The rapid growth of wearable devices has opened exciting opportunities for context-aware multi-device collaboration, where multiple devices can provide enhanced user experience tailored to user needs and conditions. However, it also presents a unique challenge of reliably determining whether a set of wearables is being used by the same individual. In real-world scenarios, device sharing, exchanging, or unintended use can cause privacy risks and degraded functionality. Existing solutions primarily rely on accelerometer data to match movement patterns across devices, but they perform poorly during stationary or varied non-repetitive activities. In this paper, we introduce BioQ, a method that unobtrusively detects wearable co-location by generating and matching bio-cues. These bio-cues are generated from on-body wearable sensor data and embedded into a common latent space. Furthermore, when devices share the same sensor types, BioQ can effectively integrate multiple sensor sources to improve cue generation and matching. Experimental results show that BioQ outperforms baselines in bio-cue generation and matching and is resource-effective in model training, inference, and energy use. Our code is available at https://github.com/Nokia-Bell-Labs/contextual-biological-cues.

The advent of tiny artificial intelligence (AI) accelerators enables AI to run at the extreme edge, offering reduced latency, lower power cost, and improved privacy. When integrated into wearable devices, these accelerators open exciting opportunities, allowing various AI apps to run directly on the body. We present Synergy that provides AI apps with besteffort performance via system-driven holistic collaboration over AI accelerator-equipped wearables. To achieve this, Synergy provides device-agnostic programming interfaces to AI apps, giving the system visibility and controllability over the app's resource use. Then, Synergy maximizes the inference throughput of concurrent AI models by creating various execution plans for each app considering AI accelerator availability and intelligently selecting the best set of execution plans. Synergy further improves throughput by leveraging parallelization opportunities over multiple computation units. Our evaluations with 7 baselines and 8 models demonstrate that, on average, Synergy achieves a 23.0× improvement in throughput, while reducing latency by 73.9% and power consumption by 15.8%, compared to the baselines

Overlapping cameras offer exciting opportunities to view a scene from different angles, allowing for more advanced, comprehensive and robust analysis. However, existing video analytics systems for multi-camera streams are mostly limited to (i) per-camera processing and aggregation and (ii) workload-agnostic centralized processing architectures. In this paper, we present Argus, a distributed video analytics system withcross-camera collaborationon smart cameras. We identify multi-camera, multi-target tracking as the primary task of multi-camera video analytics and develop a novel technique that avoids redundant, processing-heavy identification tasks by leveraging object-wise spatio-temporal association in the overlapping fields of view across multiple cameras. We further develop a set of techniques to perform these operations across distributed cameras without cloud support at low latency by (i) dynamically ordering the camera and object inspection sequence and (ii) flexibly distributing the workload across smart cameras, taking into account network transmission and heterogeneous computational capacities. Evaluation of three real-world overlapping camera datasets with two Nvidia Jetson devices shows that Argus reduces the number of object identifications and end-to-end latency by up to 7.13× and 2.19× (4.86× and 1.60× compared to the state-of-the-art), while achieving comparable tracking quality.

Tiny machine learning (TinyML) aims to run ML models on small devices and is increasingly favored for its enhanced privacy, reduced latency, and low cost. Recently, the advent of tiny AI accelerators has revolutionized the TinyML field by significantly enhancing hardware processing power. These accelerators, equipped with multiple parallel processors and dedicated per-processor memory instances, offer substantial performance improvements over traditional microcontroller units (MCUs). However, their limited data memory often necessitates downsampling input images, resulting in accuracy degradation. To address this challenge, we propose Data channel EXtension (DEX), a novel approach for efficient CNN execution on tiny AI accelerators. DEX incorporates additional spatial information from original images into input images through patch-wise even sampling and channel-wise stacking, effectively extending data across input channels. By leveraging underutilized processors and data memory for channel extension, DEX facilitates parallel execution without increasing inference latency. Our evaluation with four models and four datasets on tiny AI accelerators demonstrates that this simple idea improves accuracy on average by 3.5%p while keeping the inference latency the same on the AI accelerator. The source code is available at https://github.com/Nokia-Bell-Labs/data-channel-extension.

