Sirajum Munir

Addressing burstiness for reliable communication and latency bound generation in wireless sensor networks

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.

DepSys: Dependency Aware Integration of Cyber-Physical Systems for Smart Homes

As sensor and actuator networks mature, they become a core utility of smart homes like electricity and water and enable the running of many CPS applications. Like other Cyber-Physical Systems (CPSs), when a number of applications share physical world entities, it raises many systems of systems interdependency problems. Such problems arise in the cyber part mainly because each application has assumptions on the physical world entities without knowing how other applications work. In this work, we propose DepSys, a utility sensing and actuation infrastructure for smart homes that provides comprehensive strategies to specify, detect, and resolve conflicts in a home setting. Based on real home data, we demonstrate the severity of conflicts when multiple CPSs are integrated and the significant ability of detecting and resolving such conflicts using DepSys.

Taxi Dispatch With Real-Time Sensing Data in Metropolitan Areas: A Receding Horizon Control Approach

Traditional taxi systems in metropolitan areas often suffer from inefficiencies due to uncoordinated actions as system capacity and customer demand change. With the pervasive deployment of networked sensors in modern vehicles, large amounts of information regarding customer demand and system status can be collected in real time. This information provides opportunities to perform various types of control and coordination for large-scale intelligent transportation systems. In this paper, we present a receding horizon control (RHC) framework to dispatch taxis, which incorporates highly spatiotemporally correlated demand/supply models and real-time Global Positioning System (GPS) location and occupancy information. The objectives include matching spatiotemporal ratio between demand and supply for service quality with minimum current and anticipated future taxi idle driving distance. Extensive trace-driven analysis with a data set containing taxi operational records in San Francisco, CA, USA, shows that our solution reduces the average total idle distance by 52%, and reduces the supply demand ratio error across the city during one experimental time slot by 45%. Moreover, our RHC framework is compatible with a wide variety of predictive models and optimization problem formulations. This compatibility property allows us to solve robust optimization problems with corresponding demand uncertainty models that provide disruptive event information.

ACN: An Associative Classifier with Negative Rules

Classification using association rules has added a new dimension to the ongoing research for accurate classifiers. Experiments have shown that these classifiers are significantly more accurate than decision tree classifiers. The idea behind most of the existing approaches has been the mining of positive class association rules from the training set and then selecting a subset of the mined rules for future predictions. However, in most cases, it is found that the final classifier contains some weak and inaccurate rules that were selected for covering some training instances for which no better rules were available. These rules make poor predictions of unseen test instances and only for these rules, the overall classification accuracy is drastically reduced. The idea of this paper is to eliminate these weak and inaccurate positive rules as far as possible by accurate negative rules. The generation of negative associations from datasets has been attacked from different perspectives by various authors and this has proved to be a very computationally expensive task. This paper approaches the problem of generating negative rules from a classification perspective, how to generate a sufficient number of high quality negative rules efficiently so that classification accuracy is enhanced. We extend the a priori algorithm for this and show that our classifier ldquoassociative classifier with negative rulesrdquo (ACN) is not only time-efficient but also achieves significantly better accuracy than four other state-of-the-art classification methods by experimenting on benchmark UCI datasets.

Dmodel: Online Taxicab Demand Model from Big Sensor Data in a Roving Sensor Network

Investigating passenger demand is essential for the taxicab business. Existing solutions are typically based on dated and inaccurate offline data collected by manual investigations. To address this issue, we propose Dmodel, using roving taxicabs as real-time mobile sensors to (i) infer passenger arriving moments by interactions of vacant taxicabs, and (ii) infer passenger demand by a customized online training with both historical and real-time data. Such huge taxicab data (almost 1TB per year) pose a big data challenge. To address this challenge, model employs a novel parameter called pickup pattern (accounts for various real-world logical information, e.g., bad weather) to increase the inference accuracy. We evaluate Dmodel with a real-world 450 GB dataset of 14, 000 taxicabs, and results show that compared to the ground truth, Dmodel achieves a 76% accuracy on the demand inference and outperforms a statistical model by 39%.

