Low-power wireless communication has been widely used in cyber-physical systems that require time-critical data delivery. Achieving this goal is challenging because of link burstiness and interference. Based on significant empirical evidence of 21 days and over 3.6 M packet transmissions per link, we propose both routing and scheduling algorithms that produce latency bounds of the real-time periodic streams and accounts for both link bursts and interference. The solution is achieved through the definition of a new metric B max that characterizes links by their maximum burst length, and by choosing a novel least-burst-route that minimizes the sum of worst-case burst lengths over all links in the route. With extensive data-driven analysis, we show that our algorithms outperform existing solutions by achieving accurate latency bound with much less energy consumption. In addition, a testbed evaluation consisting of 48 nodes spread across a floor of a building shows that we obtain 100% reliable packet delivery within derived latency bounds. We also demonstrate how performance deteriorates and discuss its implications for wireless networks with insufficient high-quality links.

Residential homes constitute roughly one-fourth of the total energy usage worldwide. Providing appliance-level energy breakdown has been shown to induce positive behavioral changes that can reduce energy consumption by 15%. Existing approaches for energy breakdown either require hardware installation in every target home or demand a large set of energy sensor data available for model training. However, very few homes in the world have installed sub-meters (sensors measuring individual appliance energy); and the cost of retrofitting a home with extensive sub-metering eats into the funds available for energy saving retrofits. As a result, strategically deploying sensing hardware to maximize the reconstruction accuracy of sub-metered readings in non-instrumented homes while minimizing deployment costs becomes necessary and promising. In this work, we develop an active learning solution based on low-rank tensor completion for energy breakdown. We propose to actively deploy energy sensors to appliances from selected homes, with a goal to improve the prediction accuracy of the completed tensor with minimum sensor deployment cost. We empirically evaluate our approach on the largest public energy dataset collected in Austin, Texas, USA, from 2013 to 2017. The results show that our approach gives better performance with fixed number of sensors installed, when compared to the state-of-the-art, which is also proven by our theoretical analysis.

The performance of autonomous cars can be greatly enhanced through wireless coordination. However, mobility has traditionally been a challenge for wireless networks due to rapid fluctuation of the signal quality. Current control systems handle this challenge by slowing down the vehicle to preserve safety. However, in this research, we demonstrate that we can robustly characterize the channel quality by mapping the multipath signals to the dynamics of the physical environment, thus controlling the trajectory of the mobile agent to a safe efficient motion path. This allows mobile systems to realize the performance benefits of wireless coordination while providing safety.

Many homes today are logically or physically "zoned" based on properties such as HVACs, activities, or physical layouts. Accurately sensing the occupancy of these zones can yield energy savings, aid in automatic heating and lighting control, energy disaggregation, etc. Existing systems that attempt to sense occupancy are power consuming, non real-time, pet unfriendly and/or sensitive to ambient heat, light and air flow. In this paper, we address these by building Doorpler, a time, space and power-aware radar-based system that detects occupancy at zone transition points by sensing crossings and their direction. It detects a crossing via the Doppler Principle, and infers the direction of crossing by measuring the angle-of-arrival of the human reflection. We evaluate Doorpler by conducting a scripted study and two in-situ studies for 200 hours, collecting over 1600 doorway crossings. We obtain a precision, recall and direction accuracy of over 99% in the scripted studies, and over 95% in the in-situ studies. Our results estimate that Doorpler can fall in the energy-harvestable range of indoor environments with an average power consumption of 6.1mW. With an execution time of 13.8ms, Doorpler has the potential to enable several real-time smart home applications like smart-lighting and HVAC control.

research-article Share on A Tree-Structured Neural Network Model for Household Energy Breakdown Authors: Yiling Jia University of Virginia, USA University of Virginia, USAView Profile , Nipun Batra IIT Gandhinagar, India IIT Gandhinagar, IndiaView Profile , Hongning Wang University of Virginia, USA University of Virginia, USAView Profile , Kamin Whitehouse University of Virginia, USA University of Virginia, USAView Profile Authors Info & Claims WWW '19: The World Wide Web ConferenceMay 2019 Pages 2872–2878https://doi.org/10.1145/3308558.3313405Published:13 May 2019Publication History 14citation306DownloadsMetricsTotal Citations14Total Downloads306Last 12 Months61Last 6 weeks6 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.

Homes constitute roughly one-third of the total energy usage worldwide. Providing an energy breakdown – energy consumption per appliance, can help save up to 15% energy. Given the vast differences in energy consumption patterns across different regions, existing energy breakdown solutions require instrumentation and model training for each geographical region, which is prohibitively expensive and limits the scalability. In this paper, we propose a novel region independent energy breakdown model via statistical transfer learning. Our key intuition is that the heterogeneity in homes and weather across different regions most significantly impacts the energy consumption across regions; and if we can factor out such heterogeneity, we can learn region independent models or the homogeneous energy breakdown components for each individual appliance. Thus, the model learnt in one region can be transferred to another region. We evaluate our approach on two U.S. cities having distinct weather from a publicly available dataset. We find that our approach gives better energy breakdown estimates requiring the least amount of instrumented homes from the target region, when compared to the state-of-the-art.

Decimeter-level localization has become a reality, in part due to the ability to eliminate the effects of multipath interference. In this paper, we demonstrate the ability to use multipath reflections to enhance localization rather than throwing them away. We present Multipath Triangulation, a new localization technique that uses multipath reflections to localize a target device with a single receiver that does not require any form of coordination with any other devices. In this paper, we leverage multipath triangulation to build the first decimeter-level WiFi localization system, called MonoLoco, that requires only a single access point (AP) and a single channel, and does not impose any overhead, data sharing, or coordination protocols beyond standard WiFi communication. As a bonus, it also determines the orientation of the target relative to the AP. We implemented MonoLoco using Intel 5300 commodity WiFi cards and deploy it in four environments with different multipath propagation. Results indicate median localization error of 0.5m and median orientation error of 6.6 degrees, which are comparable to the best performing prior systems, all of which require multiple APs and/or multiple frequency channels. High accuracy can be achieved with only a handful of packets.