We present GrooveMeter, a novel system that automatically detects vocal and motion reactions to music and supports music engagement-aware applications. We use smart earbuds as sensing devices, already widely used for music listening, and devise reaction detection techniques by leveraging an inertial measurement unit (IMU) and a microphone on earbuds. To explore reactions in daily music-listening situations, we collect the first-kind-of dataset containing 926-minute-long IMU and audio data with 30 participants. With the dataset, we discover unique challenges in detecting music-listening reactions and devise sophisticated processing pipelines to enable accurate and efficient detection. Our comprehensive evaluation shows GrooveMeter achieves the macro F1 scores of 0.89 for vocal reaction and 0.81 for motion reaction with leave-one-subject-out (LOSO) cross-validation (CV). More importantly, it shows higher accuracy and robustness compared to alternative methods. We also present the potential use cases.

We present SensiX++, a multi-tenant runtime for adaptive model execution with integrated MLOps on edge devices, e.g., a camera, a microphone, or IoT sensors. SensiX++ operates on two fundamental principles: highly modular componentisation to externalise data operations with clear abstractions and document-centric manifestation for system-wide orchestration. First, a data coordinator manages the lifecycle of sensors and serves models with correct data through automated transformations. Next, a resource-aware model server executes multiple models in isolation through model abstraction, pipeline automation, and feature sharing. An adaptive scheduler then orchestrates the best-effort executions of multiple models across heterogeneous accelerators, balancing latency and throughput. Finally, microservices with REST APIs serve synthesised model predictions, system statistics, and continuous deployment. Collectively, these components enable SensiX++ to serve multiple models efficiently with fine-grained control on edge devices while minimising data operation redundancy, managing data and device heterogeneity, and reducing resource contention. We benchmark SensiX++ with 10 different vision and acoustics models across various multi-tenant configurations on different edge accelerators (Jetson AGX and Coral TPU) designed for sensory devices. We report on the overall throughput and quantified benefits of various automation components of SensiX++ and demonstrate its efficacy in significantly reducing operational complexity and lowering the effort to deploy, upgrade, reconfigure, and serve embedded models on edge devices.

Multiple sensory devices on and around us are on the rise and require us to redesign a system to make an inference of ML models accurate, robust, and efficient at the deployment time. While this multiplicity opens up an exciting opportunity to leverage sensor redundancy, it is still extremely challenging to benefit from such multiplicity and boost the runtime performance of deployed ML models without model retraining and engineering. From our experience, we uncovered two prime caveats, device and data variabilities, that affect the runtime performance of ML models. We develop an ML system that addresses these variabilities without modifying deployed models by building on prior algorithmic work. It decouples model execution from sensor data and employs two essential operations between them: a) device-to-device data translation for principled mapping of training and inference data and b) quality-aware dynamic selection of the execution pipeline as a function of runtime accuracy. We evaluate the system on wearable devices with motion and audio-based models. The results show that ML models achieve a 7-13% increase in runtime accuracy solely by running on our system, and the increase goes up to 30% in dynamic environments, at the expense of 3 mW on the host device.

Sleep deficiency and disorders are one of the most unsolved public health challenges of modern times. Music therapy is a promising approach, offering a cheap and non-invasive solution to improve sleep quality. However, the choice of therapeutic sleep music is highly limited for users because such music needs to be specially chosen and made by sleep therapists. It could potentially lead to the inefficiency of music therapy if users get bored after listening to the same set of music repeatedly. In this paper, we take the first step towards generating personalized sleep therapy music. Firstly, through an in-depth feature analysis, we investigate the importance of various musical and acoustic features of therapy music. Grounded on our findings, we design a style transfer framework called SleepGAN which induces therapeutic features into music from different genres. We show that, compared to baselines, the music generated by SleepGAN has a higher similarity to the sleep music designed by experts.

