Research Papers

The cloud is migrating to the edge of the network, where routers themselves may become the virtualisation infrastruc- ture, in an evolution labelled as “the fog”. However, many other complementary technologies are reaching a high level of maturity. Their interplay may dramatically shift the in- formation and communication technology landscape in the following years, bringing separate technologies into a com- mon ground. This paper offers a comprehensive definition of the fog, comprehending technologies as diverse as cloud, sensor networks, peer-to-peer networks, network virtualisa- tion functions or configuration management techniques. We highlight the main challenges faced by this potentially break- through technology amalgamation. Read More ›

Relaying and orthogonal frequency division multiple access (OFDMA) are the accepted technologies for emerging wireless communications standards. The activities in many wireless standardization bodies and forums, for example IEEE 802.16 j/m and LTE-Advanced, attest to this fact. The availability or lack thereof of efficient radio resource management (RRM) could make or mar the opportunities in these networks. Although distributed schemes are more attractive, it is essential to seek outstanding performance benchmarks to which various decentralized schemes can be compared. Therefore, this paper provides a comprehensive centralized RRM algorithm for downlink OFDMA cellular fixed relay networks in a way to ensure user fairness with minimal impact on network throughput. In contrast, it has been observed that pure opportunistic schemes and fairness-aware schemes relying solely on achievable and allocated capacities may not attain the desired fairness, e.g., proportional fair scheduling. The proposed scheme is queue-aware and performs three functions jointly; dynamic routing, fair scheduling, and load balancing among cell nodes. We show that the proposed centralized scheme is different from the traditional centralized schemes in terms of the substantial savings in complexity and feedback overhead. Read More ›

Researchers in both academia and industry have accepted OFDMA as the most appropriate air-interface for the emerging broadband wireless access networks and standards. A number of IEEE working groups and various research forums are focusing on developing relay and mesh-enabled networks with cooperative communication features. Among these research efforts are IEEE 802.11s, IEEE 802.16j/m, and 3GPP's advanced long term evolution (LTE-advanced). The combination of OFDMA with relaying techniques provides rich opportunities for cost-effective and high-performance networks. To exploit such opportunities requires intelligent radio resource management (RRM) algorithms. Although a number of publications have highlighted the important and challenging issues involved in designing RRM algorithms for OFDMA networks, only recently a number of papers have investigated relay-enhanced OFDMA-based multicellular networks. By and large, the literature indicates that these issues constitute a hot research topic that will continue to attract interest. This paper provides a survey of the current literature on OFDMA networks enhanced with decode-and-forward relaying and provides their link to earlier literature in non-OFDMA networks. In addition, a rich list of references is provided to direct the readers toward some of the emerging techniques. Read More ›

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Mobile cloud computing aims at improving the performance of mobile applications and to enhance the resource utilization of service providers. In this paper, we consider a mobile cloud computing environment in which the service providers can form a coalition to create a resource pool to support the mobile applications. First, an admission control mechanism is used to provide services of mobile applications to the users given the available long-term reserved resources in a pool. An optimization formulation is introduced to obtain the optimal decision of admission control. Then, for a given coalition of service providers, the revenue obtained from utilizing the resource pool has to be shared among the service providers. A coalitional game model is developed for sharing the revenue. In addition, since the service providers can decide on short-term capacity expansion of the resource pool, a game model is introduced to obtain the optimal strategies of service providers on capacity expansion such that their profits are maximized. Read More ›

Mobile cloud computing aims at improving the performance of mobile applications and to enhance the resource utilization of service providers. In this paper, we consider a mobile cloud computing environment in which the service providers can form a coalition to create a resource pool to support the mobile applications. First, an admission control mechanism is used to provide services of mobile applications to the users given the available long-term reserved resources in a pool. An optimization formulation is introduced to obtain the optimal decision of admission control. Then, for a given coalition of service providers, the revenue obtained from utilizing the resource pool has to be shared among the service providers. A coalitional game model is developed for sharing the revenue. In addition, since the service providers can decide on short-term capacity expansion of the resource pool, a game model is introduced to obtain the optimal strategies of service providers on capacity expansion such that their profits are maximized. Read More ›

