Game theoretic and auction-based algorithms towards opportunistic communications in LPWA LoRa networks

Low Power Wide Area (LPWA) networks have been the enabling technology for large-scale sensor and actuator networks. Low cost, energy-efficiency and longevity of such networks make them perfect candidates for smart city applications. LoRa is a new LPWA standard based on spread spectrum technology, which is suitable for sensor nodes enabling long battery life and bi-directional communication but with low data rates. In this paper, we will demonstrate a use-case inspired model in which, end-nodes with multiple radio transceivers (LoRa/WiFi/BLE) have the option to interconnect via multiple networks to improve communications resilience under the diverse conditions of a smart city of a billion devices. To facilitate this, each node has the ability to switch radio communications opportunistically and adaptively, and this is based on the application requirements and dynamic radio parameters

Machine Learning for Intelligent IoT Networks with Edge Computing

The intelligent Internet of Things (IoT) network is envisioned to be the internet of intelligent things. In this paradigm, billions of end devices with internet connectivity will provide interactive intelligence and revolutionise the current wireless communications. In the intelligent IoT networks, the unprecedented volume and variety of data is generated, making centralized cloud computing ine cient or even infeasible due to network congestion, resource-limited IoT devices, ultra-low latency applications and spectrum scarcity. Edge computing has been proposed to overcome these issues by pushing centralized communication and computation resource physically and logically closer to data providers and end users. However, compared with a cloud server, an edge server only provides nite computation and spectrum resource, making proper data processing and e cient resource allocation necessary. Machine learning techniques have been developed to solve the dynamic and complex problems and big data analysis in IoT networks. Speci - cally, Reinforcement Learning (RL) has been widely explored to address the dynamic decision making problems, which motivates the research on machine learning enabled computation o oading and resource management. In this thesis, several original contributions are presented to nd the solutions and address the challenges. First, e cient spectrum and power allocation are investigated for computation o oading in wireless powered IoT networks. The IoT users o oad all the collected data to the central server for better data processing experience. Then a matching theory-based e cient channel allocation algorithm and a RL-based power allocation mechanism are proposed. Second, the joint optimization problem of computation o oading and resource allocation is investigated for the IoT edge computing networks via machine learning techniques. The IoT users choose to o oad the intensive computation tasks to the edge server while keep simple task execution locally. In this case, a centralized user clustering algorithm is rst proposed as a pre-step to group the IoT users into di erent clusters according to user priorities for achieving spectrum allocation. Then the joint computation o oading, computation resource and power allocation for each IoT user is formulated as an RL framework and solved by proposing a deep Q-network based computation o oading algorithm. At last, to solve the simultaneous multiuser computation o oading problem, a stochastic game is exploited to formulate the joint problem of computation o oading mechanism of multiple sel sh users and resource (including spectrum, computation and radio access technologies resources) allocation into a non-cooperative multiuser computation o oading game. Therefore, a multi-agent RL framework is developed to solve the formulated game by proposing an independent learners based multi-agent Q-learning algorithm

Delivering IoT Services in Smart Cities and Environmental Monitoring through Collective Awareness, Mobile Crowdsensing and Open Data

The Internet of Things (IoT) is the paradigm that allows us to interact with the real world by means of networking-enabled devices and convert physical phenomena into valuable digital knowledge. Such a rapidly evolving field leveraged the explosion of a number of technologies, standards and platforms. Consequently, different IoT ecosystems behave as closed islands and do not interoperate with each other, thus the potential of the number of connected objects in the world is far from being totally unleashed. Typically, research efforts in tackling such challenge tend to propose a new IoT platforms or standards, however, such solutions find obstacles in keeping up the pace at which the field is evolving. Our work is different, in that it originates from the following observation: in use cases that depend on common phenomena such as Smart Cities or environmental monitoring a lot of useful data for applications is already in place somewhere or devices capable of collecting such data are already deployed. For such scenarios, we propose and study the use of Collective Awareness Paradigms (CAP), which offload data collection to a crowd of participants. We bring three main contributions: we study the feasibility of using Open Data coming from heterogeneous sources, focusing particularly on crowdsourced and user-contributed data that has the drawback of being incomplete and we then propose a State-of-the-Art algorith that automatically classifies raw crowdsourced sensor data; we design a data collection framework that uses Mobile Crowdsensing (MCS) and puts the participants and the stakeholders in a coordinated interaction together with a distributed data collection algorithm that prevents the users from collecting too much or too less data; (3) we design a Service Oriented Architecture that constitutes a unique interface to the raw data collected through CAPs through their aggregation into ad-hoc services, moreover, we provide a prototype implementation

