Contextualising water use in residential settings: a survey of non-intrusive techniques and approaches

Water monitoring in households is important to ensure the sustainability of fresh water reserves on our planet. It provides stakeholders with the statistics required to formulate optimal strategies in residential water management. However, this should not be prohibitive and appliance-level water monitoring cannot practically be achieved by deploying sensors on every faucet or water-consuming device of interest due to the higher hardware costs and complexity, not to mention the risk of accidental leakages that can derive from the extra plumbing needed. Machine learning and data mining techniques are promising techniques to analyse monitored data to obtain non-intrusive water usage disaggregation. This is because they can discern water usage from the aggregated data acquired from a single point of observation. This paper provides an overview of water usage disaggregation systems and related techniques adopted for water event classification. The state-of-the art of algorithms and testbeds used for fixture recognition are reviewed and a discussion on the prominent challenges and future research are also included

SmartSantander: IoT experimentation over a smart city testbed

This paper describes the deployment and experimentation architecture of the Internet of Things experimentation facility being deployed at Santander city. The facility is implemented within the SmartSantander project, one of the projects of the Future Internet Research and Experimentation initiative of the European Commission and represents a unique in the world city-scale experimental research facility. Additionally, this facility supports typical applications and services of a smart city. Tangible results are expected to influence the definition and specification of Future Internet architecture design from viewpoints of Internet of Things and Internet of Services. The facility comprises a large number of Internet of Things devices deployed in several urban scenarios which will be federated into a single testbed. In this paper the deployment being carried out at the main location, namely Santander city, is described. Besides presenting the current deployment, in this article the main insights in terms of the architectural design of a large-scale IoT testbed are presented as well. Furthermore, solutions adopted for implementation of the different components addressing the required testbed functionalities are also sketched out. The IoT experimentation facility described in this paper is conceived to provide a suitable platform for large scale experimentation and evaluation of IoT concepts under real-life conditions.This work is funded by research project SmartSantander, under FP7-ICT-2009-5 of the 7th Framework Programme of the European Community. Authors would like to acknowledge the collaboration with the rest of partners within the consortium leading to the results presented in this paper

Query Interface for Smart City Internet of Things Data Marketplaces: A Case Study

Cities are increasingly getting augmented with sensors through public, private, and academic sector initiatives. Most of the time, these sensors are deployed with a primary purpose (objective) in mind (e.g., deploy sensors to understand noise pollution) by a sensor owner (i.e., the organization that invests in sensing hardware, for example, a city council). Over the last few years, communities undertaking smart city development projects have understood the importance of making the sensor data available to a wider community – beyond their primary usage. Different business models have been proposed to achieve this, including creating data marketplaces. The vision is to encourage new start-ups and small and medium-scale businesses to create novel products and services using sensor data to generate additional economic value. Currently, data are sold as pre-defined independent datasets (e.g., noise level and parking status data may be sold separately). This approach creates several challenges, such as (i) difficulties in pricing, which leads to higher prices (per dataset), (ii) higher network communication and bandwidth requirements, and (iii) information overload for data consumers (i.e., those who purchase data). We investigate the benefit of semantic representation and its reasoning capabilities towards creating a business model that offers data on-demand within smart city Internet of Things (IoT) data marketplaces. The objective is to help data consumers (i.e., small and medium enterprises (SMEs)) acquire the most relevant data they need. We demonstrate the utility of our approach by integrating it into a real-world IoT data marketplace (developed by synchronicity-iot.eu project). We discuss design decisions and their consequences (i.e., trade-offs) on the choice and selection of datasets. Subsequently, we present a series of data modeling principles and recommendations for implementing IoT data marketplaces

SocIoTal - The development and architecture of a social IoT framework

In this paper the development and architecture of the SocIoTal platform is presented. SocIoTal is a European FP7 project which aims to create a socially-aware citizen-centric Internet of Things infrastructure. The aim of the project is to put trust, user-control and transparency at the heart of the system in order to gain the confidence of everyday users and developers. By providing adequate tools and mechanisms that simplify complexity and lower the barriers of entry, it will encourage citizen participation in the Internet of Things. This adds a novel and rich dimension to the emerging IoT ecosystem, providing a wealth of opportunities for the creation of new services and applications. These services and applications will be able to address the needs of society therefore improving the quality of life in cities and communities. In addition to technological innovation, the SocIoTal project sought to innovate the way in which users and developers interact and shape the direction of the project. The project worked on new formats in obtaining data, information and knowledge. The first step consisted of gaining input, feedback and information on IoT as a reality in business. This led to a validated iterative methodology which formed part of the SocIoTal toolkit.This work was supported by the SocIoTal project under grant agreement No 609112

Online Anomaly Detection with an Incremental Centred Kernel Hypersphere

Anomaly detection is an important aspect of data analysis. Kernel methods have been shown to exhibit good anomaly detection performance, however, they have high computational complexity. When anomaly detection is performed on a data stream, computational complexity is a key issue. Our approach uses the kernel hypersphere, which does not require a computationally complex operation in order to form the model. We introduce an incremental update and downdate to the model to further reduce computational complexity. Evaluations on synthetic and real-world datasets show that the incremental kernel hypersphere exhibits competitive performance when compared to other anomaly detectors

Online anomaly rate parameter tracking for anomaly detection in wireless sensor networks

Anomaly detection in a Wireless Sensor Network is an important aspect of data analysis in order to facilitate intrusion and event detection. A key challenge is creating optimal classifiers constructed from training sets in which the anomaly rates are varying due to the existence of non-stationary distributions in the data. In this paper we propose an adaptive algorithm that can dynamically adjust the anomaly rate parameter, which can be represented by a model parameter of a one-class quarter-sphere support vector machine. This algorithm operates in an online, iterative manner producing an optimal model for a training set, which is presented sequentially. Our evaluations demonstrate that our algorithm is capable of constructing optimal models for a training set that minimizes the error rate on the classification set compared to a static model, where the anomaly rate is kept stationary

Internet of things cognitive transformation technology research trends and applications

The Internet of Things (IoT) is changing how industrial and consumer markets are developing. Robotic devices, drones and autonomous vehicles, blockchains, augmented and virtual reality, digital assistants and machine learning (artificial intelligence or AI) are the technologies that will provide the next phase of development of IoT applications. The combination of these disciplines makes possible the development of autonomous systems combining robotics and machine learning for designing similar systems. This new hyperconnected world offers many benefits to businesses and consumers, and the processed data produced by hyperconnectivity allows stakeholders in the IoT value network ecosystems to make smarter decisions and provide better customer experience.publishedVersio