The development of edge devices with dedicated hardware accelerators has pushed the deployment and inference of Deep Neural Network (DNN) models closer to users and real-world sensory systems than ever before (e.g., wearables, IoT). Recently, a further subset of these devices has emerged: ultra-low power DNN accelerators. These microcontrollers possess a dedicated hardware accelerator and are able to operate with only μJ's of energy in milliseconds of time. With their small form-factor, such devices could be used for battery-powered machine learning (ML) applications. In this work, we take a close look at one such device: the MAX78000 by Maxim Integrated. We characterize the device's performance by running five DNN models of various sizes and architectures, and analyze its operational latency, power consumption, and memory footprint. To better understand the performance characteristics, we take a step further and investigate how different layer types (operation type, kernel size, number of input and output channels) and the selection of accelerator processors affect the execution time.

Video cameras are becoming ubiquitous in our daily lives. With the recent advancement of Artificial Intelligence (AI), live video analytics are enabling various useful services, including traffic monitoring and campus surveillance. However, current video analytics systems are highly limited in leveraging the enormous opportunities of the deployed cameras due to (i) centralized processing architecture (i.e., cameras are treated as dumb streaming-only sensors), (ii) hard-coded analytics capabilities from tightly coupled hardware and software, (iii) isolated and fragmented camera deployment from different service providers, and (iv) independent processing of camera streams without any collaboration. In this paper, we envision a full-fledged system for software-defined video analytics with cross-camera collaboration that overcomes the aforementioned limitations. We illustrate its detailed system architecture, carefully analyze the key system requirements with representative app scenarios, and derive potential research issues along with a summary of the status quo of existing works.

Conversational agents are rich in content today. However, they are entirely oblivious to users’ situational context, limiting their ability to adapt their response and interaction style. To this end, we explore the design space for a context augmented conversational agent, including analysis of input segment dynamics and computational alternatives. Building on these, we propose a solution that redesigns the input segment intelligently for ambient context recognition, achieved in a two-step inference pipeline. We first separate the non-speech segment from acoustic signals and then use a neural network to infer diverse ambient contexts. To build the network, we curated a public audio dataset through crowdsourcing. Our experimental results demonstrate that the proposed network can distinguish between 9 ambient contexts with an average F1 score of 0.80 with a computational latency of 3 milliseconds. We also build a compressed neural network for on-device processing, optimised for both accuracy and latency. Finally, we present a concrete manifestation of our solution in designing a context-aware conversational agent and demonstrate use cases.

We envision that our earbuds recognize how much we understand learning materials while taking online lectures for effective learning and teaching, e.g., to pinpoint the part for which we need to put more effort to learn. To this end, we explore the feasibility of recognizing the perceived level of understanding of online learners based on IMU sensor data from earbuds. We present an exploratory study to identify head-related behaviors that can be detected by in-ear IMU data, which are associated with the perceived level of understanding for online lectures.

We propose an in-ear sensing method that automatically detects vocal reactions that people often exhibit when listening to music. We observe what kind of vocal reactions are often brought during music listening and investigate the challenges of applying an existing representative acoustic classification model to vocal reaction recognition. We present our vocal reaction recognition method and the preliminary evaluation to assess its performance.

Today's smartphone application (hereinafter `app') markets do not provide information on power consumption of apps, which is essential for users. Continuous sensing apps make this problem more severe because significant power is consumed without the users' awareness. We propose PowerForecaster to break through such an exhaustive cycle. It provides users with personalized estimation of sensing apps' power cost at pre-installation time. It is challenging to provide such estimation in advance because the actual power cost of a sensing app varies depending on user behavior such as physical activities and phone use patterns. To address this, we develop a novel power emulator as a core component of PowerForecaster. It achieves accurate, personalized power estimation by reproducing users' behaviors and emulating the target app's power use. We optimize the system to make the power emulation fast and its trace collection energy efficient. We further address the problem of dealing with large-scale emulation requests from worldwide deployment. We develop a novel selective emulation approach to minimize the server-side resource cost. We performed extensive experiments and the experimental results show that PowerForecaster achieves the power estimation accuracy of 93.4 percent and saves on 60 percent of the emulator instance usage.

