Data Acquisition Network Research Articles

Distributed real‐time monitoring system for atmospheric particles

Haze occurs frequently in many cities, and becomes the authors' great common concern. A distributed real-time monitoring system for atmospheric particles has been designed, implemented and tested. The proposed system consists of a front-end data wireless acquisition network, an embedded web server system, a central server and remote monitoring terminals. The front-end data-acquisition network is made up of mobile data-acquisition nodes distributed in atmospheric particle monitoring areas which collect atmospheric particle data periodically. The data is transmitted by Jennet wireless network and sent to the Internet through the embedded web servers attached to the coordinator nodes via the serial ports. Only a common Internet browser is required for a remote user to check the atmospheric particle data. The central server is responsible for storing the pollution data for further usage of pollution analysis, evaluation and early warning. In this study, the PM1.0, PM2.5 and PM10 particle data were successfully collected, transmitted and checked by the monitoring system in Lanzhou, China. The results show that the designed system can meet the needs of functional requirements of atmospheric particle monitoring.

Time Triggered Hybrid Scheduler with Dynamic Frequency Scaling for Distributed Real Time Embedded Systems

In this paper, the time triggered hybrid scheduler (TTH) is implemented in the distributed real time embedded systems along with the operation of dynamic frequency scaling (DFS) since it overcomes the problem of time triggered cooperative scheduler (TTC) for distributed systems and also the decreases the power consumption. This scheduler is implemented along with the token ring network protocol for the communication scheduling. In hard real time systems, ideally, no deadline may ever be missed and hence the co-operative and pre-emptive are combined in this paper to achieve optimum efficiency. DFS is used to provide robustness to the network and also reduces the power utilization. This TTH scheduler is more advantageous than TTC scheduler in distributed system, since long task problem can be overcome by means of this algorithm. The model is simulated using the TORSCHE toolbox of the MATLAB. Some of the applications of this scheduling are data acquisition and sampling in wireless sensor networks, control system engineering, patient monitoring using wireless sensor networking and so on.

Monitoring and controlling energy distribution: Implementation of a distribution management system based on Common Information Model

The management of renewable and distributed energy resources is changing the way in which electric distribution systems are being operated. Many companies commonly create several custom system solutions to manage power distribution. The functions provided by these systems are network visualization, state estimation, system control and data acquisition, among others. Despite their relevance, many mid-size or small distribution companies have problems to install one of these solutions because they are either very expensive, incompatible, or limited by difficulties in data exchange. As a possible solution to this problem, this paper presents the development of a Distribution Management System (DMS) based on open source technology. This system combines four main components: OpenDSS-framework, ActiveMQ-broker, applications for visualizing and editing electrical network, and Common Information Model (CIM). The tool is part of Tenergia R&D project 1This work is financed by R&D projects FITS 2013 – UREE10 – FONARSEC – Ministry of Science, Technology and Productive Innovation of Argentina, and PIDDEF 36/14 – Ministry of Defense of Argentina.1 and has a collection of applications designed to monitor and control the distribution networks located in Argentina.

Data Acquisition Based on Stable Matching of Bipartite Graph in Cooperative Vehicle-Infrastructure Systems.

Existing studies on data acquisition in vehicular networks often take the mobile vehicular nodes as data carriers. However, their autonomous movements, limited resources and security risks impact the quality of services. In this article, we propose a data acquisition model using stable matching of bipartite graph in cooperative vehicle-infrastructure systems, namely, DAS. Contents are distributed to roadside units, while vehicular nodes support supplementary storage. The original distribution problem is formulated as a stable matching problem of bipartite graph, where the data and the storage cells compose two sides of vertices. Regarding the factors relevant with the access ratio and delay, the preference rankings for contents and roadside units are calculated, respectively. With a multi-replica preprocessing algorithm to handle the potential one-to-many mapping, the matching problem is addressed in polynomial time. In addition, vehicular nodes carry and forward assistant contents to deliver the failed packets because of bandwidth competition. Furthermore, an incentive strategy is put forward to boost the vehicle cooperation and to achieve a fair bandwidth allocation at roadside units. Experiments show that DAS achieves a high access ratio and a small storage cost with an acceptable delay.