Voice over Internet Protocol (VoIP) has been widely used by many mobile consumer devices in IEEE 802.11 wireless local area networks (WLAN) due to its low cost and convenience. However, delays of all VoIP flows dramatically increase when network capacity is approached. Additionally, unfair traffic distribution between downlink and uplink flows in WLANs impacts the perceived VoIP quality. This paper proposes an intelligent bandwidth management scheme for VoIP services (iVoIP) that improves bandwidth utilization and provides fair downlink–uplink channel access. iVoIP is a cross-layer solution which includes two components: (1) iVoIP-Admission Control, which protects the quality of existing flows and increases the utilization of wireless network resources; (2) iVoIP-Fairness scheme, which balances the channel access opportunity between access point (AP) and wireless stations. iVoIP-Admission Control limits the number of VoIP flows based on an estimation of VoIP capacity. iVoIP-Fairness implements a contention window adaptation scheme at AP which uses stereotypes and considers several major quality of service parameters to balance the network access of downlink and uplink flows, respectively. Extensive simulations and real tests have been performed, demonstrating that iVoIP has both very good VoIP capacity estimation and admission control results. Additionally, iVoIP improves the downlink/uplink fairness level in terms of throughput, delay, loss, and VoIP quality. Read More ›

Fourth generation (4G) wireless networks will provide high-bandwidth connectivity with quality-of-service (QoS) support to mobile users in a seamless manner. In such a scenario, a mobile user will be able to connect to different wireless access networks such as a wireless metropolitan area network (WMAN), a cellular network, and a wireless local area network (WLAN) simultaneously. We present a game-theoretic framework for radio resource management (that is, bandwidth allocation and admission control) in such a heterogeneous wireless access environment. First, a noncooperative game is used to obtain the bandwidth allocations to a service area from the different access networks available in that service area (on a long-term basis). The Nash equilibrium for this game gives the optimal allocation which maximizes the utilities of all the connections in the network (that is, in all of the service areas). Second, based on the obtained bandwidth allocation, to prioritize vertical and horizontal handoff connections over new connections, a bargaining game is formulated to obtain the capacity reservation thresholds so that the connection-level QoS requirements can be satisfied for the different types of connections (on a long-term basis). Third, we formulate a noncooperative game to obtain the amount of bandwidth allocated to an arriving connection (in a service area) by the different access networks (on a short-term basis). Based on the allocated bandwidth and the capacity reservation thresholds, an admission control is used to limit the number of ongoing connections so that the QoS performances are maintained at the target level for the different types of connections. Read More ›

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Consumer demand for enjoying any content anywhere, any time, and on any device is driving the need for reliable connectivity between content sources and consumption devices inside the home. A key content consumption device in the home is the TV. In this paper, we describe the connectivity challenges associated with Smart TVs to ensure a positive user experience and present some solutions that achieve these requirements. We also describe how the TV can incorporate the latest networking technologies to enable new types of user experiences. Read More ›

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Device-to-device (D2D) communications underlaying a cellular infrastructure has been proposed as a means of taking advantage of the physical proximity of communicating devices, increasing resource utilization, and improving cellular coverage. Relative to the traditional cellular methods, there is a need to design new peer discovery methods, physical layer procedures, and radio resource management algorithms that help realize the potential advantages of D2D communications. In this article we use the 3GPP Long Term Evolution system as a baseline for D2D design, review some of the key design challenges, and propose solution approaches that allow cellular devices and D2D pairs to share spectrum resources and thereby increase the spectrum and energy efficiency of traditional cellular networks. Simulation results illustrate the viability of the proposed design. Read More ›

This paper presents a Quality of Experience (QoE) driven approach for multi-user resource optimization in Dynamic Adaptive Streaming over HTTP (DASH) over next generation wireless networks. Our objective is to enhance the user experience in adaptive HTTP streaming by jointly considering the characteristics of the media content and the available wireless resources in the operator network. Specifically, we propose a proactive QoE-based approach for rewriting the client HTTP requests at a proxy in the mobile network. The advantage of the proposed approach is its applicability for over-the-top (OTT) streaming as it requires no adaptation of the media content. We compare our proposed scheme to both reactive QoE-optimized and to standard-DASH HTTP streaming. Our contributions are: 1) We first show that standard OTT DASH leads to unsatisfactory performance since the content agnostic resource allocation by the LTE scheduler is far from optimal, and we can achieve a clear QoE improvement when considering the content characteristics. 2) We additionally show that proactively rewriting the client requests gives control of the video content adaptation to the network operator which has better information than the client on the load and radio conditions in the cell. This results in additional gains in user perceived video quality. 3) A standard unmodified DASH client remains unaware of the proposed rewriting of the HTTP requests and can decode and play the redirected media segments. Read More ›

