A DHT-Based Discovery Service for the Internet of Things

Current trends towards the Future Internet are envisaging the conception of novel services endowed with context-aware and autonomic capabilities to improve end users' quality of life. The Internet of Things paradigm is expected to contribute towards this ambitious vision by proposing models and mechanisms enabling the creation of networks of "smart things" on a large scale. It is widely recognized that efficient mechanisms for discovering available resources and capabilities are required to realize such vision. The contribution of this work consists in a novel discovery service for the Internet of Things. The proposed solution adopts a peer-to-peer approach for guaranteeing scalability, robustness, and easy maintenance of the overall system. While most existing peer-to-peer discovery services proposed for the IoT support solely exact match queries on a single attribute (i.e., the object identifier), our solution can handle multiattribute and range queries. We defined a layered approach by distinguishing three main aspects: multiattribute indexing, range query support, peer-to-peer routing. We chose to adopt an over-DHT indexing scheme to guarantee ease of design and implementation principles. We report on the implementation of a Proof of Concept in a dangerous goods monitoring scenario, and, finally, we discuss test results for structural properties and query performance evaluation

Distributed spatial indexing for the Internet of Things data management

The Internet of Things (IoT) has become a new enabler for collecting real-world observation and measurement data from the physical world. The IoT allows objects with sensing and network capabilities (i.e. Things and devices) to communicate with one another and with other resources (e.g. services) on the digital world. The heterogeneity, dynamicity and ad-hoc nature of underlying data, and services published by most of IoT resources make accessing and processing the data and services a challenging task. The IoT demands distributed, scalable, and efficient indexing solutions for large-scale distributed IoT networks. We describe a novel distributed indexing approach for IoT resources and their published data. The index structure is constructed by encoding the locations of IoT resources into geohashes and then building a quadtree on the minimum bounding box of the geohash representations. This allows to aggregate resources with similar geohashes and reduce the size of the index. We have evaluated our proposed solution on a large-scale dataset and our results show that the proposed approach can efficiently index and enable discovery of the IoT resources with 65% better response time than a centralised approach and with a high success rate (around 90% in the first few attempts)

Service-Oriented Middleware for the Mobile Internet of Things: A Scalable Solution

International audienceThe Internet of Things (IoT) is characterized by a wide penetration in the regular user's life through an increasing number of mobile Things, such as mobile phones hosting sensors and actuators. However, the shift to the mobile IoT does not come without challenges, as many already existing issues remain unresolved and are amplified by the IoT scale and the mobility of its Things. The most challenging issues are handling the abundance of users and Things, providing interoperability across the heterogeneous Things, and overcoming the unknown dynamic environment due to the mobility of Things. This paper addresses the above challenges as we revisit the commonly used Service-Oriented Architecture (SOA). This leads to the design, implementation and evaluation of MobIoT, a new service-oriented middleware. MobIoT modifies standard SOA functionalities, namely service discovery, composition and access, to better address the challenges posed by the IoT, especially its scale. Specifically, MobIoT adopts probabilistic methods to decrease the number of involved devices, while building on semantic knowledge to support interoperability and fulfill users' queries for Thing-based measurements/actions

Dragon: Multidimensional Range Queries on Distributed Aggregation Trees,

Distributed query processing is of paramount importance in next-generation distribution services, such as Internet of Things (IoT) and cyber-physical systems. Even if several multi-attribute range queries supports have been proposed for peer-to-peer systems, these solutions must be rethought to fully meet the requirements of new computational paradigms for IoT, like fog computing. This paper proposes dragon, an ecient support for distributed multi-dimensional range query processing targeting ecient query resolution on highly dynamic data. In dragon nodes at the edges of the network collect and publish multi-dimensional data. The nodes collectively manage an aggregation tree storing data digests which are then exploited, when resolving queries, to prune the sub-trees containing few or no relevant matches. Multi-attribute queries are managed by linearising the attribute space through space lling curves. We extensively analysed dierent aggregation and query resolution strategies in a wide spectrum of experimental set-ups. We show that dragon manages eciently fast changing data values. Further, we show that dragon resolves queries by contacting a lower number of nodes when compared to a similar approach in the state of the art

An efficient indexing model for the fog layer of industrial Internet of Things.

Fog Computing is gaining popularity and is being increasingly deployed in various latency-sensitive application domains including industrial IoTs. However, efficient discovery of services is one of the prevailing issues in the fog nodes of indus-trial IoTs which restrain their efficiencies in availing appropriate services to the clients. To address this issue, this paper proposes a novel effi-cient multilevel index model based on equivalence relation, named the DM-index model, for service maintenance and retrieval in the fog layer of industrial IoTs to eliminate redundancy, narrow the search space, reduce both the traversed number of services and retrieval time, ultimately to improve the service discovery efficiency. The efficiency of the proposed index model has been verified theoretically and evaluated experimentally, which demonstrates that the proposed model is effective in achieving much better service discovery and retrieval performance than the sequential and inverted index models.N/

Modèle collaboratif pour l'Internet of Things (IoT)

L'Internet of Things (IoT) consiste principalement à connecter des objets physiques à l'Internet. Le Web of Things (WoT) est un IoT plus spécifique qui vise à apporter des technologies et des normes du Web à l'IoT, L'émergence de l'IoT et du WoT offre un grand potentiel pour le développement de nouveaux services et applications connectant le monde physique au monde virtuel, un processus qui n'était pas possible auparavant. De nos jours, il existe de nombreuses plateformes et applications pour l’IoT. Cependant, et au mieux de notre connaissance, ils se limitent généralement dans leur champ d'application à un simple schéma de stockage et de récupération des données. Dans une tentative de profiter de cette occasion, ce mémoire de maîtrise présente un modèle théorique qui offre un ensemble de primitives et une nouvelle stratégie de collaboration pour partager les données dans le monde de l’IoT. Basé sur une stratégie de décentralisation, ce modèle propose une approche de propagation des données qui se concrétise dans les trois phases suivantes : i) la découverte de services, ii) la sélection de services et iii) la consommation de services, et cela au-delà d’une simple politique de contrôle d’accès. Ce travail présente aussi un langage dédié appelé IoTCollab, qui est conçu pour faciliter la programmation et l'intégration des différents concepts introduits par le modèle de partage de données

Large-Scale Indexing, Discovery, and Ranking for the Internet of Things (IoT)

Network-enabled sensing and actuation devices are key enablers to connect real-world objects to the cyber world. The Internet of Things (IoT) consists of the network-enabled devices and communication technologies that allow connectivity and integration of physical objects (Things) into the digital world (Internet). Enormous amounts of dynamic IoT data are collected from Internet-connected devices. IoT data are usually multi-variant streams that are heterogeneous, sporadic, multi-modal, and spatio-temporal. IoT data can be disseminated with different granularities and have diverse structures, types, and qualities. Dealing with the data deluge from heterogeneous IoT resources and services imposes new challenges on indexing, discovery, and ranking mechanisms that will allow building applications that require on-line access and retrieval of ad-hoc IoT data. However, the existing IoT data indexing and discovery approaches are complex or centralised, which hinders their scalability. The primary objective of this article is to provide a holistic overview of the state-of-the-art on indexing, discovery, and ranking of IoT data. The article aims to pave the way for researchers to design, develop, implement, and evaluate techniques and approaches for on-line large-scale distributed IoT applications and services