Monitoring Server Research Articles

Monitoring of Servers and Server Rooms by IoT System that Can Configure and Control its Terminal Sensors Behind a NAT Using a Wiki Page on the Internet

Monitoring of Servers and Server Rooms by IoT System that Can Configure and Control its Terminal Sensors Behind a NAT Using a Wiki Page on the Internet

Heart Rate Monitoring with Smart Wearables using Edge Computing

Heart is a vital component of every human health. The development of wearables and its sensor enables the possibility of easy-to-use real-time monitoring. The goal of this study is to improve an IoT monitoring system by enabling real-time heart rate monitoring and analysis, also to assess the use of PPG sensors in smart wearables compared to other clinical-tested heart rate sensors. The PPG sensor will be used to record heart rate data of the user physically. The measurements are then sent to the application for pre-processing. The application can then transmit the pre-processed measurements to the cloud server for monitoring or further analysis, i.e. to assess the health of users’ heart. The measurement comparison with measurement collected by a BCG sensor is carried out in this paper. While neither are standard for heart rate measurements, the findings of the evaluation show that the PPG sensor achieves quite similar input data and assessment results during awake stages. The Fitbit sensor tested often underestimates, with sometimes delayed or doesn’t detect a sudden increase in heart rate during sleeping.

A Cloud Image Data Protection Algorithm with Multilevel Encryption Scheme and Automated- Selection Mechanism

In this paper, we present a cloud image data protection algorithm with a multilevel encryption scheme and automated-selection mechanism to maintain the privacy of cloud data contents. This algorithm is also useful for the protection of personal or commercial data uploaded to the cloud server for real-time applications, monitoring, and transmission. Fundamental and well-known in cryptography, the confusion–diffusion scheme, as well as an automated-selection mechanism (sliding pixel window) were selected as the main motor of the proposed algorithm to cipher images. First, a sliding pixel window is selected to expedite a two-stepped process, whether in small or big images. The confusion stage was designed to drastically change data from plain image to cipher image. The conversion of pixels from decimal to binary and their vertical and horizontal relocation were performed to help in this stage, not only by randomly moving bits, but also by changing the pixel values when they returned to their corresponding decimal values. Meanwhile, the diffusion stage was designed to destroy all possible existing patterns in the sliding pixel window after the confusion stage. Two hyperchaotic systems, together with a logistic map (multilevel scheme), produce pseudorandom numbers to separately conceal the original data of each subplain image through first- and second-level encryption processes. The two-stepped algorithm was designed to be easily implemented by practitioners. Furthermore, the experimental analysis demonstrates the effectiveness and feasibility of the proposed encryption algorithm after being tested using the benchmark “Lena” image, as well as the “Bruce Lee” image, the latter of which is completely different to the first one, statistically speaking.

An Integrated Emergency Call System based on Public Switched Telephone Network for Elevators

Today, most of elevators have an emergency call facility for emergency situations. However, if the network installed in the elevator is also out of power, it cannot be used for the elevator remote monitoring and management. So, we develop an integrated and unified emergency call system, which can transmit not only telephone call but also data signals using PSTN(Public Switched Telephone Network) in order to remote monitoring and management of elevators, even though a power outage occurs. The proposed integrated emergency call system to process multiple data such as voice and operational information is a multi-channel board system which is composed of an emergency phone signal processing module and an operational information processing module in the control box of elevator. In addition, the RMS(remote management server) systems based on the Web consist of a dial-up server and a remote monitoring server where manages the elevator's operating information, status records, and operational faults received via the proposed integrated and unified emergency call system in real time. So even if there's a catastrophic emergency, the proposed RMS systems shall ensure and maintain the safety of passengers inside the elevator. Also, remote control of the elevator by this system should be more efficient and secure. In near future, all elevator emergency call system need to support multifunctional capabilities to transmit operational data as well as phone calls for the safety of passengers. In addition, for safer elevators, it is necessary to improve them more efficiently by combining them with high-tech technologies such as the Internet of Things and artificial intelligence.