Homes constitute more than one-thirds of the total energy consumption. Producing an energy breakdown for a home has been shown to reduce household energy consumption by up to 15%, among other benefits. However, existing approaches to produce an energy breakdown require hardware to be installed in each home and are thus prohibitively expensive. In this paper, we propose a novel application of feature-based matrix factorisation that does not require any additional hard- ware installation. The basic premise of our approach is that common design and construction patterns for homes create a repeating structure in their energy data. Thus, a sparse basis can be used to represent energy data from a broad range of homes. We evaluate our approach on 516 homes from a publicly available data set and find it to be more effective than five baseline approaches that either require sensing in each home, or a very rigorous survey across a large number of homes coupled with complex modelling. We also present a deployment of our system as a live web application that can potentially provide energy breakdown to millions of homes.

The key to time series clustering is how to characterize the similarity between any two time series. In this paper, we explore a new similarity metric called “cross-predictability”: the degree to which a future value in each time series is predicted by past values of the others. However, it is challenging to estimate such cross-predictability among time series in the high-dimensional regime, where the number of time series is much larger than the length of each time series. We address this challenge with a sparsity assumption: only time series in the same cluster have significant cross-predictability with each other. We demonstrate that this approach is computationally attractive, and provide a theoretical proof that the proposed algorithm will identify the correct clustering structure with high probability under certain conditions. To the best of our knowledge, this is the first practical high-dimensional time series clustering algorithm with a provable guarantee. We evaluate with experiments on both synthetic data and real-world data, and results indicate that our method can achieve more than 80% clustering accuracy on real-world data, which is 20% higher than the state-of-art baselines.

Super-resolution subspace methods are popular in estimating multipath parameters such as angle of arrival and time of flight. However, they require decorrelation techniques to resolve coherent multipath components. The conventional decorrelation techniques reduce the effective aperture of the MIMO array, thus reducing the resolution and number of resolved paths. In this paper, we introduce MIMO smoothing as a new technique to bring decorrelation effect by leveraging the spacial and frequential diversity in MIMO transmitters and receivers. Via extensive experiments on WiFi links, we show that MIMO smoothing can increase the accuracy of multipath resolution.

A large amount of energy could be saved by detecting home occupancy and automatically controlling the lights, HVAC, water heating, and other mechanical systems. Existing systems rely on motion information, which usually fail to detect occupied rooms with stationary people. In this project, we study the possibility of converting commodity WiFi access points to occupancy sensors by exploiting multipath reflections as individual spatial sensors. The proposed method measures fine-grained distortions caused by human body on phase and amplitude of WiFi signals. Our initial results suggest that formulating WiFi parameters into angle of arrival provides a more sensitive metric to measure occupancy.

As sensor networks are increasingly deployed for critical applications, reliability and latency guarantee become more important than ever to meet industrial requirements. In this paper, we investigated the impact of link burstiness on stream scheduling using a data trace of 3,600,000 packets collected from an indoor testbed. We demonstrate that a good tradeoff between reliability and latency can be achieved by allocating certain time slots on each link for stream transmissions based on its burst length and frequency distributions. With this observation, we design transmission scheduling and routing algorithms for data streams to meet a specified reliability requirement while minimizing end-to- end latency. For the multi-stream scheduling problem, we prove its NP-hardness and design an algorithm that achieves the reliability guarantee and an O(log n) approximation of minimizing the maximum end-to-end latency for any stream. Trace- driven simulations show that our solution meets specified end-to-end reliability requirements with latency up to 9.18 times less than existing solutions.

Doorway tracking systems track people’s room location by instrumenting the doorways rather than instrumenting the rooms themselves—resulting in fewer sensors and less monitoring while still providing location information on occupants. In this article, we explore what is required to make doorway tracking a practical solution. We break a doorway tracking system into multiple independent design components, including both sensor and algorithmic design. Informed by this design, we construct a doorway tracking system and analyze how different combinations of these design components affect tracking accuracy. We perform a six-day in situ study in a ten-room house with two volunteers to analyze how these design components respond to the natural types and frequencies of errors in a real-world setting. To reflect the needs of different application classes, we analyze these design components using three different evaluation metrics: room accuracy, duration accuracy, and transition accuracy. Results indicate that doorway tracking can achieve 99.5% room accuracy on average in controlled settings and 96% room accuracy in in situ settings. This is contrasted against the 76% in situ setting room accuracy of Doorjamb, a doorway tracking system whose design implements only a limited number of components in our proposed doorway tracking system design space. We describe the differences between the data in the in situ and controlled settings, and provide guidelines about how to design a doorway tracking system for a given application’s accuracy requirements.

In this paper, we consider a variant of the multi-target tracking problem in which the tracking region is divided into zones and targets can only be monitored as they transition between these zones. We call this the transition tracking problem. The key challenge in Transition Tracking is to estimate the number of targets in the tracking region without being able to sense all targets simultaneously. In this paper, we propose an approach to the Transition Tracking problem called TransTrack. Unlike most other tracking algorithms that maximize the likelihood of the sensor data, TransTrack applies penalty functions to find the minimum number of targets that can explain the sensor data. These penalties allow tracks with larger numbers of targets only ifthey have sufficiently fewer errors than other, alternative tracks. To evaluate this approach, we apply TransTrack to a data set containing 3275 transitions between rooms in a home. We observe an average room tracking accuracy of up to 94.5%.

