Design and implementation of an advanced MQTT broker for distributed pub/sub scenarios

MQTT is one of the most popular communication protocols for Internet of Things applications. Based on a publish/subscribe pattern, it relies on a single broker to exchange messages among clients according to topics of interest. However, such a centralized approach does not scale well and is prone to single point of failure risks, calling for solutions where multiple brokers cooperate together in a distributed fashion. In this paper, we present a complete solution for a distributed MQTT broker systems. We target several functional primitives which are key in such a scenario: broker discovery and failure recovery, overlay tree network creation and message routing. Moreover, we also focus on the case where multiple topics are present in the system. In such a scenario, a single tree-based overlay network connecting the different brokers may not be the most efficient solution. To cope with this issue, we propose a topic-based routing scheme for MQTT distributed brokers. The proposed solution creates multiple overlay networks in the distributed system, each one linking together only the brokers whose connected clients have interest in the same topics. We implement the complete system as an extension of the popular HiveMQ MQTT broker and perform several experiments to test its performance in scenarios characterized by a different publishers/subscribers configurations as well as number of topics existing in the system

THREE TEMPORAL PERSPECTIVES ON DECENTRALIZED LOCATION-AWARE COMPUTING: PAST, PRESENT, FUTURE

Durant les quatre dernières décennies, la miniaturisation a permis la diffusion à large échelle des ordinateurs, les rendant omniprésents. Aujourd’hui, le nombre d’objets connectés à Internet ne cesse de croitre et cette tendance n’a pas l’air de ralentir. Ces objets, qui peuvent être des téléphones mobiles, des véhicules ou des senseurs, génèrent de très grands volumes de données qui sont presque toujours associés à un contexte spatiotemporel. Le volume de ces données est souvent si grand que leur traitement requiert la création de système distribués qui impliquent la coopération de plusieurs ordinateurs. La capacité de traiter ces données revêt une importance sociétale. Par exemple: les données collectées lors de trajets en voiture permettent aujourd’hui d’éviter les em-bouteillages ou de partager son véhicule. Un autre exemple: dans un avenir proche, les données collectées à l’aide de gyroscopes capables de détecter les trous dans la chaussée permettront de mieux planifier les interventions de maintenance à effectuer sur le réseau routier. Les domaines d’applications sont par conséquent nombreux, de même que les problèmes qui y sont associés. Les articles qui composent cette thèse traitent de systèmes qui partagent deux caractéristiques clés: un contexte spatiotemporel et une architecture décentralisée. De plus, les systèmes décrits dans ces articles s’articulent autours de trois axes temporels: le présent, le passé, et le futur. Les systèmes axés sur le présent permettent à un très grand nombre d’objets connectés de communiquer en fonction d’un contexte spatial avec des temps de réponses proche du temps réel. Nos contributions dans ce domaine permettent à ce type de système décentralisé de s’adapter au volume de donnée à traiter en s’étendant sur du matériel bon marché. Les systèmes axés sur le passé ont pour but de faciliter l’accès a de très grands volumes données spatiotemporelles collectées par des objets connectés. En d’autres termes, il s’agit d’indexer des trajectoires et d’exploiter ces indexes. Nos contributions dans ce domaine permettent de traiter des jeux de trajectoires particulièrement denses, ce qui n’avait pas été fait auparavant. Enfin, les systèmes axés sur le futur utilisent les trajectoires passées pour prédire les trajectoires que des objets connectés suivront dans l’avenir. Nos contributions permettent de prédire les trajectoires suivies par des objets connectés avec une granularité jusque là inégalée. Bien qu’impliquant des domaines différents, ces contributions s’articulent autour de dénominateurs communs des systèmes sous-jacents, ouvrant la possibilité de pouvoir traiter ces problèmes avec plus de généricité dans un avenir proche. -- During the past four decades, due to miniaturization computing devices have become ubiquitous and pervasive. Today, the number of objects connected to the Internet is in- creasing at a rapid pace and this trend does not seem to be slowing down. These objects, which can be smartphones, vehicles, or any kind of sensors, generate large amounts of data that are almost always associated with a spatio-temporal context. The amount of this data is often so large that their processing requires the creation of a distributed system, which involves the cooperation of several computers. The ability to process these data is important for society. For example: the data collected during car journeys already makes it possible to avoid traffic jams or to know about the need to organize a carpool. Another example: in the near future, the maintenance interventions to be carried out on the road network will be planned with data collected using gyroscopes that detect potholes. The application domains are therefore numerous, as are the prob- lems associated with them. The articles that make up this thesis deal with systems that share two key characteristics: a spatio-temporal context and a decentralized architec- ture. In addition, the systems described in these articles revolve around three temporal perspectives: the present, the past, and the future. Systems associated with the present perspective enable a very large number of connected objects to communicate in near real-time, according to a spatial context. Our contributions in this area enable this type of decentralized system to be scaled-out on commodity hardware, i.e., to adapt as the volume of data that arrives in the system increases. Systems associated with the past perspective, often referred to as trajectory indexes, are intended for the access to the large volume of spatio-temporal data collected by connected objects. Our contributions in this area makes it possible to handle particularly dense trajectory datasets, a problem that has not been addressed previously. Finally, systems associated with the future per- spective rely on past trajectories to predict the trajectories that the connected objects will follow. Our contributions predict the trajectories followed by connected objects with a previously unmet granularity. Although involving different domains, these con- tributions are structured around the common denominators of the underlying systems, which opens the possibility of being able to deal with these problems more generically in the near future

