S. M. Shahriar Nirjon

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.

Bagging and Boosting Negatively Correlated Neural Networks

In this paper, we propose two cooperative ensemble learning algorithms, i.e., NegBagg and NegBoost, for designing neural network (NN) ensembles. The proposed algorithms incrementally train different individual NNs in an ensemble using the negative correlation learning algorithm. Bagging and boosting algorithms are used in NegBagg and NegBoost, respectively, to create different training sets for different NNs in the ensemble. The idea behind using negative correlation learning in conjunction with the bagging/boosting algorithm is to facilitate interaction and cooperation among NNs during their training. Both NegBagg and NegBoost use a constructive approach to automatically determine the number of hidden neurons for NNs. NegBoost also uses the constructive approach to automatically determine the number of NNs for the ensemble. The two algorithms have been tested on a number of benchmark problems in machine learning and NNs, including Australian credit card assessment, breast cancer, diabetes, glass, heart disease, letter recognition, satellite, soybean, and waveform problems. The experimental results show that NegBagg and NegBoost require a small number of training epochs to produce compact NN ensembles with good generalization.

MusicalHeart: a hearty way of listening to music

MusicalHeart is a biofeedback-based, context-aware, automated music recommendation system for smartphones. We introduce a new wearable sensing platform, Septimu, which consists of a pair of sensor-equipped earphones that communicate to the smartphone via the audio jack. The Septimu platform enables the MusicalHeart application to continuously monitor the heart rate and activity level of the user while listening to music. The physiological information and contextual information are then sent to a remote server, which provides dynamic music suggestions to help the user maintain a target heart rate. We provide empirical evidence that the measured heart rate is 75% -- 85% correlated to the ground truth with an average error of 7.5 BPM. The accuracy of the person-specific, 3-class activity level detector is on average 96.8%, where these activity levels are separated based on their differing impacts on heart rate. We demonstrate the practicality of MusicalHeart by deploying it in two real world scenarios and show that MusicalHeart helps the user achieve a desired heart rate intensity with an average error of less than 12.2%, and its quality of recommendation improves over time.

MultiNets: Policy Oriented Real-Time Switching of Wireless Interfaces on Mobile Devices

In this paper we present Multi Nets, a system which is capable of switching between wireless network interfaces on mobile devices in real-time. Multi Nets is motivated by the need of smart phone platforms to save energy, offload data traffic, and achieve higher throughput. We describe the architecture of Multi Nets and demonstrate the methodology to perform switching in Linux based mobile OSes such as Android. Our analysis on mobile data traces collected from real users shows that with real-time switching we can save 27.4% of the energy, offload 79.82% of the data traffic, or achieve 7 times more throughput on average. We deploy Multi Nets in a real world scenario and our experimental results show that depending on the user requirements, it outperforms the state-of-the-art Android system either by saving up to 33.75% energy, or achieving near-optimal offloading, or achieving near-optimal throughput while substantially reducing TCP interruptions due to switching.

Auditeur: a mobile-cloud service platform for acoustic event detection on smartphones

Auditeur is a general-purpose, energy-efficient, and context-aware acoustic event detection platform for smartphones. It enables app developers to have their app register for and get notified on a wide variety of acoustic events. Auditeur is backed by a cloud service to store user contributed sound clips and to generate an energy-efficient and context-aware classification plan for the phone. When an acoustic event type has been registered, the smartphone instantiates the necessary acoustic processing modules and wires them together to execute the plan. The phone then captures, processes, and classifies acoustic events locally and efficiently. Our analysis on user-contributed empirical data shows that Auditeurs energy-aware acoustic feature selection algorithm is capable of increasing the device lifetime by 33.4%, sacrificing less than 2% of the maximum achievable accuracy. We implement seven apps with Auditeur, and deploy them in real-world scenarios to demonstrate that Auditeur is versatile, 11.04% - 441.42% less power hungry, and 10.71% - 13.86% more accurate in detecting acoustic events, compared to state-of-the-art techniques. We present a user study to demonstrate that novice programmers can implement the core logic of interesting apps with Auditeur in less than 30 minutes, using only 15 - 20 lines of Java code.

COIN-GPS: indoor localization from direct GPS receiving

Due to poor signal strength, multipath effects, and limited on-device computation power, common GPS receivers do not work indoors. This work addresses these challenges by using a steerable, high-gain directional antenna as the front-end of a GPS receiver along with a robust signal processing step and a novel location estimation technique to achieve direct GPS-based indoor localization. By leveraging the computing power of the cloud, we accommodate longer signals for acquisition, and remove the requirement of decoding timestamps or ephemeris data from GPS signals. We have tested our system in 31 randomly chosen spots inside five single-story, indoor environments such as stores, warehouses and shopping centers. Our experiments show that the system is capable of obtaining location fixes from 20 of these spots with a median error of less than 10 m, where all normal GPS receivers fail.