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The design and development of wearable biosensor systems for health monitoring has garnered lots of attention in the scientific community and the industry during the last years. Mainly motivated by increasing healthcare costs and propelled by recent technological advances in miniature biosensing devices, smart textiles, microelectronics, and wireless communications, the continuous advance of wearable sensor-based systems will potentially transform the future of healthcare by enabling proactive personal health management and ubiquitous monitoring of a patient's health condition. These systems can comprise various types of small physiological sensors, transmission modules and processing capabilities, and can thus facilitate low-cost wearable unobtrusive solutions for continuous all-day and any-place health, mental and activity status monitoring. This paper attempts to comprehensively review the current research and development on wearable biosensor systems for health monitoring. A variety of system implementations are compared in an approach to identify the technological shortcomings of the current state-of-the-art in wearable biosensor solutions. An emphasis is given to multiparameter physiological sensing system designs, providing reliable vital signs measurements and incorporating real-time decision support for early detection of symptoms or context awareness. In order to evaluate the maturity level of the top current achievements in wearable health-monitoring systems, a set of significant features, that best describe the functionality and the characteristics of the systems, has been selected to derive a thorough study. The aim of this survey is not to criticize, but to serve as a reference for researchers and developers in this scientific area and to provide direction for future research improvements Read More ›

Is M2M hype or the future of our information society? What does it take to turn the M2M vision into reality? In this article we discuss the business motivations and technology challenges for machine-to-machine communications. We highlight key M2M application requirements and major technology gaps. We analyze the future directions of air interface technology improvements and network architectures evolution to enable the mass deployment of M2M services. In particular, we consider the salient features of M2M traffic that may not be supported efficiently by present standards, and provide an overview of potential enhancements. Finally, we discuss standards development for M2M. Read More ›

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It is envisioned that home networks will shift from current machine-to-human communications to the machine-to-machine paradigm with the rapid penetration of embedded devices in home surroundings. In this article, we first identify the fundamental challenges in home M2M networks. Then we present the architecture of home M2M networks decomposed into three subareas depending on the radio service ranges and potential applications. Finally, we focus on QoS management in home M2M networks, considering the increasing number of multimedia devices and growing visual requirements in a home area. Three standards for multimedia sharing and their QoS architectures are outlined. Cross-layer joint admission and rate control design is reported for QoS-aware multimedia sharing. This proposed strategy is aware of the QoS requirements and resilience of multimedia services. Illustrative results indicate that the joint design is able to intelligently allocate radio bandwidth based on QoS demands in resource-constrained home M2M networks. Read More ›

To enable full mechanical automation where each smart device can play multiple roles among sensor, decision maker, and action executor, it is essential to construct scrupulous connections among all devices. Machine-to-machine communications thus emerge to achieve ubiquitous communications among all devices. With the merit of providing higher-layer connections, scenarios of 3GPP have been regarded as the promising solution facilitating M2M communications, which is being standardized as an emphatic application to be supported by LTE-Advanced. However, distinct features in M2M communications create diverse challenges from those in human-to-human communications. To deeply understand M2M communications in 3GPP, in this article, we provide an overview of the network architecture and features of M2M communications in 3GPP, and identify potential issues on the air interface, including physical layer transmissions, the random access procedure, and radio resources allocation supporting the most critical QoS provisioning. An effective solution is further proposed to provide QoS guarantees to facilitate M2M applications with inviolable hard timing constraints. Read More ›

To provide live streaming service to mobile users, traditionally each user pulls content from a server over his cellular network. In order to overcome the scalability problem of last-hop bandwidth bottleneck, mobile peer-to-peer (P2P) streaming can be used where mobile devices relay their stream received in a multi-hop manner by means of a secondary channel (such as Wi-Fi or bluetooth). We investigate the design of distributed algorithm termed LocalTree, which minimizes the number of broadcasters while meeting a certain video quality requirement under peer churns. We first formulate the problem and show that it is NP-hard, and hence propose LocalTree which achieves robustness similar to an unstructured mesh and low delay similar to a global tree. Simulation results show that LocalTree outperforms other algorithms substantially in terms of number of broadcasters used (by as much as 50%). Read More ›

Internet of Things (IoT) brings more than an explosive proliferation of endpoints. It is disruptive in several ways. In this chapter we examine those disruptions, and propose a hierarchical distributed architecture that extends from the edge of the network to the core nicknamed Fog Computing. In particular, we pay attention to a new dimension that IoT adds to Big Data and Analytics: a massively distributed number of sources at the edge. Read More ›

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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 ›