Coverage and penetration measurements for Low Power Wide Area Network signals

Wireless sensor networks (WSN) are evermore widespread and play from day to day a bigger role in our lives. Up to date, such networks use multi-hop short-range wireless technologies such as ZigBee, Z-Wave or Bluetooth. This technologies are unpractical to use, if one wants the coverage of an area in the size of a city. Lately, new technologies called Low Power Wide Area Networks (LPWAN) emerged. They have greater range, longer battery life (they can work on single battery for few years) and they mostly use single-hop wireless communication. One such technology, which we will focus on in this master's thesis, is a Long Range Radio or LoRa for short. The goal of our work is to develop an open source, free to use, scalable research platform for conducting short or long term LoRa signal penetration and coverage measurements. With this platform finished, we focused on performing a LoRa signal penetration and coverage measurements in different environments. For all this measurements the same sets of LoRa modulation parameter settings were used, so one can easily compare the signal propagation in different environments. We mainly focused on urban and indoor measurements, but few range tests were also conducted. In one of this range tests, the distance of 39 kilometers was achieved. At this point the signal was still quite strong and packet reception was still satisfying, but due to technical problems, measurement had to be finished

Dispensador electrónico de bolsas para la recolección de heces caninas en espacios públicos

El presente proyecto de investigación tiene como objeto el diseño de un dispensador electrónico, que ayuda en los procesos de control y suministro de bolsas utilizadas en la recolección de heces de mascotas. El sistema se podría emplear en parques y otros lugares públicos de las ciudades. El dispensador está constituido por subsistemas: electrónicos, mecánicos y computacionales; los cuales permiten el proceso de suministro de bolsas. Para su funcionamiento, el dispositivo utiliza como actuadores, motores que proveerán bolsas, también, abren y cierran el depósito de las heces, así mismo, tiene un sistema de autenticación de usuarios compuesto por un módulo de identificación por radiofrecuencia (RFID), se conecta a una base de datos en la nube a través de una red LoRaWAN haciendo uso del protocolo MQTT verificando la existencia del usuario en la plataforma y la disponibilidad de bolsas. La energía necesaria para la operación del módulo se abastecerá mediante un panel solar, una batería y un sistema de conversión de potencia. Dentro de esta propuesta se plantea un diseño partiendo de un análisis de requisitos, seguido de un estudio metodológico que permitió desarrollar los diferentes componentes, se investigó en áreas de IoT, protocolos y seguridad en el IoT y sistemas de recolección de heces caninas.The purpose of this research project is the design of an electronic dispenser, which helps in the control and supply processes of bags used in the collection of pet feces. The system could be used in parks and other public places in cities. The dispenser is made up of electronic, mechanical and computational subsystems, which enable the bag supply process. For its operation, the device uses as actuators, motors that will provide bags, also, open and close the deposit of feces, likewise, it has a user authentication system composed of a radio frequency identification module (RFID), it connects to a database in the cloud through a LoRaWAN network making use of the MQTT protocol verifying the existence of the user on the platform and the availability of bags. The energy required for the operation of the module will be supplied by a solar panel, a battery and a power conversion system. Within this proposal a design is proposed based on a requirements analysis, followed by a methodological study that allowed the development of the different components, research was conducted in the areas of IoT, protocols and security in the IoT and dog feces collection systems