This paper presents findings from a field deployment that explored a design approach we call eco-coaching: giving personalized suggestions for specific actions that would reduce wasted energy. We studied ThermoCoach, which performs eco-coaching for thermostat scheduling. It senses and models occupancy patterns in a home, and provides occupants alternative suggestions for configuring their thermostat. Our study shows that eco-coaching accomplished four things. First, it made it easier for users to implement an effective thermostat schedule. Second, it supported user agency in negotiating energy savings and comfort goals. Third, it facilitated learning different scheduling strategies as well as weighing different options. Finally, it challenged users' beliefs about how well they were doing. These outcomes, in turn, were successful in getting users to employ and experiment with more efficient setback strategies. Going forward, we propose ways that eco-coaching systems could better support users in customizing and assessing the systems' recommendations.

The first step to saving energy in the home is often to create an energy breakdown: the amount of energy used by each individual appliance in the home. Unfortunately, current techniques that produce an energy breakdown are not scalable: they require hardware to be installed in each and every home. In this paper, we propose a more scalable solution called Gemello that estimates the energy breakdown for one home by matching it with similar homes for which the breakdown is already known. This matching requires only the monthly energy bill and household characteristics such as square footage of the home and the size of the household. We evaluate this approach using 57 homes and results indicate that the accuracy of Gemello is comparable to or better than existing techniques that use sensing infrastructure in each home. The information required by Gemello is often publicly available and, as such, it can be immediately applied to many homes around the world.

Aerial drones, ground robots, and aquatic rovers enable mobile applications that no other technology can realize with comparable flexibility and costs. In existing platforms, the low-level control enabling a drone's autonomous movement is currently realized in a time-triggered fashion, which simplifies implementations. In contrast, we conceive a notion of reactive control that supersedes the time-triggered approach by leveraging the characteristics of existing control logic and of the hardware it runs on. Using reactive control, control decisions are taken only upon recognizing the need to, based on observed changes in the navigation sensors. As a result, the rate of execution dynamically adapts to the circumstances. Compared to time-triggered control, this allows us to: i) attain more timely control decisions, ii) improve hardware utilization, iii) lessen the need to over-provision control rates. Based on 260+ hours of real-world experiments using three aerial drones, three different control logic, and three hardware platforms, we demonstrate, for example, up to 41% improvements in control accuracy and up to 22% improvements in flight time.

Commercial and industrial buildings account for a considerable portion of all energy consumed in the U.S., and thus reducing this energy consumption is a national grand challenge. Based on the large deployment of sensors in modern commercial buildings, many organizations are applying data analytic solutions to the thousands of sensing and control points to detect wasteful and incorrect operations for energy savings. Scaling this approach is challenging, however, because the metadata about these sensing and control points is inconsistent between buildings, or even missing altogether. Moreover, normalizing the metadata requires significant integration effort.

In order for objects to perform personalized or contextual functions based on identity, they must solve what we call the object user identification problem: understanding who is actually using them. In this paper, we propose a new technique that uses data from wearable wrist sensors to perform object user identification. We hypothesize that objects have unique hallmarks that are imprinted in the hand gestures of its users. By detecting the presence of an object's hallmark in the wrist sensor data, we can identify who used the object. We evaluate this concept with a smart home application: recognizing who is using an object or appliance in a multi-person home by combining smart meter data and wearables. We conduct three different studies with 10 participants: 1) a study with scripted object use 2) a study with high-level tasked activities and unscripted object use, and 3) a 5-day in-situ study. These studies indicate that our approach performs object user identification with an average accuracy of 85--90%.

In typical US homes, water heating is the largest energy consumer besides space heating and cooling, and it accounts for approximately 17% of residential energy consumption on average. In this paper, we propose and evaluate a new technique to reduce energy waste due to pipe loss: delivering lower temperature water whenever possible. We created a Smarter Water Heater (SWH) that uses data fusion techniques to infer 1) the fixture being used 2) the mixed water temperature at the fixture 3) the pipe volume for that fixture. After learning a model for each fixture, it solves a control optimization problem to decide when and at what temperature to deliver water to minimize energy use without sacrificing comfort of the user. We evaluated the SWH in three stages. First, we built a physical prototype and measured energy efficiency. We then deployed 18 sensors into a home's piping system for a 50-day in-situ study to stress test the SWH's sensing sub-system. Finally, we collected traces of hot water use from 5 different homes over 10 days each to determine how different water usage habits and piping structures affect energy savings. The results indicate that the SWH reduces total water heating energy by 8--14% with little to no effect on user comfort.

Many applications in wireless sensor networks require communication performance that is both consistent and of high quality. Unfortunately, performance of current network protocols can vary significantly because of various interferences and environmental changes. Current protocols estimate link quality based on the reception of probe packets over a short time period. This method is neither efficient nor accurate enough to capture the dramatic variations of link quality. Therefore, we propose a link metric called competence that characterizes links over a longer period of time. We combine competence with current short-term estimations in routing algorithm designs. To further improve network performance, we have designed a distributed route maintenance framework based on feedback control solutions. This framework allows every link along an end-to-end (E2E) path to adjust its link protocol parameters, such as transmission power and number of retransmissions, to ensure specified E2E reliability and latency under dynamic link qualities. Our solutions are evaluated in both extensive simulations and real system experiments. In real system evaluations with 48 T-Motes, our overall solution improves E2E packet delivery ratio over existing solutions by up to 40% while reducing transmission energy consumption by up to 22%. Importantly, our solution also achieves more stable and better transient performance than current approaches.

