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The Second International Conference on Smart Portable, Wearable,
Implantable and Disability-oriented SPWID 2016 |
| Tutorials |
T1. Fog-computing versus SDN/NFV in 5G
Eugen Borcoci, University ‘Politehnica’ - Bucharest, Romania
T2. Risk Assessment and Analysis for Smart Grids
György Kálmán, mnemonic AS | Norwegian University of Science and Technology (NTNU), Norway
T3. 3D-Crime Scene/Disaster Site Reconstruction using Open Source Software
Dirk Labudde, University of Applied Sciences Mittweida, Germany
Details
T1. Fog-computing versus SDN/NFV in 5G
Eugen Borcoci, University ‘Politehnica’ - Bucharest, Romania
Pre-requisites: general knowledge on IP networking architectures, protocols , introductory knowledge on SDN, NFV, Cloud computing and 5G technlogies.
The 5G architectures and technologies are seen today as main wireless networks candidates to solve future challenging requirements: high traffic capacity, low-latency, scalability, flexibility, openess and universal support for data and media applications and services (fixed/mobile), including V2X, M2M and IoT communications. Security, privacy, resiliency, robustness and data integrity are also important requirements. The 5G networks should provide open communication systems including heterogeneous cellular networks, clouds and data-centres, home networks and gateways, satellite systems, etc. Flexible management and control is also a major operational requirement. 5G will be fully driven by software; a unified operating system is needed, in a number of points of presence, especially at the network edge. In order to achieve the required performance, scalability and agility of the management and control, 5G can rely on technologies like Software Defined Networking (SDN), Network Function Virtualization (NFV) and Cloud concepts implemented in Mobile Edge Computing (MEC).
SDN separates the control and data planes, thus enabling external control of data flows through logical software entities, i.e., remote vendor-neutral controllers. It abstracts the network components, their functions and the protocols to control the data plane. Programability of the network is an important SDN advantage. In 5G wireless networks, the SDN-type control is attractive, given its ability to support network virtualisation, automating and creation of new services on top of the virtualised resources. NFV is a complementary technology to SDN; it can enhance and make the 5G networking more flexible by virtualising many network functions and deploying them into software packages. Such functions can be dynamically assembled and chained to implement legacy services or novel ones. Cloud computing approach is already seen as attractive for 5G (Cloud based Radio Access Network is an example of a major proposal).
The recent Fog/edge computing architectures extends the cloud concepts to provide to end-users, data, compute, storage, and application services, based on resources hosted at the network edge, access points or end devices such as set-top-boxes and even terminal devices. Fog computing offers significant advantages, important for 5G networks, such as data movement reduction across the network resulting in reduced congestion and latency. It eliminates the bottlenecks resulting from centralized computing systems.
However, applying principles and realizing a good cooperation of the SDN/NFV/MEC and Fog computing in 5G environment raises many challenges and open research issues. This tutorial is an overview of such combined approach and different solutions in 5G networks.
T2. Risk Assessment and Analysis for Smart Grids
György Kálmán, mnemonic AS | Norwegian University of Science and Technology (NTNU), Norway
This tutorial will provide an overview of the state of the art in risk analysis for smart grids with focus on the consumer side. The tutorial will explain what changes the physical dimension of the electricity grid is implying in the security evaluation and how safety-availability and security might be balanced in the same system. The most important customer side equipment is the smart meter. The tutorial provides an analysis of the attack surface of smart meters, vulnerability analysis and recommendations. An example industrial router is shown with vulnerabilities found in the management.
T3. 3D-Crime Scene/Disaster Site Reconstruction using Open Source Software
Dirk Labudde, University of Applied Sciences Mittweida, Germany
Major damaging events with many victims or environmental contamination hazards, such as natural disasters, airplane crashes, train accidents or terroristic acts, are shocking events to society. Fast and comprehensive information acquisition of event sites ensures appropriate and safe actions for rescue forces and investigators, and increases resilience of our society with respect to such disorders in the long term.
There are generally five phases that follows such an event.
- Victim rescue
- Victim identification
- Forensics
- Investigation
- Derivation and implementation of prevention models
The use of unmanned aerial vehicles, so-called drones, for gathering as much information as possible about the event site to support the whole resilience cycle is a fast and safe way to elucidate such unknown environments. The spatiotemporal data gathered in this way can be used for supporting decision makers with respect to targeted and safe management of rescue teams and the fast locating of victims in Phase1. For forensic purposes, a 3D spatiotemporal model can be generated in Phase 3. This model provides the basis for simulations in supporting investigation in Phase 4. In Phase 2 the identification of an unknown deceased person is the main priority. Generally, this is an important task in forensic anthropology. There are various methods for identification, such as fingerprinting, odontostomatology and genetic fingerprinting, which presuppose the existence of reference material of the missing person; however, if there is no evidence of a person’s identity the only possibility is often the utilization of forensic facial soft tissue reconstruction. This method is based on the high recognition level of a human face on the basis of bone structure characteristics of the skull and its anatomical features.
In this tutorial a novel process for computer-aided 3D facial soft tissue reconstruction based on digital photographs of a skull is presented. The tool chain completely employs open source software and poses a cost-efficient and flexible alternative to conventional reconstruction methods. As an example, we present a complete facial soft tissue reconstruction of a deceased person. Here, reconstruction is based on seventy-six photographs of the skull taken with a Nikon D7100 SLR digital camera. The results show that for actual comparison images similar reconstruction results can be achieved.
