Design and implementation of a smart surveillance system controlled with embedded devices

Grado en Ingeniería de Tecnologías de Telecomunicació

Utilising web analytics in the agile development of e-commerce sites : a software developer’s perspective

E-commerces have gained popularity exponentially since the dawn of the world wide web. To stay competitive, increase revenue and make their e-commerce site as good as possible, organisations have begun to utilise web analytics to make the development of the site data driven. Agile software development has often been the desired way of building software in the last decades. Organisations are in increasing numbers trying to move to a more agile way of working in order to build better software. In this thesis we examine how the web analytics of an e-commerce site can be utilised as well as possible in agile software development teams. We examine this web analytics process especially from the point of view of software developers in these teams. The research around this topic was conducted as qualitative research by interviewing four different software developers each having experience in developing e-commerces. Drawing from their experiences and opinions, we formulate some observations and guidelines for how organisations can potentially improve their efficiency in utilising web analytics as a part of their development process

Diseño e implementación de una red de sensores basada en protocolos IoT para monitorización de mercancías

Máster Universitario en Ingeniería de TelecomunicaciónInternet de las Cosas se ha convertido en una tecnología que acompaña la vida de todas las personas. Buscamos facilitar las tareas, automatizarlas, y evitar en lo posible los riesgos de los humanos a la hora de llevarlas a cabo, además de hacer el trabajo humano lo más cómodo posible. Cada vez necesitamos tener a nuestro alcance más datos, acerca de cualquier cosa que podamos imaginar, lo que conlleva a implementar tecnología en todas ellas que monitoricen y nos faciliten esos datos. Este Trabajo de Fin de Máster se centra en la aplicación del Internet de las Cosas para disminuir la cantidad de productos desechados por llegar en mal estado, debido a una mala manipulación en la cadena de transporte. Actualmente se deterioran a diario productos que no pueden ser vendidos, debido a que en su transporte no han viajado en las condiciones adecuadas. El sistema desarrollado en este trabajo trata de disminuir este impacto monitorizando cómo viaja en cada momento el envío realizado, así como de contribuir al desarrollo sostenible. Entre las dificultades que plantea hacer un sistema para ello, se encuentran que estará en movimiento, serán múltiples envíos a la vez los que deban ser controlados, evaluar las constantes que son más interesantes a tener en cuenta, cómo y dónde analizar los datos recibidos, y lo más importante, que sean presentados una vez analizados de tal forma que tengan utilidad y se pueda acceder a ellos desde cualquier lugar. Para resolver esto, se plantea un sistema que se divide en en tres grandes bloques: por un lado el sistema que sensoriza la mercancía, midiendo así la temperatura y humedad a la que viaja, y si se producen golpes en la misma; por otro lado, un dispositivo en el contenedor que transporta las mercancías, que recibirá los datos de todos los envíos que contiene, y enviará en tiempo real la localización del contenedor junto con los datos de las medidas recibidas hacia un servidor en la nube. Una vez los datos llegan a la nube, estos son analizados y presentados en un cuadro de mando accesible vía Internet desde cualquier lugar

Blockchain-based solutions for UAV-assisted connected vehicle networks in smart cities: a review, open issues, and future perspectives

It had been predicted that by 2020, nearly 26 billion devices would be connected to the Internet, with a big percentage being vehicles. The Internet of Vehicles (IoVa) is a concept that refers to the connection and cooperation of smart vehicles and devices in a network through the generation, transmission, and processing of data that aims at improving traffic congestion, travel time, and comfort, all the while reducing pollution and accidents. However, this transmission of sensitive data (e.g., location) needs to occur with defined security properties to safeguard vehicles and their drivers since attackers could use this data. Blockchain is a fairly recent technology that guarantees trust between nodes through cryptography mechanisms and consensus protocols in distributed, untrustful environments, like IoV networks. Much research has been done in implementing the former in the latter to impressive results, as Blockchain can cover and offer solutions to many IoV problems. However, these implementations have to deal with the challenge of IoV node’s resource constraints since they do not suffice for the computational and energy requirements of traditional Blockchain systems, which is one of the biggest limitations of Blockchain implementations in IoV. Finally, these two technologies can be used to build the foundations for smart cities, enabling new application models and better results for end-users

Enabling Edge-Intelligence in Resource-Constrained Autonomous Systems

The objective of this research is to shift Machine Learning algorithms from resource-extensive server/cloud to compute-limited edge nodes by designing energy-efficient ML systems. Multiple sub-areas of research in this domain are explored for the application of drone autonomous navigation. Our principal goal is to enable the UAV to autonomously navigate using Reinforcement Learning, without incurring any additional hardware or sensor cost. Most of the lightweight UAVs are limited in their resources such as compute capabilities and onboard energy source, and the conventional state-of-the-art ML algorithms cannot be directly implemented on them. This research addresses this issue by devising energy-efficient ML algorithms, modifying existing ML algorithms, designing energy-efficient ML accelerators, and leveraging the hardware-algorithm co-design. RL is notorious for being data-hungry and requires trials and error for it to converge. Hence it cannot be directly implemented on real drones until the issues of safety, data limitations, and reward generation is addressed. Instead of learning the task from scratch, just like humans, RL algorithms can benefit from prior knowledge which can help them converge to their goals in less time and consume less energy. Multiple drones can be collectively used to help each other by sharing their locally learned knowledge. Such distributive systems can help agents learn their respective local tasks faster but may become vulnerable to attacks in the presence of adversarial agents which needs to be addressed. Finally, the improvement in the energy efficiency of RL-based systems achieved from the algorithmic approaches is limited by the underlying hardware and computing architectures. Hence, these need to be redesigned in an application-specific way exploring and exploiting the nature of the most used ML operators This can be done by exploring new computing devices and considering the data reuse and dataflow of ML operators within the architectural design. This research discusses these issues by addressing them and presenting better alternatives. It is concluded that energy consumption at multiple levels of hierarchy needs to be addressed by exploring algorithmic, hardware-based, and algorithm-hardware co-design approaches.Ph.D