Qatar Foundation Annual Research Conference Proceedings Volume 2014 Issue 1

With data proliferation at an unprecedented rate, data accessibility while in motion has increased the demand of VANETs of late. VANETs use moving cars as nodes to create a mobile network. VANETs are designed with the goals of enhancing driver safety and providing passenger comfort. Providing security to VANETs is important in terms of providing user anonymity, authentication, integrity and privacy of data. Attacks in VANETs include the Sybil attack, DDoS attack, misbehaving and faulty nodes, sinkhole attack, spoofing, traffic analysis attack, position attack and illusion attack. Illusion attack is a recent threat in which the attacker generates fraud traffic messages to mislead the passengers thereby changing the passenger's driving behaviour. Thus, the attacker achieves his goal by moving the target vehicle along a traffic free route and creating traffic jams in areas where he wishes. Illusion attack is devised mainly by thieves and terrorists who require a clean get away path. The existing method used to prevent Illusion attack is the Plausibility Validation Network (PVN). In PVN the message validation is based on a set of rules depending on the message type. This is an overhead as the rule set for all possible message types have to be stored and updated in the database. Hence, an efficient mechanism is required to prevent illusion attacks. In this paper, our proposed system: TRUC verifies the message by a Message Content Validation (MCV) Algorithm thus ensuring the safety of the car drivers and passengers. The possibilities of attacker creating an Illusion attack is explored in all dimensions and the security goals are analyzed by our proposed design, TRUC

Background & Objectives Qatar's IT infrastructure is rapidly growing to encompass the evolution of businesses and economical growth the country is increasingly witnessing throughout its industries. It is now evident that the country's e-government requirements and associated data management systems are becoming large in number, highly dynamic in nature, and exceptionally attractive for cybercrime activities. Protecting the sensitive data e-government portals are relying on for daily activities is not a trivial task. The techniques used to perform cybercrimes are becoming sophisticated relatively with the firewalls protecting them. Reaching high-level of data protection, in both wired and wireless networks, in order to face recent cybercrime approaches is a challenge that is continuously proven hard to achieve. In a common IT infrastructure, the deployed network devices contain a number of event logs that reside locally within its memory. These logs are in large numbers, and therefore, analyzing them is a time consuming task for network administrators. In addition, a single network event often generates a redundancy of similar event logs that belong to the same class within short time intervals. This makes it difficult to manage them during forensics investigation. In most cybercrime cases, a single alert log does not contain sufficient information about malicious actions background and invisible network attackers. The information for a particular malicious action or attacker is often distributed among multiple alert logs and among multiple network devices. Forensic investigators mission is to reconstruct incident scenarios is now very complex considering the number as well as the quality of these event logs. Methods My research will focus on involving mathematics and algorithm science for each proposed sub models in the alerts correlation model. After collecting alert logs from network sensors; then it will be stored in the alert logs warehouse. The stored alert log contains redundancy data and irrelevant information. The alert correlation model used to filter out all redundancy data and irrelevant information from the alert logs. This model contains two stages; format standardization and redundancy management. The format standardization process aims unified different event logs format into one format, while the redundancy management process aims to reduce the duplication of the single event. Furthermore, this research will try to utilized criminology science to enhance security level of the proposed model and forensics experiments tools to validate the proposal approach. Results In response to attacks and potential of attacks against network infrastructure and assets, my research focuses on how to build an organized legislative e-government environment. The idea of this approach is to forensically utilize the current network security output by collect, analysis and present evidence of network attack in an efficient manner. After data mining process we can utilize our preprocessing results for e-government awareness purpose. Conclusions This research proposed Qatar e-government alerts correlation model. The proposed model used to process and normalize the captured network event logs. The main point of designing the model is to find a way to forensically visualize the evidence and attack scenario in e-government infrastructure.