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Users increasingly store data collections such as digital photographs on multiple personal devices, each of which typically offers a storagemanagement interface oblivious to the contents of the user’s other devices. As a result, collections become disorganized and drift out of sync. This paper presents Eyo, a novel personal storage system that provides device transparency: a user can think in terms of “file X”, rather than “file X on device Y ”, and will see the same set of files on all personal devices. Eyo allows a user to view and manage the entire collection of objects from any of their devices, even from disconnected devices and devices with too little storage to hold all the object content. Eyo synchronizes these collections across any network topology, including direct peer-to-peer links. Eyo provides applications with a storage API with first-class access to object version history in order to resolve update conflicts automatically. Experiments with several applications using Eyo— media players, a photo editor, a podcast manager, and an email interface—show that device transparency requires only minor application changes, and matches the storage and bandwidth capabilities of typical portable devices. Read More ›

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Despite increasing usage of mobile computing, exploiting its full potential is difficult due to its inherent problems such as resource scarcity, frequent disconnections, and mobility. Mobile cloud computing can address these problems by executing mobile applications on resource providers external to the mobile device. In this paper, we provide an extensive survey of mobile cloud computing research, while highlighting the specific concerns in mobile cloud computing. We present a taxonomy based on the key issues in this area, and discuss the different approaches taken to tackle these issues. We conclude the paper with a critical analysis of challenges that have not yet been fully met, and highlight directions for future work. Read More ›

The convergence of mobile computing and cloud computing is predicated on a reliable, high-bandwidth, end-to-end network, which is difficult to guarantee in hostile environments. However, virtual-machine-based cloudlets located in close proximity to associated mobile devices can overcome this deep-rooted problem. Read More ›

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Mobile applications are becoming increasingly datacentric – often relying on cloud services to store, share, and analyze data. App developers have to frequently manage the local storage on the device (e.g., SQLite databases, file systems), as well as data synchronization with cloud services. Developers have to address common issues such as data packaging, handling network failures, supporting disconnected operations, propagating changes, and detecting and resolving conflicts. To free mobile developers from this burden, we are building Simba, a platform to rapidly develop and deploy datacentric mobile apps. Simba provides a unified storage and synchronization API for both structured data and unstructured objects. Apps can specify a data model spanning tables and objects, and atomically sync such data with the cloud without worrying about network disruptions. Simba is also frugal in consuming network resources. Read More ›

The availability of real-time continuous speech recognition on mobile and embedded devices has opened up a wide range of research opportunities in human-computer interactive applications. Unfortunately, most of the work in this area to date has been confined to proprietary software, or has focused on limited domains with constrained grammars. In this paper, we present a preliminary case study on the porting and optimization of CMU Sphinx-11, a popular open source large vocabulary continuous speech recognition (LVCSR) system, to hand-held devices. The resulting system operates in an average 0.87 times real-time on a 206 MHz device, 8.03 times faster than the baseline system. To our knowledge, this is the first hand-held LVCSR system available under an open-source license Read More ›

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Mobile computing continuously evolve through the sustained effort of many researchers. It seamlessly augments users' cognitive abilities via compute-intensive capabilities such as speech recognition, natural language processing, etc. By thus empowering mobile users, we could transform many areas of human activity. This article discusses the technical obstacles to these transformations and proposes a new architecture for overcoming them. In this architecture, a mobile user exploits virtual machine (VM) technology to rapidly instantiate customized service software on a nearby cloudlet and then uses that service over a wireless LAN; the mobile device typically functions as a thin client with respect to the service. A cloudlet is a trusted, resource-rich computer or cluster of computers that's well-connected to the Internet and available for use by nearby mobile devices. Our strategy of leveraging transiently customized proximate infrastructure as a mobile device moves with its user through the physical world is called cloudlet-based, resource-rich, mobile computing. Crisp interactive response, which is essential for seamless augmentation of human cognition, is easily achieved in this architecture because of the cloudlet's physical proximity and one-hop network latency. Using a cloudlet also simplifies the challenge of meeting the peak bandwidth demand of multiple users interactively generating and receiving media such as high-definition video and high-resolution images. Rapid customization of infrastructure for diverse applications emerges as a critical requirement, and our results from a proof-of-concept prototype suggest that VM technology can indeed help meet this requirement. Read More ›

