Sensor networks security based on sensitive robots agents. A conceptual model

Multi-agent systems are currently applied to solve complex problems. The security of networks is an eloquent example of a complex and difficult problem. A new model-concept Hybrid Sensitive Robot Metaheuristic for Intrusion Detection is introduced in the current paper. The proposed technique could be used with machine learning based intrusion detection techniques. The new model uses the reaction of virtual sensitive robots to different stigmergic variables in order to keep the tracks of the intruders when securing a sensor network.Comment: 5 page

Hydroelectric power plant management relying on neural networks and expert system integration

The use of Neural Networks (NN) is a novel approach that can help in taking decisions when integrated in a more general system, in particular with expert systems. In this paper, an architecture for the management of hydroelectric power plants is introduced. This relies on monitoring a large number of signals, representing the technical parameters of the real plant. The general architecture is composed of an Expert System and two NN modules: Acoustic Prediction (NNAP) and Predictive Maintenance (NNPM). The NNAP is based on Kohonen Learning Vector Quantization (LVQ) Networks in order to distinguish the sounds emitted by electricity-generating machine groups. The NNPM uses an ART-MAP to identify different situations from the plant state variables, in order to prevent future malfunctions. In addition, a special process to generate a complete training set has been designed for the ART-MAP module. This process has been developed to deal with the absence of data about abnormal plant situations, and is based on neural nets trained with the backpropagation algorithm.Publicad

Condition Assessment Models for Sewer Pipelines

Underground pipeline system is a complex infrastructure system that has significant impact on social, environmental and economic aspects. Sewer pipeline networks are considered to be an extremely expensive asset. This study aims to develop condition assessment models for sewer pipeline networks. Seventeen factors affecting the condition of sewer network were considered for gravity pipelines in addition to the operating pressure for pressurized pipelines. Two different methodologies were adopted for models’ development. The first method by using an integrated Fuzzy Analytic Network Process (FANP) and Monte-Carlo simulation and the second method by using FANP, fuzzy set theory (FST) and Evidential Reasoning (ER). The models’ output is the assessed pipeline condition. In order to collect the necessary data for developing the models, questionnaires were distributed among experts in sewer pipelines in the state of Qatar. In addition, actual data for an existing sewage network in the state of Qatar was used to validate the models’ outputs. The “Ground Disturbance” factor was found to be the most influential factor followed by the “Location” factor with a weight of 10.6% and 9.3% for pipelines under gravity and 8.8% and 8.6% for pipelines under pressure, respectively. On the other hand, the least affecting factor was the “Length” followed by “Diameter” with weights of 2.2% and 2.5% for pipelines under gravity and 2.5% and 2.6% for pipelines under pressure. The developed models were able to satisfactorily assess the conditions of deteriorating sewer pipelines with an average validity of approximately 85% for the first approach and 86% for the second approach. The developed models are expected to be a useful tool for decision makers to properly plan for their inspections and provide effective rehabilitation of sewer networks.1)- NPRP grant # (NPRP6-357-2-150) from the QatarNational Research Fund (Member of Qatar Foundation) 2)-Tarek Zayed, Professor of Civil Engineeringat Concordia University for his support in the analysis part, the Public Works 3)-Authority of Qatar (ASHGAL) for their support in the data collection

Report on the Workshop held at the University of Leeds on 6 November organised on behalf of the Transport Sub-Committee of the Environment Committee of the Science and Engineering Research Council

The meeting was the second in a series of three; called to review the prospects for future development of the Special Programme in the Application of Information Technology to Transport set up by the Transport Sub-committee of the Science and Engineering Research Council (SERC). The other seminars were: at University College London on Traffic Engineering Applications; held in July 1986; and at the University of Newcastle up Tyne, on Public Transport Information Systems, held in December 1986. The objectives of the workshop were to (i) review current perceptions of and future prospects for the role of expert systems in transport; and (ii) provide such feed-back to the SERC Transport Sub-committee as would assist it in forming a view about future work in this area. (Continues..

Report on the Workshop held at the University of Leeds on 6 November organised on behalf of the Transport Sub-Committee of the Environment Committee of the Science and Engineering Research Council

The meeting was the second in a series of three; called to review the prospects for future development of the Special Programme in the Application of Information Technology to Transport set up by the Transport Sub-committee of the Science and Engineering Research Council (SERC). The other seminars were: at University College London on Traffic Engineering Applications; held in July 1986; and at the University of Newcastle up Tyne, on Public Transport Information Systems, held in December 1986. The objectives of the workshop were to (i) review current perceptions of and future prospects for the role of expert systems in transport; and (ii) provide such feed-back to the SERC Transport Sub-committee as would assist it in forming a view about future work in this area. (Continues..

Security Enhancement in Surveillance Cloud Using Machine Learning Techniques

Most industries are now switching from traditional modes to cloud environments and cloud-based services. It is essential to create a secure environment for the cloud space in order to provide consumers with a safe and protected environment for cloud-based transactions. Here, we discuss the suggested approaches for creating a reliable and safe environment for a surveillance cloud. When assessing the security of vital locations, surveillance data is crucial. We are implementing machine learning methods to improve cloud security to more precisely classify image pixels, we make use of Support Vector Machines (SVM) and Fuzzy C-means Clustering (FCM). We also extend the conventional two-tiered design by adding a third level, the CloudSec module, to lower the risk of potential disclosure of surveillance data.In our work we  evaluates how well our proposed model (FCM-SVM) performed against contemporary models like ANN, KNN, SVD, and Naive Bayes. Comparing our model to other cutting-edge models, we found that it performed better, with an average accuracy of 94.4%

Bio-inspired multi-agent systems for reconfigurable manufacturing systems

The current market’s demand for customization and responsiveness is a major challenge for producing intelligent, adaptive manufacturing systems. The Multi-Agent System (MAS) paradigm offers an alternative way to design this kind of system based on decentralized control using distributed, autonomous agents, thus replacing the traditional centralized control approach. The MAS solutions provide modularity, flexibility and robustness, thus addressing the responsiveness property, but usually do not consider true adaptation and re-configuration. Understanding how, in nature, complex things are performed in a simple and effective way allows us to mimic nature’s insights and develop powerful adaptive systems that able to evolve, thus dealing with the current challenges imposed on manufactur- ing systems. The paper provides an overview of some of the principles found in nature and biology and analyses the effectiveness of bio-inspired methods, which are used to enhance multi-agent systems to solve complex engineering problems, especially in the manufacturing field. An industrial automation case study is used to illustrate a bio-inspired method based on potential fields to dynamically route pallets