Activity Recognition and Prediction in Real Homes

In this paper, we present work in progress on activity recognition and prediction in real homes using either binary sensor data or depth video data. We present our field trial and set-up for collecting and storing the data, our methods, and our current results. We compare the accuracy of predicting the next binary sensor event using probabilistic methods and Long Short-Term Memory (LSTM) networks, include the time information to improve prediction accuracy, as well as predict both the next sensor event and its mean time of occurrence using one LSTM model. We investigate transfer learning between apartments and show that it is possible to pre-train the model with data from other apartments and achieve good accuracy in a new apartment straight away. In addition, we present preliminary results from activity recognition using low-resolution depth video data from seven apartments, and classify four activities - no movement, standing up, sitting down, and TV interaction - by using a relatively simple processing method where we apply an Infinite Impulse Response (IIR) filter to extract movements from the frames prior to feeding them to a convolutional LSTM network for the classification.Comment: 12 pages, Symposium of the Norwegian AI Society NAIS 201

A Smart Game for Data Transmission and Energy Consumption in the Internet of Things

The current trend in developing smart technology for the Internet of Things (IoT) has motivated a lot of research interest in optimizing data transmission or minimizing energy consumption, but with little evidence of proposals for achieving both objectives in a single model. Using the concept of game theory, we develop a new MAC protocol for IEEE 802.15.4 and IoT networks in which we formulate a novel expression for the players' utility function and establish a stable Nash equilibrium (NE) for the game. The proposed IEEE 802.15.4 MAC protocol is modeled as a smart game in which analytical expressions are derived for channel access probability, data transmission probability, and energy used. These analytical expressions are used in formulating an optimization problem (OP) that maximizes data transmission and minimizes energy consumption by nodes. The analysis and simulation results suggest that the proposed scheme is scalable and achieves better performance in terms of data transmission, energy-efficiency, and longevity, when compared with the default IEEE 802.15.4 access mechanism.Peer reviewe

Adjoining Internet of Things with Data Mining : A Survey

The Interactive Data Corporative (IDC) conjectures that by 2025 the worldwide data circle will develop to 163ZB (that is a trillion gigabytes) which is ten times the 16.1ZB of information produced in 2016. The Internet of Things is one of the hot topics of this living century and researchers are heading for mass adoption 2019 driven by better than-expected business results. This information will open one of a kind of user experience and another universe of business opening. The huge information produced by the Internet of Things (IoT) are considered of high business esteem, and information mining calculations can be connected to IoT to extract hidden data from information. This paper concisely discusses the work done in sequential manner of time in different fields of IOT along with its outcome and research gap. This paper also discusses the various aspects of data mining functionalities with IOT. The recommendation for the Challenges in IOT that can be adopted for betterment is given. Finally, this paper presents the vision for how IOT will have impact on changing the distant futur

Datanet: Deep Learning Based Encrypted Network Traffic Classification in SDN Home Gateway

A smart home network will support various smart devices and applications, e.g., home automation devices, E-health devices, regular computing devices, and so on. Most devices in a smart home access the Internet through a home gateway (HGW). In this paper, we propose a software-defined network (SDN)-HGW framework to better manage distributed smart home networks and support the SDN controller of the core network. The SDN controller enables efficient network quality-of-service management based on real-time traffic monitoring and resource allocation of the core network. However, it cannot provide network management in distributed smart homes. Our proposed SDN-HGW extends the control to the access network, i.e., a smart home network, for better end-to-end network management. Specifically, the proposed SDN-HGW can achieve distributed application awareness by classifying data traffic in a smart home network. Most existing traffic classification solutions, e.g., deep packet inspection, cannot provide real-time application awareness for encrypted data traffic. To tackle those issues, we develop encrypted data classifiers (denoted as DataNets) based on three deep learning schemes, i.e., multilayer perceptron, stacked autoencoder, and convolutional neural networks, using an open data set that has over 200 000 encrypted data samples from 15 applications. A data preprocessing scheme is proposed to process raw data packets and the tested data set so that DataNet can be created. The experimental results show that the developed DataNets can be applied to enable distributed application-aware SDN-HGW in future smart home networks

Smartphone-Oriented Development of Video Data Based Services

The massive introduction of video capturing devices in Internet of Things (IoT) environments leads to development of various video data based services. In this paper, we consider the need and background on the video data based services in IoT environments. Based on the smart spaces approach, we introduce the architecture and distributed configurations to construct such services using primarily local devices and to deliver such services using smartphones. We discuss possible data models that can be used on such mediatory components as a local video server and a semantic information broker

