This article considers a problem of periodically estimating energy consumption breakdowns for main appliances inside building using a single power meter and the knowledge of the ON/OFF states of individual appliances. In the first part of this article, we formulate the problem as a constrained convex optimization problem with tunable parameters. Then we propose an online algorithm that adaptively determines the optimization parameters to robustly estimate the breakdown information. The proposed solution is evaluated by experiment using a scaled-down proof-of-concept prototype with real measurements. In the second part, we provide detailed analysis to understand the performance of our proposed algorithm. We first develop a stochastic model to describe evolution of appliances’ ON/OFF states using continuous-time Markov chain. Then we derive analytical bounds of estimation error and the probability of a rank-deficient binary matrix. Those analytical bounds are verified by extensive simulations. Finally, we study the effect of collinearity of binary data matrix on estimation performance. Simulation results suggest that our algorithm is robust against the collinearity of binary dataset.

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.

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.

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.

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.

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.

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.

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.

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.

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.

This paper constructs a model for studying the energy efficiency of sensor node architectures featuring a pair of low-end, low-power processor and radio and a pair of high-end, energy efficient processor and radio. Such nodes can have a highly dynamic range of operation ranging from the collection of simple temperature measurements or motion detection all the way up to sophisticated signal processing of sensor data. Our model explores the energy efficiency tradeoffs related to the decision of which processor and which radio should be used for each task. For this we derive a general Semi-Markov Decision process model for maximizing the asymptotic lifetime of two alternate designs, one with dynamic and one with static interconnect. The resulting models are validated with simulation and are applied to the reported measurements from an existing platform with two radios and two processors. Our results show how to quantify the gains of such design with respect to the power consumption properties of each component. Furthermore, based on our power budget calculation, we conclude that the deisgn of reconfigurable interconnect between multiple processors and radios would result in efficiency gains despite the energy overhead such device may incur.

In this demonstration we present the BehaviorScope, a system for interpreting human activity patterns using a sensor network and its application to elder monitoring in assisted living. The BehaviorScope provides a runtime, user-programmable framework that processes streams of timestamped sensor data along with prior context information to infer activities and generate appropriate notifications. Human activities are described in high-level scripts that are directly mapped to a hierarchy of probabilistic grammars that parse low-level sensor measurements into high-level distinguishable activities. Activities of interest are pre-programmed into a specification that is used by the system to interpret the incoming sensor data stream. The system interprets the activities to generate summaries and other triggered notifications that are propagated to stakeholders via email and cell-phone text messages.

This paper presents BScope, a new system for interpreting human activity patterns using a sensor network. BScope provides a runtime, user-programmable framework that processes streams of timestamped sensor data along with prior context information to infer activities and generate appropriate notifications. The users of the system are able to describe human activities with high-level scripts that are directly mapped to hierarchical probabilistic grammars used to parse low-level sensor measurements into high-level distinguishable activities. Our approach is presented, though not limited, in the context of an assisted living application in which a small, privacy-preserving camera sensor network of five nodes is used to monitor activity in the entire house over a period of 25 days. Privacy is preserved by the fact that camera sensors only provide discrete high-level features, such as motion information in the form of image locations, and not actual images. In this deployment, our primary sensing modality is a distributed array of image sensors with wide-angle lenses that observe people's locations in the house during the course of the day. We demonstrate that our system can successfully generate summaries of everyday activities and trigger notifications at runtime by using more than 1.3 million location measurements acquired through our real home deployment.

The paper explores an algorithm and methodology for realtime video compression and communication over sensor network. Video is encoded using the address-event representation (AER) and using custom image sensors capable of detecting intensity-differences (motion) information. The work focuses on keeping a constant low bit-rate over sensor network channels based on ZigBee radios, so that the motion information in an image can degrade gracefully when the resolution or the image content is varied over time. The paper shows that a 320times240 video stream can be compressed up to 50 times while being intelligible for interpreting motion patterns. This compression requires zero computation when the image is encoded with AER. The work will be applied to remote monitoring of home-care patients and surveillance

Camera sensors constitute an information rich sensing modality with many potential applications in sensor networks. Their effectiveness in a sensor network setting however greatly relies on their ability to calibrate with respect to each other, and other sensors in the field. This paper examines node localization and camera calibration using the shared field of view of camera pairs. Using a new distributed camera sensor network we compare two approaches from computer vision and propose an algorithm that combines a sparse set of distance measurements with image information to accurately localize nodes in 3D. Our algorithms are evaluated using a network of iMote2 nodes equipped with COTS camera modules. The sensor nodes identify themselves to cameras using modulated LED emissions. Our indoor experiments yielded a 2-7cm error in a 6x6m room. Our outdoor experiments in a 30x30m field resulted in errors 20-80cm, depending on the method used.

