Andreas Savvides

University of New Haven, New Haven, Connecticut, United States

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Publications (77)18.48 Total impact

  • Deokwoo Jung, Andreas Savvides
    ACM Transactions on Sensor Networks 11/2014; 11(1):1-36. DOI:10.1145/2630880 · 1.46 Impact Factor
  • Deokwoo Jung, Andreas Savvides, Athanasios Bamis
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    ABSTRACT: Given the ongoing widespread deployment of low frequency electricity sub-metering devices at residential and commercial buildings, fine-grained usage information of end-loads can bring a new powerful sensing modality in Cyber-Physical Systems (CPS). Motivated by the opportunity, this paper describes an algorithm of estimating the ON/OFF sequences for typical household end-loads in close-to-real-time using an off-the-shelf power meter. Unlike previous algorithms that lacks in scalability to support diverse applications in CPS our algorithm is designed to provide control knobs to support various trade-offs between accuracy and computation load or delay to satisfy the different application requirements. We experimentally verify the proposed algorithm using a collection of home appliances. Our experiment result shows that our algorithm is able to detect ON/OFF sequences of 7 appliances nearly without error and 3 appliances with moderate error rate less than 6% among 12 typical household appliances.
    01/2012; DOI:10.1145/2228360.2228393
  • Deokwoo Jung, Athanasios Bamis, Andreas Savvides
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    ABSTRACT: This poster paper describes a method for estimating the ON/OFF usage profile for appliances inside a home using an off-the-Unlike previous approaches that put their emphasis on the detection of individual events using high frequency sampling, our approach aims to reliably detect sequences of ON/OFF events from traces of less reliable ON/OFF events identified on data from low-frequency sampling. The proposed algorithm is evaluated experimentally using a collection of home appliances.
    Proceedings of the Third ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Buildings; 11/2011
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    ABSTRACT: The proliferation of the smart grid creates new opportunities for large buildings to act as smart end-points that provide mutually beneficial services for building occupants and the grid. In this article we describe how Cyber-Physical systems that provide rapid access to information and decision-making can enable buildings to autonomously interact with the grid. By participating in new real-time electricity markets on the utility side of the meter and day-ahead markets, buildings have the potential of achieving local efficiency and reliability gains while also facilitating the effective utilization of renewables.
    ACM SIGBED Review 06/2011; DOI:10.1145/2000367.2000375
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    ABSTRACT: This paper presents an automated methodology for extracting the spatiotemporal activity model of a person using a wireless sensor network deployed inside a home. The sensor network is modeled as a source of spatiotemporal symbols whose output is triggered by the monitored person’s motion over space and time. Using this stream of symbols, the problem of human activity modeling is formulated as a spatiotemporal pattern-matching problem on top of the sequence of symbolic information the sensor network produces, and is solved using an exhaustive search algorithm. The effectiveness of the proposed methodology is demonstrated on a real 30-day dataset extracted from an ongoing deployment of a sensor network inside a home monitoring an elder. The developed algorithm examines the person’s data over these 30days and automatically extracts the person’s daily pattern. KeywordsHuman activity model–Spatiotemporal activity patterns–Sensor networks
    Universal Access in the Information Society 06/2011; 10(2):125-138. DOI:10.1007/s10209-010-0197-5 · 0.40 Impact Factor
  • A. Bamis, A. Savvides
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    ABSTRACT: A large collection of mobile sensing applications depend on the knowledge of the user's whereabouts and are heavily based on GPS location measurements. Although knowledge of location is very desirable, in many mobile applications excessive GPS sampling is very energy taxing thus posing a barrier to application sustainability. To mitigate this problem, in this paper we examine how to reduce GPS sensing redundancies by extracting the state of a person and using it to drive GPS sampling on mobile phones. Using a GPS dataset we first describe how to extract the spatio-temporal states of the user. We then use the knowledge of the user's state to reduce GPS sampling rate, helping to make mobile applications more sustainable.
    Pervasive Computing and Communications Workshops (PERCOM Workshops), 2011 IEEE International Conference on; 04/2011
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    ABSTRACT: The in-house monitoring of elders using intelligent sensors is a very de- sirable service that has the potential of increasing autonomy and independence while minimizing the risks of living alone. Because of this promise, the eorts of building such systems have been spanning for decades, but there is still a lot of room for improvement. Driven by the recent technology advances in many of the required components, in this paper we present a scalable framework for detailed behavior interpretation of elders. We report on our early deployment experiences and present our current progress in three main components: sensors, middleware and behavior interpretation mechanisms that aim to make eective monitoring and assistive services a reality.
    Personal and Ubiquitous Computing 09/2010; 14(6):473-487. DOI:10.1007/s00779-010-0282-z · 1.62 Impact Factor
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    Deokwoo Jung, T. Teixeira, A. Savvides
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    ABSTRACT: This work describes a new approach for localizing people by cooperative sensor fusion of lightweight camera and wearable accelerometer measurements. We present the algorithm to identify people moving around as they are detected by cameras deployed in the infrastructure. The algorithm uses a correlation metric to develop an ID matching algorithm that can associate people in the scene to their global ID emitted from a wireless accelerometer sensor node worn on their belts. First we conduct a set of preliminary experiments to verify that the quantities of interest easily measurable by off-the-shelf components. We validate our metric and the performance of the proposed ID matching algorithm using simulations on testbed data that also includes a crowded scenario.