We explore a new variability observed in motion signals acquired from modern wearables. Wearing variability refers to the variations of the device orientation and placement across wearing events. We collect the accelerometer data on a smartwatch and an earbud and analyse how motion signals change due to the wearing variability. Our analysis shows that the wearing variability can bring an unexpected change to motion signals, not only from different users but also from different wearing sessions of the same user. We also provide empirical ranges of changes in device orientations.

We propose Tiger, an eyewear system for helping users follow the 20-20-20 rule to alleviate the Computer Vision Syndrome symptoms. It monitors user's screen viewing activities and provides real-time feedback to help users follow the rule. For accurate screen viewing detection, we devise a light-weight multi-sensory fusion approach with three sensing modalities, color, IMU, and lidar. We also design the real-time feedback to effectively lead users to follow the rule. Our evaluation shows that Tiger accurately detects screen viewing events, and is robust to the differences in screen types, contents, and ambient light. Our user study shows positive perception of Tiger regarding its usefulness, acceptance, and real-time feedback.

The increasing availability of multiple sensory devices on or near a human body has opened brand new opportunities to leverage redundant sensory signals for powerful sensing applications. For instance, personal-scale sensory inferences with motion and audio signals can be done individually on a smartphone, a smartwatch, and even an earbud - each offering unique sensor quality, model accuracy, and runtime behaviour. At execution time, however, it is incredibly challenging to assess these characteristics to select the best device for accurate and resource-efficient inferences. To this end, we look at a quality-aware collaborative sensing system that actively interplays across multiple devices and respective sensing models. It dynamically selects the best device as a function of model accuracy at any given context. We propose two complementary techniques for the runtime quality assessment. Borrowing principles from active learning, our first technique runs on three heuristic-based quality assessment functions that employ confidence, margin sampling, and entropy of models' output. Our second technique is built with a siamese neural network and acts on the premise that runtime sensing quality can be learned from historical data. Our evaluation across multiple motion and audio datasets shows that our techniques provide 12% increase in overall accuracy through dynamic device selection at the average expense of 13 mW power on each device as compared to traditional single-device approaches.

No abstract available.

No abstract available.

We present eSense - an open and multi-sensory in-ear wearable platform for personal-scale behaviour analytics. eSense is a true wireless stereo (TWS) earbud and supports dual-mode Bluetooth and Bluetooth Low Energy. It is also augmented with a 6-axis in-ertial measurement unit and a microphone. We demonstrate the eSense platform, the data exploration tool with the open APIs for the real-time visualisation of multi-modal sensory data, and its manifestation in a 360° workplace well-being application.

We introduce Zaturi, a system enabling parents to create an audio book for their babies by utilizing micro spare time at work. We define micro spare time at work as tiny fragments of time with low cognitive loads that frequently occur at work, such as waiting for an elevator. We show that putting together micro spare time at work helps a working parent (1) build a tangible symbol conveying his/her thoughts to the beloved baby and (2) develop his/her own feelings of parental achievement without compromising regular working hours. Zaturi lets the parent immediately be aware of micro spare time and provides a crafted interface to seamlessly record the book piece by piece, so that the baby can enjoy listening to the book recorded in the parent's own voice. Through an extensive design process, we characterize the notion of micro spare time and build a working prototype of Zaturi. We also report parents' perceptions and family reactions after a two-week deployment.