Increasingly people manage and share information across a wide variety of computing devices from cell phones to Internet services. Selective replication of content is essential because devices, especially portable ones, have limited resources for storage and communication. Cimbiosys is a novel replication platform that permits each device to define its own content-based filtering criteria and to share updates directly with other devices. In the face of fluid network connectivity, redefinable content filters, and changing content, Cimbiosys ensures two properties not achieved by previous systems. First, every device eventually stores exactly those items whose latest version matches its filter. Second, every device represents its replication-specific metadata in a compact form, with state proportional to the number of devices rather than the number of items. Such compact representation results in low data synchronization overhead, which permits ad hoc replication between newly encountered devices and frequent replication between established partners, even over low bandwidth wireless networks. Read More ›

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A fog computing based radio access network (F-RAN) is presented in this article as a promising paradigm for the fifth generation (5G) wireless communication system to provide high spectral and energy efficiency. The core idea is to take full advantages of local radio signal processing, cooperative radio resource management, and distributed storing capabilities in edge devices, which can decrease the heavy burden on fronthaul and avoid large-scale radio signal processing in the centralized baseband unit pool. This article comprehensively presents the system architecture and key techniques of F-RANs. In particular, key techniques and their corresponding solutions, including transmission mode selection and interference suppression, are discussed. Open issues in terms of edge caching, software-defined networking, and network function virtualization, are also identified. Read More ›

Information-centric network architectures are an increasingly important approach for future Internet architectures. Several approaches are based on a non-hierarchical identifier (ID) namespace that requires some kind of global Name Resolution Service (NRS) to translate the object IDs into network addresses. Building a world-wide NRS for such a namespace with 1015 expected IDs is challenging because of requirements such as low latency, efficient network utilization, and anycast routing. In this paper, we present an NRS called Multi-level Distributed Hash Table (MDHT). It provides name-based anycast routing, can support constant hop resolution, and fulfills the afore mentioned requirements. A scalability assessment shows that our system can scale to the Internet level, managing 1015 objects with today's storage technology and 1/10th of today's DNS nodes. The evaluation indicates that a non-hierarchical namespace can be adopted on a global scale, opening up several design alternatives for information-centric network architectures. Read More ›

Cellular networks have witnessed tremendous traffic growth recently, fueled by smartphones, tablets and new high speed broadband cellular access technologies. A key application driving that growth is video streaming. Yet very little is known about the characteristics of this traffic class. In this paper, we examine video traffic generated by three million users across one of the world's largest 3G cellular networks. This first deep dive into cellular video streaming shows that HLS, an adaptive bitrate streaming protocol, accounts for one third of the streaming video traffic and that it is common to see changes in encoding bitrates within a session. We also observe that most of the content is streamed at less than 255 Kbps and that only 40% of the videos are fully downloaded. Another key finding is that there exists significant potential for caching to deliver this content. Read More ›

The ubiquity of broadband Internet and the proliferation of connected devices like laptops, tablets or TV result in a high demand of multimedia content such as high definition video on demand (VOD) for which the Internet has been poorly designed with the Internet Protocol (IP). Information-Centric Networking and more precisely Content Centric Networking (CCN) overtake the limitation of IP by considering content as the essential element of the network instead of the topology. CCN and its content caching capabilities is particularly adapted to Over-The-Top (OTT) services like YouTube or Netflix that distribute high-definition multimedia content to millions of consumers, independently of their location. However, bringing content as the most important component of the network implies fundamental changes in the Internet and the transition to a fully CCN Internet might take a long time. Despite this transition period where CCN and IP will co-exist, we show that OTT service providers and consumers have strong incentives for migrating to CCN. We also propose a transition mechanism that leverages caching and enable loosely collaboration. Read More ›

Video traffic already represents a significant fraction of today's traffic and is projected to exceed 90% in the next five years. In parallel, user expectations for a high quality viewing experience (e.g., low startup delays, low buffering, and high bitrates) are continuously increasing. Unlike traditional workloads that either require low latency (e.g., short web transfers) or high average throughput (e.g., large file transfers), a high quality video viewing experience requires sustained performance over extended periods of time (e.g., tens of minutes). This imposes fundamentally different demands on content delivery infrastructures than those envisioned for traditional traffic patterns. Our large-scale measurements over 200 million video sessions show that today's delivery infrastructure fails to meet these requirements: more than 20% of sessions have a rebuffering ratio ≥ 10% and more than 14% of sessions have a video startup delay ≥ 10s. Using measurement-driven insights, we make a case for a video control plane that can use a global view of client and network conditions to dynamically optimize the video delivery in order to provide a high quality viewing experience despite an unreliable delivery infrastructure. Our analysis shows that such a control plane can potentially improve the rebuffering ratio by up to 2× in the average case and by more than one order of magnitude under stress. Read More ›

