Faisal Kaleem

A fuzzy extension of VIKOR for target network selection in heterogeneous wireless environments

Abstract In a highly integrated ubiquitous wireless environment, the selection of a network that can fulfill end-users’ service requests while keeping their overall satisfaction at a high level, is vital. The wrong selection can lead to undesirable conditions such as unsatisfied users, weak Quality of Service (QoS), network congestions, dropped and/or blocked calls, and wastage of valuable network resources. The selection of these networks is performed during the handoff process when a Mobile Station (MS) switches its current Point of Attachment (PoA) to a different network due to the degradation or complete loss of signal and/or deterioration of the provided QoS. Traditional schemes perform the handoff necessity estimation and trigger the network selection process based on a single metric such as Received Signal Strength (RSS). These schemes are not efficient enough, as they do not take into consideration the traffic characteristics, user preferences, network conditions and other important system metrics. This paper presents a novel multi-attribute vertical handoff algorithm for heterogeneous wireless networks which achieves seamless mobility while maximizing end-users’ satisfaction. Two modules are designed to estimated the necessity of handoff and to select the target network. These modules utilize parallel Fuzzy Logic Controllers (FLCs) with reduced rule-set in combination with a network ranking algorithm developed based on Fuzzy VIKOR (FVIKOR). Simulation results are provided and compared with a benchmark.

A fuzzy MADM ranking approach for vertical mobility in next generation hybrid networks

The wireless technologies in a heterogeneous wireless network usually differ in terms of, but not limited to, their offered bandwidths, operating frequencies and costs, coverage areas, and latencies. Currently, no single wireless technology claims to provide cost-effective services, which offers high bandwidths and low latencies to all mobile users in a large coverage area. This is where the need for well-organized vertical handoffs (VHOs) between heterogeneous wireless technologies become evident. This paper presents a new VHO algorithm which takes into account a complete set of system attributes to fulfill two tasks. The first task is to perform the VHO Necessity Estimation (VHONE) utilizing several parallel Fuzzy Logic Controllers (FLCs) with reduced rule sets to estimate the VHO necessity. The second task is the selection of the best network as the target for VHO, where a ranking algorithm is developed based on TOPSIS. Priority Weights for the different system attributes are calculated based on a Fuzzy Analytical Hierarchy Process (FAHP). Later, simulations based on four traffic classes (conversational, streaming, background, and interactive) in the presence of three wireless networks (WLAN, WMAN, and WWAN), are carried out using a comprehensive wireless simulation test-bed that contains all the necessary Radio Resource Management (RRM) modules.

A key management-based two-level encryption method for AMI

In the key management-based security scheme of Advanced Metering Infrastructure (AMI), it is assumed that the established third party and the links between smart meter and the third party are fully trusted. But in wired/wireless communication, man-in-the middle can interfere, and the monitors and controls in the system can expose its vulnerability. Because of this, we propose a security scheme based on two independent and partially trusted but simple servers, which includes two-level encryption without increasing packet overhead. One server (master) manages the data encryption between the meter to ES (energy supplier) and the other server manages randomized data transfer. We also propose a simple node-to-node authentication method based on electromagnetic signal strength.

Smart augmented reality glasses in cybersecurity and forensic education

Augmented reality is changing the way its users see the world. Smart augmented-reality glasses, with high resolution Optical Head Mounted display, supplements views of the real-world using video, audio, or graphics projected in front of users eye. The area of Smart Glasses and heads-up display devices is not a new one, however in the last few years, it has seen an extensive growth in various fields including education. Our work takes advantage of a students ability to adapt to new enabling technologies to investigate improvements teaching techniques in STEM areas and enhance the effectiveness and efficiency in teaching the new course content. In this paper, we propose to focus on the application of Smart Augmented-Reality Glasses in cybersecurity education to attract and retain students in STEM. In addition, creative ways to learn cybersecurity education via Smart Glasses will be explored using a Discovery Learning approach. This mode of delivery will allow students to interact with cybersecurity theories in an innovative, interactive and effective way, enhancing their overall live experience and experimental learning. With the help of collected data and in-depth analysis of existing smart glasses, the ongoing work will lay the groundwork for developing augmented reality applications that will enhance the learning experiences of students. Ultimately, research conducted with the glasses and applications may help to identify the unique skillsets of cybersecurity analysts, learning gaps and learning solutions.

Security Breach Possibility with RSS-Based Localization of Smart Meters Incorporating Maximum Likelihood Estimator

Deployment of smart meters has been greatly increased over the recent years. Most of the installed smart meters have been equipped with Advanced Metering Infrastructure (AMI) which enables a bidirectional wireless communication to gather the usage data from gas, electricity and water meters. The insecure wireless channel used by AMI meters jeopardizes the privacy of costumers and brings up cybersecurity issues since it allows hackers to monitor the energy usage data from different houses. To show the penetrability of the system, Received Signal Strength (RSS) - based localization of smart meters incorporating Maximum Likelihood (ML) estimator has been proposed in this paper. By decoding the received signal from a smart meter, one can localize the unoccupied houses or track the people’s daily routines. The effectiveness of the proposed ML location estimator has been examined through MATLAB simulation, under the assumption of a log-normal path loss model and Frequency Shift Keying (FSK) modulation and demodulation. Particle Swarm Optimization (PSO) has been used to find the ML estimation. Finally, the effect of the variance, the number of the sensors and the path loss exponent has been studied on the average Miss Distance Error (MDE).