m3 of liquid. Tap water was used as liquid flow in pipeline and conventional flow meters were also installed at the flow rig. Radiotracer was injected as a sharp pulse into the inlet p.peline. The pulse was monitored at the inlet and various points along the outlet pipeline using collimated scintillation detector. The peak to peak and total count methods were applied for radiotracer techniques and showed the comparable results with conventional flow meter.
Activity recognition in smart homes aims to infer the particular activities of the inhabitant, the aim being to monitor their activities and identify any abnormalities, especially for those living alone. In order for a smart home to support its inhabitant, the recognition system needs to learn from observations acquired through sensors. One question that often arises is which sensors are useful and how many sensors are required to accurately recognise the inhabitant's activities? Many wrapper methods have been proposed and remain one of the popular evaluators for sensor selection due to its superior accuracy performance. However, they are prohibitively slow during the evaluation process and may run into the risk of overfitting due to the extent of the search. Motivated by this characteristic, this paper attempts to reduce the cost of the evaluation process and overfitting through tree alignment. The performance of our method is evaluated on two public datasets obtained in two distinct smart home environments.
As the enterprise of the "Internet of Things" is rapidly gaining widespread acceptance, sensors are being deployed in an unrestrained manner around the world to make efficient use of this new technological evolution. A recent survey has shown that sensor deployments over the past decade have increased significantly and has predicted an upsurge in the future growth rate. In health-care services, for instance, sensors are used as a key technology to enable Internet of Things oriented health-care monitoring systems. In this paper, we have proposed a two-stage fundamental approach to facilitate the implementation of such a system. In the first stage, sensors promptly gather together the particle measurements of an android application. Then, in the second stage, the collected data are sent over a Femto-LTE network following a new scheduling technique. The proposed scheduling strategy is used to send the data according to the application's priority. The efficiency of the proposed technique is demonstrated by comparing it with that of well-known algorithms, namely, proportional fairness and exponential proportional fairness.
According to the survey on various health centres, smart log-based multi access physical monitoring system determines the health conditions of humans and their associated problems present in their lifestyle. At present, deficiency in significant nutrients leads to deterioration of organs, which creates various health problems, particularly for infants, children, and adults. Due to the importance of a multi access physical monitoring system, children and adolescents' physical activities should be continuously monitored for eliminating difficulties in their life using a smart environment system. Nowadays, in real-time necessity on multi access physical monitoring systems, information requirements and the effective diagnosis of health condition is the challenging task in practice. In this research, wearable smart-log patch with Internet of Things (IoT) sensors has been designed and developed with multimedia technology. Further, the data computation in that smart-log patch has been analysed using edge computing on Bayesian deep learning network (EC-BDLN), which helps to infer and identify various physical data collected from the humans in an accurate manner to monitor their physical activities. Then, the efficiency of this wearable IoT system with multimedia technology is evaluated using experimental results and discussed in terms of accuracy, efficiency, mean residual error, delay, and less energy consumption. This state-of-the-art smart-log patch is considered as one of evolutionary research in health checking of multi access physical monitoring systems with multimedia technology.
The volume of patient monitoring video acquired in hospitals is very huge and hence there is a need for better compression of the same for effective storage and transmission. This paper presents a new motion segmentation technique, which improves the compression of patient monitoring video. The proposed motion segmentation technique makes use of a binary mask, which is obtained by thresholding the standard deviation values of the pixels along the temporal axis. Two compression methods, which make use of the proposed motion segmentation technique, are presented. The first method uses MPEG-4 coder and 9/7-biorthogonal wavelet for compressing the moving and stationary portions of the video respectively. The second method uses 5/3-biorthogonal wavelet for compressing both the moving and the stationary portions of the video. The performances of these compression algorithms are evaluated in terms of PSNR and bitrate. From the experimental results, it is found that the proposed motion technique improves the performance of the MPEG-4 coder. Among the two compression methods presented, the MPEG-4 based method performs better for bitrates less than 767 Kbps whereas for bitrates above 767 Kbps the performance of the wavelet based method is found superior.
