Rabie A. Ramadan

Agent Based Multipath Routing in wireless sensor networks

Recently, mobile agents have been used to solve many problems in wireless sensor networks. They are usually transferred from a node to another to aggregate the sensed data and simplify the complexity of the routing algorithms. Intelligent agents can reduce the communication cost over a very low bandwidth links among the sensors. In this paper, we propose an Agent Based Multipath Routing (ABMR) algorithm for wireless sensor network. The algorithm considers many of the sensors and the monitored field parameters such as energy, reliability, and number of hops, as well as the data importance. The algorithm builds a reliable multiple paths from the source to the destination. The number of paths is selected based on the importance of the sensed data. Intelligent agents are designed to construct the multipath as well as to send the sensed data to its destination. To show the effectiveness of the proposed algorithm, ABMR is compared to non-agent based algorithm (NABMR) as well as to one of the recent multipath routing algorithm through an extensive set of experiments.

Hybrid Intelligent Intrusion Detection Scheme

This paper introduces a hybrid scheme that combines the advantages of deep belief network and support vector machine. An application of intrusion detection imaging has been chosen and hybridization scheme have been applied to see their ability and accuracy to classify the intrusion into two outcomes: normal or attack, and the attacks fall into four classes; R2L, DoS, U2R, and Probing. First, we utilize deep belief network to reduct the dimensionality of the feature sets. This is followed by a support vector machine to classify the intrusion into five outcome; Normal, R2L, DoS, U2R, and Probing. To evaluate the performance of our approach, we present tests on NSL-KDD dataset and show that the overall accuracy offered by the employed approach is high.

Advanced Machine Learning Technologies and Applications

The recognition of a character begins with analyzing its form and extracting the features that will be exploited for the identification. Primitives can be described as a tool to distinguish an object of one class from another object of another class. It is necessary to define the significant primitives. The size of vector primitives can be large if a large number of primitives are extracted including redundant and irrelevant features. As a result, the performance of the recognition system becomes poor, and as the number of features increases, so does the computing time. Feature selection, therefore, is required to ensure the selection of a subset of features that gives accurate recognition. In our work we propose a feature selection approach based genetic algorithm to improve the discrimination capacity of the Multilayer Perceptron Neural Networks (MLP).

Efficient deployment algorithms for mobile sensor networks

Sensor deployment problem is one of the important problems in Wireless Sensor Networks (WSN) since it represents the first phase that most of the network operations depends on. Sensor deployment strategies can be classified into two classes which are deterministic and autonomous (random) deployment. In the deterministic deployment, the deployment field is assumed accessible as well as the number of sensors is small to be manually deployed in specific locations. On the other hand, with large number of sensors and in inaccessible fields, the random deployment to the sensors turns out to be the solution. However, random deployment requires sensors to be automatically located (move) for coverage and connectivity purposes. In addition, after a period of time, the sensors topology might change due to some sensor hardware failure or deplaned energy. Therefore, redeployment and/or sensors relocation process is essential. Nevertheless, mobility consumed energy as well as sensor load balancing are essential factors to be considered during the initial deployment and relocation processes. This paper proposes two deployment algorithms to manage those situations. Those algorithms achieve sensor energy balancing and small amount of deployment energy consumption. A set of simulation experiments are conducted to compare between the proposed algorithm and the existing work in terms of coverage performance, average moving distance, and message complexity.

Brain Computer Interface : control Signals Review

Abstract Brain Computer Interface (BCI) is defined as a combination of hardware and software that allows brain activities to control external devices or even computers. The research in this field has attracted academia and industry alike. The objective is to help severely disabled people to live their life as regular persons as much as possible. Some of these disabilities are categorized as neurological neuromuscular disorders. A BCI system goes through many phases including preprocessing, feature extraction, signal classifications, and finally control. Large body of research are found at each phase and this might confuse researchers and BCI developers. This article is a review to the state-of-the-art work in the field of BCI. The main focus of this review is on the Brain control signals, their types and classifications. In addition, this survey reviews the current BCI technology in terms of hardware and software where the most used BCI devices are described as well as the most utilized software platforms are explained. Finally, BCI challenges and future directions are stated. Due to the limited space and large body of literature in the field of BCI, another two review articles are planned. One of these articles reviews the up-to-date BCI algorithms and techniques for signal processing, feature extraction, signals classification, and control. Another article will be dedicated to BCI systems and applications. The three articles are written as base and guidelines for researchers and developers pursue the work in the field of BCI.

