Using synchronized lightweight state observers to minimise wireless sensor resource utilisation

A major trend in the evolution of the Web is the rapidly growing numbers of web-enabled sensors which provide a rich ability to monitor and control our physical environment. The devices are often cheap, lightweight, rapidly deployed and densely interconnected. The current dominant models of Web-based data monitoring are not well-adapted to the operational needs of these devices, particularly in terms of resource utilization. In this paper we describe an approach to the optimization of the resources utilized by these devices based on the use of synchronized state-observers. By embedding state observers with a minimized footprint into both the sensors and the monitoring Web client, we show that it is possible to minimize the utilization of limited sensor resources such as power and bandwidth, and hence to improve the performance and potential applications of these devices

Fault management preventive maintenance approach in mobile networks using sequential pattern mining

Mobile networks' fault management can take advantage of Machine Learning (ML) algorithms making its maintenance more proactive and preventive. Currently, Network Operations Centers (NOCs) still operate in reactive mode, where the troubleshoot is only performed after the problem identification. The network evolution to a preventive maintenance enables the problem prevention or quick resolution, leading to a greater network and services availability, a better operational efficiency and, above all, ensures customer satisfaction. In this paper, different algorithms for Sequential Pattern Mining (SPM) and Association Rule Learning (ARL) are explored, to identify alarm patterns in a live Long Term Evolution (LTE) network, using Fault Management (FM) data. A comparative performance analysis between all the algorithms was carried out, having observed, in the best case scenario, a decrease of 3.31% in the total number of alarms and 70.45% in the number of alarms of a certain type. There was also a considerable reduction in the number of alarms per network node in a considered area, having identified 39 nodes that no longer had any unresolved alarm. These results demonstrate that the recognition of sequential alarm patterns allows taking the first steps in the direction of preventive maintenance in mobile networks.info:eu-repo/semantics/publishedVersio

Isolated systems with wind power. Main report

The overall objective of this research project is to study the development of methods and guidelines rather than "universal solutions" for the use of wind energy in isolated communities. The main specific objective of the project is to develop and present amore unified and generally applicable approach for assessing the technical and economical feasibility of isolated power supply systems with wind energy. As a part of the project the following tasks were carried out: Review of literature, fieldmeasurements in Egypt, development of an inventory of small isolated systems, overview of end-user demands, analysis of findings and development of proposed guidelines. The project is reported in one main report and four topical reports, all of themissued as Risø reports. This is the Main Report Risø-R-1256, summing up the activities and findings of the project and outlining an Implementation Strategy for Isolated Systems with Wind Power, applicable for international organisations such as donoragencies and development banks

Modulation-mode assignment in SVD-assisted multiuser MIMO-OFDM systems

In order to comply with the demand on increasing available data rates in particular in wireless technologies, systems with multiple transmit and receive antennas, also called MIMO (multiple-input multiple-output) systems, have become indispensable for future generations of wireless systems. Due to the strongly increasing demand in high-data rate transmission systems, frequency non-selective MIMO links have reached a state of maturity and frequency selective MIMO links are in the focus of interest. In this field, the combination of MIMO transmission and OFDM (orthogonal frequency division multiplexing) can be considered as an essential part of fulfilling the requirements of future generations of wireless systems. However, single-user scenarios have reached a state of maturity. By contrast multiple users’ scenarios require substantial further research, where in comparison to ZF (zero-forcing) multiuser transmission techniques, the individual user’s channel characteristics are taken into consideration in this contribution. The performed joint optimization of the number of activated MIMO layers and the number of transmitted bits per subcarrier along with the appropriate allocation of the transmit power shows that not necessarily all user-specific MIMO layers per subcarrier have to be activated in order to minimize the overall BER under the constraint of a given fixed data throughput

Automatic Fire Detection: A Survey from Wireless Sensor Network Perspective

Automatic fire detection is important for early detection and promptly extinguishing fire. There are ample studies investigating the best sensor combinations and appropriate techniques for early fire detection. In the previous studies fire detection has either been considered as an application of a certain field (e.g., event detection for wireless sensor networks) or the main concern for which techniques have been specifically designed (e.g., fire detection using remote sensing techniques). These different approaches stem from different backgrounds of researchers dealing with fire, such as computer science, geography and earth observation, and fire safety. In this report we survey previous studies from three perspectives: (1) fire detection techniques for residential areas, (2) fire detection techniques for forests, and (3) contributions of sensor networks to early fire detection

Everything counts in small amounts

This paper describes an encoding tool which utilises the "data is code" principle of symbolic expressions available in Lisp-like languages to allow the scripting of tightly packed, cross-platform network protocols. This dynamic approach provides specific flexibility when working on embedded systems as it reduces the amount of cross compilation and deploy cycles that occur following more traditional development approaches. In addition, the separation of how the data is encoded from the compiled application facilitates a concept known as extensibility of the network protocol without requiring special handling

Use of AI Techniques for Residential Fire Detection in Wireless Sensor Networks

Early residential fire detection is important for prompt extinguishing and reducing damages and life losses. To detect fire, one or a combination of sensors and a detection algorithm are needed. The sensors might be part of a wireless sensor network (WSN) or work independently. The previous research in the area of fire detection using WSN has paid little or no attention to investigate the optimal set of sensors as well as use of learning mechanisms and Artificial Intelligence (AI) techniques. They have only made some assumptions on what might be considered as appropriate sensor or an arbitrary AI technique has been used. By closing the gap between traditional fire detection techniques and modern wireless sensor network capabilities, in this paper we present a guideline on choosing the most optimal sensor combinations for accurate residential fire detection. Additionally, applicability of a feed forward neural network (FFNN) and Naïve Bayes Classifier is investigated and results in terms of detection rate and computational complexity are analyzed