Bassem Mokhtar

Towards a Data Semantics Management System for Internet Traffic

Although current Internet operations generate voluminous data, they remain largely oblivious of traffic data semantics. This poses many inefficiencies and challenges due to emergent or anomalous behavior impacting the vast array of Internet elements such as services and protocols. In this paper, we propose a Data Semantics Management System (DSMS) for learning Internet traffic data semantics to enable smarter semantics- driven networking operations. We extract networking semantics and build and utilize a dynamic ontology of network concepts to better recognize and act upon emergent or abnormal behavior. Our DSMS utilizes: (1) Latent Dirichlet Allocation algorithm (LDA) for latent features extraction and semantics reasoning; (2) big tables as a cloud-like data storage technique to maintain large-scale data; and (3) Locality Sensitive Hashing algorithm (LSH) for reducing data dimensionality. Our preliminary evaluation using real Internet traffic shows the efficacy of DSMS for learning behavior of normal and abnormal traffic data and for accurately detecting anomalies at low cost.

Big data and semantics management system for computer networks

We define Big Networks as those that generate big data and can benefit from big data management in their operations. Examples of big networks include the current Internet and the emerging Internet of things and social networks. The ever-increasing scale, complexity and heterogeneity of the Internet make it harder to discover emergent and anomalous behavior in the network traffic. We hypothesize that endowing the otherwise semantically-oblivious Internet with memory management mimicking the human memory functionalities would help advance the Internet capability to learn, conceptualize and effectively and efficiently store traffic data and behavior, and to more accurately predict future events. Inspired by the functionalities of human memory, we proposed a distributed network memory management system, termed NetMem, to efficiently store Internet data and extract and utilize traffic semantics in matching and prediction processes. In particular, we explore Hidden Markov Models (HMM), Latent Dirichlet Allocation (LDA), and simple statistical analysis-based techniques for semantic reasoning in NetMem. Additionally, we propose a hybrid intelligence technique for semantic reasoning integrating LDA and HMM to extract network semantics based on learning patterns and features with syntax and semantic dependencies. We also utilize locality sensitive hashing for reducing dimensionality. Our simulation study using real network traffic demonstrates the benefits of NetMem and highlights the advantages and limitations of the aforementioned techniques.

Evaluation of a Traffic-Aware Smart Highway Lighting System

Highway lighting consumes considerable amounts of energy, yet smart lighting techniques provide significant potential for reducing this consumption. This paper introduces a preliminary algorithm, simulation studies and a small-scale hardware prototype for a smart highway lighting management system based on road occupancy. Wireless Sensor Network (WSN) detects the presence of vehicles along the road, and controls lighting accordingly. The system is evaluated through two different simulation studies: using a realistic model for vehicles traffic based on cellular automata (Nagel-Shreckenberg model), and using state-of-the-art Simulation of Urban Mobility (SUMO) traffic simulator. Simulations provide estimation for expected energy saving rates at different cases and scenarios. According to simulation results, the proposed system can save up to 57.4% of power consumption compared to conventional lighting systems.

Towards a Smart Highway Lighting System Based on Road Occupancy: Model Design and Simulation

Energy saving is a major aspect of smart cities, so optimizing highway lighting is essential, as it consumes considerable amounts of energy. However, there is a remarkable potential for reducing this consumption through smart lighting techniques. This paper introduces preliminary design and simulation for a smart highway lighting management system based on road occupancy. Wireless Sensors Network (WSN) detects the presence of vehicles along the road, and controls lamps accordingly. The system is simulated and optimized using a realistic probabilistic model for vehicles traffic, taking the advantage of simulation to provide estimation for expected energy saving rates; in contrary to previous works depending only on rough calculations or real-time results after implementation. According to simulation results, the proposed system can save up to 57.4% of power consumption compared to conventional lighting systems.

System-Aware Smart Network Management for Nano-Enriched Water Quality Monitoring

This paper presents a comprehensive water quality monitoring system that employs a smart network management, nano-enriched sensing framework, and intelligent and efficient data analysis and forwarding protocols for smart and system-aware decision making. The presented system comprises two main subsystems, a data sensing and forwarding subsystem (DSFS), and Operation Management Subsystem (OMS). The OMS operates based on real-time learned patterns and rules of system operations projected from the DSFS to manage the entire network of sensors. The main tasks of OMS are to enable real-time data visualization, managed system control, and secure system operation. The DSFS employs a Hybrid Intelligence (HI) scheme which is proposed through integrating an association rule learning algorithm with fuzzy logic and weighted decision trees. The DSFS operation is based on profiling and registering raw data readings, generated from a set of optical nanosensors, as profiles of attribute-value pairs. As a case study, we evaluate our implemented test bed via simulation scenarios in a water quality monitoring framework. The monitoring processes are simulated based on measuring the percentage of dissolved oxygen and potential hydrogen (PH) in fresh water. Simulation results show the efficiency of the proposed HI-based methodology at learning different water quality classes.

