Energy-efficient Continuous Context Sensing on Mobile Phones

With the ever increasing adoption of smartphones worldwide, researchers have found the perfect sensor platform to perform context-based research and to prepare for context-based services to be also deployed for the end-users. However, continuous context sensing imposes a considerable challenge in balancing the energy consumption of the sensors, the accuracy of the recognized context and its latency. After outlining the common characteristics of continuous sensing systems, we present a detailed overview of the state of the art, from sensors sub-systems to context inference algorithms. Then, we present the three main contribution of this thesis. The first approach we present is based on the use of local communications to exchange sensing information with neighboring devices. As proximity, location and environmental information can be obtained from nearby smartphones, we design a protocol for synchronizing the exchanges and fairly distribute the sensing tasks. We show both theoretically and experimentally the reduction in energy needed when the devices can collaborate. The second approach focuses on the way to schedule mobile sensors, optimizing for both the accuracy and energy needs. We formulate the optimal sensing problem as a decision problem and propose a two-tier framework for approximating its solution. The first tier is responsible for segmenting the sensor measurement time series, by fitting various models. The second tier takes care of estimating the optimal sampling, selecting the measurements that contributes the most to the model accuracy. We provide near-optimal heuristics for both tiers and evaluate their performances using environmental sensor data. In the third approach we propose an online algorithm that identifies repeated patterns in time series and produces a compressed symbolic stream. The first symbolic transformation is based on clustering with the raw sensor data. Whereas the next iterations encode repetitive sequences of symbols into new symbols. We define also a metric to evaluate the symbolization methods with regard to their capacity at preserving the systems' states. We also show that the output of symbols can be used directly for various data mining tasks, such as classification or forecasting, without impacting much the accuracy, but greatly reducing the complexity and running time. In addition, we also present an example of application, assessing the user's exposure to air pollutants, which demonstrates the many opportunities to enhance contextual information when fusing sensor data from different sources. On one side we gather fine grained air quality information from mobile sensor deployments and aggregate them with an interpolation model. And, on the other side, we continuously capture the user's context, including location, activity and surrounding air quality. We also present the various models used for fusing all these information in order to produce the exposure estimation

OptiMoS: Optimal Sensing for Mobile Sensors

Both sensor coverage maximization and energy cost minimization are the fundamental requirements in the design of real-life mobile sensing applications, e.g., (1) deploying environ- mental sensors (like CO2, fine particle measurement) on public transports to monitor air pollution, (2) analyzing smartphone embedded sensors (like GPS, accelerometer) to recognize people daily activities. However sensor coverage and energy cost are intuitively contradictory. The higher frequency mobile sensing takes, the more energy is used; and vise versa. In this paper, we design a novel two-step mobile sensing process (“OptiMoS”) to achieve optimal mobile sensing that can effectively balance sensor coverage and energy cost. In the first step, OptiMoS divides the continuous mobile sensor readings into several segments, where the readings in one segment are highly- correlated rather than readings amongst different segments. In the second step, OptiMoS identifies optimal sampling for the sensor readings in each segment, where the selected readings can guarantee reasonably high sensor coverage with limited sampling rate. Various greedy & near-optimal segmentation and sampling methods are designed in OptiMoS, and are evaluated using real- life environmental data from mobile sensors. In this paper, we design a novel two-step mobile sensing process (``OptiMoS'') to achieve optimal mobile sensing that can effectively balance sensor converge and energy cost. In the first step, OptiMoS divides the continuous mobile sensor readings into several segments, where the readings in one segment are highly-correlated rather than readings amongst different segments. %the two neighboring segments, in terms of data modeling. In the second step, OptiMoS identifies optimal sampling for the sensor readings in each segment, where the selected readings can guarantee reasonably high sensor coverage with limited sampling rate. Various greedy \& near-optimal {\em segmentation} and {\em sampling} methods are designed in OptiMoS, and are evaluated using real-life environmental data from mobile sensors

Online Unsupervised State Recognition in Sensor Data

Smart sensors, such as smart meters or smart phones, are nowadays ubiquitous. To be "smart", however, they need to process their input data with limited storage and computational resources. In this paper, we convert the stream of sensor data into a stream of symbols, and further, to higher level symbols in such a way that common analytical tasks such as anomaly detection, forecasting or state recognition, can still be carried out on the transformed data with almost no loss of accuracy, and using far fewer resources. We identify states of a monitored system and convert them into symbols (thus, reducing data size), while keeping "interesting" events, such as anomalies or transition between states, as it is. Our algorithm is able to find states of various length in an online and unsupervised way, which is crucial since behavior of the system is not known beforehand. We show the effectiveness of our approach using real-world datasets and various application scenarios