This study presents a prioritisation framework for mobile patient monitoring systems (MPMSs) based on multicriteria analysis in architectural components. This framework selects the most appropriate system amongst available MPMSs for the telemedicine environment. Prioritisation of MPMSs is a challenging task due to (a) multiple evaluation criteria, (b) importance of criteria, (c) data variation and (d) unmeasurable values. The secondary data presented as the decision evaluation matrix include six systems (namely, Yale-National Aeronautics and Space Administration (NASA), advanced health and disaster aid network, personalised health monitoring, CMS, MobiHealth and NTU) as alternatives and 13 criteria (namely, supported number of sensors, sensor front-end (SFE) communication, SFE to mobile base unit (MBU) communications, display of biosignals on the MBU, storage of biosignals on the MBU, intra-body area network (BAN) communication problems, extra-BAN communication problems, extra-BAN communication technology, extra-BAN communication protocols, back-end system communication technology, intended geographic area of use, end-to-end security and reported trial problems) based on the architectural components of MPMSs. These criteria are adopted from the most relevant studies and are found to be applicable to this study. The prioritisation framework is developed in three stages. (1) The unmeasurable values of the MPMS evaluation criteria in the adopted decision evaluation matrix based on expert opinion are represented by using the best-worst method (BWM). (2) The importance of the evaluation criteria based on the architectural components of the MPMS is determined by using the BWM. (3) The VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method is utilised to rank the MPMSs according to the determined importance of the evaluation criteria and the adopted decision matrix. For validation, mean ± standard deviation is used to verify the similarity of systematic prioritisations objectively. The following results are obtained. (1) The BWM represents the unmeasurable values of the MPMS evaluation criteria. (2) The BWM is suitable for weighing the evaluation criteria based on the architectural components of the MPMS. (3) VIKOR is suitable for solving the MPMS prioritisation problem. Moreover, the internal and external VIKOR group decision making are approximately the same, with the best MPMS being 'Yale-NASA' and the worst MPMS being 'NTU'. (4) For the objective validation, remarkable differences are observed between the group scores, which indicate the similarity of internal and external prioritisation results.
Promoting patient care is a priority for all healthcare providers with the overall purpose of realising a high degree of patient satisfaction. A medical centre server is a remote computer that enables hospitals and physicians to analyse data in real time and offer appropriate services to patients. The server can also manage, organise and support professionals in telemedicine. Therefore, a remote medical centre server plays a crucial role in sustainably delivering quality healthcare services in telemedicine. This article presents a comprehensive review of the provision of healthcare services in telemedicine applications, especially in the medical centre server. Moreover, it highlights the open issues and challenges related to providing healthcare services in the medical centre server within telemedicine. Methodological aspects to control and manage the process of healthcare service provision and three distinct and successive phases are presented. The first phase presents the identification process to propose a decision matrix (DM) on the basis of a crossover of 'multi-healthcare services' and 'hospital list' within intelligent data and service management centre (Tier 4). The second phase discusses the development of a DM for hospital selection on the basis of integrated VIKOR-Analytic Hierarchy Process (AHP) methods. Finally, the last phase examines the validation process for the proposed framework.
Remotely monitoring a patient's condition is a serious issue and must be addressed. Remote health monitoring systems (RHMS) in telemedicine refers to resources, strategies, methods and installations that enable doctors or other medical professionals to work remotely to consult, diagnose and treat patients. The goal of RHMS is to provide timely medical services at remote areas through telecommunication technologies. Through major advancements in technology, particularly in wireless networking, cloud computing and data storage, RHMS is becoming a feasible aspect of modern medicine. RHMS for the prioritisation of patients with multiple chronic diseases (MCDs) plays an important role in sustainably providing high-quality healthcare services. Further investigations are required to highlight the limitations of the prioritisation of patients with MCDs over a telemedicine environment. This study introduces a comprehensive and inclusive review on the prioritisation of patients with MCDs in telemedicine applications. Furthermore, it presents the challenges and open issues regarding patient prioritisation in telemedicine. The findings of this study are as follows: (1) The limitations and problems of existing patients' prioritisation with MCDs are presented and emphasised. (2) Based on the analysis of the academic literature, an accurate solution for remote prioritisation in a large scale of patients with MCDs was not presented. (3) There is an essential need to produce a new multiple-criteria decision-making theory to address the current problems in the prioritisation of patients with MCDs.
In recent years, fire detection technologies have helped safeguard lives and property from hazards. Early fire warning methods, such as smoke or gas sensors, are ineffectual. Many fires have caused deaths and property damage. IoT is a fast-growing technology. It contains equipment, buildings, electrical systems, vehicles, and everyday things with computing and sensing capabilities. These objects can be managed and monitored remotely as they are connected to the Internet. In the Internet of Things concept, low-power devices like sensors and controllers are linked together using the concept of Low Power Wide Area Network (LPWAN). Long Range Wide Area Network (LoRaWAN) is an LPWAN product used on the Internet of Things (IoT). It is well suited for networks of things connected to the Internet, where terminals send a minute amount of sensor data over large distances, providing the end terminals with battery lifetimes of years. In this article, we design and implement a LoRaWAN-based system for smart building fire detection and prevention, not reliant upon Wireless Fidelity (Wi-Fi) connection. A LoRa node with a combination of sensors can detect smoke, gas, Liquefied Petroleum Gas (LPG), propane, methane, hydrogen, alcohol, temperature, and humidity. We developed the system in a real-world environment utilizing Wi-Fi Lora 32 boards. The performance is evaluated considering the response time and overall network delay. The tests are carried out in different lengths (0-600 m) and heights above the ground (0-2 m) in an open environment and indoor (1st Floor-3rd floor) environment. We observed that the proposed system outperformed in sensing and data transfer from sensing nodes to the controller boards.