Real-Time Fine Grained Occupancy Estimation Using Depth Sensors on ARM Embedded Platforms

Occupancy estimation is an important primitive for a wide range of applications including building energy efficiency, safety, and security. In this paper, we explore the potential of using depth sensors to detect, estimate, identify, and track occupants in buildings. While depth sensors have been widely used for human detection and gesture recognition, computer vision algorithms are typically run on a powerful computer like XBOX or Intel R CoreTM i7 processor. In this work, we develop a prototype system called FORK using off-the-shelf components that performs the entire depth data processing on a cheaper and low power ARM processor in real-time. As ARM processors are extremely weak in running computer vision algorithms, FORK is designed to detect humans and track them in a very efficient way by leveraging a novel lightweight model based approach instead of traditional approaches based on histogram of oriented gradients (HOG) features. Unlike other camera based approaches, FORK is much less privacy invasive (even if the sensor is compromised). Based on a complete implementation, real-world deployment, and extensive evaluation at realistic scenarios, we observe that FORK achieves over 99% accuracy in real-time (4-9 FPS) in occupancy estimation.

EyePhy: Detecting Dependencies in Cyber-Physical System Apps due to Human-in-the-Loop

As app based paradigms are becoming popular, millions of apps are developed from many domains including energy, health, security, and entertainment. The US FDA expects that there will be 500 million smart phone users downloading healthcare related apps by 2015. Many of these apps are Cyber-Physical System (CPS) apps. In addition to sensing, communication, and computation, they perform interventions to control human physiological parameters, which can cause dependency problems as multiple interventions of multiple apps can increase or decrease each others effects, some of which can be harmful to the user. Such dependency problems occur mainly because each app is unaware about how other apps work and when an app performs an intervention to control its target parameters, it may affect other physiological parameters without even knowing it. We present EyePhy, a system that detects dependencies across interventions by having a closer eye on the physiological parameters of the human in the loop. To do that, EyePhy uses a physiological simulator HumMod that can model the complex interactions of the human physiology using over 7800 variables. EyePhy reduces app developers’ efforts in specifying dependency metadata compared to state of the art solutions and offers personalized dependency analysis for the user. We demonstrate the magnitude of dependencies that arise during multiple interventions in a human body and the significant ability of detecting these dependencies using EyePhy.

Reducing Energy Waste for Computers by Human-in-the-Loop Control

Although current cyber physical systems (CPSs) act as the bridge between humans and environment, their implementation mostly assumes humans as an external component to the control loops. We use a case study of energy waste on computer workstations to motivate the incorporation of humans into the control loops. The benefits include better response accuracy and timeliness of the CPS systems. However, incorporating humans into tight control loops remains a challenge as it requires understanding complex human behavior. In our case study, we collect empirical data to understand human behavior regarding distractions in computer usage and develop a human-in-the-loop control that can put workstations into sleep by early detection of distraction. Our control loop implements strategies such as an adaptive timeout interval, multilevel sensing, and addressing background processing. Evaluation on multiple subjects show an accuracy of 97.28% in detecting distractions, which cuts the energy waste of computers by 80.19%.

Taxi dispatch with real-time sensing data in metropolitan areas: a receding horizon control approach

Traditional taxi systems in metropolitan areas often suffer from inefficiencies due to uncoordinated actions as system capacity and customer demand change. With the pervasive deployment of networked sensors in modern vehicles, large amounts of information regarding customer demand and system status can be collected in real time. This information provides opportunities to perform various types of control and coordination for large-scale intelligent transportation systems. In this paper, we present a receding horizon control (RHC) framework to dispatch taxis, which incorporates highly spatiotemporally correlated demand/supply models and real-time Global Positioning System (GPS) location and occupancy information. The objectives include matching spatiotemporal ratio between demand and supply for service quality with minimum current and anticipated future taxi idle driving distance. Extensive trace-driven analysis with a data set containing taxi operational records in San Francisco, CA, USA, shows that our solution reduces the average total idle distance by 52%, and reduces the supply demand ratio error across the city during one experimental time slot by 45%. Moreover, our RHC framework is compatible with a wide variety of predictive models and optimization problem formulations. This compatibility property allows us to solve robust optimization problems with corresponding demand uncertainty models that provide disruptive event information.

FailureSense: Detecting Sensor Failure Using Electrical Appliances in the Home

With the proliferation of inexpensive sensors, sensors are increasingly being used in smart homes. Recent experience on long term sensor deployment in residential homes has identified the potential risk of various types of sensor failure. Motivated by real examples, we develop new schemes to detect not only fail-stop failure, but obstructed-view and moved-location failures that are not the traditional fault detection foci. Our proposed solution, FailureSense, uses a novel idea of using electrical appliances to detect sensor failure at home. We learn the regular patterns of sensor firing with respect to appliance activation events and report a failure when we observe a significant deviation from the regularity. By using data from three real home deployments of over 71 days and 2818 recorded turn on and off events of 19 monitored appliances, we observe that FailureSense can detect obstructed-view, moved-location, and fail-stop failures with 82.84%, 90.53%, and 86.87% precision, respectively, with an average of 88.81% recall.