Internet Monitoring Research Articles

Characterization of a low consumption wireless sensor node for the intensive transmission of physiological signals

<span>This paper describes the development and implementation of low power consumption wireless sensor nodes for the periodic monitoring of physiological signals with intensive data transmission, using Wi-Fi and ZigBee wireless communication modules, obtaining operation characteristics from the energy point of view that allow to increase the life time of the sensor node. The sensor nodes are designed and built using low energy consumption electronic devices to evaluate their energy performance using current data, transmission time, data transmission period and the relationship with the sensor node's lifetime when transmitting electrocardiographic (ECG), temperature and pulse type physiological signals. The development of this work generates recommendations for the design, development and construction of sensor nodes where the energy consumption of the wireless communication modules is evaluated. In this way, results are obtained that can allow the data transmission period, current consumption and size of data sent to be related to the operating time, defining the operating conditions and wireless technologies that allow the optimization of energy consumption when data is sent to Internet monitoring applications.</span>

Enhanced output performance on LbL multilayer PVDF-TrFE piezoelectric films for charging supercapacitor

The piezoelectric nanogenerator (PENG) has the potential to become a promising power supply for monitoring and sensors in Internet of Things (IoT) systems through wireless networks. In order to further increase the utilization of energy harvesters in an IoT system, we introduce a novel approach that greatly enhances the piezoelectric output performances by employing the layer-by-layer (LbL) method. Poly(vinylidenefluoride-co-trifluoroethylene) (PVDF-TrFE) polymer film, which has piezoelectric properties and mechanical flexibility, was used for the active layer in PENG. The maximum open-circuit voltage and closed-circuit current of the LbL multilayer PENG reached 34 V and 100 nA, respectively. In particular, the closed-circuit current of the LbL multilayer PENG was dramatically improved to be five times higher than that of the single-layer PENG. Furthermore, a supercapacitor was employed to investigate the energy storage capability of PENGs using different methods. The proposed LbL multilayer PENG is expected to be a candidate for a promising power supply for self-powered systems in the IoT system.

A secure cloud-based solution for real-time monitoring and management of Internet of underwater things (IOUT)

Internet of underwater things (IoUT) is an emerging technique with widespread applications. In such type of systems, efficient and secure use of network resources remains to be a greater challenge. This is due to the reason that it works on the basis of a network of smart interconnected objects. This creates the necessity for energy-aware and secure communication techniques across IoUT environment. In order to solve this issue, in this paper, a secure cloud-based solution for real-time monitoring and management of IoUT across smart cities is provided. The proposed system mainly focuses on secure data transmission with lesser energy requirements. This makes smart cities to go green and efficient. The major contribution of the proposed system is divided into two parts: First, an energy-aware efficient framework for IoUT system is defined. Next, we define an enhanced attribute-based encryption scheme with effective user attribute management for secure data transmission across IoUT systems. The simulations are performed, and the results state that the proposed approach provides better security and privacy measures than the existing system. In future, this work could be extended to deal with increasing amount of complexity measures with increased user attributes.

The impact of telehealth remote patient monitoring on glycemic control in type 2 diabetes: a systematic review and meta-analysis of systematic reviews of randomised controlled trials

BackgroundThere is a growing body of evidence to support the use of telehealth in monitoring HbA1c levels in people living with type 2 diabetes. However, the overall magnitude of effect is yet unclear due to variable results reported in existing systematic reviews. The objective of this study is to conduct a systematic review and meta-analysis of systematic reviews of randomised controlled trials to create an evidence-base for the effectiveness of telehealth interventions on glycemic control in adults with type 2 diabetes.MethodsElectronic databases including The Cochrane Library, MEDLINE, EMBASE, HMIC, and PsychINFO were searched to identify relevant systematic reviews published between 1990 and April 2016, supplemented by references search from the relevant reviews. Two independent reviewers selected and reviewed the eligible studies. Of the 3279 references retrieved, 4 systematic reviews reporting in total 29 unique studies relevant to our review were included. Both conventional pairwise meta-analyses and network meta-analyses were performed.ResultsEvidence from pooling four systematic reviews found that telehealth interventions produced a small but significant improvement in HbA1c levels compared with usual care (MD: -0.55, 95% CI: -0.73 to − 0.36). The greatest effect was seen in telephone-delivered interventions, followed by Internet blood glucose monitoring system interventions and lastly interventions involving automatic transmission of SMBG using a mobile phone or a telehealth unit.ConclusionCurrent evidence suggests that telehealth is effective in controlling HbA1c levels in people living with type 2 diabetes. However there is need for better quality primary studies as well as systematic reviews of RCTs in order to confidently conclude on the impact of telehealth on glycemic control in type 2 diabetes.