Wireless systems are deployed in environments like buildings, bridges and forests - each of which has very different wireless properties. In this paper, we propose a technique to customize simulation for a new RF environment, before a physical layer model has been created for it. Our approach is to create physical layer traces or PhyTraces that capture the RF environment of a single node. These PhyTraces can then be composed in multiple ways to simulate protocol-layer performance in the target environment. This approach offers an alternative to conventional trace-based network simulation that is very realistic, but does not permit protocol-level changes after the traces are collected. We show that PhyTraces can be collected very quickly and can be used in simulation with low computational overhead to model networks of various sizes and densities. To evaluate this approach, we collect PhyTraces in 3 different physical environments using 2 different low-power radios at 4 different transmission power levels. Our analysis indicates that the PhyTraces can predict the performance of neighbor discovery algorithms, distance vector routing algorithms, and flooding algorithms in real deployments, including some effects of complex dynamics due to packet loss and wireless collisions.

Sensor network research has facilitated advancements in various domains, such as industrial monitoring, environmental sensing, etc., and research challenges have shifted from creating infrastructure to utilizing it. Extracting meaningful information from sensor data, or control applications using the data, depends on the metadata available to interpret it, whether provided by novel networks or legacy instrumentation. Commercial buildings provide a valuable setting for investigating automated metadata acquisition and augmentation, as they typically comprise large sensor networks, but have limited, obscure metadata that are often meaningful only to the facility managers. Moreover, this primitive metadata is imprecise and varies across vendors and deployments.

Autonomous drones are a powerful new breed of mobile sensing platform that can greatly extend the capabilities of traditional sensing systems. Unfortunately, it is still non-trivial to coordinate multiple drones to perform a task collaboratively. We present a novel programming model called team-level programming that can express collaborative sensing tasks without exposing the complexity of managing multiple drones, such as concurrent programming, parallel execution, scaling, and failure recovering. We create the Voltron programming system to explore the concept of team-level programming in active sensing applications. Voltron offers programming constructs to create the illusion of a simple sequential execution model while still maximizing opportunities to dynamically re-task the drones as needed. We implement Voltron by targeting a popular aerial drone platform, and evaluate the resulting system using a combination of real deployments, user studies, and emulation. Our results indicate that Voltron enables simpler code and produces marginal overhead in terms of CPU, memory, and network utilization. In addition, it greatly facilitates implementing correct and complete collaborative drone applications, compared to existing drone programming systems.

Breadcrumb systems (BCS) aid first responders by communicating their physiological parameters to remotely located base stations. In this paper, we describe the design, implementation, and evaluation of an automatic and robust multi-user breadcrumb system for indoor first response applications. Our solution includes a breadcrumb dispenser with a link estimator that is used to decide when to deploy breadcrumbs to maintain reliable wireless connectivity. The solution includes accounting for realities of buildings and dispensing such as the height difference between where the dispenser is worn and the floor where the dispensed nodes are found. We also include adaptive power management to maintain link quality over time. Moreover, we propose UF, a distributed cooperative deployment algorithm, to achieve longer breadcrumb chain lengths while maintaining fairness and high system reliability via selecting appropriate benefit and cost functions. We deployed and evaluated our system in real buildings with several different first responder mobility patterns. Experimental results from our study show that compared to the state of the art solution , our breadcrumb system achieves 200 percent link redundancy with only 23 percent additional deployed nodes. Our deployed breadcrumb chain can achieve 90 percent PRR when one node fails in the chain. In addition, by applying the UF coordination algorithm, the system can maintain connectivity for up to 87 percent longer distances than baseline greedy coordination approach while maintaining 96 percent packet delivery ratio.

Off-the-shelf home automation technology is making it easier than ever for people to convert their own homes into smart homes. However, manual configuration is tedious and error-prone. In this article, we present and compare a family of solutions that automatically generate a map of the home and the devices within it using data from the smart home sensors themselves (e.g., light and motion sensors). These solutions can be used to automatically configure home automation systems or to automatically produce an intuitive map-like interface for visualizing sensor data and interacting with controllers. We call our approach Smart Blueprints because it automatically maps out the unique configuration of each smart home. We demonstrate the Smart Blueprints using a variety of sensor combinations, including light sensors, motion sensors, and magnetometers deployed on the doors and/or windows of the home. For evaluation of each combination on sensor-map generation, we deployed more than 200 sensors in seven different houses at different locations and compared the ability to use a variety of techniques to map out the configuration. We show that, in almost all houses, our system can automatically narrow the configuration down to 1--5 candidates per home using only one week of collected data.

Many occupant-oriented smarthome applications such as automated lighting, heating and cooling, and activity recognition need room location information of residents within a building. Surveillance based tracking systems used to track people in commercial buildings, are privacy invasive in homes. In this paper, we present the RF Doormat - a RF threshold system that can accurately track people's room locations by monitoring their movement through the doorways in the home. We also present a set of guidelines and a visualization to easily and rapidly setup the RF-Doormat system on any doorway. To evaluate our system, we perform 580 doorway crossings across 11 different doorways in a home. Results indicate that our system can detect doorway crossings made by people with an average accuracy of 98%. To our knowledge, the RF Doormat is the first highly accurate room location tracking system that can be used for long time periods without the need for privacy invasive cameras.

The monitoring of electrical and water fixtures in the home is being applied for a variety of "smart home" applications, such as recognizing activities of daily living (ADLs) or conserving energy or water usage. Fixture monitoring techniques generally fall into two categories: fixture recognition and fixture disaggregation. However, existing techniques require users to explicitly identify each individual fixture, either by placing a sensor on it or by manually creating training data for it. In this paper, we present a new fixture discovery system that automatically infers the existence of electrical and water fixtures in the home. We call the system FixtureFinder. The basic idea is to use data fusion between the smart meters and other sensors or infrastructure already in the home, such as the home security or automation system, and to find repeating patterns in the fused data stream. To evaluate FixtureFinder, we deployed the system into 4 different homes for 7-10 days of data collection. Our results show that FixtureFinder is able to identify and differentiate major light and water fixtures in less than 10 days, including multiple copies of light bulbs and sinks that have identical power/water profiles.