Optimal and approximate approaches for deployment of heterogeneous sensing devices

A modeling framework for the problem of deploying a set of heterogeneous sensors in a field with time-varying differential surveillance requirements is presented. The problem is formulated as mixed integer mathematical program with the objective to maximize coverage of a given field. Two metaheuristics are used to solve this problem. The first heuristic adopts a genetic algorithm (GA) approach while the second heuristic implements a simulated annealing (SA) algorithm. A set of experiments is used to illustrate the capabilities of the developed models and to compare their performance. The experiments investigate the effect of parameters related to the size of the sensor deployment problem including number of deployed sensors, size of the monitored field, and length of the monitoring horizon. They also examine several endogenous parameters related to the developed GA and SA algorithms.

Basics of Brain Computer Interface

Brain-Computer Interface (BCI) is a fast-growing emergent technology in which researchers aim to build a direct channel between the human brain and the computer. It is a collaboration in which a brain accepts and controls a mechanical device as a natural part of its representation of the body. The BCI can lead to many applications especially for disabled persons. Most of these applications are related to disable persons in which they can help them in living as normal people. Wheelchair control is one of the famous applications in this field. In addition, the BCI research aims to emulate the human brain. This would be beneficial in many fields including the Artificial Intelligence and Computational Intelligence. Throughout this chapter, an introduction to the main concepts behind the BCI is given, the concepts of the brain anatomy is explained, and the BCI different signals are stated. In addition, the used hardware and software for the BCI are elaborated.

Efficient deployment of connected sensing devices using circle packing algorithms

In this paper, we explore different sensor deployment problems and how these problems can be solved optimally using the current packing approaches in terms of small-scale problems. In addition, we consider the deployment of either homogenous or heterogeneous sensing devices. The deployment objectives are to maximize the coverage of the monitored field and use the best of the sensing devices characteristics as well as developing a connected deployment scheme. We propose a novel algorithm named Sequential Packing-based Deployment Algorithm (SPDA) for the deployment of heterogeneous sensors in order to maximize the coverage of the monitored field and connectivity of the deployed sensors. The algorithm is inspired from the packing theories in computational geometry where it benefits from many of the observations properties that are captured from the optimal packing solutions. The algorithm efficiency is examined using different case studies.

Finding the Best Sink Location in WSNs with Reliability Route Analysis

Abstract Wireless Sensor Network (WSN) became one of the emerged networks that are used in many critical applications. One of the challenges of the network is the energy source of its sensors since sensors depends, in most of the cases, on a double AA batteries and they are supposed to live for long time. One of the important methods to save sensors energy is to reduce the messages flow transferred to the sink node in a multi-hop wireless sensor networks. To do so, this paper investigates the best location to the sink node to maximize the reliability of a message delivery before it is being received and processed by a sink. The paper introduces the optimal location solution through utilizing the Mixed Integer Linear Programming (MILP) solution to the problem in small- scale WSNs. Consequently, maximum reliability of a path may lead to the minimum energy consumed for retransmission along the routing path. However, in large-scale networks, the paper introduces the Genetic Algorithm (GA) as one of the heuristics solution. The Fitness function of the GA calculates the negative value of the log of the reliability of a path and the GA tries to find the sink position with the minimum fitness value to minimize the energy spent by each sensor in the routing towards the sink. An extensive set of experiments are introduced and the MILP solution results are compared to GA approach for the GA performance measure. The comparison showed that the GA have found near optimal solution in reasonable time. In addition, GA is utilized in large-scale problems as well.

A fuzzy based hierarchical coordination and control system for a robotic agent team in the robot Hockey competition

This paper presents the system used by the team of the German University in Cairo (GUC) within the FESTO Hockey Challenge league that took place within RoboCup 2009. The goal of the FESTO Hockey Challenge is to have a competition between robotic teams where each team consists of three robots to compete in an Ice Hockey game. All robots are of the same mechanical, sensor and electronic capabilities so that the focus of the competition is to develop novel artificial intelligence techniques for robot control and coordination. The GUC team scored the 2nd place in this competition after losing by penalty shoot outs in the final. The proposed control approach for GUC team employed Hierarchical Fuzzy Logic Controllers (HFLCs) in which the low level behaviours are implemented using FLCs and the coordination between the behaviours is implemented by a high level fuzzy layer. The coordination between the robotic agents team members is implemented by a hierarchical situation based dynamic role allocation mechanism. The paper will describe the employed approaches and will report on the results achieved.