Abstract

This paper proposes an integrated network management framework for inter-domain outbound traffic engineering. The framework consists of three functional blocks (monitoring, optimization and implementation) to make the outbound traffic engineering adaptive to network condition changes such as inter-domain traffic demand variation, inter-domain routing changes and link failures. The objective is to keep the inter-domain link utilization balanced under any of these changes while reducing service disruptions and reconfiguration overheads. Simulation results demonstrate that the proposed framework can achieve better load balancing with less service disruptions and re-configuration overheads in comparison to alternative approaches. Document type: Part of book or chapter of book

Abstract

Proceedings of 7th International Symposium on Ambient Intelligence (ISAmI 2016), Seville, Spain on June 1st–3rd Stress is one of the most important factors in car accidents. When the driver is in this mental state, their skills and abilities are reduced. In this paper, we propose an algorithm to predict stress level on a road. Prediction model is based on deep learning. The stress level estimation considers the previous driver's driving behavior before reaching the road section, the road state (weather and traffic), and the previous driving made by the driver. We employ this algorithm to build a speed assistant. The solution provides an optimum average speed for each road stage that minimizes the stress. Validation experiment has been conducted using five different datasets with 100 samples. The proposal is able to predict the stress level given the average speed by 84.20% on average. The system reduces the heart rate (15.22%) and the aggressiveness of driving. The proposed solution is implemented on Android mobile devices and uses a heart rate chest strap. The research leading to these results has received funding from the “HERMES-SMART DRIVER/CITIZEN” projects TIN2013-46801-C4-2-R /1-R funded by the Spanish MINECO, from the grant PRX15/00036 from the Ministerio de Educación Cultura y Deporte. Document type: Part of book or chapter of book

Abstract

In this paper, we propose a solution to reduce the stress level of the driver, minimize fuel consumption and improve safety. The system analyzes the driving style and the driver's workload during the trip while driving. If it discovers an area where the stress increases and the driving style is not appropriate from the point of view of energy efficiency and safety for a particular driver, the location of this area is saved in a shared database. On the other hand, the implemented solution warns a particular user when approaching a region where the driving is difficult (high fuel consumption and stress) using the shared database based on previous recorded knowledge of similar drivers in that area. In this case, the proposal provides an optimal deceleration profile if the vehicle speed is not adequate. Therefore, he or she may adjust the vehicle speed with both a positive impact on the driver workload and fuel consumption. The Data Envelopment Analysis algorithm is used to estimate the efficiency of driving and the driver's workload in in each area. We employ this method because there is no preconceived form on the data in order to calculate the efficiency and stress level. A validation experiment has been conducted using both a driving simulator and a real environment with 12 participants who made 168 driving tests. The system reduced the slowdowns (38%), heart rate (4.70%), and fuel consumption (12.41%) in the real environment. The proposed solution is implemented on Android mobile devices and does not require the installation of infrastructure on the road. It can be installed on any model of vehicle. The research leading to these results has received funding from the “HERMES-SMART DRIVER” project TIN2013- 46801-C4-2-R within the Spanish "Plan Nacional de I+D+I" under the Spanish Ministerio de Economía y Competitividad and from the Spanish Ministerio de Economía y Competitividad funded projects (co-financed by the Fondo Europeo de Desarrollo Regional (FEDER)) IRENE (PT-2012-1036-370000), COMINN (IPT-2012-0883-430000) and REMEDISS (IPT-2012-0882-430000) within the INNPACTO program. Document type: Part of book or chapter of book

Abstract

This paper demonstrates a novel car-following model based on driver or passengers’ comfort. As we know, hasty deceleration during emergency brake will cause passengers feel uncomfortable. According to the relationship between brake acceleration and people’s comfortable feeling, the comfortable model is setup. The model calculates the following car’s acceleration by measuring the distance between the following car and the preceding car, the velocity of the following car, and controls the car’s acceleration to make driver and passengers feel comfort. The paper combine the model with the pervasive computing concept, provoke the pervasive computing driver behavior modeling idea and turn it into reality to increase the adaptability and reliability of car’s parts, when car equipped with this device, the prospect is not only the assistant driver or comfortable driver are realized in the car-following circumstance, but also the whole car’s performance will be improved. Document type: Part of book or chapter of book