We present our ongoing work on the design of macroprogramming abstractions to program sensing and actuating applications using robot swarms. Robots can sample the environment and act on it where no other sensor can reach, e.g., to monitor the environment at altitude with aerial robots. Programming the individual behavior of multiple coordinating robots is difficult. We design LiftOff, a macroprogramming abstraction that allows to program robot swarms collectively, by creating the illusion of a single computing device that occupies the entire physical space of interest. We achieve this by giving variables and values in a programming language a spatial semantics. In LiftOff, values may be associated to a location, and programmers use the same variable to access different values at different locations, sparing the need to manually create a mapping from variables to spatial values. LiftOff applications execute synchronously or based on lazy evaluation. The former allows precise program analysis, e.g., using model checking, whilst the latter potentially executes faster. In this paper, we report on LiftOff's initial design and prototypes.

Inexpensive wireless sensing products are dramatically reducing the cost of in-home sensing. However, these sensors have been found to fail often and prohibitive maintenance costs may negate the cost benefits of inexpensive hardware and do-it-yourself installation. In this paper, we describe a new technique called SMART that uses application-level semantics to detect, assess, and adapt to sensor failures. SMART detects sensor failures at run-time by analyzing the relative behavior of multiple classifier instances trained to recognize the same set of activities based on different subsets of sensors. Once a failure is detected, SMART assesses its importance and adapts the classifier ensemble in attempt to avoid maintenance dispatch. Evaluation on three homes from two public datasets shows that SMART decreases the number of maintenance dispatches by 55% on average, identifies non-fail-stop failures at run-time with more than 85% accuracy, and improves the activity recognition accuracy under sensor failures by 15% on average.

Development of smarthome home application depends on the ability to identify resident activity and track occupancy of rooms as people move within a residence. Existing solutions to home activity recognition are evaluated using controlled experiments and having participants maintain logs of daily activities as ground truth. In our study, we evaluate the effectiveness of using mid-range RFID as a research tool to perform in-situ evaluation of smarthome systems. We propose that using bracelets and anklets embedded with passive RFID tags can provide an accurate ground truth system, which can help evaluate the performance of research solutions for smarthomes with higher accuracy - in presence of natural variability of people in activities and movement in homes.

Daylight harvesting is the use of natural sunlight to reduce the need for artificial lighting in buildings. The key challenge of daylight harvesting is to provide stable indoor lighting levels even though natural sunlight is not a stable light source. In this paper, we present a new technique called SunCast that improves lighting stability by predicting changes in future sunlight levels. The system has two parts: 1) it learns predictable sunlight patterns due to trees, nearby buildings, or other environmental factors, and 2) it controls the window transparency based on a quadratic optimization over predicted sunlight levels. To evaluate the system, we record daylight levels at 39 different windows for up to 12 weeks at a time, and apply our control algorithm on the data traces. Our results indicate that SunCast can reduce glare by 59% over a baseline approach with only a marginal increase in artificial lighting energy.

Indoor tracking systems will be an essential part of the home of the future, enabling location-aware and individually-tailored services. However, today there are no tracking solutions that are practical for "every day" use in the home. In this paper, we introduce the Doorjamb tracking system that uses ultrasonic range finders mounted above each doorway, pointed downward to sense people as they walk through the doorway. The system differentiates people by measuring their heights, infers their walking direction using signal processing, and identifies their room locations based on the sequence of doorways through which they pass. Doorjamb provides room-level tracking without requiring any user participation, wearable devices, privacy-intrusive sensors, or high-cost sensors. We create a proof-of-concept implementation and empirically evaluate Doorjamb with experiments that include over 3000 manually-recorded doorway crossings. Results indicate that the system can perform room-level tracking with 90% accuracy on average.

Body sensor networks (BSNs) are emerging cyber–physical systems that promise to improve quality of life through improved healthcare, augmented sensing and actuation for the disabled, independent living for the elderly, and reduced healthcare costs. However, the physical nature of BSNs introduces new challenges. The human body is a highly dynamic physical environment that creates constantly changing demands on sensing, actuation, and quality of service (QoS). Movement between indoor and outdoor environments and physical movements constantly change the wireless channel characteristics. These dynamic application contexts can also have a dramatic impact on data and resource prioritization. Thus, BSNs must simultaneously deal with rapid changes to both top–down application requirements and bottom–up resource availability. This is made all the more challenging by the wearable nature of BSN devices, which necessitates a vanishingly small size and, therefore, extremely limited hardware resources and power budget. Current research is being performed to develop new principles and techniques for adaptive operation in highly dynamic physical environments, using miniaturized, energy-constrained devices. This paper describes a holistic cross-layer approach that addresses all aspects of the system, from low-level hardware design to higher level communication and data fusion algorithms, to top-level applications.

Breadcrumb systems (BCS) aid first responders by communicating their physiological parameters to remotely located base stations. However, state-of-the-art research only focuses on deploying breadcrumb systems on the assumption of uncoordinated users, which is inefficient. In this paper, we present the first design, implementation, and evaluation of reliable multiuser breadcrumb systems (MUBCS) which exploits efficient and automatic coordination among system users to achieve better utilization of limited breadcrumbs. We propose UF, a distributed cooperative deployment algorithm, to achieve longer breadcrumb chain length while maintaining fairness and high system reliability via selecting appropriate benefit and cost functions. UF also requires no prior assumptions about users' mobility models, making the design practical for real applications. We deployed and evaluated our system in real buildings with several different first responder mobility patterns. Experimental results indicate that this approach can maintain connectivity for up to 87% longer distances than baseline greedy coordination approach while maintaining 96% packet delivery ratio.

