-Sami M. Mostafa

SOPK: Second Opportunity Pairwise Key Scheme for Topology Control Protocols

Sensor networks typically consist of a very large number of nodes with no centralized supervision. As a result, sensor networks are highly prone to an enormous number of logical and physical attacks. These attacks vary from eavesdropping on sensitive information, imputing inaccurate information, to the unintentional failure of nodes as in Denial of Service (DoS) attacks. Many approaches have been proposed for assuring the Hop-to-hop encryption using different short keys in each node along the path from source to destination, for example the random key pre-distribution scheme. This random key pre-distribution scheme and its enhanced editions were applied with assumptions of no prior deployment knowledge. The paper proposes a scheme that uses prior deployment knowledge in terms of the energy level carried by each node for modifying the polynomial pool based key pre-distribution scheme proposed in [1]. The paper shows that the node energy level observation can be used to control the selection of polynomial keys held by this node. The proposed scheme shows that it is suitable to be applied on topology control protocols such as the A3 protocol [2]. The proposed scheme reduces the energy consumption and computational overhead through controlling the use of security keys according to specific networks energy threshold that positively reflects on the performance of the whole WSN.

Recognizing Driving Behavior and Road Anomaly using Smartphone Sensors

Roadtrafficaccidentsarecaused1.25milliondeathsperyearworldwide.Toimproveroadsafety andreducingroadaccidents,arecognitionmethodfordrivingeventsisintroducedinthispaper.The proposedmethoddetectedandclassifiedbothdrivingbehaviorsandroadanomaliespatternsbasedon smartphonesensors(accelerometerandgyroscope).k-NearestNeighborandDynamicTimeWarping algorithmswereutilizedformethodevaluation.Experimentswereconductedtoevaluatek-nearest neighboranddynamictimewarpingalgorithmsaccuracyforroadanomaliesanddrivingbehaviors detection,moreover,drivingbehaviorsclassification.Evaluationresultsshowedthatk-nearestneighbor algorithmdetectedroadanomaliesanddrivingbehaviorswithtotalaccuracy98.67%.Dynamictime warpingalgorithmclassified(normalandabnormal)drivingbehaviorswithtotalaccuracy96.75%. KeywoRDS Anomaly Detection, Behavior Classification, Driving Behavior, Road Anomalies, Smartphone Sensors

An Experimental Comparison Between Seven Classification Algorithms for Activity Recognition

The daily activities recognition is one of the most important areas that attract the attention of researchers. Automatic classification of activities of daily living (ADL) can be used to promote healthier lifestyle, though it can be challenging when it comes to intellectual disability personals, the elderly, or children. Thus developing a technique to recognize activities with high quality is critical for such applications. In this work, seven algorithms are developed and evaluated for classification of everyday activities like climbing the stairs, drinking water, getting up from bed, pouring water, sitting down on a chair, standing up from a chair, and walking. Algorithms of concern are K-nearest Neighbor, Artificial Neural Network, and Naive Bayes, Dynamic Time Warping,

Single Trial Authentication with Mental Password Writing

1 recognizer, Support Vector Machine, and a novel classifier (D

Low Intensity Laser Irradiation Influence Proliferation of Mesenchymal Stem Cells: Comparison of Experimental Data to Intelligent Agent-Based Model Predictions

1). We explore different algorithm activities with regard to recognizing everyday activities. We also present a technique based on

Study of the effects of pairwise key pre-distribution scheme on the performance of a topology control protocol

1 and DTW to enhance the recognition accuracy of ADL. Our result show that we can achieve up to 83 % accuracy for seven different activities.

A Pairwise Key Pre-distribution Scheme Based on Prior Deployment Knowledge

This paper presents an authentication system that uses brain waves as a biometric discriminant trait. It utilizes Electroencephalogram EEG signals generated from mental writing of the user-owned password. Independent Component Analysis ICA and baseline correction has been used for preprocessing and noise removal. The effect of two types of features, multivariate autoregressive MVAR model parameters and power spectral density PSD features, have been studied for this activity. Performance results based on single trial analysis have revealed that imagined password writing can reach average Half Total Error Rate HTER of 5i¾ź% for PSD features vs 3i¾ź% obtained with MVAR coefficients. The experiments have shown that mental password writing can be used for increasing the user acceptance for enrollment conditions while maintaining high performance results.

Enhanced “ULTRA GRIDSEC”: Enhancing high Performance Symmetric Key Cryptography Schema using pure Peer To Peer Computational Grid Middleware (HIMAN)

Over the past several decades, evidences have shown that low intensity laser can stimulate a number of biological processes, including stem cell proliferation. In order to fully utilize stem cells in research and medical studies, understanding these processes is essential. However, for gaining this fundamental understanding in a rapid and cost-effective manner, model predictions and computer simulations are required as they may yield useful information and represent powerful supportive tools. This chapter provides some of the experiments employed to measure influence of low intensity laser on proliferation of mesenchymal stem cells which can vary considerably according to many parameters and biological conditions such as laser nature of emission, irradiation time, wavelength, and energy density. These experiments were compared to intelligent agent-based model predictions and detailed information about the model description and comparison results are provided. The model was capable of predicting the data for the scenarios fairly well although a few were somewhat problematic. This study recommends a wave length ranging from 600 to 680 nm, and an energy density ranging from 0.3 to 4.0 J/\( \mathrm cm^{2}\) for enhancing proliferation of mesenchymal stem cells.

Authentication Systems: Principles and Threats

Collecting information from open and possibly hostile environments makes the wireless Sensor Network (WSN) vulnerable to different types of security threats [1]. To provide secure communications for the WSNs, all messages have to be encrypted with a secret key. Message encryption using the public key cryptosystems [2] in WSN is not applicable due to sensors constrained resources. A random key pre-distribution scheme and its enhanced versions to deal with pairwise key establishment [3] are of popular approaches that have higher resilience for nodes compromising. On the other hand, the topology control protocols are special forms of WSNs that add some constraints for controlling the construction of wireless networks. This paper aims to identify whether it is applicable to apply a key pre-distribution technique on a topology control protocol and evaluates its performance.

ROSS: A rapid protein structure alignment algorithm

Still, the security problems remain one of the major barriers somehow preventing the complete utilization of wireless sensor networks (WSN) technology. Securing the communication channel through encrypting messages sent between nodes grow to be a must. Message encryption using the public key cryptosystems [1] in WSN is infeasible due to its constrained resources. A random key pre-distribution scheme [2] is of popular approaches that perfectly securing a WSN and conserving its resources. The random key pre-distribution scheme or its enhanced editions is applied with assumptions of no prior deployment knowledge. The paper proposes a scheme that uses prior deployment knowledge in terms of the energy level carried by each node for modifying the polynomial pool based key pre-distribution scheme proposed in [3]. The paper shows that the node energy level observation can be used to control the creation and the selection of polynomial keys hold by this node. For the purpose of evaluating the proposed scheme its applied on the A3 protocol as one of known topology control protocols [4]. The proposed scheme avoids the unnecessary key assignment and it reduces the number of active nodes per topology construction that positively reflects on the performance of the whole WSN.