Managing Event-Driven Applications in Heterogeneous Fog Infrastructures

The steady increase in digitalization propelled by the Internet of Things (IoT) has led to a deluge of generated data at unprecedented pace. Thereby, the promise to realize data-driven decision-making is a major innovation driver in a myriad of industries. Based on the widely used event processing paradigm, event-driven applications allow to analyze data in the form of event streams in order to extract relevant information in a timely manner. Most recently, graphical flow-based approaches in no-code event processing systems have been introduced to significantly lower technological entry barriers. This empowers non-technical citizen technologists to create event-driven applications comprised of multiple interconnected event-driven processing services. Still, today’s event-driven applications are focused on centralized cloud deployments that come with inevitable drawbacks, especially in the context of IoT scenarios that require fast results, are limited by the available bandwidth, or are bound by the regulations in terms of privacy and security. Despite recent advances in the area of fog computing which mitigate these shortcomings by extending the cloud and moving certain processing closer to the event source, these approaches are hardly established in existing systems. Inherent fog computing characteristics, especially the heterogeneity of resources alongside novel application management demands, particularly the aspects of geo-distribution and dynamic adaptation, pose challenges that are currently insufficiently addressed and hinder the transition to a next generation of no-code event processing systems. The contributions of this thesis enable citizen technologists to manage event-driven applications in heterogeneous fog infrastructures along the application life cycle. Therefore, an approach for a holistic application management is proposed which abstracts citizen technologists from underlying technicalities. This allows to evolve present event processing systems and advances the democratization of event-driven application management in fog computing. Individual contributions of this thesis are summarized as follows: 1. A model, manifested in a geo-distributed system architecture, to semantically describe characteristics specific to node resources, event-driven applications and their management to blend application-centric and infrastructure-centric realms. 2. Concepts for geo-distributed deployment and operation of event-driven applications alongside strategies for flexible event stream management. 3. A methodology to support the evolution of event-driven applications including methods to dynamically reconfigure, migrate and offload individual event-driven processing services at run-time. The contributions are introduced, applied and evaluated along two scenarios from the manufacturing and logistics domain

