Edge analytics/sensing, stream mining, and augmented reality

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Consider a network of prosumers of media content in which users dynamically create and request content objects. The request process is governed by the objects' popularity and varies across network regions and over time. In order to meet user requests, content objects can be stored and transported over the network, characterized by the capacity and energy efficiency of the storage and transport resources. The energy efficient dynamic in-network caching problem aims at finding the evolution of the network configuration, in terms of the content objects being cached and transported over each network element at any given time, that meets user requests, satisfies network resource capacities and minimizes overall energy use. We provide 1) an information-centric optimization framework for the energy efficient dynamic in-network caching problem, 2) an offline solution, EE-OFD, based on an integer linear program (ILP) that obtains the maximum efficiency gains that can be achieved with global knowledge of user requests and network resources, and 3) an efficient fully distributed online solution, EEOND, that allows network nodes to make local caching decisions based on their current estimate of the global energy benefit. Our solutions take into account the network heterogeneity, in terms of capacity, energy efficiency and content popularity, and adapt to changing network conditions minimizing overall energy use. Read More ›

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This paper presents design, implementation, and evaluation of AmbientSense, a real-time ambient sound recognition system on a smartphone. AmbientSense continuously recognizes user context by analyzing ambient sounds sampled from a smartphone's microphone. The phone provides a user with realtime feedback on recognised context. AmbientSense is implemented as an Android app and works in two modes: in autonomous mode processing is performed on the smartphone only. In server mode recognition is done by transmitting audio features to a server and receiving classification results back. We evaluated both modes in a set of 23 daily life ambient sound classes and describe recognition performance, phone CPU load, and recognition delay. The application runs with a fully charged battery up to 13.75 h on a Samsung Galaxy SII smartphone and up to 12.87 h on a Google Nexus One phone. Runtime and CPU load were similar for autonomous and server modes. Read More ›

Mobile phones play a pivotal role in supporting ubiquitous and unobtrusive sensing of human activities. However, maintaining a highly accurate record of a user's behavior throughout the day imposes significant energy demands on the phone's battery. In this paper, we present the design, implementation, and evaluation of METIS: an adaptive mobile sensing platform that efficiently supports social sensing applications. The platform implements a novel sensor task distribution scheme that dynamically decides whether to perform sensing on the phone or in the infrastructure, considering the energy consumption, accuracy, and mobility patterns of the user. By comparing the sensing distribution scheme with sensing performed solely on the phone or exclusively on the fixed remote sensors, we show, through benchmarks using real traces, that the opportunistic sensing distribution achieves over 60% and 40% energy savings, respectively. This is confirmed through a real world deployment in an office environment for over a month: we developed a social application over our frameworks, that is able to infer the collaborations and meetings of the users. In this setting the system preserves over 35% more battery life over pure phone sensing. Read More ›

Real-time stream mining such as surveillance and personal health monitoring, which involves sophisticated mathematical operations, is computation-intensive and prohibitive for mobile devices due to the hardware/computation constraints. To satisfy the growing demand for stream mining in mobile networks, we propose to employ a cloud-based stream mining system in which the mobile devices send via wireless links unclassified media streams to the cloud for classification. We aim at minimizing the classification-energy cost, defined as an affine combination of classification cost and energy consumption at the cloud, subject to an average stream mining delay constraint (which is important in real-time applications). To address the challenge of time-varying wireless channel conditions without a priori information about the channel statistics, we develop an online algorithm in which the cloud operator can dynamically adjust its resource provisioning on the fly and the mobile devices can adapt their transmission rates to the instantaneous channel conditions. It is proved that, at the expense of increasing the average stream mining delay, the online algorithm achieves a classification-energy cost that can be pushed arbitrarily close to the minimum cost achieved by the optimal offline algorithm. Extensive simulations are conducted to validate the analysis. Read More ›