Motivated by freeing the user from specialized devices and leveraging natural and contextually relevant human movements, gesture recognition systems are becoming popular as a fundamental approach for providing HCI alternatives. Indeed, there is a rising trend in the adoption of gesture recognition systems into various consumer electronics and mobile devices. These systems, along with research enhancing them by exploiting the wide range of sensors available on such devices, generally adopt various techniques for recognizing gestures including computer vision, inertial sensors, ultra-sonic, and infrared. While promising, these techniques experience various limitations such as being tailored for specific applications, sensitivity to lighting, high installation and instrumentation overhead, requiring holding the mobile device, and/or requiring additional sensors to be worn or installed. We present WiGest, a ubiquitous WiFi-based hand gesture recognition system for controlling applications running on off-the-shelf WiFi-equipped devices. WiGest does not require additional sensors, is resilient to changes within the environment, and can operate in non-line-of-sight scenarios. The basic idea is to leverage the effect of the in-air hand motion on the wireless signal strength received by the device from an access point to recognize the performed gesture. As shown in Figure 1, WiGest parses combinations of signal primitives along with other parameters, such as the speed and magnitude of each primitive, to detect various gestures, which can then be mapped to distinguishable application actions. There are several challenges we address WiGest including handling noisy RSSI values due to multipath interference and other electromagnetic noise in the wireless medium; handling gesture variations and their attributes for different humans or the same human at different times; handling interference due to the motion of other people within proximity of the user's device; and finally being energy-efficient to suit mobile devices. To address these challenges, WiGest leverages different signal processing techniques that can preserve signal details while filtering out the noise and variations in the signal. We implement WiGest on off-the-shelf laptops and evaluate frequencies on the RSSI, creating a signal composed of three primitives: rising edge, falling edge, and pause. We evaluate its performance with different users in an apartment and engineering building settings. Various realistic scenarios are tested covering more than 1000 primitive actions and gestures each in the presence of interfering users in the same room as well as other people moving in the same floor during their daily life. Our results show that WiGest can detect the basic primitives with an accuracy of 90% using a single AP for distances reaching 26 ft including through-the-wall non-line-of-sight scenarios. This increases to 96% using three overheard APs, a typical case for many WiFi deployments. When evaluating the system using a multimedia player application case study, we achieve a classification accuracy of 96%.

Cooperative communication is a promising technology that has attracted significant attention recently thanks to its ability to achieve spatial diversity in wireless networks with only single-antenna nodes. The different nodes of a cooperative system can share their resources so that a virtual Multiple Input Multiple Output (MIMO) system is created which leads to spatial diversity gains. To exploit this diversity, a variety of cooperative protocols have been proposed in the literature under different design criteria and channel information availability assumptions. Among these protocols, two of the most-widely used are the amplify-and-forward (AF) and decode-and-forward (DF) protocols. However, in large-scale relay networks, the relay selection process becomes highly complex. In fact, in many applications such as device-to-device (D2D) communication networks and wireless sensor networks, a large number of cooperating nodes are used, which leads to a dramatic increase in the complexity of the relay selection process. To solve this problem, the sparsity of the relay selection vector has been exploited to reduce the multiple relay selection complexity for large AF cooperative networks while also improving the bit error rate performance. In this work, we extend the study from AF to large-scale decode-and-forward (DF) relay networks. Based on exploiting the sparsity of the relay selection vector, we propose and compare two different techniques (referred to as T1 and T2) that aim to improve the performance of multiple relay selection in large-scale decode-and-forward relay networks. In fact, when only few relays are selected from a large number of relays, the relay selection vector becomes sparse. Hence, utilizing recent advances in sparse signal recovery theory, we propose to use different signal recovery algorithms such as the Orthogonal Matching Pursuit (OMP) to solve the relay selection problem. Our theoretical and simulated results demonstrate that our two proposed sparsity-aware relay selection techniques are able to improve the outage performance and reduce the computation complexity at the same time compared with conventional exhaustive search (ES) technique. In fact, compared to ES technique, T1 reduces the selection complexity by O(K^2 N) (where N is the number of relays and K is the number of selected relays) while outperforming it in terms of outage probability irrespective of the relays' positions. Technique T2 provides higher outage probability compared to T1 but reduces the complexity making a compromise between complexity and outage performance. The best selection threshold for T2 is also theoretically calculated and validated by simulations which enabled T2 to also improve the outage probability compared with ES techniques. Acknowledgment This publication was made possible by NPRP grant #6-070-2-024 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors.