Abstract

The advent of mobile computing and sensing technologies, in conjunction with omni-present and high-speed mobile networks, allow nowadays the capture of human mobility data at an extremely high fidelity. Modern mobile computing services not only have the capacity to store spatio-temporal mobile data, these nowadays also have the capability to process incoming data in near-real time. As a result, we have a better chance to develop effective strategies and build intelligent systems that play critical roles in areas like public health, traffic engineering, urban planning and economic forecasting. On the other hand, detailed movement data often poses a threat to the privacy and security of users and companies, given that mobile devices are associated with real human custodians. One fundamental question is whether human mobility computing and privacy can co-exist under the same roof, given different cultural, religious, legal, technological and socio-economic backgrounds of societies. This panel will explore how the academia and industry are tackling human mobility computing and privacy challenges at a global scale. It will also identify and debate the key challenges and opportunities, in terms of applications, queries, architectures, to which the mobile data management community should contribute. © 2015 IEEE. 2 42 43 "p"Sponsors: Aalborg University et al. IEEE Computer Society IEEE Technical Committee on Data Engineering The National Science Foundation University of Pittsburgh Conference code: 118117

Abstract

This paper discusses some challenges that user mobility imposes over the user-centric protection model against security threats. This model is based on the idea of offloading the security applications from the end user device, and placing them in a trusted network node at the network's edge. Our research perspective is particularly centered around three interrelated mobility challenges, i) the allocation of the security applications “close” to the user, i.e., on network nodes with enhanced processing capabilities, ii) seamless mobility with negligible disruption of ongoing network connections, and iii) dynamic orchestration and management with support of security applications migration. Based on our arguments, we expose the main requirements and trade-offs to be considered in the attempt to support mobility in such environment. We propose a flexible solution that leverages Software Defined Networking, Network Function Virtualization and Computing at the Network Edge to offer a seamless on-path security protection to mobile users. Our preliminary experiments' results considering a WiFi mobile user show that seamless security migration and mobility are feasible in a simple real scenario. Vertical mobility and more complex use cases scenarios are envisioned for future research. Peer Reviewed Document type: Conference object

Abstract

International audience; As mobile applications deliver increasingly complex functionalities, the demands for even more intensive computation would quickly transcend energy capability of mobile devices. On one hand and in an attempt to address such issues, fog computing paradigm is introduced to mitigate the limited energy and computation resources available within constrained mobile devices, by moving computation resources closer to their users at the edge of the access network. On another hand, most of electric vehicles (EVs), with increasing computation, storage and energy capabilities, spend more than 90% of time on parking lots. In this paper, we conceive the basic idea of using the underutilized computation resources of parked EVs as fog nodes in order to provide on-demand computation at the vicinity of the access network. The proposed Vehicular Fog Computing (VFC) architecture aggregates the abundant unused resources of parked vehicles, and uses it to serve mobile users' demands. The resource allocation problem is formulated as a Markov Decision Process (MDP) and dynamic programming is used to solve the underling decision problem. Extensive simulation results show the effectiveness of the proposed approach by improving the global reward value by 51% and scoring an energy gain of 66% compared to two other models.

Abstract

International audience; High volumes of pedestrians, cyclists and other vulnerable road users (VRUs) have much higher casualty rates per mile; not surprising given their lack of protection from an accident. In order to alleviate the problem, sensing capabilities of smartphones can be used to detect, warn and safeguard these road users. In this research we propose an infrastructure-less fog-based architecture named PV-Alert (Pedestrian-Vehicle Alert) where fog nodes process delay sensitive data obtained from smartphones for alerting pedestrians and drivers before sending the data to the cloud for further analysis. Fog computing is considered in developing the architecture since it is an emerging paradigm that has paramount of advantages for low-latency applications which demand mobility support, geo-distribution and location awareness. We have also defined an algorithm for accident prediction and alerting traffic accidents involving vehicles and pedestrians. PV-Alert can be deployed quickly since it is based only on users' smartphones and doesn't require special infrastructure. Simulation results show that the proposed architecture is able to render alerts in real time whichever the wireless communication technology used to connect smartphones with fog nodes (Wi-Fi or LTE). Moreover, analytical and performance evaluations depict that the new architecture outperforms other related architectures in terms of reliability, scalability and latency.