    INFOCOM, 2010 Proceedings IEEE; 04/2010
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    Thiago Teixeira, Deokwoo Jung, Andreas Savvides
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    ABSTRACT: We present a method to identify and localize people by leveraging existing CCTV camera infrastructure along with inertial sensors (accelerometer and magnetometer) within each person's mobile phones. Since a person's motion path, as observed by the camera, must match the local motion measurements from their phone, we are able to uniquely identify people with the phones' IDs by detecting the statistical dependence between the phone and camera measurements. For this, we express the problem as consisting of a two-measurement HMM for each person, with one camera measurement and one phone measurement. Then we use a maximum a posteriori formulation to find the most likely ID assignments. Through sensor fusion, our method largely bypasses the motion correspondence problem from computer vision and is able to track people across large spatial or temporal gaps in sensing. We evaluate the system through simulations and experiments in a real camera network testbed.
    UbiComp 2010: Ubiquitous Computing, 12th International Conference, UbiComp 2010, Copenhagen, Denmark, September 26-29, 2010, Proceedings; 01/2010
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    Deokwoo Jung, Andreas Savvides
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    ABSTRACT: This paper considers the problem of estimating the power breakdowns for the main appliances inside a building using a small number of power meters and the knowledge of the ON/OFF states of individual appliances. First we solve the breakdown estimation problem within a tree configuration using a single power meter and the knowledge of ON/OFF states and use the solution to derive an estimation quality metric. Using this metric, we then propose an algorithm for optimally placing additional power meters to increase the estimation certainty for individual appliances to the required level. The proposed solution is evaluated using real measurements, numerical simulations and by constructing a scaled down proof-of-concept prototype using binary sensors.
    Proceedings of the 8th International Conference on Embedded Networked Sensor Systems, SenSys 2010, Zurich, Switzerland, November 3-5, 2010; 01/2010
  • Athanasios Bamis, Andreas Savvides
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    ABSTRACT: In this paper we present a classification of human movement in physical space into spatio-temporal activities (STAs) and classes thereof. Drawing from our experiences with real human data from GPS traces we define a clustering approach for STA extraction based on the amount of motion of the user in space and time. Our solution captures these properties in a lightweight online algorithm that can run inside mobile devices. We then cluster the discovered STAs into classes based on a similarity metric that aims to identify which activities (STAs) are consistent in time. In contrast to previous approaches of discovering important places, this work also utilizes the temporal properties of the data to extract more realistic STAs and STA classes. Our work is evaluated through simulations and real GPS traces.
    Proceedings of the 31st IEEE Real-Time Systems Symposium, RTSS 2010, San Diego, California, USA, November 30 - December 3, 2010; 01/2010
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    Athanasios Bamis, Jia Fang, Andreas Savvides
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    ABSTRACT: This poster abstract introduces the problem of macroscopic sensing composition, where a sensor capable to detect complex events is synthesized dynamically by a collection of simpler sensors using a data-driven approach. Our solution is geared towards discovering the structure of human activities by considering the triggering of simple sensors over a diverse set of spatial and temporal scales. The goal is to identify routines from their components by leveraging the fact that the components have the same temporal persistence as the routines themselves. To this end we have devised an algorithm for determining if an event occurs consistently within a time interval where the interval is periodic but the event is not. The goal of the algorithm is to identify events with this property and also determine the minimum interval in which they occur. Our first results using testbed data and simulations indicate that this approach can uncover components of routines by identifying which events are parts of the same routine through their temporal properties.
    Proceedings of the 9th International Conference on Information Processing in Sensor Networks, IPSN 2010, April 12-16, 2010, Stockholm, Sweden; 01/2010
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    Athanasios Bamis, Jia Fang, Andreas Savvides
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    ABSTRACT: This paper describes an algorithm for determining if an event occurs persistently within an interval where the interval is periodic but the event is not. The goal of the algorithm is to identify events with this property and also determine the minimum interval in which they occur. This solution is geared towards discovering human routines by considering the triggering of simple sensors over a diverse set of spatial and temporal scales. After describing the problem and the proposed solution, in this paper we demonstrate using testbed data and simulations that this approach uncovers components of routines by identifying which events are parts of the same routine through their temporal properties.
    Proceedings of the 19th ACM Conference on Information and Knowledge Management, CIKM 2010, Toronto, Ontario, Canada, October 26-30, 2010; 01/2010
