iSEA: IoT-based smartphone energy assistant for prompting energy-aware behaviors in commercial buildings

Providing personalized energy-use information to individual occupants enables the adoption of energy-aware behaviors in commercial buildings. However, the implementation of individualized feedback still remains challenging due to the difficulties in collecting personalized data, tracking personal behaviors, and delivering personalized tailored information to individual occupants. Nowadays, the Internet of Things (IoT) technologies are used in a variety of applications including real-time monitoring, control, and decision-making due to the flexibility of these technologies for fusing different data streams. In this paper, we propose a novel IoT-based smartphone energy assistant (iSEA) framework which prompts energy-aware behaviors in commercial buildings. iSEA tracks individual occupants through tracking their smartphones, uses a deep learning approach to identify their energy usage, and delivers personalized tailored feedback to impact their usage. iSEA particularly uses an energy-use efficiency index (EEI) to understand behaviors and categorize them into efficient and inefficient behaviors. The iSEA architecture includes four layers: physical, cloud, service, and communication. The results of implementing iSEA in a commercial building with ten occupants over a twelve-week duration demonstrate the validity of this approach in enhancing individualized energy-use behaviors. An average of 34% energy savings was measured by tracking occupants’ EEI by the end of the experimental period. In addition, the results demonstrate that commercial building occupants often ignore controlling over lighting systems at their departure events that leads to wasting energy during non-working hours. By utilizing the existing IoT devices in commercial buildings, iSEA significantly contributes to support research efforts into sensing and enhancing energy-aware behaviors at minimal costs

On the Feature Discovery for App Usage Prediction in Smartphones

With the increasing number of mobile Apps developed, they are now closely integrated into daily life. In this paper, we develop a framework to predict mobile Apps that are most likely to be used regarding the current device status of a smartphone. Such an Apps usage prediction framework is a crucial prerequisite for fast App launching, intelligent user experience, and power management of smartphones. By analyzing real App usage log data, we discover two kinds of features: The Explicit Feature (EF) from sensing readings of built-in sensors, and the Implicit Feature (IF) from App usage relations. The IF feature is derived by constructing the proposed App Usage Graph (abbreviated as AUG) that models App usage transitions. In light of AUG, we are able to discover usage relations among Apps. Since users may have different usage behaviors on their smartphones, we further propose one personalized feature selection algorithm. We explore minimum description length (MDL) from the training data and select those features which need less length to describe the training data. The personalized feature selection can successfully reduce the log size and the prediction time. Finally, we adopt the kNN classification model to predict Apps usage. Note that through the features selected by the proposed personalized feature selection algorithm, we only need to keep these features, which in turn reduces the prediction time and avoids the curse of dimensionality when using the kNN classifier. We conduct a comprehensive experimental study based on a real mobile App usage dataset. The results demonstrate the effectiveness of the proposed framework and show the predictive capability for App usage prediction.Comment: 10 pages, 17 figures, ICDM 2013 short pape

Context-awareness for mobile sensing: a survey and future directions

The evolution of smartphones together with increasing computational power have empowered developers to create innovative context-aware applications for recognizing user related social and cognitive activities in any situation and at any location. The existence and awareness of the context provides the capability of being conscious of physical environments or situations around mobile device users. This allows network services to respond proactively and intelligently based on such awareness. The key idea behind context-aware applications is to encourage users to collect, analyze and share local sensory knowledge in the purpose for a large scale community use by creating a smart network. The desired network is capable of making autonomous logical decisions to actuate environmental objects, and also assist individuals. However, many open challenges remain, which are mostly arisen due to the middleware services provided in mobile devices have limited resources in terms of power, memory and bandwidth. Thus, it becomes critically important to study how the drawbacks can be elaborated and resolved, and at the same time better understand the opportunities for the research community to contribute to the context-awareness. To this end, this paper surveys the literature over the period of 1991-2014 from the emerging concepts to applications of context-awareness in mobile platforms by providing up-to-date research and future research directions. Moreover, it points out the challenges faced in this regard and enlighten them by proposing possible solutions

Travel Mode Identification with Smartphone Sensors

Personal trips in a modern urban society typically involve multiple travel modes. Recognizing a traveller\u27s transportation mode is not only critical to personal context-awareness in related applications, but also essential to urban traffic operations, transportation planning, and facility design. While the state of the art in travel mode recognition mainly relies on large-scale infrastructure-based fixed sensors or on individuals\u27 GPS devices, the emergence of the smartphone provides a promising alternative with its ever-growing computing, networking, and sensing powers. In this thesis, we propose new algorithms for travel mode identification using smartphone sensors. The prototype system is built upon the latest Android and iOS platforms with multimodality sensors. It takes smartphone sensor data as the input, and aims to identify six travel modes: walking, jogging, bicycling, driving a car, riding a bus, taking a subway. The methods and algorithms presented in our work are guided by two key design principles. First, careful consideration of smartphones\u27 limited computing resources and batteries should be taken. Second, careful balancing of the following dimensions (i) user-adaptability, (ii) energy efficiency, and (iii) computation speed. There are three key challenges in travel mode identification with smartphone sensors, stemming from the three steps in a typical mobile mining procedure. They are (C1) data capturing and preprocessing, (C2) feature engineering, and (C3) model training and adaptation. This thesis is our response to the challenges above. To address the first challenge (C1), in Chapter 4 we develop a smartphone app that collects a multitude of smartphone sensor measurement data, and showcase a comprehensive set of de-noising techniques. To tackle challenge (C2), in Chapter 5 we design feature extraction methods that carefully balance prediction accuracy, computation time, and battery consumption. And to answer challenge (C3), in Chapters 6,7 and 8 we design different learning models to accommodate different situations in model training. A hierarchical model with dynamic sensor selection is designed to address the energy consumption issue. We propose a personalized model that adapts to each traveller\u27s specific travel behavior using limited labeled data. We also propose an online model for the purpose of addressing the model updating problem with large scaled data. In addressing the challenges and proposing solutions, this thesis provides an comprehensive study and gives a systematic solution for travel mode detection with smartphone sensors