TypingRing: A Wearable Ring Platform for Text Input

This paper presents TypingRing, a wearable ring platform that enables text input into computers of different forms, such as PCs, smartphones, tablets, or even wearables with tiny screens. The basic idea of TypingRing is to have a user wear a ring on his middle finger and let him type on a surface - such as a table, a wall, or his lap. The user types as if a standard QWERTY keyboard is lying underneath his hand but is invisible to him. By using the embedded sensors TypingRing determines what key is pressed by the user. Further, the platform provides visual feedback to the user and communicates with the computing device wirelessly. This paper describes the hardware and software prototype of TypingRing and provides an in-depth evaluation of the platform. Our evaluation shows that TypingRing is capable of detecting and sending key events in real-time with an average accuracy of 98.67%. In a field study, we let seven users type a paragraph with the ring, and we find that TypingRing yields a reasonable typing speed (e.g., 33-50 keys per minute) and their typing speed improves over time.

MultiNets: A system for real-time switching between multiple network interfaces on mobile devices

MultiNets is a system supporting seamless switch-over between wireless interfaces on mobile devices in real-time. MultiNets is configurable to run in three different modes: (i) Energy Saving mode--for choosing the interface that saves the most energy based on the condition of the device, (ii) Offload mode--for offloading data traffic from the cellular to WiFi network, and (iii) Performance mode--for selecting the network for the fastest data connectivity. MultiNets also provides a powerful API that gives the application developers: (i) the choice to select a network interface to communicate with a specific server, and (ii) the ability to simultaneously transfer data over multiple network interfaces. MultiNets is modular, easily integrable, lightweight, and applicable to various mobile operating systems. We implement MultiNets on Android devices as a show case. MultiNets does not require any extra support from the network infrastructure and runs existing applications transparently. To evaluate MultiNets, we first collect data traces from 13 actual Android smartphone users over three months. We then use the collected traces to show that, by automatically switching to WiFi whenever it is available, MultiNets can offload on average 79.82p of the data traffic. We also illustrate that, by optimally switching between the interfaces, MultiNets can save on average 21.14 KJ of energy per day, which is equivalent to 27.4p of the daily energy usage. Using our API, we demonstrate that a video streaming application achieves 43--271p higher streaming rate when concurrently using WiFi and 3G interfaces. We deploy MultiNets in a real-world scenario and our experimental results show that depending on the user requirements, it outperforms the state-of-the-art Android system either by saving up to 33.75p energy, achieving near-optimal offloading, or achieving near-optimal throughput while substantially reducing TCP interruptions due to switching.

Kinsight: Localizing and Tracking Household Objects Using Depth-Camera Sensors

We solve the problem of localizing and tracking household objects using a depth-camera sensor network. We design and implement Kin sight that tracks household objects indirectly -- by tracking human figures, and detecting and recognizing objects from human-object interactions. We devise two novel algorithms: (1) Depth Sweep -- that uses depth information to efficiently extract objects from an image, and (2) Context Oriented Object Recognition -- that uses location history and activity context along with an RGB image to recognize object sat home. We thoroughly evaluate Kinsights performance with a rich set of controlled experiments. We also deploy Kinsightin real-world scenarios and show that it achieves an average localization error of about 13 cm.

Kintense: a robust, accurate, real-time and evolving system for detecting aggressive actions from streaming 3D skeleton data

Kintense is a robust, accurate, real-time, and evolving system for detecting aggressive actions such as hitting, kicking, pushing, and throwing from streaming 3D skeleton joint coordinates obtained from Kinect sensors. Kintense uses a combination of: (1) an array of supervised learners to recognize a predefined set of aggressive actions, (2) an unsupervised learner to discover new aggressive actions or refine existing actions, and (3) human feedback to reduce false alarms and to label potential aggressive actions. This paper describes the design and implementation of Kintense and provides empirical evidence that the system is 11% - 16% more accurate and 10% - 54% more robust to changes in distance, body orientation, speed, and person when compared to standard techniques such as dynamic time warping (DTW) and posture based gesture recognizers. We deploy Kintense in two multi-person households and demonstrate how it evolves to discover and learn unseen actions, achieves up to 90% accuracy, runs in real-time, and reduces false alarms with up to 13 times fewer user interactions than a typical system.