Towards a cloud‑based automated surveillance system using wireless technologies

Cloud Computing can bring multiple benefits for Smart Cities. It permits the easy creation of centralized knowledge bases, thus straightforwardly enabling that multiple embedded systems (such as sensor or control devices) can have a collaborative, shared intelligence. In addition to this, thanks to its vast computing power, complex tasks can be done over low-spec devices just by offloading computation to the cloud, with the additional advantage of saving energy. In this work, cloud’s capabilities are exploited to implement and test a cloud-based surveillance system. Using a shared, 3D symbolic world model, different devices have a complete knowledge of all the elements, people and intruders in a certain open area or inside a building. The implementation of a volumetric, 3D, object-oriented, cloud-based world model (including semantic information) is novel as far as we know. Very simple devices (orange Pi) can send RGBD streams (using kinect cameras) to the cloud, where all the processing is distributed and done thanks to its inherent scalability. A proof-of-concept experiment is done in this paper in a testing lab with multiple cameras connected to the cloud with 802.11ac wireless technology. Our results show that this kind of surveillance system is possible currently, and that trends indicate that it can be improved at a short term to produce high performance vigilance system using low-speed devices. In addition, this proof-of-concept claims that many interesting opportunities and challenges arise, for example, when mobile watch robots and fixed cameras would act as a team for carrying out complex collaborative surveillance strategies.Ministerio de Economía y Competitividad TEC2016-77785-PJunta de Andalucía P12-TIC-130

Teaching telecommunication standards: bridging the gap between theory and practice

©2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Telecommunication standards have become a reliable mechanism to strengthen collaboration between industry and research institutions to accelerate the evolution of communications systems. Standards are needed to enable cooperation while promoting competition. Within the framework of a standard, the companies involved in the standardization process contribute and agree on appropriate technical specifications to ensure diversity and compatibility, and facilitate worldwide commercial deployment and evolution. Those parts of the system that can create competitive advantages are intentionally left open in the specifications. Such specifications are extensive, complex, and minimalistic. This makes telecommunication standards education a difficult endeavor, but it is much demanded by industry and governments to spur economic growth. This article describes a methodology for teaching wireless communications standards. We define our methodology around six learning stages that assimilate the standardization process and identify key learning objectives for each. Enabled by software-defined radio technology, we describe a practical learning environment that facilitates developing many of the needed technical and soft skills without the inherent difficulty and cost associated with radio frequency components and regulation. Using only open source software and commercial of-the-shelf computers, this environment is portable and can easily be recreated at other educational institutions and adapted to their educational needs and constraints. We discuss our and our students' experiences when employing the proposed methodology to 4G LTE standard education at Barcelona Tech.Peer ReviewedPostprint (author's final draft

Intelligent Management and Efficient Operation of Big Data

This chapter details how Big Data can be used and implemented in networking and computing infrastructures. Specifically, it addresses three main aspects: the timely extraction of relevant knowledge from heterogeneous, and very often unstructured large data sources, the enhancement on the performance of processing and networking (cloud) infrastructures that are the most important foundational pillars of Big Data applications or services, and novel ways to efficiently manage network infrastructures with high-level composed policies for supporting the transmission of large amounts of data with distinct requisites (video vs. non-video). A case study involving an intelligent management solution to route data traffic with diverse requirements in a wide area Internet Exchange Point is presented, discussed in the context of Big Data, and evaluated.Comment: In book Handbook of Research on Trends and Future Directions in Big Data and Web Intelligence, IGI Global, 201