Due to the high bandwidth requirements and stringent delay constraints of multi-user wireless video transmission applications, ensuring that all video senders have sufficient transmission opportunities to use before their delay deadlines expire is a longstanding research problem. We propose a novel solution that addresses this problem without assuming detailed packet-level knowledge, which is unavailable at resource allocation time (i.e. prior to the actual compression and transmission). Instead, we translate the transmission delay deadlines of each sender's video packets into a monotonically-decreasing weight distribution within the considered time horizon. Higher weights are assigned to the slots that have higher probability for deadline-abiding delivery. Given the sets of weights of the senders' video streams, we propose the low-complexity Delay-Aware Resource Allocation (DARA) approach to compute the optimal slot allocation policy that maximizes the deadline-abiding delivery of all senders. A unique characteristic of the DARA approach is that it yields a non-stationary slot allocation policy that depends on the allocation of previous slots. This is in contrast with all existing slot allocation policies such as round-robin or rate-adaptive round-robin policies, which are stationary because the allocation of the current slot does not depend on the allocation of previous slots. We prove that the DARA approach is optimal for weight distributions that are exponentially decreasing in time. We further implement our framework for real-time video streaming in wireless personal area networks that are gaining significant traction within the new Internet-of-Things (IoT) paradigm. For multiple surveillance videos encoded with H.264/AVC and streamed via the 6tisch framework that simulates the IoT-oriented IEEE 802.15.4e TSCH medium access control, our solution is shown to be the only one that ensures all video bitstreams are delivered with acceptable quality in a deadline-- biding manner. Read More ›

The 5th generation wireless communication networks are expected to face the challenge of providing satisfactory - in terms of user experience - connections to ten or even hundred times more connected devices than today. To meet such challenge, a revolutionary change in the network architecture is required. In this paper, we discuss potential directions for the future wireless network architecture and key techniques. An edge cloud and underlay network architecture is proposed by leveraging techniques such as network virtualization, edge computing, local traffic offloading, cloudbased control and processing for improving the overall network efficiency and user experience. This technology advance forms the foundation of the next generation wireless network architecture. Case studies on key enabling techniques of the proposed edge cloud and underlay network architecture are presented together with preliminary design concepts and numerical results. Read More ›

The authors propose a new approach to building augmented reality (AR) systems using a component-based software framework. This has advantages for all parties involved with AR systems. A project manager can reuse existing components in new applications; an end user can reconfigure his system by plugging modules together, an application developer can view the system at a high level of abstraction; and a component developer can focus on technical problems. Our proposed framework consists of reusable distributed services for key subproblems of AR, the middleware to combine them, and an extensible software architecture. We have implemented services for tracking, modeling real and virtual objects, modeling structured navigation or maintenance instructions, and multimodal user interfaces. As a working proof of our concept, we have built an indoor and outdoor campus navigation system using different modes of tracking and user interaction Read More ›

In this paper we describe the first stand-alone Augmented Reality (AR) system with self-tracking running on an unmodified personal digital assistant (PDA) with a commercial camera. The project exploits the ready availability of consumer devices with a minimal need for infrastructure. The application provides the user with a three-dimensional augmented view of the environment. Our system achieves good overlay registration accuracy by using a popular marker-based tracking toolkit (ARToolKit), which runs directly on the PDA. We introduce an optional client/server architecture that is based on wireless networking and is able to dynamically and transparently offload the tracking task in order to provide better performance in select areas. The hardware/software framework is modular and can be easily combined with many elements of an existing AR framework. As a demonstration of the effectiveness, we present a 3D navigation application that guides a user through an unknown building to a chosen location. Read More ›

In this paper, we present three techniques for 6DOF natural feature tracking in real time on mobile phones. We achieve interactive frame rates of up to 30 Hz for natural feature tracking from textured planar targets on current generation phones. We use an approach based on heavily modified state-of-the-art feature descriptors, namely SIFT and Ferns plus a template-matching-based tracker. While SIFT is known to be a strong, but computationally expensive feature descriptor, Ferns classification is fast, but requires large amounts of memory. This renders both original designs unsuitable for mobile phones. We give detailed descriptions on how we modified both approaches to make them suitable for mobile phones. The template-based tracker further increases the performance and robustness of the SIFT- and Ferns-based approaches. We present evaluations on robustness and performance and discuss their appropriateness for Augmented Reality applications. Read More ›

