Moayad Aloqaily

A location-aware user tracking and prediction system

Modern location-aware services and applications use context and prediction methods to adapt to the needs of users and changes in the environment. The growing availability of WLAN and mobile devices offers significant opportunities for locationaware services. But the use of WLAN or RFID technologies alone provides a less accurate estimation of a users current location. In this paper, we introduce an ontology-based location tracking system. It makes use of both WLAN and RFID technologies, and includes a prediction method for identifying a users current location and predicted future location. Our system architecture better serves the client by using location and context information.

Social Behaviometrics for Personalized Devices in the Internet of Things Era

As the integration of smart mobile devices to the Internet of Things (IoT) applications is becoming widespread, mobile device usage, interactions with other devices, and mobility patterns of users carry significant amount of information about the daily routines of the users who are in possession of these devices. This rich set of data, if observed over a time period, can be used to effectively verify a user. In previous works, verification of users on personalized electronic devices via biometric properties, such as fingerprint and iris, has been successfully employed to increase the security of access. However, with the integration of social networks with the IoT infrastructure and their popularity on smart handheld devices, identification based on behavior over social networks is emerging as a novel concept. In this paper, we propose an intelligent add-on for the smart devices to enable continuous verification of users. In the experiments, we use data from built-in sensors and usage statistics of five different social networking applications on mobile devices. The collected feature set is aggregated over time and analyzed using machine learning techniques. We show that when smart devices are equipped with continuous verification intelligence, it is possible to verify users with less than 10% false rejection probabilities, and the users can keep using the devices with no interruption for biometric authentication 90% of the time. In the case of anomalous behavioral patterns, the proposed system can verify genuine users with up to 97% success ratio using an aggregated behavior pattern on five different social network applications.

Multiagent/Multiobjective Interaction Game System for Service Provisioning in Vehicular Cloud

The increasing number of applications based on the Internet of Things, as well as advances in wireless communication, information and communication technology, and mobile cloud computing, has allowed mobile users to access a wider range of resources when mobile. As the use of vehicular cloud computing has become more popular due to its ability to improve driver and vehicle safety, researchers and industry have a growing interest in the design and development of vehicular networks for emerging applications. Vehicle drivers can now access a variety of on demand resources en route via vehicular network service providers. The adaptation of vehicular cloud services faces many challenges, including cost, privacy, and latency. The contributions of this paper are as follows. First, we propose a game theory-based framework to manage on-demand service provision in a vehicular cloud. We present three different game approaches, each of which helps drivers minimize their service costs and latency, and maximize their privacy. Second, we propose a quality-of-experience framework for service provision in a vehicular cloud for various types of users, a simple but effective model to determine driver preferences. Third, we propose using the trusted third party concept to represent drivers and service providers, and ensure fair game treatment. We develop and evaluate simulations of the proposed approaches under different network scenarios with respect to privacy, service cost, and latency, by varying the vehicle density and driver preferences. The results show that the proposed approach outperforms conventional models, since the game theory system introduces a bounded latency of ≤3%, achieves service cost savings up to 65%, and preserves driver privacy by reducing revealed information by up to 47%.

An Auction-Driven Multi-Objective Provisioning Framework in a Vehicular Cloud

Vehicular Cloud Computing (VCC) denotes a shared pool of computing resources in a set of wirelessly connected vehicles, which are available for on- demand and rapid access based on the pay-as-you-go fashion. VCC consolidates the benefits of Mobile Cloud Computing (MCC) and vehicular communications. Prior to the VCC concept being widely adopted, several research issues and challenges need to be carefully addressed in such environment. Privacy, service cost and provisioning delay are identified as the most crucial challenges to be addressed. State of the art includes several proposals to address these issues however a combined solution that aims to address these requirements holistically does not exist. This paper refines our recently presented frameworks in the works of Aloqaily et al. (2014) and Aloqaily et al.(2015), and extends it by building a generalized multi-objective framework, namely Auction-driven QoE Provisioning (AQoEP). A Trusted Third Party (TTP) approach has been adopted to address such challenges. Simulations results are extensively studied to proof the concept of the proposed framework. Through simulations, we proof that AQoEP in a vehicular cloud better fulfils the vehicular service requirements than any other proposed approach.

A Generalized Framework for Quality of Experience (QoE)-Based Provisioning in a Vehicular Cloud

Recently, Vehicular Cloud Computing (VCC) are promising to convey relatively more communication, on-demand and based on pay-as-you-go fashion. Such environment concepts is being widely adopted, which is resulting in several research issues and challenges. Privacy, service cost and provisioning delay are identified as the most crucial challenges to be addressed. This paper extends our previous work [10] and builds a generalized of quality-of-experience (QoE) design. QoE requirements are collected via numerous vehicular nodes in the vehicular cloud and re-formulated by a weighted combination of these factors, i.e., delay, price and information revealed to the Trusted Third Party (TTP). Extensive simulations are run in order to evaluate the performance of our proposed framework. Through simulations, we show that QoE-based service provisioning in a vehicular cloud fulfils the service requirements by making a compromise between delay, service cost and information revealed to the TTP.