IoT and Sensor Networks in Industry and Society

The exponential progress of Information and Communication Technology (ICT) is one of the main elements that fueled the acceleration of the globalization pace. Internet of Things (IoT), Artificial Intelligence (AI) and big data analytics are some of the key players of the digital transformation that is affecting every aspect of human's daily life, from environmental monitoring to healthcare systems, from production processes to social interactions. In less than 20 years, people's everyday life has been revolutionized, and concepts such as Smart Home, Smart Grid and Smart City have become familiar also to non-technical users. The integration of embedded systems, ubiquitous Internet access, and Machine-to-Machine (M2M) communications have paved the way for paradigms such as IoT and Cyber Physical Systems (CPS) to be also introduced in high-requirement environments such as those related to industrial processes, under the forms of Industrial Internet of Things (IIoT or I2oT) and Cyber-Physical Production Systems (CPPS). As a consequence, in 2011 the German High-Tech Strategy 2020 Action Plan for Germany first envisioned the concept of Industry 4.0, which is rapidly reshaping traditional industrial processes. The term refers to the promise to be the fourth industrial revolution. Indeed, the first industrial revolution was triggered by water and steam power. Electricity and assembly lines enabled mass production in the second industrial revolution. In the third industrial revolution, the introduction of control automation and Programmable Logic Controllers (PLCs) gave a boost to factory production. As opposed to the previous revolutions, Industry 4.0 takes advantage of Internet access, M2M communications, and deep learning not only to improve production efficiency but also to enable the so-called mass customization, i.e. the mass production of personalized products by means of modularized product design and flexible processes. Less than five years later, in January 2016, the Japanese 5th Science and Technology Basic Plan took a further step by introducing the concept of Super Smart Society or Society 5.0. According to this vision, in the upcoming future, scientific and technological innovation will guide our society into the next social revolution after the hunter-gatherer, agrarian, industrial, and information eras, which respectively represented the previous social revolutions. Society 5.0 is a human-centered society that fosters the simultaneous achievement of economic, environmental and social objectives, to ensure a high quality of life to all citizens. This information-enabled revolution aims to tackle today’s major challenges such as an ageing population, social inequalities, depopulation and constraints related to energy and the environment. Accordingly, the citizens will be experiencing impressive transformations into every aspect of their daily lives. This book offers an insight into the key technologies that are going to shape the future of industry and society. It is subdivided into five parts: the I Part presents a horizontal view of the main enabling technologies, whereas the II-V Parts offer a vertical perspective on four different environments. The I Part, dedicated to IoT and Sensor Network architectures, encompasses three Chapters. In Chapter 1, Peruzzi and Pozzebon analyse the literature on the subject of energy harvesting solutions for IoT monitoring systems and architectures based on Low-Power Wireless Area Networks (LPWAN). The Chapter does not limit the discussion to Long Range Wise Area Network (LoRaWAN), SigFox and Narrowband-IoT (NB-IoT) communication protocols, but it also includes other relevant solutions such as DASH7 and Long Term Evolution MAchine Type Communication (LTE-M). In Chapter 2, Hussein et al. discuss the development of an Internet of Things message protocol that supports multi-topic messaging. The Chapter further presents the implementation of a platform, which integrates the proposed communication protocol, based on Real Time Operating System. In Chapter 3, Li et al. investigate the heterogeneous task scheduling problem for data-intensive scenarios, to reduce the global task execution time, and consequently reducing data centers' energy consumption. The proposed approach aims to maximize the efficiency by comparing the cost between remote task execution and data migration. The II Part is dedicated to Industry 4.0, and includes two Chapters. In Chapter 4, Grecuccio et al. propose a solution to integrate IoT devices by leveraging a blockchain-enabled gateway based on Ethereum, so that they do not need to rely on centralized intermediaries and third-party services. As it is better explained in the paper, where the performance is evaluated in a food-chain traceability application, this solution is particularly beneficial in Industry 4.0 domains. Chapter 5, by De Fazio et al., addresses the issue of safety in workplaces by presenting a smart garment that integrates several low-power sensors to monitor environmental and biophysical parameters. This enables the detection of dangerous situations, so as to prevent or at least reduce the consequences of workers accidents. The III Part is made of two Chapters based on the topic of Smart Buildings. In Chapter 6, Petroșanu et al. review the literature about recent developments in the smart building sector, related to the use of supervised and unsupervised machine learning models of sensory data. The Chapter poses particular attention on enhanced sensing, energy efficiency, and optimal building management. In Chapter 7, Oh examines how much the education of prosumers about their energy consumption habits affects power consumption reduction and encourages energy conservation, sustainable living, and behavioral change, in residential environments. In this Chapter, energy consumption monitoring is made possible thanks to the use of smart plugs. Smart Transport is the subject of the IV Part, including three Chapters. In Chapter 8, Roveri et al. propose an approach that leverages the small world theory to control swarms of vehicles connected through Vehicle-to-Vehicle (V2V) communication protocols. Indeed, considering a queue dominated by short-range car-following dynamics, the Chapter demonstrates that safety and security are increased by the introduction of a few selected random long-range communications. In Chapter 9, Nitti et al. present a real time system to observe and analyze public transport passengers' mobility by tracking them throughout their journey on public transport vehicles. The system is based on the detection of the active Wi-Fi interfaces, through the analysis of Wi-Fi probe requests. In Chapter 10, Miler et al. discuss the development of a tool for the analysis and comparison of efficiency indicated by the integrated IT systems in the operational activities undertaken by Road Transport Enterprises (RTEs). The authors of this Chapter further provide a holistic evaluation of efficiency of telematics systems in RTE operational management. The book ends with the two Chapters of the V Part on Smart Environmental Monitoring. In Chapter 11, He et al. propose a Sea Surface Temperature Prediction (SSTP) model based on time-series similarity measure, multiple pattern learning and parameter optimization. In this strategy, the optimal parameters are determined by means of an improved Particle Swarm Optimization method. In Chapter 12, Tsipis et al. present a low-cost, WSN-based IoT system that seamlessly embeds a three-layered cloud/fog computing architecture, suitable for facilitating smart agricultural applications, especially those related to wildfire monitoring. We wish to thank all the authors that contributed to this book for their efforts. We express our gratitude to all reviewers for the volunteering support and precious feedback during the review process. We hope that this book provides valuable information and spurs meaningful discussion among researchers, engineers, businesspeople, and other experts about the role of new technologies into industry and society

