AAL open source system for the monitoring and intelligent control of nursing homes

[EN] SAFE-ECH is an innovative intelligent AAL open source system for monitoring nursing homes, that creates an Ambient Intelligent environment in a residence by collecting and storing sensor monitoring data, performing intelligent data analysis and specific actions to enhance the safety, comfort and efficient care of aged people. Our system implements open standards of the Open Geospatial Consortium complying with Observations & Measurements Schema (O&M), SensorML and Sensor Web Enablement (SWE) specifications. Our system adapts to the specific needs of each nursing home, integrating the required sensors, actuators, rules and services. It is scalable and allows the management of several residences simultaneously.This research was partially funded by the European Union's Horizon 2020 research and innovation programme as part of the INTERIoT project under Grant Agreement 687283, and by SAFE-ECH funded by the Spanish Ministerio de Industria, Economía y Competitividad (MINECO) under Grant Agreement RTC-2015-4502-1González-Usach, R.; Collado, V.; Esteve Domingo, M.; Palau Salvador, CE. (2017). AAL open source system for the monitoring and intelligent control of nursing homes. IEEE Systems, Man, and Cybernetics Society. 1-6. https://doi.org/10.1109/ICNSC.2017.8000072S1

Integration of heterogeneous devices and communication models via the cloud in the constrained internet of things

As the Internet of Things continues to expand in the coming years, the need for services that span multiple IoT application domains will continue to increase in order to realize the efficiency gains promised by the IoT. Today, however, service developers looking to add value on top of existing IoT systems are faced with very heterogeneous devices and systems. These systems implement a wide variety of network connectivity options, protocols (proprietary or standards-based), and communication methods all of which are unknown to a service developer that is new to the IoT. Even within one IoT standard, a device typically has multiple options for communicating with others. In order to alleviate service developers from these concerns, this paper presents a cloud-based platform for integrating heterogeneous constrained IoT devices and communication models into services. Our evaluation shows that the impact of our approach on the operation of constrained devices is minimal while providing a tangible benefit in service integration of low-resource IoT devices. A proof of concept demonstrates the latter by means of a control and management dashboard for constrained devices that was implemented on top of the presented platform. The results of our work enable service developers to more easily implement and deploy services that span a wide variety of IoT application domains

Sharing Human-Generated Observations by Integrating HMI and the Semantic Sensor Web

Current “Internet of Things” concepts point to a future where connected objects gather meaningful information about their environment and share it with other objects and people. In particular, objects embedding Human Machine Interaction (HMI), such as mobile devices and, increasingly, connected vehicles, home appliances, urban interactive infrastructures, etc., may not only be conceived as sources of sensor information, but, through interaction with their users, they can also produce highly valuable context-aware human-generated observations. We believe that the great promise offered by combining and sharing all of the different sources of information available can be realized through the integration of HMI and Semantic Sensor Web technologies. This paper presents a technological framework that harmonizes two of the most influential HMI and Sensor Web initiatives: the W3C’s Multimodal Architecture and Interfaces (MMI) and the Open Geospatial Consortium (OGC) Sensor Web Enablement (SWE) with its semantic extension, respectively. Although the proposed framework is general enough to be applied in a variety of connected objects integrating HMI, a particular development is presented for a connected car scenario where drivers’ observations about the traffic or their environment are shared across the Semantic Sensor Web. For implementation and evaluation purposes an on-board OSGi (Open Services Gateway Initiative) architecture was built, integrating several available HMI, Sensor Web and Semantic Web technologies. A technical performance test and a conceptual validation of the scenario with potential users are reported, with results suggesting the approach is soun

Feature Selection of Distributed Denial of Service (DDos) IoT Bot Attack Detection Using Machine Learning Techniques