Internet of things (IoT) based adaptive energy management system for smart homes

PhD ThesisInternet of things enhances the flexibility of measurements under different environments, the development of advanced wireless sensors and communication networks on the smart grid infrastructure would be essential for energy efficiency systems. It makes deployment of a smart home concept easy and realistic. The smart home concept allows residents to control, monitor and manage their energy consumption with minimal wastage. The scheduling of energy usage enables forecasting techniques to be essential for smart homes. This thesis presents a self-learning home management system based on machine learning techniques and energy management system for smart homes. Home energy management system, demand side management system, supply side management system, and power notification system are the major components of the proposed self-learning home management system. The proposed system has various functions including price forecasting, price clustering, power forecasting alert, power consumption alert, and smart energy theft system to enhance the capabilities of the self-learning home management system. These functions were developed and implemented through the use of computational and machine learning technologies. In order to validate the proposed system, real-time power consumption data were collected from a Singapore smart home and a realistic experimental case study was carried out. The case study had proven that the developed system performing well and increased energy awareness to the residents. This proposed system also showcases its customizable ability according to different types of environments as compared to traditional smart home models. Forecasting systems for the electricity market generation have become one of the foremost research topics in the power industry. It is essential to have a forecasting system that can accurately predict electricity generation for planning and operation in the electricity market. This thesis also proposed a novel system called multi prediction system and it is developed based on long short term memory and gated recurrent unit models. This proposed system is able to predict the electricity market generation with high accuracy. Multi Prediction System is based on four stages which include a data collecting and pre-processing module, a multi-input feature model, multi forecast model and mean absolute percentage error. The data collecting and pre-processing module preprocess the real-time data using a window method. Multi-input feature model uses single input feeding method, double input feeding method and multiple feeding method for features input to the multi forecast model. Multi forecast model integrates long short term memory and gated recurrent unit variations such as regression model, regression with time steps model, memory between batches model and stacked model to predict the future generation of electricity. The mean absolute percentage error calculation was utilized to evaluate the accuracy of the prediction. The proposed system achieved high accuracy results to demonstrate its performance

Plataforma de suporte à gestão de cargas no setor residencial suportada por RNA

Nos últimos 70 anos o consumo de energia foi superior aos 12 mil anos anteriores [1]. Seguindo a mesma tendência o consumo de energia elétrica também tem aumentado. Este facto é justificável pela utilização generalizada de energia elétrica nos mais variados setores, os setores industrial e doméstico apresentam a maior quantidade de energia consumida nos últimos anos. Atualmente, essa dependência prende-se com o facto de existirem inúmeros equipamentos que necessitam de eletricidade ininterruptamente para a sua correta operação, como exemplo temos: sistemas de segurança, equipamentos frigoríficos, entre outros. O aumento de equipamentos presentes nas habitações também é um fator relevante, acarretando maiores consumos ao nível de energia elétrica e consequentemente um aumento da fatura elétrica. Todas as mudanças nos hábitos de consumo obrigaram a repensar os sistemas de fornecimento de energia aos locais de consumo. Ao longo dos últimos anos, a liberalização do mercado português, associada ao mercado de energia elétrica, tem culminado no surgimento de novos tipos de tarifas, nomeadamente o mercado de tarifas dinâmicas. Com o intuito de obter melhorias durante a utilização de energia elétrica, foram utilizadas Redes Neuronais que permitiram uma aprendizagem constante com a utilização de preços precedentes para a realização dessa mesma aprendizagem. Desta forma, recorreu-se a softwares de Inteligência Artificial, cada vez mais procurados e que têm permitido a realização de inúmeras operações tais como a previsão de acontecimentos. Tendo em conta os pontos apresentados anteriormente, surgiu a ideia de criar uma ferramenta que permitisse prever preços de energia elétrica e, concludentemente, realizasse uma gestão de cargas que seriam deslocadas e alocadas em espaços temporais onde o preço de eletricidade fosse o mais reduzido. Esta ferramenta tem como principal objetivo proporcionar uma redução da fatura de eletricidade, tendo por base uma boa gestão de cargas baseada na previsão proveniente da rede neuronal implementada.In the last 70 years energy consumption has been higher than in the previous 12,000 years [1]. Following the same trend, the consumption of electrical energy has also increased. This fact is justified by the widespread use of electricity in the most varied sectors, among them the industrial and domestic sectors adding up to the largest amount of energy consumed in recent years. Currently, this dependence happens because there are several equipments that need electricity uninterruptedly for correct operation, such as security systems, refrigerating equipment and others. The increase in equipments present in homes is also a relevant factor, leading to greater consumption of electricity and consequently an increase in the electric bill. All the changes in consumption habits have forced a change in the way energy reaches our homes. Over the past few years, the liberalization of the Portuguese market, associated with the electricity market, has culminated in the emergence of new types of tariffs, namely the dynamic tariff market. In order to obtain improvements during the use of electricity, Neural Networks were used, which allowed constant learning and the use of previous prices. In this way, Artificial Intelligence software was used, which is increasingly sought after, and which has allowed for numerous operations such as event forecasting. Due to the points presented above, the idea arose of creating a tool that would make it possible to predict electricity prices and, conclusively, to manage loads that would be displaced and allocated in temporal spaces where the price of electricity was lower. The implementation of this tool has as its main objective to provide a reduction in the electricity bill whenever good load management occurs based on the forecast coming from the implemented neuronal network