Hamdy K. Elminir

Balancing Energy Consumption in Heterogeneous Wireless Sensor Networks Using Genetic Algorithm

In a heterogeneous Wireless Sensor Network (WSN), factors such as initial energy, data processing capability, etc. greatly influence the network lifespan. Despite the success of various clustering strategies of WSN, the numerous possible sensor clusters make searching for an optimal network structure an open challenge. In this paper, we propose a Genetic Algorithm based method that optimizes heterogeneous sensor node clustering. Compared with five state-of-the-art methods, our proposed method greatly extends the network life, and the average improvement with respect to the second best performance based on the first-node-die and the last-node-die is 33.8% and 13%, respectively. The balanced energy consumption greatly improves the network life and allows the sensor energy to deplete evenly. The computational efficiency of our method is comparable to the others and the overall average time across all experiments is 0.6 seconds with a standard deviation of 0.06.

Secure Routing in Wireless Sensor Networks: A State of the Art

Wireless Sensor Networks (WSNs) have attracted intensive interest from both academia and industry due to their wide application in civil and military scenarios. Recently, there is a great interest related to routing process in WSNs. Security aspects in routing protocols have not been given enough attention, since most of the routing protocols in WSNs have not been designed with security requirements in mind. This paper surveys the current challenges of WSN. It will focus on the attempts of building an Artificial Intelligence (AI) based routing protocols in WSNs. This paper also identifies the WSN security trends and threats. The paper also addresses some examples of how to use artificial intelligence in the routing algorithms and shows the advantages and the drawbacks of each. A proposed framework also has been introduced. Finally, the future directions of WSNs have been declared.

SignsWorld Facial Expression Recognition System (FERS)

Live facial expression recognition is an effective and essential research area in human computer interaction (HCI), and the automatic sign language recognition (ASLR) fields. This paper presents a fully automatic facial expression and direction of sight recognition system, that we called SignsWorld Facial Expression Recognition System (FERS). The SignsWorld FERS is divided into three main components: Face detection that is robust to occlusion, key facial features points extraction and facial expression with direction of sight recognition. We present a powerful multi-detector technique to localize the key facial feature points so that contours of the facial components such as the eyes, nostrils, chin, and mouth are sampled. Based on the extracted 66 facial features points, 20 geometric formulas (GFs), 15 ratios (Rs) are calculated, and the classifier based on rule-based reasoning approach are then formed for both of the gaze direction and the facial expression (Normal, Smiling, Sadness or Surprising). Sign...

SECURITY EVALUATION AND ENCRYPTION EFFICIENCY ANALYSIS OF RC4 STREAM CIPHER FOR CONVERGED NETWORK APPLICATIONS

The trend toward converged networks where voice,IP,video and wireless are combined over the same network infrastructure offers significant - and highly attractive - benefits.But this union introduces new vulnerabilities and new opportunities to break into the network and so we need converged security.In this paper we investigate the possibility of using RC4 as a stream key generator for converged network applications.The RC4 was statistically tested against wide range of tests for inspecting the validity of the encryption,and it’s mathematically measured for encryption efficiency.All the consequent proves that RC4with suitable key length is highly secure and efficient enough to be used within the converged network.

Unsupervised Object Annotation through Context Analysis

The goal of object level annotation is to locate and identify instances of an object category within an image. Nowadays, Most of the current object level annotation systems annotate the object according to the visual appearance in the image. Recognizing an object in an image based visual appearance yield ambiguity in object detection due to appearance confusion for example “sky” object may be annotated as “water” according to similarity in visual appearance. As a result, these systems don’t recognize the objects in an image accurately due to the lack of scene context. In the task of visual object recognition, scene context can play important role in resolving the ambiguities in object detection. In order to solve the ambiguity problem, this paper presents a new technique for a context based object level annotation that considers both the semantic context and spatial context analysis to reduce ambiguous in object annotation. General Terms Image Annotation and Retrieval