A Lossless Network for Data Acquisition

The bursty many-to-one communication pattern, typical for data acquisition systems, is particularly demanding for commodity TCP/IP and Ethernet technologies. We expand the study of lossless switching in software running on commercial off-the-shelf servers, using the ATLAS experiment as a case study. In this paper, we extend the popular software switch, Open vSwitch, with a dedicated, throughput-oriented buffering mechanism for data acquisition. We compare the performance under heavy congestion on typical Ethernet switches to a commodity server acting as a switch. Our results indicate that software switches with large buffers perform significantly better. Next, we evaluate the scalability of the system when building a larger topology of interconnected software switches, exploiting the integration with software-defined networking technologies. We build an IP-only leaf-spine network consisting of eight software switches running on distinct physical servers as a demonstrator.

Recognition Method of Abnormal Data Based on Interval Estimation

The emergence of substantial abnormal data in network data acquisition system is due to the cognitive level of users mostly. The abnormal data not only cause the waste of storage and the operation resources, but also seriously affect the results of statistical analysis, How to identify the massive abnormal data is the premise to eliminate the abnormal data. Accordingly a detection method of abnormal data based on confidence interval, which is aimed to filter the abnormal data from the group, was proposed. Compared with the traditional method of simple random sampling, the sample space is selected from the overall sample by the above-mentioned method has more credibility. And different from the commonly used detection method which is aimed at abnormal point, this method can batch test the unexpected data. The experimental results suggest that this method can effectively filter out the abnormal data particularly in the cases where the data size is huge and the data deviation is not obvious. And on the basis of above-mentioned method, the detection result has little difference with the actual situation and the accuracy is over 90%.

Multi-objective optimized connectivity restoring of disjoint segments using mobile data collectors in wireless sensor network

Wireless sensor networks (WSNs) have been used extensively in a range of applications, which realizes data acquisition, processing, transmission, and analysis in an interesting area. Harsh surroundings and their inherent vulnerability often mean that these networks suffer from simultaneous node failure possibly causing the network to become partitioned into multiple disjointed segments. This in turn can prevent the gathering of data from the sensors and subsequent transmission to the sink, causing the whole network to fail. In this paper, a strategy is presented for restoring multi-objective optimization connectivity of these segments using mobile data collectors (MDCs), by considering the segments as collections of sensor nodes and not as some representative node. Different from existing uses of MDCs for restoration, the delay in data collection and task balance is considered, and the network connectivity and data acquisition path optimization problem are transformed into an improved multi-travelling salesman problem (iMTSP). An improved multi-objective optimization genetic algorithm for solving the optimal collection data collector position and moving paths is proposed, which introduces virtual segments and hierarchical chromosome structure, improved population diversity, and custom coding and decoding. The simulation results show that the proposed method can effectively solve the iMTSP of the Pareto optimal solution and can provide a new strategy for connectivity-restoring technology in WSNs. Compared with NSGA-II, the diversity of the proposed gene algorithm represents a clear improvement.

Unmanned Aerial Vehicle Systems for Remote Estimation of Flooded Areas Based on Complex Image Processing.