Bot detection by monitoring and grouping domain name server record response queries in DNS traffic

A Botnet is network of bots or infected systems, each running on a compromised host machine, controlled by command and control server. Botnet can be used for email spamming to launching DDoS attacks. Botnet attacks classified as topology based, protocol based, and architecture based. Designing a detection system for bots is becoming challenging as botnet attacks are upgrading the attacking methodology by hiding and changing identities (command and control server) periodically. This paper is proposing, bot detection methodology by monitoring domain name server record response (DNSRR) query traffic, which form a group activity in DNS traffic simultaneously sent by bot machines. The analysis is based on type of botnet attack, detection target, feature source, feature extraction, feature correlation, machine learning techniques. Few researchers proposed bot detection techniques based on DNS queries initiated by bots, but these can be easily avoided by changing bot program, architecture, protocol and encrypted network traffic. The proposed approach is versatile and robust than the existing detection approaches so that the presence of variety of bots can be detected by monitoring the group activities of DNSRR queries in DNS traffic. From the experiment and results, it is shown that proposed methodology able to detect bot efficiently while they are connected to controlling server or migrating to new server. The results are encouraging because of low false positive detection rate.

Next generation integrated smart manufacturing based on big data analytics, reinforced learning, and optimal routes planning methods

ABSTRACTIn this study, Big Data Analytics has been applied to implement smart manufacturing services performed by local commercial laundry Small and Medium Sized Enterprises (SMEs), which, to be specific, is called Smart Laundry Services (SLSs). This laundry service first uses traffic big data inputs from the City Traffic monitoring Server and abstract patterns to develop dynamic optimal scheduling routes for logistic terminals after taking pick-up orders from hotel supply managers. On the other hand, the laundry floor, which isfor fully automatic operation through integrated smart manufacturing with reinforced learning algorithms, will automatically classify the arriving orders according to the Radio Frequency Identification (RFID) tags on the orders, which contain data for each order such as material type, forbidden policies, detergent suggestion, and other relevant specifications. The orders are immediately processed and assigned to a laundry terminal with least waiting time and with optimal laundry parameters learned by the algorithm. The processed orders are then shipped back to the hotels by static scheduling routes with the optimal patterns estimated from real-time traffic big data. The entire operation, from initial orders to dropping back orders, are monitored by hotel supply managers and laundry SMEs’ personnel on their computers or mobile devices such as cell phones via an APP. As a result, this proposed integrated Smart Manufacturing architecture characterized with cutting-edge technologies such as big data analytics and intelligent production will make the commercial laundry SMEs more competitive in local markets.

Practical wireless safety monitoring system of long-span girders subjected to construction loading a building under construction

The primary goal of this paper is to address the challenges associated with the design and application of a practical wireless measurement system for the continuous safety monitoring of a large-scale structural members under construction. A practical wireless sensor network system (WSNS) was designed and real-time structural health monitoring (SHM) on long–span girders in a building under construction was performed. A WSNS consists of vibrating wire strain gauges (VWSGs) along with a sensor node, a master node, and a monitoring server. The field application of the WSNS using WVWSs was conducted on long-span plate girder and beam members of the K Art Hall during the construction of the building. It was possible to monitor the strain changes of the long-span plate girders and beams in real time as a function of the construction process and temperature changes.

Implementation of web server monitoring system using crawling technology

Implementation of web server monitoring system using crawling technology

Development of master–slave monitoring systems for automobile exhaust using integration of ZigBee and GSM networks

Automobile exhaust has become one of the important factors that affect atmosphere environment and cause environmental pollution. Therefore, it is necessary to do a good job of automobile exhaust detection. However, existing surveillance systems for such a poisonous gas still have some drawbacks like a limited network function, wiring complexity, a small signal coverage, poor compatibility. To overcome above disadvantages, this paper presents a master–slave wireless network monitoring system by integrating ZigBee and GSM technologies. The detected gas status from the Remote Detection Unit can be transmitted to Master Monitoring Station via self-organizing ZigBee network. The Master Monitoring Station connected with the Monitoring Server is to process the data received and thus send mobile phone real-time alarm information via GSM module. Simultaneously, the Monitoring Server can display the gas status and record it in the database online. Also, it can adjust gas warning level according to the temperature and humility of environment. Experimental results show that the proposed system is feasible in terms of stability reliability, self-organizing and wide area range, achieving the expected design goals in data acquisition and transmission, especially in simplifying equipment installation and wiring, improving system mobility and portability and remote monitoring.

Formal Assessment and Measurement of Data Utilization and Value for Mines

Most contemporary mines have considerable amounts of data. Structured and unstructured large data sets are colloquially named big data. Much of the data in mining operations come from a variety of systems, each with different databases and reporting environments. Standard technology deployments create a “silo-ification” of data leading to poor system benefit. Through modern server monitoring and systematic approach, data utilization and value can quantifiably be measured. The Data Utilization and Value Index (DUVI) can quantify business intelligence best practices and user interaction. This study seeks to provide a data management tool to measure data utilization across the process of converting data into action.

