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)

Towards Optimally Efficient Field Estimation with Threshold-Based Pruning in Real Robotic Sensor Networks

The efficiency of distributed sensor networks depends on an optimal trade-off between the usage of resources and data quality. The work in this paper addresses the problem of optimizing this trade-off in a self-configured distributed robotic sensor network, with respect to a user-defined objective function. We investigate a quadtree network topology and implement a fully distributed threshold-based field estimation algorithm. Simulations with field data as well as real robot experiments are performed, validating our distributed control strategy and evaluating the threshold-based formula for real world scenarios. We propose a theoretical analysis that predicts the system’s behavior in real world case studies. The experiments and this prediction show very good correspondence, enabling the accurate employment of the objective function, optimizing the trade-off based on user needs

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