The lack of information on ground truth gas dispersion and experiment verification information has impeded the development of mobile olfaction systems, especially for real-world conditions. In this paper, an integrated testbed for mobile gas sensing experiments is presented. The integrated 3 m × 6 m testbed was built to provide real-time ground truth information for mobile olfaction system development. The testbed consists of a 72-gas-sensor array, namely Large Gas Sensor Array (LGSA), a localization system based on cameras and a wireless communication backbone for robot communication and integration into the testbed system. Furthermore, the data collected from the testbed may be streamed into a simulation environment to expedite development. Calibration results using ethanol have shown that using a large number of gas sensor in the LGSA is feasible and can produce coherent signals when exposed to the same concentrations. The results have shown that the testbed was able to capture the time varying characteristics and the variability of gas plume in a 2 h experiment thus providing time dependent ground truth concentration maps. The authors have demonstrated the ability of the mobile olfaction testbed to monitor, verify and thus, provide insight to gas distribution mapping experiment.
By applying a hexagon-diamond search (HDS) method to an ultrasound image, the path of an object is able to be monitored by extracting images into macro-blocks, thereby achieving image redundancy is reduced from one frame to another, and also ascertaining the motion vector within the parameters searched. The HDS algorithm uses six search points to form the six sides of the hexagon pattern, a centre point, and a further four search points to create diamond pattern within the hexagon that clarifies the focus of the subject area.
Gentamicin is an aminoglycoside antibiotic which is commonly used in the treatment of serious Gram-negative infections. However, gentamicin like other aminoglycosides, has a narrow therapeutic index and is potentially ototoxic and nephrotoxic. Blood levels following administration of gentamicin has been shown to be highly unpredictable and monitoring of gentamicin levels is necessary to ensure effective therapeutic levels as well as to avoid toxicity. The Department of Microbiology, Universiti Kebangsaan Malaysia offers such a monitoring service. This paper analyses the results of 135 such estimations performed between August 1979 and May 1981. It is shown that a significant proportion of patients were receiving either too much or too little gentamicin. Empirical determinations of dosages is unsatisfactory and as the microbiological assay method of determining gentamicin levels is both easy to perform and inexpensive, such a service should be offered by all general hospitals in Malaysia.
It is important to monitor the spatial resolution of a gamma camera on a weekly basis to acquire medical images with accurate quantitative information. A simple and fast computer program with a graphical user interface to analyze spatial resolution was successfully developed using MATLAB. The results were compared with those obtained from the standard processing system available in our gamma camera. The spatial resolution calculated using MATLAB was 1.24% lower than using the standard processing system. The developed program is cost effective, faster, and provides an easy platform for the physicists and technologists to analyze the spatial resolution based on the image of the line source.
Battery Monitoring System (BMoS) is an electronic system that monitors rechargeable battery cells or packs with various parameters, such as battery voltage, current and State-of-Charge (SoC). This system can be used to avoid overcharging or over-discharging of batteries to increase its shelf life. However, BMoS on the market is very expensive and not suitable for low cost embedded systems. As the Arduino Uno is widely used for low cost microcontroller boards, easy programming environment, and open-source platforms for building electronic projects, therefore, this study focuses on Arduino Uno BMoS based system. This system consists of current and voltage sensors, an Arduino Uno microcontroller and a liquid crystal display (LCD). In order to develop this system, there are three objectives to be achieved. First, the relationship between input and output of the sensors must be derived mathematically. The mathematical expression obtained can be verified by connecting and disconnecting the circuit with load and monitoring the value of output sensors. Then, a complete prototype of the BMoS was developed by connecting the LCD, current and voltage sensors to the Arduino Uno microcontroller. The complete prototype was tested using an 11.1 V of Lithium-ion battery and a DC motor as a load. From the results, the current sensor shows zero value when no load is connected as no current flow. The LCD also displays 11.1V of battery voltage when fully charged. Using the developed system, the user can monitor the current, the voltage and the SoC of the battery to ensure the battery is not overcharged and overused. The development of the BMoS can help to monitor the operation and performance of the batteries in any electronic systems. At the end of this study, the complete BMoS prototype gives benefits to the user and makes work easier.