Fault-tolerant flexible lossless cluster compression method for monitoring data in smart grid

Big data in smart grid dispatch monitoring systems is susceptible to interference from processing delays and slow response times. Hence, a new fault-tolerant flexible lossless cluster compression method is proposed. This paper presents the Five-tuples (S, D, O, T, M) model, and builds a monitoring data processing platform based on hive. By deploying the dispatch host and monitoring servers under the cloud computing environment, where data nodes are respectively transformed by Deflate, Gzip, BZip2 and LZO lossless compression method. Taking the power dispatch automation system of Long-hai line as example, experimental results show that the cluster lossless compression ratio of BZip2 is greater than 81%; when data records reach twelve million, the compression ratio can be further improved to certain extent by using RCFile storage hive format, which has significant flexible features. Therefore, the new method proposed in this paper can improve the flexibility and fault-tolerant ability of big monitoring data processing in smart grid.

Power audit: an estimation model-based tool as a support for monitoring power consumption in a distributed network infrastructure

Understanding the details of power consumption in IT distributed infrastructure has become essential to make efficient power management decisions. Indeed, increasingly, energy costs are a major factor in the total cost of ownership (TCO) of IT equipments of both data centres and enterprise computing. However, measuring and monitoring the power consumption of systems in medium-scale to large-scale distributed infrastructures is often difficult due to large and dispersed deployment of heterogeneous equipments such as personal computers (PCs), routers, switches, printers, etc. The various aspects discussed in this study are then organised around: 1) proposing an approach for measuring power consumption of devices in distributed infrastructure, especially for computers as a first step; 2) collecting the measures on the monitoring server through the network in a supervisory objective using simple network management protocol (SNMP). We have designed and developed software named 'power audit' as support of the above aspects.

EMMCS: An Edge Monitoring Framework for Multi-Cloud Environments using SNMP

Multi-cloud computing is no different than other Cloud computing (CC) models when it comes to providing users with self-services IT resources. For instance, a company can use services of one specific cloud Service Provider (CSP) for its business, as it can use more than one CSP either to get the best of each without any vendor lock-in. However, the situation is different regarding monitoring a multi-cloud environment. In fact, CSPs provide in-house monitoring tools that are natively compatible with their environment but lack support for other CSP's environments, which is problematic for any company that wants to use more than a CSP. In addition, third party cloud monitoring tools often use agents installed on each monitored virtual machine (VM) to collect monitoring data and send them to a central monitoring server that is hosted on premise or on a Cloud, which increases bottlenecks and latency while transmitting data or processing it. Therefore, this paper presents a monitoring framework for multi-cloud environments that implements edge computing and RESTFul microservices for a high efficiency monitoring and scalability. In fact, the monitoring framework “EMMCS” uses SNMP agents to collect metrics, and performs all monitoring tasks at the edge of each cloud to enhance network transmission and data processing at the central monitoring server level. The implementation of the framework is tested on different public cloud environments, namely Amazon AWS and Microsoft Azure to show the efficiency of the proposed approach.

Online Partial Discharge Insulation Condition Monitoring of Complete High-Voltage Networks

Renforth et al. in 2017 present a novel online partial discharge (OLPD) high-voltage (HV) insulation condition monitoring (CM) solution to monitor complete HV networks in petrochemical and other critical industry facilities. Continuous, 24/7 OLPD insulation monitoring of in-service cables, switchgear, transformers, and rotating machines operating at 3000 V and above provides operators with an early warning against incipient HV insulation faults that manifest themselves through high PD activity. Knowledge of the type, severity, and source of any site(s) of PD across the HV network (including remotely connected plant such as Ex/ATEX HV motors located in hazardous gas zones) enables pinpointed preventative maintenance interventions to be made to the HV insulation system during planned maintenance turnarounds to avoid unplanned outages. To monitor a complete HV network, OLPD sensors and monitoring nodes are distributed at strategic locations across the network. The CM data from the distributed nodes are passed via Ethernet/LAN to a partial discharge monitoring server (PDMS) located at the control center of the facility with a secure internet connection. The PDMS operates an advanced OLPD monitoring database for the logging, display, benchmarking, trending, and visualization of the PD monitoring data on a graphical user interface. The insulation condition criticality (from 0% to 100%) of each phase of all of the individual HV plant items monitored is presented on a series of mimic screens of the HV network single-line diagram. This paper concludes with a case study of a complete HV network OLPD monitoring design solution implemented at a large, crude-oil processing facility in Eurasia.