Homes are rich with information about people's energy consumption, medical health, and personal or family functions. In this paper, we present our experiences deploying large-scale residential sensing systems in over 20 homes. Deploying small-scale systems in homes can be deceptively easy, but in our deployments we encountered a phase transition in which deployment effort increases dramatically as residential deployments scale up in terms of 1) the number of nodes, 2) the length of time, and 3) the number of houses. In this paper, we distill our experiences down to a set of guidelines and design principles to help future deployments avoid the potential pitfalls of large-scale sensing in homes.

As wireless sensor networks mature, they are increasingly being used in real-time applications. Many of these applications require reliable transmission within latency bounds. Achieving this goal is very difficult because of link burstiness and interference. Based on significant empirical evidence of 21 days and over 3,600,000 packets transmission per link, we propose a scheduling algorithm that produces latency bounds of the real-time periodic streams and accounts for both link bursts and interference. The solution is achieved through the definition of a new metric Bmax that characterizes links by their maximum burst length, and by choosing a novel least-burst-route that minimizes the sum of worst case burst lengths over all links in the route. A testbed evaluation consisting of 48 nodes spread across a floor of a building shows that we obtain 100% reliable packet delivery within derived latency bounds. We also demonstrate how performance deteriorates and discuss its implications for wireless networks with insufficient high quality links.

Continuous and reliable operation of WSNs is notoriously difficult to guarantee due to hardware degradation and environmental changes. In this paper, we propose and demonstrate a methodology for run-time assurance (RTA), in which we validate at run time that a WSN will function correctly, despite any changes to the operating conditions since it was originally designed and deployed. We use program analysis and compiler techniques to facilitate automated testing of a WSN at run time. As a proof of concept, we implemented a framework for designing and automatically testing WSN applications. We evaluate our implementation on a network of 21 TelosB nodes, and compare performance with an existing network health monitoring solution. Our results indicate that in addition to providing the application-level verification function, RTA misses 75% fewer system failures, produces 70% fewer maintenance dispatches, and incurs 33% less messaging overhead than network health monitoring.

Breadcrumb systems (BCS) have been proposed to aid firefighters inside buildings by communicating their physiological parameters to base stations outside the buildings. In this paper, we describe the design, implementation and evaluation of an automatic and robust breadcrumb system for firefighter applications. Our solution includes a breadcrumb dispenser with an optimized link estimator that is used to decide when to deploy breadcrumbs to maintain reliable wireless connectivity. The solution includes accounting for realities of buildings and dispensing such as the height difference between where the dispenser is worn and the floor where the dispensed nodes are found. We also include adaptive power management to maintain link quality over time.

Heating, ventilation and cooling (HVAC) is the largest source of residential energy consumption. In this paper, we demonstrate how to use cheap and simple sensing technology to automatically sense occupancy and sleep patterns in a home, and how to use these patterns to save energy by automatically turning off the home's HVAC system. We call this approach the smart thermostat. We evaluate this approach by deploying sensors in 8 homes and comparing the expected energy usage of our algorithm against existing approaches. We demonstrate that our approach will achieve a 28% energy saving on average, at a cost of approximately $25 in sensors. In comparison, a commercially-available baseline approach that uses similar sensors saves only 6.8% energy on average, and actually increases energy consumption in 4 of the 8 households.

We present the Flash flooding protocol for rapid network flooding in wireless sensor networks. Traditional flooding protocols can be very slow because of neighborhood contention: nodes cannot propagate the flood until neighboring nodes have finished their transmissions. The Flash flooding protocol avoids this problem by allowing concurrent transmissions among neighboring nodes. It relies on the capture effect to ensure that each node receives the flood from at least one of its neighbors, and introduces new techniques to either recover from or prevent too many concurrent transmissions. We evaluate the Flash flooding protocol on both a 48-node wireless sensor network testbed and in a trace-based simulator. Our results indicate that the Flash flooding protocol can reduce latency by as much as 80%, achieving flooding latencies near the theoretical lower bound without sacrificing coverage, reliability or power consumption.

Many applications in wireless sensor networks require communication performance that is both consistent and high quality. Unfortunately, performance of current network protocols can vary significantly because of various interferences and environmental changes. Current protocols estimate link quality based on the reception of probe packets over a short time period. This method is neither efficient nor accurate enough to capture the dramatic variations of link quality. Therefore, we propose a link metric called competence that characterizes links over a longer period of time. We combine competence with current short term estimations in routing algorithm designs. To further improve network performance we have designed a distributed route maintenance framework based on feedback control solutions. In real system evaluations with 48 T-Motes, our overall solution improves end-to-end packet delivery ratio over existing solutions by up to 40%, while reducing energy consumption by up to 22%. Importantly, our solution also achieves more stable and better transient performance than current approaches.

Macroprogramming abstractions provide abstract distributed data structures to simplify the programming of wireless embedded networks. However, none of the current macroprogramming systems provide debugging support for application development. We have developed MDB, a GDB-like post-mortem debugger for the MacroLab macroprogramming abstraction. In this demonstration, we show how MDB enables application development and debugging at a single level of abstraction. MDB eliminates the need for a programmer to reason about low-level event traces and message passing protocols, instead allowing debugging in terms of abstract data types. We expect MDB to fill a crucial link in the development cycle as a macroprogram progresses from the drawing board to real deployment.

The current rapid development and deployment of wireless sensor networks (WSNs) and their application in mission critical systems are exacerbating the need for high confidence WSNs. Achieving high confidence WSNs will require new assurance technologies. Most current solutions deal with faults and reliability and not with application level semantics and associated assurances. We propose the use of a novel WSN design and assurance mechanism, run time assurance (RTA), to guarantee that important application-level requirements are met in mission critical applications.

Simultaneous transmissions in the same radio range of a wireless sensor network causes interference and packets are lost. Knowing the interference pattern in advance, the transmission links can be scheduled so that no packet is lost due to interference and all streams meet their deadlines. This problem is NP-hard in general and therefore we resort to heuristics. In this paper, we present a set of heuristics for scheduling periodic, real-time data streams over a wireless sensor network. Simulation results show that our heuristics produce feasible schedules in almost all cases.

