Jordi Serra

End-to-end SDN orchestration of IoT services using an SDN/NFV-enabled edge node

We propose an SDN/NFV-enabled edge node for IoT Services by means of orchestration of integrated Cloud/Fog and network resources. Network connectivity is provided between IoT gateways and deployed virtual machines allocated at the edge node.

The CTTC 5G End-to-End Experimental Platform : Integrating Heterogeneous Wireless\/Optical Networks, Distributed Cloud, and IoT Devices

The Internet of Things (IoT) will facilitate a wide variety of applications in different domains, such as smart cities, smart grids, industrial automation (Industry 4.0), smart driving, assistance of the elderly, and home automation. Billions of heterogeneous smart devices with different application requirements will be connected to the networks and will generate huge aggregated volumes of data that will be processed in distributed cloud infrastructures. On the other hand, there is also a general trend to deploy functions as software (SW) instances in cloud infrastructures [e.g., network function virtualization (NFV) or mobile edge computing (MEC)]. Thus, the next generation of mobile networks, the fifth-generation (5G), will need not only to develop new radio interfaces or waveforms to cope with the expected traffic growth but also to integrate heterogeneous networks from end to end (E2E) with distributed cloud resources to deliver E2E IoT and mobile services. This article presents the E2E 5G platform that is being developed by the Centre Tecnol?gic de Telecomunicacions de Catalunya (CTTC), the first known platform capable of reproducing such an ambitious scenario.

Evaluating Pricing Strategies for Storage in Import Container Terminals

In most container terminals around the world, storage space is scarce, and pricing policies are needed to increase performance. Specifically, congestion when inbound containers are temporarily stored in terminal yards leads to high operational costs. This paper focuses on the introduction of a yard storage tariff to encourage early pickup of containers. Different from previous approaches, the price schedule introduced has a nonzero flat rate. Both demand reactions and changes in pickup decisions are considered in the analysis. A model is developed for the objective function (profit of the terminal operator). A numerical example illustrates an optimal price schedule, shows how sensitive the profit is to the basic constituents of the model, and provides general pricing rules.

Asymptotically Optimal Linear Shrinkage of Sample LMMSE and MVDR Filters

Conventional implementations of the linear minimum mean-square (LMMSE) and minimum variance distortionless response (MVDR) estimators rely on the sample matrix inversion (SMI) technique, i.e., on the sample covariance matrix (SCM). This approach is optimal in the large sample size regime. Nonetheless, in small sample size situations, those sample estimators suffer a large performance degradation. Thus, the aim of this paper is to propose corrections of these sample methods that counteract their performance degradation in the small sample size regime and keep their optimality in large sample size situations. To this aim, a twofold approach is proposed. First, shrinkage estimators are considered, as they are known to be robust to the small sample size regime. Namely, the proposed methods are based on shrinking the sample LMMSE or sample MVDR filters towards a variously called matched filter or conventional (Bartlett) beamformer in array processing. Second, random matrix theory is used to obtain the optimal shrinkage factors for large filters. The simulation results highlight that the proposed methods outperform the sample LMMSE and MVDR. Also, provided that the sample size is higher than the observation dimension, they improve classical diagonal loading (DL) and Ledoit-Wolf (LW) techniques, which counteract the small sample size degradation by regularizing the SCM. Finally, compared to state-of-the-art DL, the proposed methods reduce the computational cost and the proposed shrinkage of the LMMSE obtains performance gains.

Fast communication: Minimum variance time of arrival estimation for positioning

Positioning systems based on time of arrival (TOA) rely on an accurate estimation of the first signal arrival which is the only one bearing position information. In this context, first arrival path detectors based on the minimum variance (MV) and the normalized minimum variance (NMV) criteria are robust against multipath propagation and non-line-of-sight (NLOS) situations. The aim of the paper is twofold. On the one hand, efficient implementations of those criteria will be presented. On the other hand, two polynomial rooting procedures based on the MV criterion will be exposed. As it will be shown, they lead to better performance than the traditional MV versions based on a grid search. In general, these methods imply finding the roots of a polynomial with complex coefficients. In order to reduce their computational burden a conformal mapping is proposed herein.

Conformal transformation for efficient ROOT-TOA estimation

Wireless location based on TOA (time-of-arrival) requires an accurate estimation of the first time delay. In this context, polynomial rooting techniques can provide an accurate estimation of the first arrival even in adverse multipath scenarios and when the direct path is highly attenuated. In general, these methods imply finding the roots of a complex polynomial. The aim of this paper is to present a conformal mapping that circumvents this problem, transforming it into a root search of a real polynomial with the resulting decrease in computational load.

Improving Security in Internet of Things with Software Defined Networking

Future Internet of Things (IoT) will connect to the Internet billions of heterogeneous smart devices with the capacity of interacting with the environment. Therefore, the proposed solutions from an IoT networking perspective must take into account the scalability of IoT nodes as well as the operational cost of deploying the networking infrastructure. This will generate a huge volume of data, which poses a tremendous challenge both from the transport, and processing of information point of view. Moreover, security issues appear, due to the fact that untrusted IoT devices are interconnected towards the aggregation networks. In this paper, we propose the usage of a Software- Defined Networking (SDN) framework for introducing security in IoT gateways. An experimental validation of the framework is proposed, resulting in the enforcement of network security at the network edge.

Intra Smart Grid Management Frameworks for Control and Energy Saving in Buildings

In the context of Smart Grids and Internet of Things IoT Systems, distributed monitoring and actuation through Wireless Sensor and Actuator Networks WSANs is fundamental to control the energy usage in buildings. Moreover, the realization of algorithms for the optimization of the energy consumption is of paramount importance. This paper presents a loosely coupled integration between a flexible management framework for WSANs, namely the IGMF Intra-Grid Management Framework, and a Dynamic Energy Scheduler with local control on sensors and actuators, namely the ITESS IoTLAB Energy Scheduling System. The integrated system allows the users to manage whole buildings applying Dynamic Energy Schedulers for different environments.

SPC12-2: Low Complexity Toa Estimation for Wireless Location

Positioning estimation based on time of arrival (TOA) estimation needs high accurate first arrival detector. High-resolution TOA estimators based on minimum variance (MV) and normalized minimum variance (NMV) provide an accurate estimation of the first arrival even in high multipath environments at the expenses of a high computational cost. The aim of this paper is to reduce the computational burden of high resolution TOA estimators. First, reduced complexity MV and NMV implementations based on the FFT are presented. Next, polynomial root versions of both estimators are proposed yielding an improvement in positioning accuracy. Finally, a near TOA maximum likelihood (ML) estimator is proposed providing a good trade off between complexity and accuracy.

Is Addis Ababa Wi-Fi Ready?

As we are heading towards future ubiquitous networks, heterogeneity is one key aspect we need to deal with. Interworking between Cellular and WLAN holds a major part in these future networks. Among other potential benefits it gives the opportunity to offload traffic from the former to the latter. To successfully accomplish that, we need to thoroughly study the availability, capacity and performance of both networks. To quantify the possibility of mobile traffic offloading, this work-in-progress presents the availability, capacity and performance investigation of Wi-Fi Access Points in the city of Addis Ababa. Analysis of the scanned data, collected by travelling through the highly populated business areas of the city, reveals the potential of existing Wi-Fi coverage and capability for many application domains.