Semantic Data Layers in Air Quality Monitoring for Smarter Cities

Air pollution is one of the key indicators for quality of life in urban environments, and is also the subject of global health concern, given the number of mortal diseases associated to exposure to pollutants. Assessing and monitoring air quality is an important step in order to better understand the impact of pollution on the health of the population. Nevertheless, in order to scale to the city level, traditional high-quality stationary sensing stations are not enough. Limitations include lack of coverage, the cost of deployment and maintenance, as well as the resolution of the observed phenomena. The OpenSense2 project aims at providing a city-level sensing deployment that combines different levels of air quality sensing: reference stations, mobile sensing on public transportation, and participatory crowdsensing. In this paper we highlight some of the key challenges of managing the data captured by such infrastructure, taking the city of Lausanne as a driving use-case. Furthermore, we present a semantics-based approach for characterizing and exposing the air quality data, so that it can be made available to citizens and application developers in a way that it can be usable and understood effectively

Toward Self-monitoring Smart Cities: the OpenSense2 Approach

The sustained growth of urban settlements in the last years has had an inherent impact on the environment and the quality of life of their inhabitants. In order to support sustainability and improve quality of life in this context, we advocate the fostering of ICT-empowered initiatives that allow citizens to self-monitor their environment and assess the quality of the resources in their surroundings. More concretely, we present the case of such a self-monitoring Smart City platform for estimating the air quality in urban environments at high resolution and large scale. Our approach is a combination of mobile and human sensing that exploits both dedicated and participatory monitoring. We identify the main challenges in such a crowdsensing scenario for Smart Cities, and in particular we analyze issues related to scalability, accuracy, accessibility, privacy, and discoverability, among others. Moreover, we show that our approach has the potential to empower citizens to diagnose their environment using mobile and portable sensing devices, combining their personal data with a public higher accuracy air quality network

An energy-aware method for the joint recognition of activities and gestures using wearable sensors

This paper presents an energy-aware method for recognizing time series acceleration data containing both activities and gestures using a wearable device coupled with a smartphone. In our method, we use a small wearable device to collect accelerometer data from a user's wrist, recognizing each data segment using a minimal feature set chosen automatically for that segment. For each collected data segment, if our model finds that recognizing the segment requires high-cost features that the wearable device cannot extract, such as dynamic time warping for gesture recognition, then the segment is transmitted to the smartphone where the high-cost features are extracted and recognition is performed. Otherwise, only the minimum required set of low-cost features are extracted from the segment on the wearable device and only the recognition result, i.e., label, is transmitted to the smartphone in place of the raw data, reducing transmission costs. Our method automatically constructs this adaptive processing pipeline solely from training data

Diversity of Raft-Like Domains in Late Endosomes

BACKGROUND: Late endosomes, the last sorting station in the endocytic pathway before lysosomes, are pleiomorphic organelles composed of tubular elements as well as vesicular regions with a characteristic multivesicular appearance, which play a crucial role in intracellular trafficking. Here, we have investigated whether, in addition to these morphologically distinguishable regions, late endosomal membranes are additionally sub-compartmentalized into membrane microdomains. METHODOLOGY/PRINCIPAL FINDINGS: Using sub-organellar fractionation techniques, both with and without detergents, combined with electron microscopy, we found that both the limiting membrane of the organel and the intraluminal vesicles contain raft-type membrane domains. Interestingly, these differentially localized domains vary in protein composition and physico-chemical properties. CONCLUSIONS/SIGNIFICANCE: In addition to the multivesicular organization, we find that late endosomes contain cholesterol rich microdomains both on their limiting membrane and their intraluminal vesicles that differ in composition and properties. Implications of these findings for late endosomal functions are discussed

Symbolic Representation of Smart Meter Data

Currently smart meter data analytics has received enormous attention because it allows utility companies to analyze customer consumption behavior in real time. However, the amount of data generated by these sensors is very large. As a result, analytics performed on top of it become very expensive. Furthermore, smart meter data contains very detailed energy consumption measurement which can lead to customer privacy breach and all risks associated with it. In this work, we address the problem on how to reduce smart meter data numerosity and its detailed measurement while maintaining its analytics accuracy. We convert the data into symbolic representation and allow various machine learning algorithms to be performed on top of it. In addition, our symbolic representation admit an additional advantage to allow also algorithms which usually work on nominal and string to be run on top of smart meter data. We provide an experiment for classification and forecasting tasks using real-world data. And finally, we illustrate several directions to extend our work further. Categories and Subject Descriptors E.4 [Coding and information theory]: Data compaction and compression—time series; H.2.8 [Database Applications]: Data mining; G.3 [Probability and Statistics]: Time series analysi

A model-based back-end for air quality data management

In this paper we present a hybrid model for real-time query processing over data stream collected by mobile air quality sensors. First, we introduce a novel indexing scheme for representing air quality and use it for generating and evaluating a static model over a yearly dataset. Then, this model is combined with a dynamic nearest-neighbor approach for real-time updates, and implemented into the Global Sensor Network (GSN) middleware, with added support for model queries