Soil contains vast ecosystems that play a key role in the Earth's water and nutrient cycles, but scientists cannot currently collect the high-resolution data required to fully understand them. In this paper, we present Suelo, an embedded networked sensing system designed for soil monitoring. An important challenge for Suelo is that many soil sensors are inherently fragile and often produce invalid or uncalibrated data. Therefore Suelo is an assisted sensing system: it actively requests the help of a human when necessary to validate, calibrate, repair, or replace sensors. This approach allows us to use available sensors without sacrificing data integrity, while minimizing the human resources required. We tested our system in multiple real soil monitoring deployments and demonstrate that, using human assistance, Suelo produced 91% fewer false negatives and false positives than common fault detection solutions on these datasets.

Creating and debugging programs for wireless embedded networks (WENs) is notoriously difficult. Macroprogramming is an emerging technology that aims to address this problem by providing high-level programming abstractions. We present MDB, the first system to support the debugging of macroprograms. MDB allows the user to set break-points and step through a macroprogram using a source-level debugging interface similar to GDB, a process we call macrodebugging. A key challenge of MDB is to step through a macroprogram in sequential order even though it executes on the network in a distributed, asynchronous manner. Besides allowing the user to view distributed state, MDB also provides the ability to search for bugs over the entire history of distributed states. Finally, MDB allows the user to make hypothetical changes to a macroprogram and to see the effect on distributed state without the need to redeploy, execute, and test the new code. We show that macrodebugging is both easy and efficient: MDB consumes few system resources and requires few user commands to find the cause of bugs. We also provide a lightweight version of MDB called MDB Lite that can be used during the deployment phase to reduce resource consumption while still eliminating the possibility of heisenbugs: changes in the manifestation of bugs caused by enabling or disabling the debugger.

In this paper, we first present a new privacy leak in residential wireless ubiquitous computing systems, and then we propose guidelines for designing future systems to prevent this problem. We show that we can observe private activities in the home such as cooking, showering, toileting, and sleeping by eavesdropping on the wireless transmissions of sensors in a home, even when all of the transmissions are encrypted. We call this the Fingerprint and Timing-based Snooping (FATS) attack. This attack can already be carried out on millions of homes today, and may become more important as ubiquitous computing environments such as smart homes and assisted living facilities become more prevalent. In this paper, we demonstrate and evaluate the FATS attack on eight different homes containing wireless sensors. We also propose and evaluate a set of privacy preserving design guidelines for future wireless ubiquitous systems and show how these guidelines can be used in a hybrid fashion to prevent against the FATS attack with low implementation costs.

In this paper, we analyze the performance of group based detection in sparse sensor networks, when the system level detection decision is made based on the detection reports generated from multiple sensing periods. Sparse deployment is essential for reducing cost of large scale sensor networks, which cover thousands of square miles. In a sparse deployment, the sensor field is only partially covered by sensorspsila sensing ranges, resulting in void sensing areas in the region, but all nodes are connected through multi-hop networking. Further, due to the unavoidable false alarms generated by a single sensor in a network, many deployed systems use group based detection to reduce system level false alarms. Despite the popularity of group based detection, few analysis works in the literature deal with group based detection. In this paper, we propose a novel approach called Markov chain based Spatial approach (MS-approach) to model group based detection in sensor networks. The M-S-approach successfully overcomes the complicated conditional detection probability of a target in each sensing period, and reduces the execution time of the analysis from many days to 1 minute. The analytical model is validated through extensive simulations. This analytical work is important because it provides an easy way to understand the performance of a system that uses group based detection without running countless simulations or deploying real systems.

We demonstrate MetroNet, which is an application that illustrates collaborative data sharing on the Wide Wide Web. MetroNet has two parts. First, our sensors gather data about pedestrian foot traffic in front of and into stores, which is made available online to shopkeepers. Second, this sensor data can be made public, at the discretion of the shopkeeper, for use by city planners, other shopkeepers, or residents of the city. MetroNet is an application through which we study fundamental problems of sharing data on the Web, such as search, data fusion, and privacy.

Effective debugging usually involves watching program state to diagnose bugs. When debugging sensor network applications, this approach is often time-consuming and errorprone, not only because of the lack of visibility into system state, but also because of the difficulty to watch the right variables at the right time. In this paper, we present declarative tracepoints, a debugging system that allows the user to insert a group of action-associated checkpoints, or tracepoints, to applications being debugged at runtime. Tracepoints do not require modifying application source code. Instead, they are written in a declarative, SQL-like language called TraceSQL independently. By triggering the associated actions when these checkpoints are reached, this system automates the debugging process by removing the human from the loop. We show that declarative tracepoints are able to express the core functionality of a range of previously isolated debugging techniques, such as EnviroLog, NodeMD, Sympathy, and StackGuard. We describe the design and implementation of the declarative tracepoints system, evaluate its overhead in terms of CPU slowdown, illustrate its expressiveness through the aforementioned debugging techniques, and finally demonstrate that it can be used to detect real bugs using case studies of three bugs based on the development of the LiteOS operating system.