Security between Dehumidify Dryers and a Monitoring Server in Plastic Manufacturing Control

When moisture over an allowable range is included in material during the plastic manufacturing process, a defective product might be produced, hence management of the dehumidifying dryer process is needed. Therefore, to maintain the material’s optimal humidity, a dehumidify dryer measures data in real time and transfers the data to a server; based on the measurement results, it suggests a smart factory model for appropriate control. However, even if the data is accurately measured, if its integrity and confidentiality are not maintained during the transfer process, control data sent to a dehumidify dryer can cause unintended malfunctions. Therefore, this study suggests an overall encryption mechanism that can maintain the integrity and confidentiality of data at the same time during the transfer process. We confirmed through an experiment with this mechanism that when data is damaged or altered during a transfer process, a person can check this. We expect that the method suggested in this paper will help the productivity of the plastic manufacturing process to increase and defective product rate to decrease.

BIM- and IoT-based monitoring framework for building performance management

ABSTRACTDuring the operation and maintenance period of buildings, a monitoring system established to monitor the internal environment can be very helpful to ensure that buildings are in good condition. However, the existing monitoring systems still need improvements in data interoperability and smart sensing. This paper established a new monitoring framework based on Building Information Modeling (BIM) and IoT (Internet of Things) Technology. In this proposed framework, BIM is used to maintain the 3D data of the building, while a time-series database called InfluxDB is introduced for storage of monitoring data. Then, a monitoring server based on Message Queuing Telemetry Transport protocol is established, providing a low-cost and reliable communication. Meanwhile, Arduino microcontrollers with some sensors are adopted as the nodes of the monitoring network, aiming to collect real-time data of the environment and upload the data to the server. Finally, a prototype system is developed to demonstrate the proposed framework. Testing of the developed system and corresponding IoT sensors shows that combination of BIM and IoT technology provides a comprehensive view of the status of the buildings and improves the efficiency of information utilization.

Applying ZigBee wireless sensor and control network for bridge safety monitoring

Internet technologies bring methods to help bridge safety management, to collect data or monitor conditions in real time, and to comprehensively record or analyze the collected data of on-site conditions in real time. In this study, the wireless sensor networks and smart building technologies are adopted to help the bridge safety information transmission and management. The study proposed a bridge safety–monitoring system conceptual framework by applying the ZigBee wireless sensor and control technology. The conceptual framework demonstrated by a prototype includes four major subsystems: (1) monitoring units; (2) photovoltaic units; (3) wireless communication system; and (4) bridge safety–monitoring server system. This system can monitor and analyze in real time the conditions of a bridge and its environment, including the waters levels nearby, pipelines, air, and other safety conditions. The detected data and images are transmitted to the server and database for users to have real-time monitoring of the bridge conditions via mobile telecommunication devices.

Research on the Adjacent Charged Object Monitoring Device Based On MEMS Electric Field Sensing Technology

In order to strengthen the safety protection of the construction of the adjacent charged object and ensure the safety of the construction. Based on the MEMS electric field sensor technology, the MEMS electric field monitoring and alarm device is developed for the two different electric field conditions of DC and AC. The device has wireless transmission, GPS/Beidou Positioning and other functions. It can realize real-time local alarm, remote server monitoring based on GPRS signal transmission, mobile APP online monitoring and so on. Both DC and AC power frequency electric fields are measured in a large range of range of 0~1000kV/m, the resolution is 20V/m, and the total uncertainty is within 2%. In the Changji-Guquan and other transmission line projects, the functions are well tested and the results are accurate.

Automation of Web-Portal Construction Processes with SQL Server for the Black Sea Ecosystem Monitoring

Automation of Web-Portal Construction Processes with SQL Server for the Black Sea Ecosystem Monitoring

An Appraisal on Recurrent Pattern Analysis Algorithm from the Net Monitor Records

Web data mining is a rising examination territory where taking out information is an essential job and a range of algorithms has been projected with a specific end goal to comprehend the an assortment of issues identified with web mining from available dataset. Here, we focus Frequent Pattern-Growth algorithm for data mining. Concerning FP-Growth, the efficiency is insufficient since mining progression is depends on large tree-frame data structure by internal memory estimate. We focuses on server monitor documents to find web convention forms of websites using web utilization mining and in exacting spotlights. Here, we had the practice to work with the projected strategy which could conceivable to eradicate the disadvantage of restriction of the presented rehearse in the area of web mining. An assortment of web usage mining practice can advance effort on numerous areas of scientific, medical & social media applications to advance toward for the research & security united zone. A briefed outline development system could help in gathering additional information on utilizing line up algorithm which shows the information state-plans effectually.