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    ABSTRACT: We present an activity-recognition system for assisted living applications and smart homes. While existing systems tend to rely on expensive computation of comparatively largedimension data sets, ours leverages information from a small number of fundamentally different sensor measurements that provide context information pertaining the person's location, and action information by observing the motion of the body and arms. Camera nodes are placed on the ceiling to track people in the environment, and place them in the context of a building map where areas and objects of interest are premarked. Additionally, a single inertial sensor node is placed on the subject's arm to infer arm pose, heading and motion frequency using an accelerometer, gyroscope and magnetometer. These four measurements are parsed using a lightweight hierarchy of finite state machines, yielding recognition rates with high precision and recall values (0.92 and 0.93, respectively).
    Distributed Smart Cameras, 2009. ICDSC 2009. Third ACM/IEEE International Conference on; 10/2009
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    ABSTRACT: The ability to localize and identify multiple people is paramount to the inference of high-level activities for informed decision-making. In this paper, we describe the PEM-ID system, which uniquely identifies people tagged with accelerometer nodes in the video output of preinstalled infrastructure cameras. For this, we introduce a new distance measure between signals comprised of timestamps of gait landmarks, and utilize it to identify each tracked person from the video by pairing them with a wearable accelerometer node.
    Distributed Smart Cameras, 2009. ICDSC 2009. Third ACM/IEEE International Conference on; 10/2009
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    A. Bamis, Jia Fang, A. Savvides
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    ABSTRACT: Deployments of camera security systems are capable of capturing long data sequences about human activity. This paper deals with processing of detected sequences at a more macroscopic level to detect chains of events based on a prior given specification. In our problem, sensed interactions between people are modeled as sequences of pairwise events that are interleaved with other interactions taking place in the background. We formulate the problem as an isomorphic subgraph matching problem and solve it to detect a chain of events, its participants and their roles. We further evaluate our solution in the presence of background interference from other interactions and give analytical and empirical results about the performance of our algorithm.
    Distributed Smart Cameras, 2009. ICDSC 2009. Third ACM/IEEE International Conference on; 10/2009
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    Athanasios Bamis, Andreas Savvides
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    ABSTRACT: In this paper we introduce the Spatio Temporal Filtering Language (STFL), which is a language framework that aims to provide the primitives for easily defining rules and sequences of rules and constraints. These sequences of rules can be used to convert low-level streams of sensor data into higher-level semantics and provide triggers for actuation. Among others STFL provides support for heterogeneous types of sensors, composability and code reusability. Special emphasis is given on the support of different categories of users by providing different types of interfaces spanning from a natural-like language aiming at end-users to a regular scripting language aiming at system developers. The expressiveness and power of STFL is presented through an assisted living scenario.
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    ABSTRACT: The extraction of temporal characteristics from sensor data streams can reveal important properties about the sensed events. Knowledge of temporal characteristics in applications where sensed events tend to periodically repeat, can provide a great deal of information towards identifying patterns, building models and using the timing information to actuate and provide services. In this paper we outline a methodology for extracting the temporal properties, in terms of start time and duration, of sensor data streams that can be used in applications such as human, habitat, environmental and traffic monitoring where sensed events repeat over a time window. Its application is demonstrated on a 30-day dataset collected from one of our assisted living sensor network deployments.
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    ABSTRACT: We propose a system to identify people in a sensor network. The system fuses motion information measured from wearable accelerometer nodes with motion traces of each person detected by a camera node. This allows people to be uniquely identified with the IDs the accelerometer-node that they wear, while their positions are measured using the cameras. The system can run in real time, with high precision and recall results. A prototype implementation using iMote2s with camera boards and wearable TI EZ430 nodes with accelerometer sensorboards is also described.
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    Deokwoo Jung, Thiago Teixeira, Andreas Savvides
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    ABSTRACT: This article presents two lifetime models that describe two of the most common modes of operation of sensor nodes today, trigger-driven and duty-cycle driven. The models use a set of hardware parameters such as power consumption per task, state transition overheads, and communication cost to compute a node's average lifetime for a given event arrival rate. Through comparison of the two models and a case study from a real camera sensor node design we show how the models can be applied to drive architectural decisions, compute energy budgets and duty-cycles, and to preform side-by-side comparison of different platforms. Based on our models we present a MATLAB Wireless Sensor Node Platform Lifetime Prediction and Simulation Package (MATSNL). This demonstrates the use of the models using sample applications drawn from existing sensor node measurements.

Publication Stats

5k Citations
18.48 Total Impact Points

Institutions

  • 2006–2011
    • University of New Haven
      New Haven, Connecticut, United States
    • Rensselaer Polytechnic Institute
      • Department of Computer Science
      New York City, NY, United States
  • 2003–2011
    • Yale University
      • Department of Electrical Engineering
      New Haven, Connecticut, United States
  • 2006–2009
    • Yale-New Haven Hospital
      New Haven, Connecticut, United States
  • 2000–2003
    • University of California, Los Angeles
      • Department of Electrical Engineering
      Los Angeles, California, United States