Floods are natural disasters which cause the most economic damage at the global level. Therefore, flood monitoring and damage estimation are very important for the population, authorities and insurance companies. The paper proposes an original solution, based on a hybrid network and complex image processing, to this problem. As first novelty, a multilevel system, with two components, terrestrial and aerial, was proposed and designed by the authors as support for image acquisition from a delimited region. The terrestrial component contains a Ground Control Station, as a coordinator at distance, which communicates via the internet with more Ground Data Terminals, as a fixed nodes network for data acquisition and communication. The aerial component contains mobile nodes—fixed wing type UAVs. In order to evaluate flood damage, two tasks must be accomplished by the network: area coverage and image processing. The second novelty of the paper consists of texture analysis in a deep neural network, taking into account new criteria for feature selection and patch classification. Color and spatial information extracted from chromatic co-occurrence matrix and mass fractal dimension were used as well. Finally, the experimental results in a real mission demonstrate the validity of the proposed methodologies and the performances of the algorithms.

Consistent and Effective Data Acquisition in Wireless Sensor Networks in the Existence of Transfaulty Nodes

Consistent and Effective Data Acquisition in Wireless Sensor Networks in the Existence of Transfaulty Nodes

An Improved Data Fusion Method IICKPAD for Privacy Protection in Wireless Sensor Networks

Aiming at the privacy and security of data acquisition and monitoring in wireless sensor networks, an improved data fusion method, IICKPDA, is proposed for privacy protection in wireless sensor networks. The method is based on the ICKPDA algorithm, which optimizes the deadline between the same layer and the different layers，which can be randomly selected by the adjacent nodes of the data fragments to be transmitted, and also the non-leaf nodes which only the data is not collected to improve the characteristics of the data. The improved algorithm avoids the redundancy of the intermediate fusion process and protects the collected monitoring data, which improves the data fusion precision and the fusion efficiency of the intermediate fusion node. Theory and related experiments demonstrate the feasibility of the method.

General Data Acquisition Platform for Wireless Sensor Network Based on CC2538

Wireless sensor networks are the hotspots of current research and have very wide application prospects. Its front end is a sensor that can sense and check the external world. This paper takes temperature and humidity as the research object, and builds a wireless sensor network data acquisition platform by combining the Internet of things and the WeChat public platform. The platform uses DHT11 temperature and humidity sensors and CC2538 sensor nodes to obtain the relevant data, through the server and database for data access. The combination with WeChat public platform not only allows us to view the temperature and humidity in the WeChat public, but also allows us to understand the environmental changes of the relevant detection area more conveniently and quickly. The effectiveness of the platform is also demonstrated by the collection of temperature and humidity data.

A Multi-Scale Analysis of 27,000 Urban Street Networks

OpenStreetMap offers a valuable source of worldwide geospatial data useful to urban researchers. This study uses the OSMnx software to automatically download and analyze 27,000 US street networks from OpenStreetMap at metropolitan, municipal, and neighborhood scales - namely, every US city and town, census urbanized area, and Zillow-defined neighborhood. It presents empirical findings on US urban form and street network characteristics, emphasizing measures relevant to graph theory, transportation, urban design, and morphology such as structure, connectedness, density, centrality, and resilience. In the past, street network data acquisition and processing have been challenging and ad hoc. This study illustrates the use of OSMnx and OpenStreetMap to consistently conduct street network analysis with extremely large sample sizes, with clearly defined network definitions and extents for reproducibility, and using nonplanar, directed graphs. These street networks and measures data have been shared in a public repository for other researchers to use.

Real-time OGC compliant online data monitoring and acquisition network for management of hydro-meteorological hazards

Natural calamities are undesirable events which often cause significant damages to the society and environment. One of the prime drivers for such a situation is rainfall which, when in excess, is responsible for initiating hydro-meteorological hazards like flash flood, landslides. In this context, a suitably designed real-time, telemetred and open-source standard rainfall and water level data acquisition network for flood-prone areas may be indispensable in managing the likely hydro-meteorological hazards. This paper demonstrates the design of such a web-based online low-cost high sampling OGC compliant data acquisition and signal transmission system with multiple remote data logging points to capture information of rainfall distribution over a pilot scale catchment. The network also monitors the water level at the exit end of the basin to facilitate the estimation of catchment run off. A study is also made to compare the distributed rainfall observations of tipping bucket rain gauges with that of an ordinary rain gauge. Finally, the possible applications of the observed data in managing hydro-meteorological hazards are indicated.