We present a macroprogramming framework called MacroLab that offers a vector programming abstraction similar to Matlab for Cyber-Physical Systems (CPSs). The user writes a single program for the entire network using Matlab-like operations such as addition, find, and max. The framework executes these operations across the network in a distributed fashion, a centralized fashion, or something between the two - whichever is most efficient for the target deployment. We call this approach deployment-specific code decomposition (DSCD). MacroLab programs can be executed on mote-class hardware such as the Telos [24] motes. Our results indicate that MacroLab introduces almost no additional overhead in terms of message cost, power consumption, memory footprint, or CPU cycles over TinyOS

Extensive empirical results reveal that interference can cause link qualities to change quickly and dramatically. For such highly dynamic links, the short term link quality estimations widely used in existing protocols require frequent measurements and may not be accurate. As a result, when these links are selected, end-to-end communication quality varies significantly. Also, route changes occur frequently, introducing traffic oscillation and excessive overhead in network protocols. To achieve good and stable network performance, it is not enough to use short term link estimation. It is essential to characterize a link's capacity to perform well at a desired level in the presence of interference and environmental changes. Therefore, we propose a performance metric called competence. We have incorporated the competence metric into routing algorithm designs. We have also designed and implemented a maintenance framework that stabilizes performance at both link and network layers. This framework allocates the desired performance level among multiple links along an active route by using an end-to-end feedback loop, and enforces the performance level of each link through adaptive transmission power control and retransmission control. In real system evaluations with 48 TMotes, our solution outperforms previous protocols significantly and achieves end-to-end stable performance for more than 99% of the time over 24 hours.

In this paper, we present the Flash flooding protocol that exploits the capture effect to produce low-latency network floods. The capture effect is the ability of some radios to correctly receive one of several concurrently transmitted messages, even if the received strengths of the two messages are almost the same. We exploit this phenomenon in a network flooding scenario by allowing nodes to propagate the flooding message concurrently, thus reducing delays due to neighborhood contention. Our experimental results indicate that Flash can reduce latency by 75-80 percent without sacrificing flooding reliability or coverage.

We demonstrate MacroLab, which is a macroprogramming framework that offers a vector programming abstraction similar to Matlab for cyber-physical systems (CPSs). The user writes a single program for an entire network using Matlab like operations such as addition, find, and max. The framework executes these operations across the network in a distributed fashion, a centralized fashion, or something between the two - whichever is most efficient for the target deployment. We call this approach deployment-specific code decomposition (DSCD). The MacroLab programming framework will facilitate the easy development of applications for CPSs by domain experts such as scientists and engineers with almost no additional overhead to the nodes in terms of message cost, power consumption, memory footprint, or CPU cycles over TinyOS programs.

Wireless sensor network (WSN) applications are notoriously difficult to develop and debug. This paper describes Clairvoyant which is a comprehensive source-level debugger for wireless, embedded networks. With Clairvoyant, a developer can wirelessly connect to a sensor network and execute standard debugging commands including break, step, watch, and backtrace, as well as new commands that are specially designed for debugging WSNs. Clairvoyant attempts to minimize its effect on the program being debugged in terms of network load, memory footprint, execution speed, clock consistency, and flash lifetime.

Sensor network debugging is notoriously difficult because many bugs manifest themselves only when they encounter the real world -- exactly when most powerful debugging tools can no longer be applied. There are currectly two common approaches to source level debugging in wireless sensor networks (WSNs), (i) simulation-based debugging [3], and (ii) wire-based debugging via logic analyzers or in-circuit emulators [1]. The former do not capture the true dynamics of a real deployment while the latter do not scale with the number of nodes and geographic size of the network. Thus, existing source-level debugging approaches for WSNs cannot be used in many real deployment environments.

We present the philosophy, design, and initial evaluation of the Trio Testbed, a new outdoor sensor network deployment that consists of 557 solar-powered motes, seven gateway nodes, and a root server. The testbed covers an area of approximately 50,000 square meters and was in continuous operation during the last four months of 2005. This new testbed in one of the largest solar-powered outdoor sensor networks ever constructed and it offers a unique platform on which both systems and application software can be tested safely at scale. The testbed is based on Trio, a new mote platform that provides sustainable operation, enables efficient in situ interaction, and supports fail-safe programming. The motivation behind this testbed was to evaluate robust multi-target tracking algorithms at scale. However, using the testbed has stressed the system software, networking protocols, and management tools in ways that have exposed subtle but serious weaknesses that were never discovered using indoor testbeds or smaller deployments. We have been iteratively improving our support software, with the eventual aim of creating a stable hardware-software platform for sustainable, scalable, and flexible testbed deployments.

In this study, we implement six ranging-based localization algorithms from the literature and evaluate them in simulations that employ real-world ultrasound ranging data. We find that small variations in the ranging model can lead to large variations in localization error. We analyze each algorithm to identify how implicit assumptions may be violated by empirical ranging data and why this changes the behavior of the algorithm.

A main challenge with developing applications for wireless embedded systems is the lack of visibility and control during execution of an application. In this paper, we present a tool suite called Marionette that provides the ability to call functions and to read or write variables on pre-compiled, embedded programs at run-time, without requiring the programmer to add any special code to the application. This rich interface facilitates interactive development and debugging at minimal cost to the node.

This paper presents a study of how empirical ranging characteristics affect multihop localization in wireless sensor networks. We use an objective metric to evaluate a well-established parametric model of ranging called Noisy Disk: if the model accurately predicts the results of a real-world deployment, it sufficiently captures ranging characteristics. When the model does not predict accurately, we systematically replace components of the model with empirical ranging characteristics to identify which components contribute to the discrepancy. We reveal that both the connectivity and noise components of Noisy Disk fail to accurately represent real-world ranging characteristics and show that these shortcomings affect localization in different ways under different circumstances.

No abstract available.

This paper proposes a neighborhood programming abstraction for sensor networks, wherein a node can identify a subset of nodes around it by a variety of criteria and share state with those nodes. This abstraction allows developers to design distributed algorithms in terms of the neighborhood abstraction itself, instead of decomposing them into component parts such as messaging protocols, data caches, and neighbor lists. In those applications that are already neighborhood-based, this abstraction is shown to facilitate good application design and to reduce algorithmic complexity, inter-component coupling, and total lines of code. The abstraction as defined here has been successfully used to implement several complex applications and is shown to capture the essence of many more existing distributed sensor network algorithms.