When Things Matter: A Data-Centric View of the Internet of Things

With the recent advances in radio-frequency identification (RFID), low-cost wireless sensor devices, and Web technologies, the Internet of Things (IoT) approach has gained momentum in connecting everyday objects to the Internet and facilitating machine-to-human and machine-to-machine communication with the physical world. While IoT offers the capability to connect and integrate both digital and physical entities, enabling a whole new class of applications and services, several significant challenges need to be addressed before these applications and services can be fully realized. A fundamental challenge centers around managing IoT data, typically produced in dynamic and volatile environments, which is not only extremely large in scale and volume, but also noisy, and continuous. This article surveys the main techniques and state-of-the-art research efforts in IoT from data-centric perspectives, including data stream processing, data storage models, complex event processing, and searching in IoT. Open research issues for IoT data management are also discussed

Dynamic data placement and discovery in wide-area networks

The workloads of online services and applications such as social networks, sensor data platforms and web search engines have become increasingly global and dynamic, setting new challenges to providing users with low latency access to data. To achieve this, these services typically leverage a multi-site wide-area networked infrastructure. Data access latency in such an infrastructure depends on the network paths between users and data, which is determined by the data placement and discovery strategies. Current strategies are static, which offer low latencies upon deployment but worse performance under a dynamic workload. We propose dynamic data placement and discovery strategies for wide-area networked infrastructures, which adapt to the data access workload. We achieve this with data activity correlation (DAC), an application-agnostic approach for determining the correlations between data items based on access pattern similarities. By dynamically clustering data according to DAC, network traffic in clusters is kept local. We utilise DAC as a key component in reducing access latencies for two application scenarios, emphasising different aspects of the problem: The first scenario assumes the fixed placement of data at sites, and thus focusses on data discovery. This is the case for a global sensor discovery platform, which aims to provide low latency discovery of sensor metadata. We present a self-organising hierarchical infrastructure consisting of multiple DAC clusters, maintained with an online and distributed split-and-merge algorithm. This reduces the number of sites visited, and thus latency, during discovery for a variety of workloads. The second scenario focusses on data placement. This is the case for global online services that leverage a multi-data centre deployment to provide users with low latency access to data. We present a geo-dynamic partitioning middleware, which maintains DAC clusters with an online elastic partition algorithm. It supports the geo-aware placement of partitions across data centres according to the workload. This provides globally distributed users with low latency access to data for static and dynamic workloads.Open Acces

Incrementando as redes centradas à informaçãopara uma internet das coisas baseada em nomes