Storage and Ingestion Systems in Support of Stream Processing: A Survey

Under the pressure of massive, exponentially increasing amounts ofheterogeneous data that are generated faster and faster, Big Data analyticsapplications have seen a shift from batch processing to stream processing,which can reduce the time needed to obtain meaningful insight dramatically.Stream processing is particularly well suited to address the challenges of fog/edgecomputing: much of this massive data comes from Internet of Things (IoT)devices and needs to be continuously funneled through an edge infrastructuretowards centralized clouds. Thus, it is only natural to process data on theirway as much as possible rather than wait for streams to accumulate on thecloud. Unfortunately, state-of-the-art stream processing systems are not wellsuited for this role: the data are accumulated (ingested), processed andpersisted (stored) separately, often using different services hosted ondifferent physical machines/clusters. Furthermore, there is only limited support foradvanced data manipulations, which often forces application developers tointroduce custom solutions and workarounds. In this survey article, wecharacterize the main state-of-the-art stream storage and ingestion systems.We identify the key aspects and discuss limitations and missing features inthe context of stream processing for fog/edge and cloud computing. The goal is tohelp practitioners understand and prepare for potential bottlenecks when usingsuch state-of-the-art systems. In particular, we discuss both functional(partitioning, metadata, search support, message routing, backpressuresupport) and non-functional aspects (high availability, durability,scalability, latency vs. throughput). As a conclusion of our study, weadvocate for a unified stream storage and ingestion system to speed-up datamanagement and reduce I/O redundancy (both in terms of storage space andnetwork utilization)

Building a Framework for High-performance In-memory Message-Oriented Middleware

Message-Oriented Middleware (MOM) is a popular class of software used in many distributed applications, ranging from business systems and social networks to gaming and streaming media services. As workloads continue to grow both in terms of the number of users and the amount of content, modern MOM systems face increasing demands in terms of performance and scalability. Recent advances in networking such as Remote Direct Memory Access (RDMA) offer a more efficient data transfer mechanism compared to traditional kernel-level socket networking used by existing widely-used MOM systems. Unfortunately, RDMA’s complex interface has made it difficult for MOM systems to utilize its capabilities. In this thesis, we introduce a framework called RocketBufs, which provides abstractions and interfaces for constructing high-performance MOM systems. Applications implemented using RocketBufs produce and consume data using regions of memory called buffers while the framework is responsible for transmitting, receiving and synchronizing buffer access. RocketBufs’ buffer abstraction is designed to work efficiently with different transport protocols, allowing messages to be distributed using RDMA or TCP using the same APIs (i.e., by simply changing a configuration file). We demonstrate the utility and evaluate the performance of RocketBufs by using it to implement a publish/subscribe system called RBMQ. We compare it against two widely-used, industry-grade MOM systems, namely RabbitMQ and Redis. Our evaluations show that when using TCP, RBMQ achieves up to 1.9 times higher messaging throughput than RabbitMQ, a message queuing system with an equivalent flow control scheme. When RDMA is used, RBMQ shows significant gains in messaging throughput (up to 3.7 times higher than RabbitMQ and up to 1.7 times higher than Redis), as well as reductions in median delivery latency (up to 81% lower than RabbitMQ and 47% lower than Redis). In addition, on RBMQ subscriber hosts configured to use RDMA, data transfers occur with negligible CPU overhead regardless of the amount of data being transferred. This allows CPU resources to be used for other purposes like processing data. To further demonstrate the flexibility of RocketBufs, we use it to build a live streaming video application by integrating RocketBufs into a web server to receive disseminated video data. When compared with the same application built with Redis, the RocketBufs-based dissemination host achieves live streaming throughput up to 73% higher while disseminating data, and the RocketBufs-based web server shows a reduction of up to 95% in CPU utilization, allowing for up to 55% more concurrent viewers to be serviced