Architecture of the Parallel Big Data Processing System for Security Monitoring of Internet of Things Networks

Internet-of-Things networks are applied in many areas of people life now. A cornerstone in a issue of a possibility of further distribution and use of these networks is the aspect of security support. However, the features of these networks complicate the use of traditional means and systems of computer protection in them. One of such features is the need to analyze very large volumes of data, heterogeneous by the nature, in real time and with the minimum computing expenses. Taking into account the features of computational capabilities of Internet-of-Things networks the architecture of the system for parallel big data processing based on the data processing technology named as Complex Event Processing and the parallel computing platform Hadoop is offered. The issues directly connected to the architecture of the system and with implementation of its principal components are considered. These components are: data collection component, data storage component, data normalization and analysis component, and data visualization component. An interconnection between components is provided by means of the Hadoop Distributed File System that is a basis for creation of the distributed data storage. The data collection component organizes the distributed data acquisition and their storage in the data storage component. The data normalization and analysis component transforms data to a uniform format and processes them by means of correlation rules. The data visualization component presents data in a graphical form more suitable for further perception by the operator. The results of the experimental evaluation of the system performance confirming a conclusion about its high performance are discussed.

Performance Evaluation of Energy-Autonomous Sensors Using Power-Harvesting Beacons for Environmental Monitoring in Internet of Things (IoT).

Environmental conditions and air quality monitoring have become crucial today due to the undeniable changes of the climate and accelerated urbanization. To efficiently monitor environmental parameters such as temperature, humidity, and the levels of pollutants, such as fine particulate matter (PM2.5) and volatile organic compounds (VOCs) in the air, and to collect data covering vast geographical areas, the development of cheap energy-autonomous sensors for large scale deployment and fine-grained data acquisition is required. Rapid advances in electronics and communication technologies along with the emergence of paradigms such as Cyber-Physical Systems (CPSs) and the Internet of Things (IoT) have led to the development of low-cost sensor devices that can operate unattended for long periods of time and communicate using wired or wireless connections through the Internet. We investigate the energy efficiency of an environmental monitoring system based on Bluetooth Low Energy (BLE) beacons that operate in the IoT environment. The beacons developed measure the temperature, the relative humidity, the light intensity, and the CO2 and VOC levels in the air. Based on our analysis we have developed efficient sleep scheduling algorithms that allow the sensor nodes developed to operate autonomously without requiring the replacement of the power supply. The experimental results show that low-power sensors communicating using BLE technology can operate autonomously (from the energy perspective) in applications that monitor the environment or the air quality in indoor or outdoor settings.

Students' and Teachers' Perceptions About Hospital Educational Environment In Selected Postgraduate Medical Institutes of Bangladesh

This descriptive type of cross sectional study was carried out to observe the hospital educational environment of selected postgraduate medical institutes of Bangladesh. Study period was from July 2016 to June 2017.Sample size was 289 postgraduate clinical students of various course (MD, FCPS & Diploma) and 20 clinical teachers of 10 selected postgraduate medical institutes of Bangladesh. Convenience sampling technique was adopted. Data collection was done with self- administered semi-structured questionnaire using Postgraduate Hospital Educational Environment Measurement (PHEEM) inventory and by In-depth interview of teachers. Three subscales of PHEEM are students' perceptions about 'role autonomy', students' perceptions about quality of teaching and students' perceptions about social support. In-depth interview of the clinical teachers was undertaken to explore the training facilities in the selected postgraduate medical institutes of Bangladesh. Study result revealed total PHEEM score was110.08.Mean score of subscales of PHEEM were 34.51, 36.29 and 26.28 for 'role autonomy', quality of teaching and social support respectively. All these values indicates that the students' perception were in the right or positive direction. Analysis of individual items had shown mean score of most of the individual item were 2-3 that indicated the aspects of the climate that could be enhanced. There were few items that had mean score 2 or less than 2 that indicated the problematic areas of hospital educational environment. In-depth interview of the teachers revealed that there was also some problematic issues on postgraduate training facilities like work overload, lack of proper monitoring or supervision of training, inadequate library, internet, ICU and investigation facilities etc. Study recommended that significant attention should be paid in the problematic areas (lack of supervised training, heavy work load of student, lack of library facility etc.) as well as improvement needed on several areas of hospital educational environment of postgraduate medical institutes of Bangladesh.Bangladesh Journal of Medical Education Vol.9(1) 2018: 7-9

