The Personal Presence System: a wide-area network resource for the real-time composition of multipoint multimedia communications

Wave overtopping is a dangerous phenomenon that, in a port environment, takes place when waves that are higher than the port's breakwater meet it and water passes over the structure. This event can lead to property damage or physical harm to port workers. It is difficult to detect an overtopping, so this chapter proposes a solution to the overtopping detection problem by describing the design and development of a system that can detect an overtopping event in real-time and in a real environment. To achieve this goal, the proposed overtopping detection system is based on devices that use ultrasonic ranging sensors and communicate using the Sigfox low-power wide-area network, together with a backend that processes the data the devices send, issuing alerts to inform the interested parties that an overtopping took place.

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Event Based Simulator for Parallel Computing over the Wide Area Network for Real Time Visualization

Lecture Notes in Computer Science - Visual Informatics: Bridging Research and Practice ◽

10.1007/978-3-642-05036-7_54 ◽

2009 ◽

pp. 573-584

Author(s):

Elankovan Sundararajan ◽

Aaron Harwood ◽

Ramamohanarao Kotagiri ◽

Anton Satria Prabuwono

Keyword(s):

Parallel Computing ◽

Real Time ◽

Wide Area ◽

Area Network ◽

Wide Area Network ◽

Real Time Visualization ◽

Event Based

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An Autonomous Wireless Device for Real-Time Monitoring of Water Needs

Sensors ◽

10.3390/s20072078 ◽

2020 ◽

Vol 20 (7) ◽

pp. 2078 ◽

Cited By ~ 2

Author(s):

Juan D. Borrero ◽

Alberto Zabalo

Keyword(s):

Low Power ◽

Real Time ◽

Irrigation Management ◽

Agricultural Practices ◽

Wireless Sensor ◽

Wide Area ◽

Area Network ◽

Wide Area Network ◽

Wireless Sensor Node ◽

Real Time Monitoring

The agri-food sector is in constantly renewing, continuously demanding new systems that facilitate farmers´ work. Efficient agricultural practices are essential to increasing farm profitability, and reducing water consumption can be achieved by real-time monitoring of water needs. However, the prices of automatic systems for collecting data from several sources (soil and climate) are expensive and their autonomy is very low. This paper presents a low-consumption solution using the Internet of Things (IoT) based on wireless sensor networks (WSNs) and long-range wide-area network (LoRaWAN) technologies. By means of low-power wide-area network (LPWAN) communication, a farmer can monitor the state of crops in real time thanks to a large number of sensors connected wirelessly and distributed across the farm. The wireless sensor node developed, called BoXmote, exhibits very low power, since it has been optimized both in terms of hardware and software. The result is a higher degree of autonomy than commercial motes. This will allow the farmer to have access to all of the information necessary to achieve an efficient irrigation management of his crops with full autonomy.

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A Semi-Supervised QoS-Aware Classification for Wide Area Networks with Limited Resources

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.h7185.0981119 ◽

2019 ◽

Vol 8 (11) ◽

pp. 970-981

Keyword(s):

Limited Resource ◽

Resource Depletion ◽

Error Rates ◽

Wide Area ◽

Area Network ◽

Traffic Classification ◽

Wide Area Network ◽

Network Resource ◽

Network Administrators

The task of network administrators to identify and determine the type of traffic traversing through the network is very critical to the rapid growth of new traffic each day. As the requirements of networks change over time, the situation of the network not able to meet some requirements is likely to occur. In a wide area network with a limited resource such as the low speed of links, frequent fragmentation of packets leading to extreme packet loss and costs is prominent resulting in the poor quality of service. As a result quantified amount of traffic flows can be classified at a time with limited features lowering the effectiveness of traffic classification. To improve upon the classification in such scenarios, we propose a hybrid semi-supervised clustering that is able to classify packet flows with restricted features and a small amount of packets while maintaining high accuracy in classification. We implement the above scenario in simulation and classify the limited flows obtained with our proposed algorithm. Evaluation results show that our proposed algorithm implemented into a classifier has good accuracy and precision values, with low processing time and error rates. The proposed strategy will enable network administrators during times of network resource depletion or upgrades provide and ensure the best quality of services and identify unwanted or malicious traffic.

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