A growing number of applications that generate massive streams of data need intelligent data processing and online analysis. Real-time surveillance systems, telecommunication systems, sensor networks and other dynamic environments are such examples. The imminent need for turning such data into useful information and knowledge augments the development of systems, algorithms and frameworks that address streaming challenges. The storage, querying and mining of such data sets are highly computationally challenging tasks. Mining data streams is concerned with extracting knowledge structures represented in models and patterns in non stopping streams of information. Generally, two main challenges are designing fast mining methods for data streams and need to promptly detect changing concepts and data distribution because of highly dynamic nature of data streams. The goal of this article is to analyze and classify the application of diverse data mining techniques in different challenges of data stream mining. In this paper, we present the theoretical foundations of data stream analysis and propose an analytical framework for data stream mining techniques. Read More ›

Mining data streams has been a focal point of research interest over the past decade. Hardware and software advances have contributed to the significance of this area of research by introducing faster than ever data generation. This rapidly generated data has been termed as data streams. Credit card transactions, Google searches, phone calls in a city, and many othersre typical data streams. In many important applications, it is inevitable to analyze this streaming data in real time. Traditional data mining techniques have fallen short in addressing the needs of data stream mining. Randomization, approximation, and adaptation have been used extensively in developing new techniques or adopting exiting ones to enable them to operate in a streaming environment. This paper reviews key milestones and state of the art in the data stream mining area. Future insights are also be presented. Read More ›

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As data is becoming more and more prolific and complex, the ability to process it and extract valuable information has become a critical requirement. However, performing such signal processing tasks requires to solve multiple challenges. Indeed, information must frequently be extracted (a) from many distinct data streams, (b) using limited resources, and (c) in real time to be of value. The aim of this chapter is to describe and optimize the specifications of signal processing systems, aimed at extracting in real time valuable information out of large-scale decentralized datasets. A first section will explain the motivations and stakes which have made stream mining a new and emerging field of research and describe key characteristics and challenges of stream mining applications. We then formalize an analytical framework which will be used to describe and optimize distributed stream mining knowledge extraction from large scale streams. In stream mining applications, classifiers are organized into a connected topology mapped onto a distributed infrastructure. We will study linear chains of classifiers and determine how the ordering of the classifiers in the chain impacts accuracy of classification and delay and determine how to choose the most suitable order of classifiers. Finally, we present a decentralized decision framework upon which distributed algorithms for joint topology construction and local classifier configuration can be constructed. Stream mining is an active field of research, at the crossing of various disciplines, including multimedia signal processing, distributed systems, machine learning etc. As such, we will indicate several areas for future research and development. Read More ›

Data-driven, adaptive computations are key to enabling the deployment of accurate and efficient stream mining systems, which invoke suitably configured queries in real-time on streams of input data. Due to the physical separation among data sources and computational resources, it is often necessary to deploy such stream mining systems in a distributed fashion, where local learners have access to disjoint subsets of the data that is to be mined, and forward their intermediate results to an ensemble learner that combines the results from the local learners. In this paper, we develop a design methodology for integrated de- sign, simulation, and implementation of dynamic data-driven adaptive stream mining systems. By systematically integrating considerations associated with local embedded processing, classifier configuration, data-driven adaptation and networked com- munication, our approach allows for effective assessment, prototyping, and implementation of alternative distributed design methods for data-driven, adaptive stream mining systems. We demonstrate our results on a dynamic data-driven application involving patient health care monitoring. Read More ›

We investigate using smartphoneWiFi signals to track human queues, which are common in many business areas such as retail stores, airports, and theme parks. Real-time monitoring of such queues would enable a wealth of new applications, such as bottleneck analysis, shift assignments, and dynamic workflow scheduling. We take a minimum infrastructure approach and thus utilize a single monitor placed close to the service area along with transmitting phones. Our strategy extracts unique features embedded in signal traces to infer the critical time points when a person reaches the head of the queue and finishes service, and from these inferences we derive a person’s waiting and service times. We develop two approaches in our system, one is directly feature-driven and the second uses a simple Bayesian network. Extensive experiments conducted both in the laboratory as well as in two public facilities demonstrate that our system is robust to real-world environments. We show that in spite of noisy signal readings, our methods can measure service and waiting times to within a 10 second resolution. Read More ›