We propose PADA, a new power evaluation tool to measure and optimize power use of mobile sensing applications. Our motivational study with 53 professional developers shows they face huge challenges in meeting power requirements. The key challenges are from the significant time and effort for repetitive power measurements since the power use of sensing applications needs to be evaluated under various real-world usage scenarios and sensing parameters. PADA enables developers to obtain enriched power information under diverse usage scenarios in development environments without deploying and testing applications on real phones in real-life situations. We conducted two user studies with 19 developers to evaluate the usability of PADA. We show that developers benefit from using PADA in the implementation and power tuning of mobile sensing applications.

Continuous mobile sensing applications are emerging. Despite their usefulness, their real-world adoption has been slow. Many users are turned away by the drastic battery drain caused by continuous sensing and processing. In this paper, we propose CoMon+, a novel cooperative context monitoring system, which addresses the energy problem through opportunistic cooperation among nearby users. For effective cooperation, we develop a benefit-aware negotiation method to maximize the energy benefit of context sharing. CoMon+ employs heuristics to detect cooperators who are likely to remain in the vicinity for a long period of time, and the negotiation method automatically devises a cooperation plan that provides mutual benefit to cooperators, while considering running applications, available devices, and user policies. Especially, CoMon+ improves the negotiation method proposed in our earlier work, CoMon [30], to exploit multiple processing plans enabled by various personal sensing devices; each plan can be alternatively used for cooperation, which in turn will maximize overall power saving. We implement a CoMon+ prototype and show that it provides significant benefit for mobile sensing applications, e.g., saving 27-71 percent of smartphone power consumption depending on cooperation cases. Also, our deployment study shows that CoMon+ saves an average 19.7 percent of battery under daily use of a prototype application compared to the case without CoMon+ running.

Emerging continuous sensing apps introduce new major factors governing phones' overall battery consumption behaviors: (1) added nontrivial persistent battery drain, and more importantly (2) different battery drain rate depending on the user's different mobility condition. In this paper, we address the new battery impacting factors significant enough to outdate users' existing battery model in real life. We explore an initial approach to help users understand the cause and effect between their physical activity and phones' battery life. To this end, we present Sandra, a novel mobility-aware smartphone battery information advisor, and study its potential to help users redevelop their battery model. We perform an extensive explorative study and deployment for 30 days with 24 users. Our findings reveal what they essentially learned, and in which situations they found Sandra very helpful. We share the lessons learned to help in the design of future mobility-aware battery advisors.

Today's smartphone application (hereinafter 'app') markets miss a key piece of information, power consumption of apps. This causes a severe problem for continuous sensing apps as they consume significant power without users' awareness. Users have no choice but to repeatedly install one app after another and experience their power use. To break such an exhaustive cycle, we propose PowerForecaster, a system that provides users with power use of sensing apps at pre-installation time. Such advanced power estimation is extremely challenging since the power cost of a sensing app largely varies with users' physical activities and phone use patterns. We observe that the time for active sensing and processing of an app can vary up to three times with 27 people's sensor traces collected over three weeks. PowerForecaster adopts a novel power emulator that emulates the power use of a sensing app while reproducing users' physical activities and phone use patterns, achieving accurate, personalized power estimation. Our experiments with three commercial apps and two research prototypes show that PowerForecaster achieves 93.4% accuracy under 20 use cases. Also, we optimize the system to accelerate emulation speed and reduce overheads, and show the effectiveness of such optimization techniques.

Recently, a number of continuous sensing applications have been actively proposed in research communities and commercially released in the market. However, due to their unique power characteristics, user behavior-dependent battery drain, they bring new challenges for users' power management on these applications. In this demonstration, we present a comprehensive approach to support users' power management for continuous sensing applications. First, at pre-installation time, we provide an instant, personalized power estimation of a continuous sensing application. Without exhaustive trial and error, users can decide judiciously to install a certain application or not. Second, at runtime, we provide mobility-aware battery information. With this information, users can better estimate the phone's remaining battery life based on their imminent mobility conditions and take necessary actions in advance such as carrying an additional battery or minimizing the use of applications.