Wearable technologies are becoming a profitable means of monitoring a person's health state, such as heart rate and physical activity. The use of the smartwatch is becoming consolidated, not only as a novelty but also as a very useful tool for daily use. In addition, other devices, such as helmets or belts, are beneficial for monitoring workers and the early detection of any anomaly. They can provide valuable information, especially in work environments, where they help reduce the rate of accidents and occupational diseases, which makes them powerful Personal Protective Equipment (PPE). The constant monitoring of the worker's health can be done in real-time, through temperature, falls, noise, impacts, or heart rate meters, activating an audible and vibrating alarm when an anomaly is detected. The gathered information is transmitted to a server in charge of collecting and processing it. In the first place, this paper provides an exhaustive review of the state of the art on works related to electronics for human activity behavior. After that, a smart multisensory bracelet, combined with other devices, developed a control platform that can improve operators' security in the working environment. Artificial Intelligence and the Internet of Things (AIoT) bring together the information to improve safety on construction sites, power stations, power lines, etc. Real-time and historic data is used to monitor operators' health and a hybrid system between Gaussian Mixture Model and Human Activity Classification. That is, our contribution is also founded on the use of two machine learning models, one based on unsupervised learning and the other one supervised. Where the GMM gave us a performance of 80%, 85%, 70%, and 80% for the 4 classes classified in real time, the LSTM obtained a result under the confusion matrix of 0.769, 0.892, and 0.921 for the carrying-displacing, falls, and walking-standing activities, respectively. This information was sent in real time through the platform that has been used to analyze and process the data in an alarm system.
Transformer failures lead to interruption of power supply. Therefore, asset management is important to monitor the efficient functioning of transformers. An important approach in asset management is condition assessment whereby the health status of the transformer is assessed via a health index. There are many methods in determining the final value of a health index. This paper examines how different assessment methods can be used in order to come up with the final health index and output of final health index. The output trend shapes are almost the same for Assessment Model A, B and C except for Assessment Model D. There is no strong correlation between the health index and age of the transformer. Generally, the value of health index of the transformer is reflected by its operation and loading history .This paper hence examines the assessment steps and results that will guide the development of a new approach to determine health index value.
Contaminated and ageing transmission line insulators often suffer from temporary or permanent loss of their insulating properties due to flashover resulting in power system failure. Surface discharges are precursors to flashover. To pre-empt any occurrence of flashovers, utility companies monitor the conditions of their insulators. There are numerous insulator surface monitoring techniques such as Leakage Current, Acoustics, and Infrared. However, these techniques may not be suitable for in-situ condition monitoring of the insulators as they are prone to noise, affected by environmental conditions or contact methods. Monitoring of the UV signals emitted by the surface discharges of these insulators has been reported to be a promising technique. However, comprehensive studies on this technique is lacking, especially on aged insulators. This paper investigated the UV signals of contaminated and aged insulators detected during surface discharge activities using UV pulse method. The time and frequency domain of the UV signals were analysed for a group of insulator samples having varying levels of contamination and phases of ageing. Results show that there is a strong correlation between the contamination level and ageing of the insulators with the amplitude and harmonic components of the UV signals. This correlation can useful to monitor in-service insulator surface conditions.
The existing optimal design of the fixed sampling interval S2-EWMA control chart to monitor the sample variance of a process is based on the average run length (ARL) criterion. Since the shape of the run length distribution changes with the magnitude of the shift in the variance, the median run length (MRL) gives a more meaningful explanation about the in-control and out-of-control performances of a control chart. This paper proposes the optimal design of the S2-EWMA chart, based on the MRL. The Markov chain technique is employed to compute the MRLs. The performances of the S2-EWMA chart, double sampling (DS) S2 chart and S chart are evaluated and compared. The MRL results indicated that the S2-EWMA chart gives better performance for detecting small and moderate variance shifts, while maintaining almost the same sensitivity as the DS S2 and S charts toward large variance shifts, especially when the sample size increases.
In this work, graphene has been utilized as the sensing material for the development of a highly-sensitive flexible pressure sensor platform. It has been demonstrated that a graphene-based pressure sensor platform that is able to measure pressure change of up to 3 psi with a sensitivity of 0.042 psi-1 and a non-linearity of less than 1% has been accomplished. The developed device, which resides on a flexible platform, will be applicable for integration in continuous wearables health-care monitoring system for the measurement of blood pressure.