Presence of toxic gases and accidental explosions in gas industries have turned the researcher to innovate an electronic nose system which can indicate the nature and the parameters of the gas passing through different vessels. Therefore, in this research we propose a low power Radio Frequency Identification (RFID) based gas sensor tag which can monitor the parameters and indicate the type of gas. The research work is divided in to three main parts. The first two parts cover the design and analysis of low power multi-sensors and processing unit, while the last part focuses on a passive RFID module which can provide communication between the sensor and the processing unit, as shown in Fig. 1. In passive RFID applications, power consumption is one of the most prominent parameter because most of the power is harvested from the coming RF signal. Therefore a ring-oscillator based low power temperature sensor is designed to measure the gas thermodynamic conditions. The oscillator is designed using the Thyristor based delay element [7], in which the current source present for temperature compensation has been displaced to make the delay element as temperature dependent. The proposed temperature sensor consumes 47nW power at 27 °C, which increases linearly with temperature. Moreover, a 4x4 array of tin-oxide gas sensor based on convex Micro hotplates (MHP), is also utilized to identify the type of gas. The array is designed such that each sensor of an array provide different pattern for the same gas. The power consumption caused by the temperature and gas sensor is in the order of few µW's. The prime advantage of MHP can be visualized by the 950 °C annealed MHP, which exhibit the thermal efficiency of 13 °C /mW. Moreover it requires a driving voltage of only 2.8V to reach 300 °C in less than 5ms, which make it compatible with power supplies required by CMOS ICs. The gas sensor will provide 16 feature points at a time, which can results in hardware complexity and throughput degradation of the processing unit. Therefore, a principle component analysis (PCA) algorithm is implemented to reduce the number of feature points. Thereafter, a binary decision tree algorithm is adopted to classify the gases. We implemented both algorithms on heterogeneous Zynq platform. It is observed that the execution of PCA on Zynq programmable SoC is 1.41 times faster than the corresponding software execution, with a resource utilization of only 23% . Finally, a passive ultrahigh-frequency (UHF) RFID transponder is developed for communicating between the sensing block and processing unit. The designed module is responsible to harvest the power from the coming RF signal and accomplish the power requirement of both sensors. The designed transponder IC achieved minimum sensitivity of -17dBm with a minimum operational power of 2.6µW.

Whilst the state of security within the Cloud is still a contentious issue, some privacy and security issues are well known or deemed to be a likely threat. When considering the ongoing threat of malicious insiders the promised security expertise might be deemed untrusted. The focus of our research is determining the extent of issues related to the underlying technology, which support Cloud environments, mainly virtualization platforms. It is often argued that virtualization is secure over conventional shared resources due to the inherent isolation. However much literature may be seen which cites examples to the contrary and as such it should be considered that, as with all software, virtualization applications are susceptible to exploitation and subversion. In fact, it might even be argued that the complexity and heterogeneous nature of the environment may even facilitate further security vulnerabilities. To illustrate and investigate this point we consider the security threat of malware within the context of cloud environments. With this evolution of malware combined with the knowledge that Cloud software is susceptible to vulnerabilities, it is argued that complex malware might exist for the Cloud and if it were successful, would shed light on the security of these technologies. Whilst there are many examples of state of the art malware detection and protection for Cloud environments, this work tends to focus on examining virtual machines (VMs) from another layer. The primary flaw identified in all of the current approaches is failing to take into account malware, which is aware of the Cloud environment; thus be in a position to subvert this detection process. Traditional malware security applications tend to take a defensive approach by looking for existing malware through signature analysis or behavior monitoring. Whilst such approaches are acceptable for traditional environments they become less effective for distributed and dynamic ones. We argued that due to this dynamic nature of the Cloud as well as its uncertain security concerns, a malware type application may be a suitable security defense and thus operate as a proactive, self-protecting element. We present an architecture for Multi-Agent Cloud-Aware Self-Propagating Agents for Self-Protection. By adapting this architecture to include constraints (such as a kill switch) the application may be effectively controlled and thus any negative effects minimized. This application will then cross the multiple layers within the network, having high privilege. Its dynamic and distributed architecture will allow it survive removal from malware whilst hunting down malicious agents and patching systems as necessary. In order to survive in the hostile and dynamic cloud environment, the software incorporates a multi-component and multi-agent architecture which has shown success in the past with malware that propagate in heterogeneous environments. The components consist of passive and active sensors to learn about the environment, distributed storage to provide redundancy and controller/constructor agents for localized coordination. The proposed architecture has been implemented with success and desired results were achieved. The research outputs hold significant potentials, particularly for complex and highly dynamic infrastructures such as those aimed for DigitalQatar.