Language delay is a developmental problem of children who do not acquire language as expected for their chronological ages. Without timely intervention, language delay can act as a lifelong risk factor. Speech-language pathologists highlight that effective parent participation in everyday parent-child conversation is important to treat children's language delay. For effective roles, however, parents need to alter their own lifelong-established conversation habits, requiring extensive period of conscious effort and staying alert. In this paper, we present new opportunities for mobile and social computing to reinforce everyday parent-child conversation with therapeutic implications for children with language delays. Specifically, we propose TalkBetter, a mobile in-situ intervention service to help parents in daily parent-child conversation through real-time meta-linguistic analysis of ongoing conversations. Through extensive field studies with speech-language pathologists and parents, we report the multilateral motivations and implications of TalkBetter. We present our development of TalkBetter prototype and report its performance evaluation.

In this paper, we present Orchestrator, an active resource orchestration framework for a PAN-scale sensor-rich mobile computing platform. Incorporating diverse sensing devices connected to a mobile phone, the platform will serve as a common base to accommodate personal context-aware applications. A major challenge for the platform is to simultaneously support concurrent applications requiring continuous and complex context processing, with highly scarce and dynamic resources. To address the challenge, we build Orchestrator, which actively coordinates applications' resource uses over the distributed mobile and sensor devices. As a key approach, it adopts an active resource use orchestration, which prepares multiple alternative plans for application requests and selectively applies them according to resource availability and demands at runtime. Through the selection, it resolves resource contention among applications and helps them efficiently share resources. With such system-level supports, applications become capable of providing long-running services under dynamic circumstances with scarce resources. Also, the platform can host a number of applications stably, exploiting its full resource capacity. We build an Orchestrator prototype on off-the-shelf mobile devices and sensor motes and show its effectiveness in terms of application supportability and resource use efficiency.

In this paper, we propose SocioPhone, a novel initiative to build a mobile platform for face-to-face interaction monitoring. Face-to-face interaction, especially conversation, is a fundamental part of everyday life. Interaction-aware applications aimed at facilitating group conversations have been proposed, but have not proliferated yet. Useful contexts to capture and support face-to-face interactions need to be explored more deeply. More important, recognizing delicate conversational contexts with commodity mobile devices requires solving a number of technical challenges. As a first step to address such challenges, we identify useful meta-linguistic contexts of conversation, such as turn-takings, prosodic features, a dominant participant, and pace. These serve as cornerstones for building a variety of interaction-aware applications. SocioPhone abstracts such useful meta-linguistic contexts as a set of intuitive APIs. Its runtime efficiently monitors registered contexts during in-progress conversations and notifies applications on-the-fly. Importantly, we have noticed that online turn monitoring is the basic building block for extracting diverse meta-linguistic contexts, and have devised a novel volume-topography-based method. We show the usefulness of SocioPhone with several interesting applications: SocioTherapist, SocioDigest, and Tug-of-War. Also, we show that our turn-monitoring technique is highly accurate and energy-efficient under diverse real-life situations.

No abstract available.

Mobile applications that sense continuously, such as location monitoring, are emerging. Despite their usefulness, their adoption in real-world deployment situations has been extremely slow. Many smartphone users are turned away by the drastic battery drain caused by continuous sensing and processing. Also, the extractable contexts from the phone are quite limited due to its position and sensing modalities. In this paper, we propose CoMon, a novel cooperative ambience monitoring platform, which newly addresses the energy problem through opportunistic cooperation among nearby mobile users. To maximize the benefit of cooperation, we develop two key techniques, (1) continuity-aware cooperator detection and (2) benefit-aware negotiation. The former employs heuristics to detect cooperators who will remain in the vicinity for a long period of time, while the latter automatically devises a cooperation plan that provides mutual benefit to cooperators, while considering running applications, available devices, and user policies. Through continuity- and benefit-aware operation, CoMon enables applications to monitor the environment at much lower energy consumption. We implement and deploy a CoMon prototype and show that it provides significant benefit for mobile sensing applications.

No abstract available.