Provisioning delay effect of partaking a Trusted Third Party in a vehicular cloud

Vehicular cloud computing has appeared as a viable mobile cloud computing solution of vehicle communications. Among several challenges in such environment, provisioning delay can be counted as a crucial challenge to be addressed. In this paper, we propose a framework of partaking a Trusted Third Party (TTP) in a vehicular cloud. This framework can aid in pinpoint of such problem and to address such challenge in a vehicular cloud based on users experience. Thus, provisioning delay is obtained via other mobile nodes and re-formulated by a weighted combination of these delays. We evaluate our proposal via simulations, and numerical results has been presented.

Vehicular clouds: State of the art, challenges and future directions

Cloud computing and Vehicular Communications are profoundly emerging fields towards realization of smart cities. While cloud computing is stage of development, vehicular cloud computing combines the benefits of Mobile Cloud Computing (MCC) and vehicular communications. Recent studies expect that vehicular cloud computing will be the gateway to the future of transportation systems. However, the development process still reveal on several issues and challenges that need to be carefully addressed in such environment. Privacy, service cost and provisioning delay are identified as the most crucial challenges to be addressed. State of the art includes several proposals to address these issues. In this position paper, we will gather and present a comprehensive study of vehicular cloud computing architecture, applications, and approaches. More importantly, we will discuss the current possible services provisioning solutions with their drawbacks, then, we will provide a thorough discussion on the benefits of using the concept of Quality of Experience (QoE) in such environment.

A policy-based location-aware framework for personalized services in cloud computing systems

Autonomous service adaptation in cloud environments requires both location-awareness and the acquisition and utilization of contextual information. Statically configured service adaptation frameworks lack the ability to adapt to changing network conditions and geographical locations. This paper presents a framework that continuously derives updated configuration policies with respect to service selection and handover to third party cloud service providers. Location tracking and prediction empower the system to provide ongoing robust services for cloud service subscribers. To achieve this goal, the proposed work relies on a policy-based real-time simulator to evaluate possible new service provider handover configurations before actually applying them to the real network. Preliminary performance evaluation results demonstrate the significant enhancement of the outcome of the proposed framework in terms of continuous services for subscribers and load balancing for service providers.

A continuous diversified vehicular cloud service availability framework for smart cities

Abstract The intelligent and connected transportation system (ICTS) is a significant and mandatory component of the smart city architecture. Multimedia content sharing, vehicle power management, and road navigation are all examples of ICTS services. As smart cities continue to deploy different technologies to improve the performance and diversity of vehicular cloud services, one of the main issues that prevails is efficient and reliable service discovery and selection for smart vehicles. Furthermore, cloud service providers (SPs) are limited to the availability, variety and quality of services made available to vehicular cloud subscribers. Smart vehicles rely on a number of SPs to acquire the required services while moving. It therefore becomes challenging for vehicular cloud subscribers to acquire services that meet their quality of experience (QoE) preferences. This paper introduces a new service provision scheme to provide continuous availability of diversified cloud services targeting vehicular cloud users through a cluster-based trusted third party (TTP) framework. TTPs act as cloud service mediators between cloud service subscribers and providers. Vehicles that are considered to have similar patterns of movement and service acquisition characteristics are grouped into service-specific clusters. TTPs communicate with service providers and cluster heads to negotiate for services with high QoE characteristics. A location prediction method is adopted to determine a vehicles future location and allow services to be negotiated for before the vehicles arrival. We provide simulation results to show that our approach can adequately discover and deliver cloud services with increased QoE results, minimal overhead burden and reduced end-to-end latency.

Trusted Third Party for service management in vehicular clouds

As vehicles get smarter, with supplementary onboard gear providing advanced applications and provisioning services related to traffic management, the requirement for simple and effective access to information has grown extensively. The new applications manage more complex operations and, unlike other mobile devices, mobile vehicle devices provide location based services, real-time functionality, provisioning services and storage, all without the shortcomings of traditional mobile devices. Vehicular cloud computing can perform a broad set of on-demand services and applications, which make this method highly applicable to urban settings. Provisioning services often encounter unexpected interruptions that increase provisioning latency and service usage duration, ultimately leading to higher charges for the driver. This paper advances our previously proposed distributed model to handle service management in vehicular clouds, by using the concept of Vehicular Trusted Third Party (VTTP) with different type of provisioning services. This model has the capability to switch between TTPs, which allows drivers to exploit the benefits of different existing services, and connect to the TTP that best meets their specific requirements. Two new service latency modes are proposed and evaluated: Service Latency Sensitive Mode (SLSM) and Neutral mode. The proposed model has been implemented and evaluated using simulations of real-time light and heavy duty services, and various simulation scenarios show that using a VTTP can significantly help drivers reduce their service latency (∼30%) and costs (∼26%).