Hatem Abdelkader

An energy-efficient coverage hole detection technique for randomly deployed wireless senor networks

Achieving full sensing coverage is a critical issue in wireless sensor networks applications such as military surveillance and environmental monitoring. In such applications, a large number of sensors are randomly deployed with helicopter passing over the area of interest. This method of deployment is the main reason for the emergence of holes in the deployed network. These holes have its effect on the performance of the applications. Therefore, it should be detected well regardless of its size or location. Many techniques were proposed to solve the problem of coverage hole detection. The performance of all these techniques is affected by a main parameter which is the average node degree measured by the nodes in the network. Researchers always tend to reduce this value. Reducing the average node degree helps in decreasing the communication cost of the detection protocol. In this paper, we propose a distributed energy-efficient coverage hole detection technique with average node degree equal 3 in most cases. To the best of our knowledge, this is the least value among all other previously proposed techniques.

Securing JPEG architecture based on enhanced chaotic hill cipher algorithm

In the last two decades, Chaos theory has received a great deal of attention from the cryptographic community. This paper presents two ideas. First idea is using chaotic functions to overcome the weaknesses of the classical Hill cipher. The second idea is proposing a new encoder-decoder architecture, called ChaoEncoDeco, for securing JPEG images. An extra stage of encryption is embedded within the traditional JPEG codec to improve security level of such system. This security stage uses one of the chaotic functions called Logistic Map. This map is used to enhance the Hill cipher and achieve more secure encryption key. The properties of both chaotic system and of the Hill cipher encryption key are all utilized to obtain ultimate secure systems. The proposed encryption algorithm is crypto-analyzed and compared to the standard Hill cipher algorithm. Complete evaluation for the proposed architecture is also performed which indicates the effectiveness of the proposed system.

Constructing automatic domain-specific sentiment lexicon using KNN search via terms discrimination vectors

ABSTRACT Web textual data content is a viable source for decision-makers’ knowledge, so are text analytic applications. Sentiment analysis (SA) is one of text mining fields, in which text is analyzed to recognize text writer implied opinion. In this paper, a new approach had been presented for automatic Arabic language sentiment lexicon constructing. Popular KNN search algorithm is utilized for this objective. Cosine distance between seeds terms and corpus terms is employed in KNN search query. Generated lexicon terms are launched from sentiment seeds and seeds terms are augmented via Arabic-specific NLP-based algorithm, which is helped to enhance seeds terms selection process. Term discrimination vector (TDV) is the main part of KNN query inputs TDV components are computed for each corpus term and it is constituted by four term weight techniques. According to the experimental results, TDV accomplished better results than TF-IDF traditional method with lower computation cost. Also, constructed lexicons outperformed premade lexicons accuracy results.

Dynamic window size of TCP for Long Term Evolution (LTE)

Long Term Evolution (LTE) is a 4Generation developed by 3 generations Partnership Project (3GPP), all-IP wireless protocol that evolved from Global System for Mobile Communications (GSM). Transmission Control Protocol (TCP) is determining the performance of the protocol in IP Network. TCP variants have been proposed in LTE network. It can be used to improve quality of service (QOS) parameters such as throughput, average delay. In this paper, an enhanced approach is proposed to improve performance TCP of LTE network. The proposed approach depends on controlling the receive buffer size in eNode B. This will be performed through dividing the memory size of eNode B between active users and adapt congestion window size at the TCP users dynamically. Simulation results demonstrate the effective of the proposed approach to enhance the system performance in terms of network throughput, low delay and packet loses.

Global distributed clustering technique for randomly deployed wireless sensor networks

Wireless sensor network applications are composed of a vast number of inexpensive battery-powered sensors. One of its primary applications is environmental monitoring for physical phenomena in rigid areas such as forests and volcanoes. In such applications, a large number of sensors are randomly scattered by aircraft over the area of monitoring. These applications mainly depend on clustering to arrange nodes into groups to facilitate their communication. Previously proposed clustering techniques are classified into two types, which are distributed or centralized techniques. Each of these types has advantages as well as some flaws. In this paper, we propose a globally distributed clustering technique. This technique depends on some global information about the network to allow each node to decide its role in the produced clusters locally. This information is assumed to be known by default by the BS for any communication or topological control activities. Simulation results show that the proposed technique achieves less power consumption and therefore longer network lifetime when compared with other clustering techniques.

An enhanced feature extraction technique for improving sentiment analysis in Arabic language

Sentiment analysis (SA) is a modern text mining disciplinary that gained notable position due its various application in social networks (SN) and many internet domains. Since, it is used for discovering audience directions, and impressions about products or any subjects discussed in the internet via social media. Personal opinions availability in SN gave SA a significant attention to discussions makers on modern corporation. In this paper, we uses machine learning techniques via many features and same corpus states in Arabic language, comparing their results, and illustrating the significance of terms merging and pruning in various figures, which helps in the field of SA performance increasing purposes.

Distributed Topological Extraction Protocol for Low-Density Wireless Sensor Network

Wireless sensor networks consist of a varying number of randomly deployed tiny sensors. One of its main applications is monitoring environmental phenomenons in remote and rugged places. In such applications, sensors are distributed with a helicopter passing over the area of study. This method of distribution causes different density subregions inside the network and unknown topology structure. Information is needed to be shared between sensors to enable them to detect and extract topological features about their random distribution. In this paper, we propose a technique which helps nodes to construct boundary cycles around each subregion of specified density-level. This cycles helps in extracting and describing the layout or the topology of the deployed network. Evaluation of our proposed technique shows that it uses less average node degree which equals 3 and achieves about 50 % decrease in energy consumption than Heuristic Boundary Cycles Finding Technique.

Fast Dimension-based Partitioning and Merging clustering algorithm

This research introduces extremely fast and scalable clustering algorithm.The proposed algorithm detects automatically clusters number.Furthermore, this algorithm uses three insensitive tuning parameters. Clustering multi-dense large scale high dimensional numeric datasets is a challenging task duo to high time complexity of most clustering algorithms. Nowadays, data collection tools produce a large amount of data. So, fast algorithms are vital requirement for clustering such data. In this paper, a fast clustering algorithm, called Dimension-based Partitioning and Merging (DPM), is proposed. In DPM, first, data is partitioned into small dense volumes during the successive processing of dataset dimensions. Then, noise is filtered out using dimensional densities of the generated partitions. Finally, merging process is invoked to construct clusters based on partition boundary data samples. DPM algorithm automatically detects the number of data clusters based on three insensitive tuning parameters which decrease the burden of its usage. Performance evaluation of the proposed algorithm using different datasets shows its fastness and accuracy compared to other clustering competitors.

CDRT: An efficient clustering algorithm for distributed real-time database sites

Recently, the demand for real-time database is increasing. Most real-time systems are inherently distributed in nature. They need data to be obtained and updated in a timely fashion. Sometimes required data are at a particular location is not available, and needed to be obtained from remote site. This may take long time that make the temporal data invalid resulting in large number of tardy transactions with their fetal effect. Clustering the database sites nodes can help distributed real-time database systems to face the challenges meeting their time requirements. Reducing the large number of network sites into many clusters with smaller number of sites will effectively decrease the response time, resulting in better meeting of time constraints. In this work, we introduce a novel clustering algorithm for distributed real-time database that depend on both the communication time cost and the timing properties of data. The results showed lower communication time, higher database performance and better meeting of timing requirements.