The ability of a sensor network to parse out observable activities into a set of distinguishable actions is a powerful feature that can potentially enable many applications of sensor networks to everyday life situations. In this paper we introduce a framework that uses a hierarchy of Probabilistic Context Free Grammars (PCFGs) to perform such parsing. The power of the framework comes from the hierarchical organization of grammars that allows the use of simple local sensor measurements for reasoning about more macroscopic behaviors. Our presentation describes how to use a set of phonemes to construct grammars and how to achieve distributed operation using a messaging model. The proposed framework is flexible. It can be mapped to a network hierarchy or can be applied sequentially and across the network to infer behaviors as they unfold in space and time. We demonstrate this functionality by inferring simple motion patterns using a sequence of simple direction vectors obtained from our camera sensor network testbed.

Although imaging is an information-rich sensing modality, the use of cameras in sensor networks is very often prohibited by factors such as power, computation cost, storage, communication bandwidth and privacy. In this paper we consider information selective and privacy-preserving address-event imagers for sensor networks. Instead of providing full images with a high degree of redundancy, our efforts in the design of these imagers specialize on selecting a handful of features from a scene and outputting these features in address-event representation. In this paper we present our initial results in modeling and evaluating address-event sensors in the context of sensor networks. Using three different platforms that we have developed, we illustrate how to model address-event cameras and how to build an emulator using these models. We also present a lightweight classification scheme to illustrate the computational advantages of address-event sensors. The paper concludes with an evaluation of the classification algorithm and a feasibility study of using COTS components to emulate address-event inside a sensor network.

We describe a sensor network based on smart imager sensors able to extract events of interest from a scene. We present a framework towards the use of image sensors for realizing sensor networks that can interpret behaviors in physical space. An array of heterogeneous event-based CMOS imagers is employed at front-end, to convert light intensity, motion and other vision primitives into pulse-density modulated stream of address-events. The wireless nodes are Yale XYZ motes employing high data rate communication and large processing capabilities. Energy-aware communication is implemented at the sensor level by employing an event-based readout with pixel-level filtering capability. This paper provides an overview of the research challenges followed by a brief description of our current research efforts towards creating a functional experimental system for investigating these intelligent networks

This paper focuses on the principled study of distance reconstruction for distance-based node localization. We address an important issue in node localization by showing that a highly incomplete set of inter-node distance measurements obtained in ad-hoc node deployments carries sufficient information for the accurate reconstruction of the missing distances, even in the presence of noise and sensor node failures. We provide an efficient and provably accurate algorithm for this reconstruction, and we show that the resulting error is bounded, decreasing at a rate that is inversely proportional to radicn, the square root of the number of nodes in the region of deployment. Although this result is applicable to many localization schemes, in this paper we illustrate its use in conjunction with the popular multidimensional scaling algorithm. Our analysis reveals valuable insights and key factors to consider during the sensor network setup phase, to improve the quality of the position estimates

Knowing the positions of the nodes in a network is essential to many next generation pervasive and sensor network functionalities. Although many network localization systems have recently been proposed and evaluated, there has been no systematic study of partially localizable networks, i.e., networks in which there exist nodes whose positions cannot be uniquely determined. There is no existing study which correctly identifies precisely which nodes in a network are uniquely localizable and which are not. This absence of a sufficient uniqueness condition permits the computation of erroneous positions that may in turn lead applications to produce flawed results. In this paper, in addition to demonstrating the relevance of networks that may not be fully localizable, we design the first framework for two dimensional network localization with an efficient component to correctly determine which nodes are localizable and which are not. Implementing this system, we conduct comprehensive evaluations of network localizability, providing guidelines for both network design and deployment. Furthermore, we study an integration of traditional geographic routing with geographic routing over virtual coordinates in the partially localizable network setting. We show that this novel cross-layer integration yields good performance, and argue that such optimizations will be likely be necessary to ensure acceptable application performance in partially localizable networks.

This paper describes the XYZ, a new open-source sensing platform specifically designed to support our experimental research in mobile sensor networks. The XYZ node is designed around the OKI ML67Q500x ARM THUMB Microprocessor and the IEEE 802.15.4 compliant CC2420 radio from Chipcon. Its new features include support for two different CPU sleep modes and a long-term ultra low power sleep mode for the entire node. This allows the XYZ and its peripheral boards to transition into deep sleep for extended time intervals. To support mobility hardware control and computations, XYZ supports a wide dynamic range and power options. In low power configuration the node resembles existing small low power nodes. When needed, the node can scale up its resources to perform more powerful computations. Mobility is enabled with an additional accessory board that allows the node to move along a horizontal string. In this paper we provide an overview of the XYZ architecture and provide an insightful power characterization of the different operational modes to allow the users to optimize their platforms for power.