Plasmonic-ceria nanoparticles for automated optical fluorescence-quenching of dissolved oxygen

Due to its optical and structural characteristics, cerium oxide (ceria) nanoparticles have been used in wide variety of applications. This paper introduces the enhancement of visible fluorescence emission of ceria nanoparticlesthrough adding plasmonicgold nanoparticles (Au NPs) under violet excitation. Au NPs lead to enhance the formation of tri-valent ionization states of cerium ions with corresponding oxygen vacancies formation. In addition, the coupling between plasmonic waves of gold and emission spectrum of ceria offers another contribution to the enhancement of fluorescence intensity.Then, gold-ceria nanoparticles have been applied as optical sensing material for dissolved oxygen in aqueous media based on fluorescence quenching mechanism.The sensed data is automatically collected and processed through a wireless sensor network-based communication infrastructure with smartdata and feedback management capability.

Mobility Prediction for Efficient Resources Management in Vehicular Cloud Computing

Vehicular Cloud Computing (VCC) has becomea significant research area recently, due to its potentialadvantages and applications, especially in the field ofIntelligent Transportation Systems (ITS). However, thehigh mobility of vehicular environment poses crucial challengesto resources allocation and management in VCC, which makesits implementation more complex than conventional clouds. Many works have been introduced to address various issuesand aspects of VCC, including resources management andVirtual Machine Migration in vehicular clouds. However, usingmobility prediction in VCC has not been studied previously. Inthis paper, we introduce a novel solution to reduce the effect ofresources mobility on the performance of vehicular cloud, usingan efficient resources management scheme based on vehiclesmobility prediction. This approach enables the vehicular cloudto take pre-planned procedures, based on the output of anArtificial Neural Network (ANN) mobility prediction model. The aim is to reduce the negative impact of sudden changes invehicles locations on vehicular cloud performance. A simulationscenario is introduced to compare between the performanceof our resources management scheme and other resourcesmanagement approaches introduced in the literature. Thesimulation environment is based on Nagel-Shreckenberg cellularautomata (CA) discrete model for traffic simulation. Simulationresults show that our proposed approach has leveraged theperformance of vehicular cloud effectively without overusingavailable vehicular cloud resources.

Hybrid Intelligence Nano-enriched Sensing and Management System for Efficient Water-Quality Monitoring

This paper presents a comprehensive water-quality monitoring system that employs a smart network management, Nano-enriched sensing framework, and intelligent and efficient data analysis and forwarding protocols for smart and system-aware decision making. The presented system comprises two main subsystems, a data sensing and forwarding subsystem (DSFS), and Operation Management subsystem (OMS). The OMS operates based on real-time learned patterns and rules of system operations projected from the DSFS to manage the entire network of sensors. For the communication framework within the designed system, we propose a Hybrid Intelligence (HI) scheme for efficient data classification and forwarding processes. The scheme integrates a machine learning algorithm, Fuzzy logic and weighted decision trees. The proposed methodology depends on profiling raw data readings, generated from a set of optical nano-sensors, as profiles of attribute value pairs. Then, data patterns are learnt adopting association rule learning algorithm clarifying the most frequent attributes and their related values. According to the discovered sets of attributes, a set of Fuzzy membership functions are directed to produce a discrete sample space and a specific membership class for each attribute based on its value. Based on information theory concepts and calculated attribute-dependent entropies and information gains, weighted decision trees are built to help take decisions of data forwarding and to generate long-term rules. As a case study, we conduct a set of simulation scenarios for detecting and forwarding data related to water quality levels. Simulation results show the efficiency of the proposed HI-based methodology at learning different water quality classes.

Compact reconfigurable multi-size pixel antenna for cognitive radio networks and IoT environments

There is a huge demand for wireless devices that have high data rates over different wireless communication systems (WCS) with reliable QoS requirements. Such devices should provide the ability to perform multi-function, and operate over different WCS. Reconfigurable antenna considers as an efficient solution to exhibit the communication capability in heterogeneous environments. Pixel reconfigurable antenna offers low profile, compact size, and reconfigurable shape. This paper proposes a multi-size reconfigurable pixel RF-switch-based antenna design, where the antenna is divided into four quarters. The effect of having different quarter configurations with such design is useful for dynamically adapting over several communication bands in IoT environments and cognitive radio networks. For reaching near optimum design and operation, a genetic algorithm is used for selecting optimum quarter and assigning a suitable configuration of each RF-switch within each quarter. This antenna design and configurations provide different operating frequencies bands for RFID, GSM, and WLAN e.g. 300, 400, 900MHz, 1.2, 1.6. 2.4, 5.2, 5.8, and 9.8. GHz with tunable bandwidth. Moreover, the obtained results of a preliminary simulation study of a cognitive radio network show enhancement in the average data throughput with increasing the number of radio bands per node.

CyNetPhy: Towards Pervasive Defense-in-Depth for Smart Grid Security

Security is a major concern in the smart grid technology extensively relying on Information and Communication Technologies (ICT). New emerging attacks show the inadequacy of the conventional defense tools that provision isolated uncooperative services to individual grid components ignoring their real-time dependency and interaction. In this article, we present a smart grid layering model and a matching multi-layer security framework, CyNetPhy, towards enabling cross-layer security of the grid.CyNetPhy tightly integrates and coordinates between a set of interrelated, and highly cooperative real-time defense solutions designed to address the grid security concerns. We advance a high-level overview of CyNetPhy and present an attack scenario against the smart grid supported by a qualitative analysis of the resolution motivating the need to a cross-layer security framework such as CyNetPhy.