Online Internet traffic monitoring system using spark streaming

Owing to the explosive growth of Internet traffic, network operators must be able to monitor the entire network situation and efficiently manage their network resources. Traditional network analysis methods that usually work on a single machine are no longer suitable for huge traffic data owing to their poor processing ability. Big data frameworks, such as Hadoop and Spark, can handle such analysis jobs even for a large amount of network traffic. However, Hadoop and Spark are inherently designed for offline data analysis. To cope with streaming data, various stream-processing-based frameworks have been proposed, such as Storm, Flink, and Spark Streaming. In this study, we propose an online Internet traffic monitoring system based on Spark Streaming. The system comprises three parts, namely, the collector, messaging system, and stream processor. We considered the TCP performance monitoring as a special use case of showing how network monitoring can be performed with our proposed system. We conducted typical experiments with a cluster in standalone mode, which showed that our system performs well for large Internet traffic measurement and monitoring.

Enhancement Method of Video Image in Haze Weather

In order to enhance the visual quality of video image collected by the internet monitoring equipment in the haze weather, the enhancement algorithm combine dark channel prior and Gamma correction was proposed. This method can improve the quality of the image and enhance the contrast of the image.

Data Acquisition and Cloud Platform for the Energy Internet in Laogang Solid Waste Recycling Base

An energy internet is designed for the biggest wastes recycling center in Shanghai, Laogang solid waste recycling base. For better monitoring and analysis of the energy internet, a data acquisition equipment is designed and fabricated basing on CAN communication. Except for data collecting, it is able to do preliminary calculation and upload the processed data to the corresponding cloud platform for further analysis. This cloud platform will realize real-time monitoring as well as display power analysis. Through the whole system, the energy internet will function well and improve the energy efficiency and make full use of the solid wastes.

Framework for Mobile Internet of Things Security Monitoring Based on Big Data Processing and Machine Learning

The paper discusses a new framework combining the possibilities of Big Data processing and machine leaning developed for security monitoring of mobile Internet of Things. The mathematical foundations and the problem statement are considered. The description of the used data set and the architecture of proposed security monitoring framework are provided. The framework specifies several machine learning mechanisms intended for solving classification tasks. The classifier operation results are exposed to plurality voting, weighted voting, and soft voting. The framework performance and accuracy is assessed experimentally.

Distributed data stream processing and edge computing: A survey on resource elasticity and future directions

Under several emerging application scenarios, such as in smart cities, operational monitoring of large infrastructure, wearable assistance, and Internet of Things, continuous data streams must be processed under very short delays. Several solutions, including multiple software engines, have been developed for processing unbounded data streams in a scalable and efficient manner. More recently, architecture has been proposed to use edge computing for data stream processing. This paper surveys state of the art on stream processing engines and mechanisms for exploiting resource elasticity features of cloud computing in stream processing. Resource elasticity allows for an application or service to scale out/in according to fluctuating demands. Although such features have been extensively investigated for enterprise applications, stream processing poses challenges on achieving elastic systems that can make efficient resource management decisions based on current load. Elasticity becomes even more challenging in highly distributed environments comprising edge and cloud computing resources. This work examines some of these challenges and discusses solutions proposed in the literature to address them.

Development and feasibility of a web application to monitor patients\u2019 cancer-related pain

BackgroundIn the outpatient setting, pain management is often inadequate in patients with cancer-related pain, because of patient- and professional-related barriers in communication and infrequent contacts. The internet may provide new opportunities for monitoring these patients.PurposeThe purpose of this study was to investigate whether internet monitoring of cancer-related pain in outpatients was feasible.MethodsWe developed an internet application that contained a pain diary, eConsult, and patient pain education. In the pain diary, patients scored their pain intensity (0–10 Numeric Rating Scale) and analgesic use daily and their side effects twice a week. Feasibility was defined as the percentage of diaries patients completed during the first 6 weeks.ResultsWe included 100 outpatients. Sixteen were not evaluable due to cognitive problems (2); withdrawal of participation (2); internet problems (2), or because they were too ill (10). During the first 6 weeks, 60% of 84 evaluable patients completed their diary for at least 65% of the days (median number of diaries, 21; range, 3–42) and asked for a median of five eConsults (range, 0–37). Patients most frequently used an eConsult for questions about pain or side effects, how to use their analgesics and to improve their self-management. Over the 6-week period, current pain intensity decreased from 3.3 (SD = 2) to 2.5 (SD = 1.6, p = 0.005), and worst pain intensity decreased from 5.7 (SD = 2) to 3.8 (SD = 2.0, p < 0.001).ConclusionInternet monitoring of pain is feasible in most outpatients with cancer-related pain. The frequent use of the pain diary in the majority of patients indicates that those patients felt confident with the regular assessment of pain.