No abstract available.

In this paper, we propose a novel efficient dataflow execution method for mobile context monitoring applications. As a key approach to minimize the execution overhead, we propose a new dataflow execution model, producer-oriented model. Compared to the conventional consumer-oriented model adopted in stream processing engines, our model significantly reduces execution overhead to process context monitoring dataflow reflecting unique characteristics of context monitoring. To realize the model, we develop DataBank, an execution container that takes charge of the management and delivery of the output data for the associated operator. We demonstrate the effectiveness of DataBank by implementing three useful applications and their dataflow graphs, i.e., MusicMap, FindMyPhone, and CalorieMonitor. Using the applications, we show that DataBank reduces the CPU utilization by more than 50%, compared to the methods based on the consumer-oriented model; DataBank enables more context monitoring applications to run concurrently.

In this demonstration, we will show MobiCon, a context monitoring platform; it runs over smartphones and sensor OSs, and facilitates development and deployment of everyday context-aware applications. For many years, lots of research efforts have been made in building low-cost, yet effective sensor networks for various application domains such as structural health monitoring of bridges, disaster recovery, automated ventilation of buildings. Integration of sensors into smartphones and the advent of wearable devices open a new opportunity for mobile applications to leverage in-situ user contexts such as his/her location, activity, social relationship, health status. In recent studies of mobile and pervasive computing, a number of useful mobile context-aware applications have been proposed, but their actual deployment is slow due to complexity of context processing and heavy resource and battery usage. To address such challenges, we have been building MobiCon for many years, upon which diverse context-aware applications are developed and deployed without concerns about complexity of context processing and resource optimization.

Emerging mobile sensing applications are changing the characteristics of smartphone workloads. Whereas typical mobile applications run alone in the foreground interacting with users, sensing applications concurrently run in the background, providing unobtrusive monitoring services. Such concurrent sensing workloads raise a new challenge incurring severe resource contention among themselves and with other foreground applications. To address the challenge, we develop SymPhoney, a coordinated sensing flow execution engine to support concurrent sensing applications. As its key approach, we develop a novel sensing-flow-aware coordination. We first introduce the new concept of frame externalization i.e., to identify and externalize semantic structures embedded in otherwise flat sensing data streams. Leveraging the identified frame structures, SymPhoney develops frame-based coordination and scheduling mechanisms, which effectively coordinates the resource use of concurrent contending applications and maximize their utilities even under severe resource contention. We implemented several sensing applications on top of the SymPhoney engine and performed extensive experiments, showing effective coordination capability of SymPhoney.

In this paper, we propose an in-situ Crowd-aware Sensing Platform, called "CrowdMon", which envisions the cooperation among mobile users in highly crowded urban areas such as metro and square. CrowdMon establishes a spontaneous connection from co-located users in a semantic proximity and enables them to share contextual information such as location, ambient music, and mood of places. To the best of our knowledge, CrowdMon is the first attempt to support crowd-aware services at a platform level. We show interesting use cases of CrowdMon and an initial system design to realize the crowd-based context sharing.

No abstract available.

In this paper, we present Orchestrator, an active resource orchestration framework for mobile context monitoring. Emerging pervasive environments will introduce a PAN-scale sensor-rich mobile platform consisting of a mobile device and many wearable and space-embedded sensors. In such environments, it is challenging to enable multiple context-aware applications requiring continuous context monitoring to simultaneously run and share highly scarce and dynamic resources. Orchestrator enables multiple applications to effectively share the resources while exploiting the full capacity of overall system resources and providing high-quality service to users. For effective orchestration, we propose an active resource use orchestration approach that actively finds appropriate resource uses for applications and flexibly utilizes them depending on dynamic system conditions. Orchestrator is built upon a prototype platform that consists of off-the-shelf mobile devices and sensor motes. We present the detailed design, implementation, and evaluation of Orchestrator. The evaluation results show that Orchestrator enables applications in a resource-efficient way.