The ability to extract topological regularity out of large randomly deployed sensor networks holds the promise to maximally leverage correlation for data aggregation and also to assist with sensor localization and hierarchy creation. This paper focuses on extracting such regular structures from physical topology through the development of a distributed clustering scheme. The topology adaptive spatial clustering (TASC) algorithm presented here is a distributed algorithm that partitions the network into a set of locally isotropic, non-overlapping clusters without prior knowledge of the number of clusters, cluster size and node coordinates. This is achieved by deriving a set of weights that encode distance measurements, connectivity and density information within the locality of each node. The derived weights form the terrain for holding a coordinated leader election in which each node selects the node closer to the center of mass of its neighborhood to become its leader. The clustering algorithm also employs a dynamic density reachability criterion that groups nodes according to their neighborhood's density properties. Our simulation results show that the proposed algorithm can trace locally isotropic structures in non-isotropic network and cluster the network with respect to local density attributes. We also found out that TASC exhibits consistent behavior in the presence of moderate measurement noise levels.

The use of low-cost imagers in sensor network applications is imminent. Small low-power imagers have many potential applications in visual confirmation of events, target recognition, security and entertainment. To make the autonomous operation of imager sensors in such applications possible, smart imager sensors should be able to calibrate their views with respect to other imagers in the network and should become aware of the locations of other non-imager sensors. Such calibration will allow cameras with multiple vantage points and sensors distributed in physical space to jointly undertake collaborative tasks.

Ad hoc localization of wireless sensor nodes is a fundamental problem in wireless sensor networks. Despite the recent proposals for the development of ad hoc localization algorithms, the fundamental behavior in systems using measurements has not been characterized. In this paper, we take a first step toward such a characterization by examining the behavior of error inducing parameters in multihop localization systems in an algorithm independent manner. We first derive the Crame Rao Bound for Gaussian measurement error for multihop localization systems using distance and angular measurements. Later on, we use these bounds on a carefully controlled set of scenarios to study the trends in the error induced by the measurement technology accuracy, network density, beacon node concentration, and beacon uncertainty. By exposing these trends, the goal of this paper is to develop a fundamental understanding of the error behavior that can provide a set of guidelines to be considered during the design and deployment of multihop localization systems.

We present a low cost wireless microsensor node architecture for distributed computation and sensing in massively distributed embedded systems. Our design focuses on the development of a versatile, low power device to facilitate experimentation and initial deployment of wireless microsensor nodes in deeply embedded systems. This paper provides the details of our architecture and introduces fine-grained node localization as an example application of distributed computation and wireless embedded sensing.

In this paper, we look at different battery capacity models that have been introduced in the literatures. These models describe the battery capacity utilization based on how the battery is discharged by the circuits that consume power. In an attempt to validate these models, we characterize a commercially available lithium coin cell battery through careful measurements of the current and the voltage output of the battery under different load profile applied by a micro sensor node. In the result, we show how the capacity of the battery is affected by the different load profile and provide analysis on whether the conventional battery models are applicable in the real world. One of the most significant finding of our work will show that DC/DC converter plays a significant role in determining the battery capacity, and that the true capacity of the battery may only be found by careful measurements.

The recent advances in radio and em beddedsystem technologies have enabled the proliferation of wireless microsensor networks. Such wirelessly connected sensors are released in many diverse environments to perform various monitoring tasks. In many such tasks, location awareness is inherently one of the most essential system parameters. It is not only needed to report the origins of events, but also to assist group querying of sensors, routing, and to answer questions on the network coverage. In this paper we present a novel approach to the localization of sensors in an ad-hoc network. We describe a system called AHLoS (Ad-Hoc Localization System) that enables sensor nodes to discover their locations using a set distributed iterative algorithms. The operation of AHLoS is demonstrated with an accuracy of a few centimeters using our prototype testbed while scalability and performance are studied through simulation.

In a wireless ad-hoc network environment, it is important for a mobile host to be able to broadcast control information within a team of nodes in a bandwidth-efficient and timely manner. Although the classical flooding strategy is the only reliable broadcast method for high rates of topology change, it leads to unnecessary bandwidth consumption for low rates of topology change. In this paper we propose two efficient, low complexity, loop-free broadcast routing protocols which outperform flooding in terms of bandwidth consumption in cases of low mobility and are quite robust under moderate mobility scenarios. Comparative results are obtained in environments with unpredictable link failures or lack of global topology knowledge due to node mobility.

The advent of wireless micro sensors promises many yet unrealized benefits. A network of such sensors or “sensor network” introduces a new set of challenges. Besides being able to communicate effectively, sensor networks have demanding sensing tasks. First, they must be aware of their environment and oftentimes are required to adapt to their surroundings. Second, they must coordinate among them to perform a greater group-sensing task. In this context, the study of sensor networks has numerous other aspects besides communication. To create a better understanding of sensor networks and to facilitate the development of new protocols and applications, detailed simulation and performance evaluation techniques need to be developed. In this paper, we introduce our ongoing efforts in the development of SensorSim, a simulation framework that introduces new models and techniques for the design and analysis of sensor networks. SensorSim inherits the core features of traditional event driven network simulators, and builds up new features that include ability to model power usage in sensor nodes, hybrid simulation that allows the interaction of real and simulated nodes, new communication protocols and real time user interaction with graphical data display. After discussing the details of SensorSim, we provide our current results, that demonstrate various capabilities of SensorSim.