Understanding the relationship between cyber-victimisation and cyber-bullying on Social Network Sites: The role of moderating factors

The strongest predictor of engagement in cyber-bullying is having experienced cyber-victimisation oneself. We examined the extent to which trait (moral disengagement, cognitive empathy, affective empathy), demographic (age, sex), and situational factors (Internet use, parental Internet monitoring) moderated the strength of the relationship between victimisation and bullying on Social Network Sites (SNSs). We surveyed 175 adolescents (M age=14.82years; SD=1.52; 53% male) who had a SNS profile. Higher moral disengagement strengthened the cyber victim-bully relationship, whereas greater parental monitoring weakened this relationship. Neither affective nor cognitive empathy, age, sex, nor time online moderated the relationship. Overall, 30% to 48% of the variance in cyber-bullying frequency was explained. The results suggest that cyber-bullying interventions need to also focus on experiences of victimisation and that reducing the adolescent's moral disengagement and educating parents about the importance of monitoring adolescent Internet use would be most effective.

物联网智能浇灌控制系统

传统果园种植低效且工作繁重，物联网技术+传统果园种植的模式有利于提高果园管理效率。本文采用STM32系类单片机、2.4 G无线模块，结合Unity3D引擎移动开发平台，设计和开发了一种物联网+Unity3D可交互智能化虚拟现实果园种植远程监控控制系统。该系统由底层部分和顶层部分组成，底层部分设计使用土壤湿度传感器和空气温湿度传感器检测果园土壤温度和外部环境温度和湿度信息，控制器根据不同果树土壤湿度设定值，调节电磁阀，控制浇水量。顶层部分设计建立三维虚拟场景，实现场景漫游、实时监视、信息显示功能。底层部分与顶层部分建立协议，通过查询果树养殖专业信息设定智能浇灌，同时也建立远程手动控制浇灌，方便管理人员随时查看数据和远程控制浇灌，降低果园养护难度。 Traditional orchard cultivation is inefficient and heavy work, and the Internet of Things technol-ogy + traditional orchard cultivation mode is conducive to improving the efficiency of the orchard management. In this paper, with STM32 series of single-chip microcomputer, 2.4 G wireless module, and Unity3D engine mobile development platform, we design and develop an orchard planting remote monitoring and control system of Internet of Things + Unity3D interactive intelligent virtual reality. The system consists of the bottom part and the top part of the composition. The bottom part of the design uses soil moisture sensors and air temperature and humidity sensors to detect the soil temperature and outdoor environment temperature and humidity information. According to different fruit soil moisture settings, the controller adjusts the solenoid valve and controls the amount of irrigation. The top part of the design establishes three-dimensional virtual scene to achieve roaming, real-time monitoring, and information display. The bottom part establishes protocols with the top part, then we can investigate fruit tree farming professional information to set the intelligent watering, and establish remote manual control watering, which facilitate the management staff at any time to view the data and remotely control watering, thus reducing the difficulty of orchards maintenance.

Network Traffic Classifier With Convolutional and Recurrent Neural Networks for Internet of Things

A network traffic classifier (NTC) is an important part of current network monitoring systems, being its task to infer the network service that is currently used by a communication flow (e.g., HTTP and SIP). The detection is based on a number of features associated with the communication flow, for example, source and destination ports and bytes transmitted per packet. NTC is important, because much information about a current network flow can be learned and anticipated just by knowing its network service (required latency, traffic volume, and possible duration). This is of particular interest for the management and monitoring of Internet of Things (IoT) networks, where NTC will help to segregate traffic and behavior of heterogeneous devices and services. In this paper, we present a new technique for NTC based on a combination of deep learning models that can be used for IoT traffic. We show that a recurrent neural network (RNN) combined with a convolutional neural network (CNN) provides best detection results. The natural domain for a CNN, which is image processing, has been extended to NTC in an easy and natural way. We show that the proposed method provides better detection results than alternative algorithms without requiring any feature engineering, which is usual when applying other models. A complete study is presented on several architectures that integrate a CNN and an RNN, including the impact of the features chosen and the length of the network flows used for training.

