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Abstract-Routing in cognitive radio networks (CRNs) necessitates a cross-layering approach. However, according to [1], CRN routing protocols proposed in literature are partially cross-layer, because the information flow is only from physical layer to network layer, e.g., about channels availabilities. In this work, we introduce a cross-layer routing protocol (CLRP), which considers both the channels that are known to be available at each node, as well as other channels that may be available. The availabilities of the latter channels are considered using a stochastic approach. CLRP computes an end to end path, and feeds the physical layer with information about which channels to sense and which nodes should perform the sensing, such that the expected route quality is enhanced. Simulation results show that CLRP outperforms other cross-layer routing protocols in terms of throughput and stability of the path being setup, and increases the probability of finding an end-to-end path.

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Networks have become an integral part of today's world. The ease of deployment, low-cost and high data rates have contributed significantly to their popularity. There are many protocols that are tailored to ease the process of establishing these networks. Nevertheless, security-wise precautions were not taken in some of them. In this paper, we expose some of the vulnerability that exists in a commonly and widely used network protocol, the Address Resolution Protocol (ARP) protocol. Effectively, we will implement a user friendly and an easy-to-use tool that exploits the weaknesses of this protocol to deceive a victim's machine and a router through creating a sort of Man-in-the-Middle (MITM) attack. In MITM, all of the data going out or to the victim machine will pass first through the attacker's machine. This enables the attacker to inspect victim's data packets, extract valuable data (like passwords) that belong to the victim and manipulate these data packets. We suggest and implement a defense mechanism and tool that counters this attack, warns the user, and exposes some information about the attacker to isolate him. GNU/Linux is chosen as an operating system to implement both the attack and the defense tools. The results show the success of the defense mechanism in detecting the ARP related attacks in a very simple and efficient way.

Over the previous decades, a need has emerged to empower human‐machine communication systems, which are essential to not only perform actions, but also obtain information especially in education applications. Moreover, any communication system has to introduce an efficient and easy way for interaction with a minimum possible error rate. The keyboard, mouse, trackball, touch‐screen, and joystick are all examples of tools which were built to provide mechanical human‐to‐machine interaction. However, a system with the ability to use oral speech, which is the natural form of communication between humans instead of mechanical communication systems, can be more practical for normal students and even a necessity for arm‐disabled students who cannot use their arms to handle traditional education tools like pens and notebooks. In this paper, we present a speech recognition system that allows arm‐disabled students to control computers by voice as a helping tool in the educational process. When a student speaks through a microphone, the speech is divided into isolated words which are compared with a predefined database of huge number of spoken words to find a match. After that, each recognized word is translated into its related tasks which will be performed by the computer like opening a teaching application or renaming a file. The speech recognition process discussed in this paper involves two separate approaches; the first approach is based on double thresholds voice activity detection and improved Mel‐frequency cepstral coefficients (MFCC), while the second approach is based on discrete wavelet transform along with modified MFCC algorithm. Utilizing the best values for all parameters in just mentioned techniques, our proposed system achieved a recognition rate of 98.7% using the first approach, and 98.86% using the second approach of which is better in ratio than the first one but slower in processing which is a critical point for a real time system. Both proposed approaches were compared with other relevant approaches and their recognition rates were noticeably higher.

Abstract-Two objectives of sensing in cognitive radio (CR) are to detect the primary user (PU) accurately and quickly, which are contradicting objectives. Therefore, many papers try to optimize this tradeoff and find the minimum sensing time which protects the PU. The trends are classified in enhancing false alarm probability ( ) and detection probability ( ), optimizing intersensing time, in-band sensing (monitoring) time optimization, and out-of-band sensing (search) time optimization. The PU model used in most of these work was a simple two states model (busy/idle renewal process). In this work, we developed a model for the PU in its idle state. The model enables the CR node to benefit from its previous measurements. It assumes that there are multi-idle states, each with specific length and known probability of staying in it. We used this model to find the best sensing time, energy detection threshold, and false alarm probability of the channel being sensed in monitoring. Also, we developed an outof-band optimization formulation. The formulation finds the best number of channels to sense, the threshold of each channel, the sensing time of each channel, and of each channel such that the PU is protected, the sensing time is minimized, and the CR will find an available channel with very high probability.