Case Studies of SCADA Firewall Configurations and the Implications for Best Practices

Firewall configuration is an important activity for any modern day business. It is particularly a critical task for the supervisory control and data acquisition (SCADA) networks that control power stations, water distribution, factory automation, etc. Lack of automation tools to assist with this critical task has resulted in unoptimised, error prone configurations that expose these networks to cyber attacks. Automation can make designing firewall configurations more reliable and their deployment increasingly cost-effective. Best practices have been proposed by the industry for developing high-level security policy ( e.g ., ANSI/ISA 62443-1-1). But these best practices lack specification in several key aspects needed to allow a firewall to be automatically configured. For instance, the standards are vague on how firewall management policies should be captured at a high-level using its specifications. In this paper, we uncover these missing pieces and propose extensions. We apply our extended best-practice specification to real-world firewall case studies to achieve multiple objectives: 1) to evaluate the usefulness of the refined best-practice in the automated specification of firewalls and 2) to illustrate that even in simple cases, SCADA networks are often insecure due to their misconfigured firewalls.

Autonomous field navigation, data acquisition and node location in wireless sensor networks

To overcome the limited transmission range of spatially separated nodes of a wireless sensor network (WSN), a small 4-wheel autonomous robot assembled the data from nodes distributed in a vineyard. First, the robot followed a predefined way-point route between the grapevine rows, in order to evaluate the sensor node locations by their received signal strength indication (RSSI). Then, the recorded and geo-referenced RSSI data were analysed and mapped. By using the evaluated node positions, an optimised second route was generated. While navigating, a laser scanner was used for obstacle detection and avoidance. Path planning with known positions of the nodes reduced the driving time by 15 times compared with the first run, because the hybrid control system used was capable of navigating within the plantation even perpendicular to the row structures. For locating the nodes, results based on trilateration were compared with the values of an attached differential global navigation satellite system (DGNSS). The results showed that it is possible to locate and geo-reference the sensor nodes with a robot, even without any prior knowledge about their absolute position. The best achieved location showed a deviation with DGNSS of 1.2 m and with RSSI trilateration of 0.6 m compared to the actual position.

Rip current tomography based on reciprocal sound transmissions using reflective waves

In order to monitor the occurrence of rip current in beaches, an acoustic measurement system for rip currents based on passive reciprocal sound transmissions using reflective waves was designed and developed. The offshore current velocity V is given by V = C0 2/L*(t21-t12)/2 where C0 is sound speed, L is the distance between the transducers, t21 is travel time at onshore transmission, and t12 is travel time at offshore transmission. The typical components of the measurement system consisted of one modified dual-frequency sounder, two broad-band acoustic transducers, six sound reflectors, and a data acquisition network. The frequency, source level, and transmission interval of transmitted signal was 50 kHz, 1 kW (max), and 2 s, respectively. The typical monitoring area is 30 m wide and 90 m long, and the depth is 1 m. This system simply required the precise position measurement of two transducers and sound reflectors. The sound speed was determined by the direct travel times between the transducers. When the field tests were conducted at Uradome Beach in Tottori Prefecture of Japan in September of 2014, the phases of sound signals were so stable to allow the automatic peak and/or phase tracking. The obtained and reconstructed velocity data were consistent with those by EM current meters.

Coastal observation systems to monitor physical, chemical, and biological parameters

We have developed a free-fall multi-parameter profiler (YODA Profiler) to measure various physical and biological parameters in coastal ocean. We found internal bores create a strong mixing event. Sediment resuspension is associated with the mixing event and also AZFP detected fish school at the front of bore. We have deployed a cable observatory system (Oshima Coastal Environment data Acquisition Network System, OCEANS) in a coastal area to monitor coastal ecosystem continuously. OCEANS can measure various physical, chemical and biological parameters simultaneously, and operates a plankton imaging system (Continuous Plankton Imaging and Classification System, CPICS). Based on acquired images of phytoplankton and zooplankton, we are investigating how planktonic biodiversity is affected by multi-scale physical processes, such as Kuroshio and internal waves. We are developing a technique to predict the biodiversity of plankton from three-dimensional hydrodynamic model using a newly developed plankton ecosys...