WebSocket vs WebRTC in the stream overlays of the Streamr Network

The Streamr Network is a decentralized publish-subscribe system. This thesis experimentally compares WebSocket and WebRTC as transport protocols in the system’s d-regular random graph type unstructured stream overlays. The thesis explores common designs for publish-subscribe and decentralized P2P systems. Underlying network protocols including NAT traversal are explored to understand how the WebSocket and WebRTC protocols function. The requirements set for the Streamr Network and how its design and implementations fulfill them are discussed. The design and implementations are validated with the use simulations, emulations and AWS deployed real-world experiments. The performance metrics measured from the real-world experiments are compared to related work. As the implementations using the two protocols are separate incompatible versions, the differences between them was taken into account during analysis of the experiments. Although the WebSocket versions overlay construction is known to be inefficient and vulnerable to churn, it is found to be unintentionally topology aware. This caused the WebSocket stream overlays to perform better in terms of latency. The WebRTC stream overlays were found to be more predictable and more optimized for small payloads as estimates for message propagation delays had a MEPA of 1.24% compared to WebSocket’s 3.98%. Moreover, the WebRTC version enables P2P connections between hosts behind NATs. As the WebRTC version’s overlay construction is more accurate, reliable, scalable, and churn tolerant, it can be used to create intentionally topology aware stream overlays to fully take over the results of the WebSocket implementation

Discreet - Pub/Sub for Edge Systems

The number of devices connected to the Internet has been growing exponentially over the last few years. Today, the amount of information available to users has reached a point that makes it impossible to consume it all, showing that we need better ways to filter what kind of information is sent our way. At the same time, while users are online and access all this information, their actions are also being collected, scrutinized and commercialized with little regard for privacy. This thesis addresses those issues in the context of a decentralized Publish/Subscribe solution for edge systems. Working at the edge of the Internet aims to prevent centralized control from a single entity and lessen the chance of abuse. Our goal was to devise a solution that achieves efficient message delivery, with good load-balancing properties, without revealing its participants subscription interests to preserve user privacy. Our solution uses cryptography and probabilistic data sets as a way to obfuscate event topics and user subscriptions. We modeled a cooperative solution, where publisher and subscriber nodes work in concert to route events among themselves, by leveraging a onehop structured overlay. By using an experimental evaluation, we attest the scalability and general performance of the proposed algorithms, including latency, false negative and false positive rates, and other useful metrics.O número de aparelhos ligados a Internet têm vindo a crescer exponencialmente ao longo dos últimos anos. Hoje em dia, a quantidade de informação que os utilizadores têm disponível, chegou a um ponto que torna impossível o seu total consumo. Isto leva a que seja necessário encontrarmos melhores formas de filtrar a informação que recebemos. Ao mesmo tempo, as ações do utilizadores estão a ser recolhidas, examinadas e comercializadas, sem qualquer respeito pela privacidade. Esta tese trata destes assuntos no contexto de um sistema Publish/Subscribe descentralizado, para sistemas na periferia. O objectivo de operar na preferia da Internet está em prevenir o controlo centralizado por uma única entidade e diminuir a oportunidade para abusos. O nosso objectivo foi conceber uma solução que realiza entrega de mensagens eficientemente, com boas propriedades na distribuição de carga e sem revelar on interesses dos participantes, de forma a preservar a sua privacidade. A nossa solução usa criptografia e estruturas de dados probabilísticas, como uma forma de ofuscar os tópicos dos eventos e as subscrições dos utilizadores. Modelamos o sistema com o objectivo de ser uma solução cooperativa, onde ambos os tipos de nós Editores e Assinantes trabalham em concertadamente para encaminhar eventos entre eles, ao fazerem uso de uma estrutura de rede sobreposta com um salto. Fazendo uma avaliação experimental testámos a escalabilidade e o desempenho geral dos algoritmos propostos, incluindo a latência, falsos negativos, falsos positivos e outras métricas úteis