A New Conception of Information Requisition in Web of Things

Objectives: The dynamic spanning tree approach from Requester Node to Responder Node has been proposed for WoT for information requisition. The objective is to eliminate data redundancy in multipath communication. Methods: A spanning tree algorithm has been proposed to create a dynamic single path from Requester Node (RN) to Responding Node (ReN) in WoT Graph. Before actual communication a virtual path is established between RN and ReN which helps in the reduction of multipaths, cycles and data duplicacy. Findings: The approach is implemented and tested on different scenarios of WoT graphs in NS-2 simulator and shows good results in terms of information requisition. In a WoT graph communication multiple path exists between sender and receiver nodes causing duplicate data packets and high traffic intensity in the network, while in WoT spanning tree there is a single path. Therefore, the network throughput and the efficiency increases manifold. Application: The rapid growth of WoT enabled internet applications, decision making, marketing, healthcare, monitoring and interlinked controlled devices generates high demand for reliable and timely information.

ECG Web Services for Patient Health Monitoring in Internet of Things

Now a day people are facing many health related problems. There is large amount of data that is not structured. The gathered information is raw and unstructured in the nature, so there is a need of converting it to meaningful services. In this paper such system is developed which will provide web service that will be available to any other person easily. Every person should get the healthcare services anywhere, every time even at their home also. For this purpose a platform is developed so that patient specially senior citizens can know their electro Cardiogram (ECG) or also can be said as heart rate, oxygen rate, temperature rate, through an android mobile application specially developed for the healthcare. This overall concept is developed under the Internet of Things as well as cloud computing environment. App which is developed named as ‘Patient Health Monitoring’. This paper gives an idea about Adriano kit which used in the project. This paper also gives an idea about the sensors which are used to measure above mentioned measures. A Bluetooth sensors is also used here for the connectivity. Server will be any network connection such as Wi-Fi.

Advance Signaling Cost for Multicast Fast Reroute Proxy Mobility Management

Background/Objectives: Mobile data traffic over IP has grown very rapidly in size. This huge increase in size creates high demand performance on network that supports mobile multicast services. This motivates the development of a better performance procedure with better signaling cost. Methods/Statistical analysis: A mathematical evaluation of signaling cost for multicast network mobility management namely Proxy Mobile IPv6 (PMIPv6). The signaling cost is derived from a novel combination of Multicast only Fast Reroute (MFR) and predictive Context Transfer (CT) with network mobility management. The signaling cost is designated base on the improved signaling call flow of the advanced method combination. It is calculated as the location updates and the packet delivery cost of the call flow. Findings: This combined procedure helps to mitigate unnecessary multicast network mobility traffic as usage increases. From the results it is clearly shows that the location update for the advanced signaling cost remain consistent regardless of the traffic usage. Where else for the standard method the signaling cost increases significantly, in parallel with the traffic increase. This is hardly because of the reduced location updates and the packet delivery cost of the novel combination procedure. Therefore through this implementation better signaling cost formula is brought forward. Application/Improvements: The advanced signaling cost helps to overcome performance degradation in multicast mobility management application such as live Internet video, Internet-video-to-TV, online video, webcam viewing, video conference and web-based video monitoring.

WITHDRAWN: A Comparison of Internet Monitoring with Continuous Glucose Monitoring in Insulin-Requiring Type 2 Diabetes Mellitus.

To compare the effects of real-time continuous glucose monitoring (RT-CGM) and an Internet blood glucose monitoring system (IBGMS) on glycated hemoglobin levels in patients with type 2 diabetes mellitus treated with insulin. Fifty-seven patients with type 2 diabetes treated with insulin were assigned randomly to 1 of 2 groups. Group 1 had the results of their self-monitoring of blood glucose level monitored biweekly using an IBGMS. Group 2 used RT-CGM and were monitored biweekly. Both groups used a secure website to upload data and to receive feedback from their endocrinologist. A1C and laboratory test results were collected at 0, 3 and 6 months. The baseline parameters were not significantly different. After a 6-month follow-up period, both IBGMS and RT-CGM showed significant within-group improvements in A1C level. In the IBGMS group, the A1C level decreased from 8.79%±1.25% to 7.96%±1.30% (p<0.05). The RT-CGM group decreased from 8.80%±1.37% to 7.49%±0.70% (p<0.001). IBGMS and RT-CGM did not show significantly different A1C levels at baseline, 3 and 6 months (p>0.05). The use of both IBGMS and RT-CGM significantly improved A1C levels in patients with type 2 diabetes treated with insulin in a randomized trial over a 6-month period. There were no significant differences in A1C values between groups after 6 months.