Advance hybrid key management architecture for SCADA network security

AbstractThis paper presents and evaluates an advance hybrid key management architecture for supervisory control and data acquisition (SCADA) networks (HSKMA), which supports all three types of communications: unicast, multicast, and broadcast. The HSKMA is based on the elliptic curve cryptography and symmetric cryptography. While the elliptic curve cryptography is used for communication between master station unit (MSU) and sub‐MSUs, the symmetric cryptographic algorithm is used for communication between sub‐MSUs and slave stations that have limited computational resources. Our analysis shows that the HSKMA has the following distinctive advantages: 1) it supports the security requirement such as availability, forward security, and backward security, 2) it supports the required speed in the MODBUS implementation, and 3) it is suitable for the environments that have limited computational resources. Copyright © 2016 John Wiley & Sons, Ltd.

Investigation on Fast Response Performance of Dam Deformation Monitoring System with Wireless Sensor and Virtualizing Technique

Dam deformation monitoring is quite important. Monitoring of dam deformation is important for ensuring people's normal lives and reducing losses. Dam deformation often experiences a relatively long time. The deformation rate is relatively small at the beginning time, and it is large at the later stage. Monitoring at the early stage of dam deformation is of great significance. The wireless sensor network has the characteristics of not being affected by the terrain, convenient arrangement and so on, which can be used to monitor the deformation of the dam. In order to improve the transmission efficiency of the wireless sensor network, data compression is helpful for the energy savings and transmission rate improving. In this paper, a dam monitoring system based on wireless sensor networks is developed. In this paper, a dam monitoring system based on wireless sensor network and GPS multi antenna is developed. In the network monitoring system and data acquisition using wireless sensor module, data transmission using data compression technology and data cache technology. At the end of the monitoring terminal, desktop cloud technology is used to process the data collected and show the real time dam deformation condition. This method can also be used in other object motion or deformation with large range and other monitoring field to improve the monitoring efficiency.

Fuzzy-Neural Petri Net Distributed Control System Using Hybrid Wireless Sensor Network and CAN Fieldbus

The reluctance of industry to allow wireless paths to be incorporated in process control loops has limited the potential applications and benefits of wireless systems. The challenge is to maintain the performance of a control loop, which is degraded by slow data rates and delays in a wireless path. To overcome these challenges, this paper presents an application–level design for a wireless sensor/actuator network (WSAN) based on the “automated architecture”. The resulting WSAN system is used in the developing of a wireless distributed control system (WDCS). The implementation of our wireless system involves the building of a wireless sensor network (WSN) for data acquisition and controller area network (CAN) protocol fieldbus system for plant actuation. The sensor/actuator system is controlled by an intelligent digital control algorithm that involves a controller developed with velocity PID-like Fuzzy Neural Petri Net (FNPN) system. This control system satisfies two important real-time requirements: bumpless transfer and anti-windup, which are needed when manual/auto operating aspect is adopted in the system. The intelligent controller is learned by a learning algorithm based on back-propagation. The concept of petri net is used in the development of FNN to get a correlation between the error at the input of the controller and the number of rules of the fuzzy-neural controller leading to a reduction in the number of active rules. The resultant controller is called robust fuzzy neural petri net (RFNPN) controller which is created as a software model developed with MATLAB. The developed concepts were evaluated through simulations as well validated by real-time experiments that used a plant system with a water bath to satisfy a temperature control. The effect of disturbance is also studied to prove the system's robustness.