A frequency divider is one of the key building blocks in phased-lock loops (PLL) and frequency synthesizers, which are essential subsystems in wireless communication and sensing systems. A frequency divider divides the output frequency of a voltage-controlled oscillator (VCO) in order to compare it with a reference clock signal. Among the different types of frequency dividers, the injection-locked frequency divider (ILFD) is becoming more popular due to its low power and high frequency characteristics. However, the ILFD has an inherent problem of narrow locking range, which basically defines a range over which a frequency-division operation is supported. In order to increase the locking range, one of the most obvious ways is to inject higher power. In fully integrated PLLs, however, the injection signal is supplied by an internal VCO, which typically has limited fixed output power and hence enhancing the locking range with large injection signal power poses difficulty. In this work, we present the development of a fully integrated 10.5-GHz divide-by-3 (1/3) injection-locked frequency divider (ILFD) that can provide extra locking range with a small fixed injection power. The ILFD consists of a previously measured on-chip 10.5-GHz VCO functioning as an injection source, a 1/3 ILFD core, and an output inverter buffer. A phase tuner implemented using an asymmetric inductor is proposed to increase the locking range through even-harmonic (the 2rd harmonic for this design) phase tuning. With a fixed internal injection signal power of only -18 dBm (measured output power of the standalone VCO with a 50-? reference), a 25% enhancement in the locking range from 12 to 15 MHz is achieved with the proposed phase tuning technique without consuming an additional DC power. The frequency tuning range of the integrated 1/3 ILFD is from 3.3 GHz to 4.2 GHz. The proposed 1/3 ILFD is realized in a 0.18-µm BiCMOS process, occupies 0.6 mm × 0.7 mm, and consumes 10.6 mA while the ILFD alone consumes 6.15 mA from 1.8-V supply. The main objective of this work is proposing a new technique of phase-tuning of the even-harmonics that can "further" increase the locking range with an "extra" amount beyond what can be achieved by other techniques. Since the developed technique can enhance the locking range further at a fixed injection power, it can be used in conjunction with other techniques for further enhancing the locking range. For instance, the locking range can be increased by using different injection powers and then further enhanced by tuning the phase of the even harmonics at each power level. The "extra" locking range, not achievable without the even-harmonic phase tuning, amounts to 25%, which is very attractive for PLL applications. Furthermore, additional tuning mechanisms such as use of a capacitor bank can be employed to achieve even wider tuning range for applications such as PLL.

INTRODUCTION: The advent of da Vinci surgical robot (Intuitive Surgical, California, USA) has allowed complex surgical procedures in urology, gynecology, cardiothoracic, and pediatric to be performed with better clinical outcomes. The end effectors of these robots exhibits enhanced dexterity with improved range of motion leading to better access and precise control during the surgery. Understanding the design and kinematics of these end effectors (imitating surgical instruments' tooltips) would assist in replication of their complex motion in a computer-generated environment. This would further support the development of computer-aided robotic surgical applications. The aim of this work is to develop a software framework comprising of the geometric three-dimensional models of the surgical robot tool-tips along with their kinematic analysis. METHODS: The geometric models of the surgical tooltips were designed based on the EndoWristTM instruments of the da Vinci surgical robot. Shapes of the link and inter-link distances of the EndoWristTM instrument were measured in detail. A three-dimensional virtual model was then recreated using CAD software (Solidworks, Dassault Systems, Massachusetts, USA). The kinematic analysis was performed considering trocar as the base-frame for actuation. The actuation mechanism of the tool composed of a prismatic joint (T1) followed by four revolute joints (Ri ; i = 1 to 4) in tandem (Figure 1). The relationship between the consequent joints was expressed in form of transformation matrices using Denavit-Hartenberg (D-H) convention. Equations corresponding to the forward and the inverse kinematics were then computed using D-H parameters and applying geometrical approach. The kinematics equations of the designed tooltips were implemented through a modular cross-platform software framework developed using C/C++. In the software, the graphical rendering was performed using openGL and a multi-threaded environment was implemented using Boost libraries. RESULTS AND DISCUSSION: Five geometric models simulating the articulated motion of the EndoWristTM instruments were designed (Figure 2). These models were selected based on the five basic interactions of the surgical tooltip with the anatomical structures, which included: Cauterization of the tissue, Stitching using needles, Applying clips on vascular structures, Cutting using scissors, and Grasping of the tissues. The developed software framework, which includes kinematics computation and graphical rendering of the designed components, was evaluated for applicability in two scenarios (Figure 3). The first scenario demonstrates the integration of the software with a patient-specific simulator for pre-operative surgical rehearsal and planning (Figure 3a). The second scenario shows the applicability of the software in generation of virtual overlays of the tooltips superimposed with the stereoscopic video stream and rendered on the surgeon's console of the surgical robot (Figure 3b). This would further assist in development of vision-based guidance for the tooltips. CONCLUSION: The geometrical modeling and kinematic analysis allowed the generation of the motion of the tooltips in a virtual space that could be used for both pre-operatively and intra-operatively, before and during the surgery, respectively. The resulting framework can also be used to simulate and test new tooltip designs.