Distributed Denial of Service (DDoS) attack can be made through numerous medium and became the one of the biggest threats for computer security. One of the most effective approaches are to develop an algorithm using Machine Learning (ML). However, low accuracy of DDoS because of feature selection classifier and time-consuming detection. This research focusses on the features selection of DDoS IoT bot attack detection using ML techniques. Two datasets from NetFlow which are NF_ToN_IoT and NF_BoT_IoT are manipulated with 2 attributes selection which are Information Gain and Gain Ratio and ranked using Ranker algorithm. These datasets are then tested using four different algorithm such as Naïve Bayes (NB). K-Nearest Neighbor (KNN), Decision Table (DT) and Random Forest (RF). The results then compared using confusion matrix evaluation Accuracy, True Positive, True Negative, Precision and Recall. The result from two datasets is selected by Top 4, Top 8 and Top 12 features selection. The best overall classifier is Naïve Bayes with the accuracy of 97.506% and 90.67% for both dataset NF_ToN_IoT and NF_BoT_IoT.&nbsp

Alpine sensor network system for high-resolution spatial snow and runoff estimation

Monitoring the snowpack is crucial for water management, flood control and hydropower optimization. Traditional regression methods often result in low accuracy runoff predictions.Existing ground-based real-time measurement systems are in majority installed at low elevations with poor physiographic representation. This thesis presents a system for better Snow Water Equivalent (SWE) and runoff estimation. The autonomous end-to-end Wireless Sensor Network (WSN) that leverages the Internet of Things (IoT) technology provides mountain hydrology measurements in near real-time. At its core lies an ultra-low power, radio channel-hoping, and self-organizing mesh secured with a rugged weather-sealed design, data replication and remote network health monitoring. Three WSNs are installed throughout the North Fork of the Feather River in Northern California upstream of the Oroville dam. Elevation, aspect, slope and vegetation determine network locations. Data show considerable spatial variability of snow depth, and that existing operational autonomous systems are non-representative spatially, with biases reaching up to 50%. Combined with existing systems, WSNs better detect precipitation timing and phase, monitor sub-daily dynamics of infiltration and surface runoff, and inform hydro power managers about actual ablation and end-of-season date across the landscape. A wet and dry year exhibit strong multi-scale inter-year spatial stationarity with major rank conservation. Elastic Net regression shows that dominant features at the sub-km2 scale are site-dependent and differ from the watershed scale. Based on the Nearest Neighbor (NN) with a Landsat assimilated historical product, explanatory variables consistently explain up to 90% of the variance in the watershed-scale SWE for both years. Lagged cross correlation of snowmelt with stream flow measurements show improvement of up to 100% compared to existing systems. Ensemble Optimal Interpolation (EnOI) update of background SWE fields from Landsat and LiDAR products provide accurate high resolution estimates of spatial SWE for areas with parsimonious sensors. Results show a minimum RMSE of 22% and 30% at 90 m and 50 m resolutions respectively. Compared with SNODAS, reduction in error is up to 55% and 80%, with LiDAR as reference

Underpinning Quality Assurance: Identifying Core Testing Strategies for Multiple Layers of Internet-of-Things-Based Applications

The Internet of Things (IoT) constitutes a digitally integrated network of intelligent devices equipped with sensors, software, and communication capabilities, facilitating data exchange among a multitude of digital systems via the Internet. Despite its pivotal role in the software development life-cycle (SDLC) for ensuring software quality in terms of both functional and non-functional aspects, testing within this intricate software–hardware ecosystem has been somewhat overlooked. To address this, various testing techniques are applied for real-time minimization of failure rates in IoT applications. However, the execution of a comprehensive test suite for specific IoT software remains a complex undertaking. This paper proposes a holistic framework aimed at aiding quality assurance engineers in delineating essential testing methods across different testing levels within the IoT. This delineation is crucial for effective quality assurance, ultimately reducing failure rates in real-time scenarios. Furthermore, the paper offers a mapping of these identified tests to each layer within the layered framework of the IoT. This comprehensive approach seeks to enhance the reliability and performance of IoT-based applications