The way we use the Internet has been evolving since its origins. Nowadays, users are more interested in accessing contents and services with high demands in terms of bandwidth, security and mobility. This evolution has triggered the emergence of novel networking architectures targeting current, as well as future, utilisation demands. Information-Centric Networking (ICN) is a prominent example of these novel architectures that moves away from the current host-centric communications and centres its networking functions around content. Parallel to this, new utilisation scenarios in which smart devices interact with one another, as well as with other networked elements, have emerged to constitute what we know as the Internet of Things (IoT). IoT is expected to have a significant impact on both the economy and society. However, fostering the widespread adoption of IoT requires many challenges to be overcome. Despite recent developments, several issues concerning the deployment of IPbased IoT solutions on a large scale are still open. The fact that IoT is focused on data and information rather than on point-topoint communications suggests the adoption of solutions relying on ICN architectures. In this context, this work explores the ground concepts of ICN to develop a comprehensive vision of the principal requirements that should be met by an IoT-oriented ICN architecture. This vision is complemented with solutions to fundamental issues for the adoption of an ICN-based IoT. First, to ensure the freshness of the information while retaining the advantages of ICN’s in-network caching mechanisms. Second, to enable discovery functionalities in both local and large-scale domains. The proposed mechanisms are evaluated through both simulation and prototyping approaches, with results showcasing the feasibility of their adoption. Moreover, the outcomes of this work contribute to the development of new compelling concepts towards a full-fledged Named Network of Things.A forma como usamos a Internet tem vindo a evoluir desde a sua criação. Atualmente, os utilizadores estão mais interessados em aceder a conteúdos e serviços, com elevados requisitos em termos de largura de banda, segurança e mobilidade. Esta evolução desencadeou o desenvolvimento de novas arquiteturas de rede, visando os atuais, bem como os futuros, requisitos de utilização. As Redes Centradas à Informação (Information-Centric Networking - ICN) são um exemplo proeminente destas novas arquiteturas que, em vez de seguirem um modelo de comunicação centrado nos dispositivos terminais, centram as suas funções de rede em torno do próprio conteúdo. Paralelamente, novos cenários de utilização onde dispositivos inteligentes interagem entre si, e com outros elementos de rede, têm vindo a aparecer e constituem o que hoje conhecemos como a Internet das Coisas (Internet of Things - IoT ). É esperado que a IoT tenha um impacto significativo na economia e na sociedade. No entanto, promover a adoção em massa da IoT ainda requer que muitos desafios sejam superados. Apesar dos desenvolvimentos recentes, vários problemas relacionados com a adoção em larga escala de soluções de IoT baseadas no protocolo IP estão em aberto. O facto da IoT estar focada em dados e informação, em vez de comunicações ponto-a-ponto, sugere a adoção de soluções baseadas em arquiteturas ICN. Neste sentido, este trabalho explora os conceitos base destas soluções para desenvolver uma visão completa dos principais requisitos que devem ser satisfeitos por uma solução IoT baseada na arquitetura de rede ICN. Esta visão é complementada com soluções para problemas cruciais para a adoção de uma IoT baseada em ICN. Em primeiro lugar, assegurar que a informação seja atualizada e, ao mesmo tempo, manter as vantagens do armazenamento intrínseco em elementos de rede das arquiteturas ICN. Em segundo lugar, permitir as funcionalidades de descoberta não só em domínios locais, mas também em domínios de larga-escala. Os mecanismos propostos são avaliados através de simulações e prototipagem, com os resultados a demonstrarem a viabilidade da sua adoção. Para além disso, os resultados deste trabalho contribuem para o desenvolvimento de conceitos sólidos em direção a uma verdadeira Internet das Coisas baseada em Nomes.Programa Doutoral em Telecomunicaçõe

CHORUS Deliverable 2.1: State of the Art on Multimedia Search Engines

Based on the information provided by European projects and national initiatives related to multimedia search as well as domains experts that participated in the CHORUS Think-thanks and workshops, this document reports on the state of the art related to multimedia content search from, a technical, and socio-economic perspective. The technical perspective includes an up to date view on content based indexing and retrieval technologies, multimedia search in the context of mobile devices and peer-to-peer networks, and an overview of current evaluation and benchmark inititiatives to measure the performance of multimedia search engines. From a socio-economic perspective we inventorize the impact and legal consequences of these technical advances and point out future directions of research

Service Abstractions for Scalable Deep Learning Inference at the Edge

Deep learning driven intelligent edge has already become a reality, where millions of mobile, wearable, and IoT devices analyze real-time data and transform those into actionable insights on-device. Typical approaches for optimizing deep learning inference mostly focus on accelerating the execution of individual inference tasks, without considering the contextual correlation unique to edge environments and the statistical nature of learning-based computation. Specifically, they treat inference workloads as individual black boxes and apply canonical system optimization techniques, developed over the last few decades, to handle them as yet another type of computation-intensive applications. As a result, deep learning inference on edge devices still face the ever increasing challenges of customization to edge device heterogeneity, fuzzy computation redundancy between inference tasks, and end-to-end deployment at scale. In this thesis, we propose the first framework that automates and scales the end-to-end process of deploying efficient deep learning inference from the cloud to heterogeneous edge devices. The framework consists of a series of service abstractions that handle DNN model tailoring, model indexing and query, and computation reuse for runtime inference respectively. Together, these services bridge the gap between deep learning training and inference, eliminate computation redundancy during inference execution, and further lower the barrier for deep learning algorithm and system co-optimization. To build efficient and scalable services, we take a unique algorithmic approach of harnessing the semantic correlation between the learning-based computation. Rather than viewing individual tasks as isolated black boxes, we optimize them collectively in a white box approach, proposing primitives to formulate the semantics of the deep learning workloads, algorithms to assess their hidden correlation (in terms of the input data, the neural network models, and the deployment trials) and merge common processing steps to minimize redundancy