Although different spectrum bands are allocated to specific services, it has been identified that these bands are unoccupied or partially used most of the time. Indeed, recent studies show that 70% of the allocated spectrum is not utilized. As wireless communication systems evolve, an efficient spectrum management solution is required in order to satisfy the need of current spectrum-greedy applications. In this context, cognitive radio (CR) has been proposed as a promising solution to optimize the spectrum utilization. Under the umbrella of cognitive radio, spectrum-sharing systems allow different wireless communication systems to coexist and cooperate in order to increase their spectral efficiency. In these spectrum-sharing systems, primary (licensed) users and secondary (unlicensed) users are allowed to coexist in the same frequency spectrum and transmit simultaneously as long as the interference of the secondary user to the primary user stays below a predetermined threshold. Several techniques have been proposed in order to meet the required quality of service of the secondary user while respecting the primary user's constraints. While these techniques, including multiple-input multiple-output (MIMO) solutions, are optimized from a spectrally-efficiency perspective, they are generally not well designed to address the related complexity and power consumption issues. Thus, the achievement of high data rates with these techniques comes at the expense of high-energy consumption and increased system complexity. Due to these challenges, a trade-off between spectral and energy efficiencies has to be considered in the design of future transmission technologies. In this context, we have recently introduced adaptive spatial modulation (ASM), which comprises both adaptive modulation (AM) and spatial modulation (SM), with the aim of enhancing the average spectral efficiency (ASE) of multiple antenna systems. This technique was shown to offer high energy efficiency and low system complexity thanks to the use of SM while achieving high data rates thanks to the use of AM. Motivated by this technique and the need of such performance in a CR scenario, we study in this abstract the concept of ASM in spectrum sharing systems. In this work, we propose the ASM-CR scheme as an energy-efficient, spectrally-efficient, and low-complexity scheme for spectrum sharing systems. The performance of the proposed scheme is analyzed in terms of ASE and average bit error rate and confirmed with selected numerical results using Monte-Carlo simulations. These results confirm that the use of such techniques comes with an improvement in terms of spectral efficiency, energy efficiency, and overall system complexity.

It is very common to study the patient's data hospitalization to get useful information to improve the health care system. According to the American Hospital Association, in 2006, over $30 billion was spent on unnecessary hospital admissions. If patients that are likely to be hospitalized can be identified, the admission will be avoided as they will get the necessary treatments earlier. In this context, in 2013, the Heritage Provider Network (HPN) launched the $3 million Heritage Health Prize in order to develop a system that uses the available patient data (health records and claims)to predict and avoid unnecessary hospitalizations. In this work we take this competition data, and we try to predict the patient's hospitalization number. The data encompasses more than 2,000,000 of patient admission records over three years. The aim is to use the data of the ?rst and second year to predict the number of hospitalization of the third year. In this context, a set of operations mainly: data transformation, outlier detection, clustering, and regression algorithms are applied. Data transformation operations are mainly: (1) As the data is big enough to be processed, dividing the data into chunks is mandatory. (2) Missing values are either replaced or removed. (3) As the data is raw and cannot be labeled, different operations of aggregation are applied. After transforming the data, outlier detection, clustering, and regression algorithms are applied in order to predict the third year hospitalization number for each patient. Results show, by applying directly regression algorithms, the relative error is only 79%. However, by applying the DBSCAN clustering algorithm followed by the regression algorithm, the relative error decreased to be 67%. This is because the attribute that has been generated by the pre-processing clustering step helped the regression algorithm to predict more accurately the number of hospitalization; and this is why the relative error has dropped. The relative error can be decreased more if we apply the clustering pre-processing step twice. That means, the clusters generated in the first clustering step are re-clustered to generated sub-clusters. Then, the regression algorithm is applied to these sub-clusters. The relative error dropped significantly from 67% to 32%. Patients share common hospitalization history are grouped into clusters. This clustering information is used to enhance the regression results.