A comparison of generative and discriminative appliance recognition models for load monitoring

Appliance-level Load Monitoring (ALM) is essential, not only to optimize energy utilization, but also to promote energy awareness amongst consumers through real-time feedback mechanisms. Non-intrusive load monitoring is an attractive method to perform ALM that allows tracking of appliance states within the aggregated power measurements. It makes use of generative and discriminative machine learning models to perform load identification. However, particularly for low-power appliances, these algorithms achieve sub-optimal performance in a real world environment due to ambiguous overlapping of appliance power features. In our work, we report a performance comparison of generative and discriminative Appliance Recognition (AR) models for binary and multi-state appliance operations. Furthermore, it has been shown through experimental evaluations that a significant performance improvement in AR can be achieved if we make use of acoustic information generated as a by-product of appliance activity. We demonstrate that our a discriminative model FF-AR trained using a hybrid feature set which is a catenation of audio and power features improves the multi-state AR accuracy up to 10 %, in comparison to a generative FHMM-AR model

Low-Power Appliance Monitoring Using Factorial Hidden Markov Models

To optimize the energy utilization, intelligent energy management solutions require appliance-specific consumption statistics. One can obtain such information by deploying smart power outlets on every device of interest, however it incurs extra hardware cost and installation complexity. Alternatively, a single sensor can be used to measure total electricity consumption and thereafter disaggregation algorithms can be applied to obtain appliance specific usage information. In such a case, it is quite challenging to discern low-power appliances in the presence of high-power loads. To improve the recognition of low-power appliance states, we propose a solution that makes use of circuit-level power measurements. We examine the use of a specialized variant of Hidden Markov Model (HMM) known as Factorial HMM (FHMM) to recognize appliance specific load patterns from the aggregated power measurements. Further, we demonstrate that feature concatenation can improve the disaggregation performance of the model allowing it to identify device states with an accuracy of 90% for binary and 80% for multi-state appliances. Through experimental evaluations, we show that our solution performs better than the traditional event based approach. In addition, we develop a prototype system that allows real-time monitoring of appliance states

Researching the transparency of personal data sharing: designing a concert receipt

The project’s overarching aim was to increase people’s awareness, trust and control over the data that they share with organisations and explore how organisations can give more control over the data individuals share when conducting personal data transactions. We focused on personal data sharing and trust using user experience (UX) design and prototyping methodology. The motivation of this project was to help citizens understand why we capture their personal data, how it benefits them, and evaluate the idea of a consent receipt. A consent receipt being a receipt that tracks a user’s consent by making a record of it, just like a regular receipt is used to track purchasing of products. Consent receipts allow: users to understand the data their share, where it goes, who has it and why users and organisations to keep a proof of consent and enable consistent consent practices organisations to simplify terms and conditions. Following this, we wanted to leverage the consent receipt standard to design and take to market a consumer-centric consent process, ultimately increasing consumers’ trust in organisations. We implemented several research methods, from mapping visitors’ different experiences within the Digital Catapult Centre, to exploratory interviews with visitors to investigate what they value in terms of data capture. This work contributed to the design of a meaningful consent receipt – from assessing its value in creating transparency and trust in different contexts, to understanding consumers’ personal data sharing patterns, and finally by informing future research

Acoustic and Device Feature Fusion for Load Recognition

Appliance-specific Load Monitoring (LM) provides a possible solution to the problem of energy conservation which is becoming increasingly challenging, due to growing energy demands within offices and residential spaces. It is essential to perform automatic appliance recognition and monitoring for optimal resource utilization. In this paper, we study the use of non-intrusive LM methods that rely on steady-state appliance signatures for classifying most commonly used office appliances, while demonstrating their limitation in terms of accurately discerning the low-power devices due to overlapping load signatures. We propose a multilayer decision architecture that makes use of audio features derived from device sounds and fuse it with load signatures acquired from energy meter. For the recognition of device sounds, we perform feature set selection by evaluating the combination of time-domain and FFT-based audio features on the state of the art machine learning algorithms. The highest recognition performance however is shown by support vector machines, for the device and audio recognition experiments. Further, we demonstrate that our proposed feature set which is a concatenation of device audio feature and load signature significantly improves the device recognition accuracy in comparison to the use of steady-state load signatures only

Mind My Value: a decentralized infrastructure for fair and trusted IoT data trading

Internet of Things (IoT) data are increasingly viewed as a new form of massively distributed and large scale digital assets, which are continuously generated by millions of connected devices. The real value of such assets can only be realized by allowing IoT data trading to occur on a marketplace that rewards every single producer and consumer, at a very granular level. Crucially, we believe that such a marketplace should not be owned by anybody, and should instead fairly and transparently self-enforce a well defined set of governance rules. In this paper we address some of the technical challenges involved in realizing such a marketplace. We leverage emerging blockchain technologies to build a decentralized, trusted, transparent and open architecture for IoT traffic metering and contract compliance, on top of the largely adopted IoT brokered data infrastructure. We discuss an Ethereum-based prototype implementation and experimentally evaluate the overhead cost associated with Smart Contract transactions, concluding that a viable business model can indeed be associated with our technical approach

A high-order discontinuous Galerkin method for the poro-elasto-acoustic problem on polygonal and polyhedral grids

The aim of this work is to introduce and analyze a finite element discontinuous Galerkin method on polygonal meshes for the numerical discretization of acoustic waves propagation through poroelastic materials. Wave propagation is modeled by the acoustics equations in the acoustic domain and the low-frequency Biot's equations in the poroelastic one. The coupling is introduced by considering (physically consistent) interface conditions, imposed on the interface between the domains, modeling both open and sealed pores. Existence and uniqueness is proven for the strong formulation based on employing the semigroup theory. For the space discretization we introduce and analyze a high-order discontinuous Galerkin method on polygonal and polyhedral meshes, which is then coupled with Newmark-$\beta$ time integration schemes. A stability analysis both for the continuous problem and the semi-discrete one is presented and error estimates for the energy norm are derived for the semidiscrete problem. A wide set of numerical results obtained on test cases with manufactured solutions are presented in order to validate the error analysis. Examples of physical interest are also presented to test the capability of the proposed methods in practical cases.Comment: The proof of the well-posedness contains an error. This has an impact on the whole paper. We need time to fix the issu

A Survey on Facilities for Experimental Internet of Things Research

International audienceThe initial vision of the Internet of Things (IoT) was of a world in which all physical objects are tagged and uniquelly identified by RFID transponders. However, the concept has grown into multiple dimensions, encompassing sensor networks able to provide real-world intelligence and goal-oriented collaboration of distributed smart objects via local networks or global interconnections such as the Internet. Despite significant technological advances, difficulties associated with the evaluation of IoT solutions under realistic conditions, in real world experimental deployments still hamper their maturation and significant roll out. In this article we identify requirements for the next generation of the IoT experimental facilities. While providing a taxonomy, we also survey currently available research testbeds, identify existing gaps and suggest new directions based on experience from recent efforts in this field

EV Charging Recommendation Concerning Preemptive Service and Charging Urgency Policy

Compared with traditional internal combustion engine vehicles, Electric Vehicles (EVs) have the advantage of eliminating harmful gases in the environment, with great development potential in recent years. However, because the battery capacity of EVs is limited at the current stage, where to charge (to select charging station) and when/whether to charge (order the charging priority of EVs) still limit the large-scale popularity of EVs. In this paper, we develop an Urgency First Charging (UFC) charging scheduling policy, which takes the remaining parking time and charging time of EVs as the standard of charging priority. With this, the CS benefits to the shortest trip duration (summation of travelling time through CS, and charging service time at CS) is selected as optimal solution. We have conducted simulations through Helsinki's traffic scenarios. The results have shown that our proposed CS-Selection scheme effectively improves the charging comfort (in terms of waiting time and trip time) and charging efficiency (in terms of not-fully charged service due to limited parking duration)

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