In the recent years the following two trends have been accelerated: on the one hand, as Content Services became at the center of Internet usages, OTTs strengthen their position. On the other hand, in several cases, Telcos are seeing their role shrinking to “dumb pipes” providers. This paper introduces advanced Telcos' positioning in future content distribution services. In particular, it focuses on the “value” that Telcos can bring to CDN providers and Content providers and analyses the required evolutions of the involved systems' architectures (network and content delivery). Value is assessed with respect to both users' trends and content ecosystem evolution. We show that existing Telcos' assets could indeed be leveraged to make the proposed move, bringing to Telcos a major differentiator in this arena. For that, an open and efficient control infrastructure is necessary, the paper presents the core required functionalities and architectural framework, as well as related challenges. Read More ›

Over-The-Top (OTT) video services are becoming more and more important in today’s broadband access networks. While original OTT services only offered short duration medium quality videos, more recently, premium content such as high definition full feature movies and live video are offered as well. For operators, who see the potential in providing Quality of Experience (QoE) assurance for an increased revenue, this introduces important new network management challenges. Traditional network management paradigms are often not suited for ensuring QoE guarantees as the provider does not have any control on the content’s origin. In this article, we focus on the management of an OTT-based video service. We present a loosely coupled architecture that can be seamlessly integrated into an existing OTT-based video delivery architecture. The framework has the goal of resolving the network bottleneck that might occur from high peaks in the requests for OTT video services. The proposed approach groups the existing Hypertext Transfer Protocol (HTTP) based video connections to be multicasted over an access network’s bottleneck and then splits them again to reconstruct the original HTTP connections. A prototype of this architecture is presented, which includes the caching of videos and incorporates retransmission schemes to ensure robust transmission. Furthermore, an autonomic algorithm is presented that allows to intelligently select which OTT videos need to be multicasted by making a remote assessment of the cache state to predict the future availability of content. The approach was evaluated through both simulation and large scale emulation and shows a significant gain in scalability of the prototype compared to a traditional video delivery architecture. Read More ›

This paper introduces a network access point (AP) control solution in the context of over-the-top (OTT) multimedia services. The solution is designed to provide network-level management mechanisms for packet traffic while using Quality of Experience (QoE) as a performance indicator. The results showed that with customer subscription scheme, traffic differentiation and QoE-driven management it is possible to both improve the perceived quality of multimedia traffic and increase the average revenue per user. Read More ›

We present a study of traffic behavior of two popular over-the-top (OTT) video streaming services (YouTube and Netflix). Our analysis is conducted on different mobile devices (iOS and Android) over various wireless networks (Wi-Fi, 3G and LTE) under dynamic network conditions. Our measurements show that the video players frequently discard a large amount of video content although it is successfully delivered to a client. We first investigate the root cause of this unwanted behavior. Then, we propose a Quality-of-Service (QoS)-aware video streaming architecture in Long Term Evolution (LTE) networks to reduce the waste of network resource and improve user experience. The architecture includes a selective packet discarding mechanism, which can be placed in packet data network gateways (P-GW). In addition, our QoS-aware rules assist video players in selecting an appropriate resolution under a fluctuating channel condition. We monitor network condition and configure QoS parameters to control availability of the maximum bandwidth in real time. In our experimental setup, the proposed platform shows up to 20.58% improvement in saving downlink bandwidth and improves user experience by reducing buffer underflow period to an average of 32 seconds. Read More ›

Cloud Computing is the long dreamed vision of computing as a utility, where users can remotely store their data into the cloud so as to enjoy the on-demand high quality applications and services from a shared pool of configurable computing resources. By data outsourcing, users can be relieved from the burden of local data storage and maintenance. However, the fact that users no longer have physical possession of the possibly large size of outsourced data makes the data integrity protection in Cloud Computing a very challenging and potentially formidable task, especially for users with constrained computing resources and capabilities. Thus, enabling public auditability for cloud data storage security is of critical importance so that users can resort to an external audit party to check the integrity of outsourced data when needed. To securely introduce an effective third party auditor (TPA), the following two fundamental requirements have to be met: 1) TPA should be able to efficiently audit the cloud data storage without demanding the local copy of data, and introduce no additional on-line burden to the cloud user; 2) The third party auditing process should bring in no new vulnerabilities towards user data privacy. In this paper, we utilize and uniquely combine the public key based homomorphic authenticator with random masking to achieve the privacy-preserving public cloud data auditing system, which meets all above requirements. To support efficient handling of multiple auditing tasks, we further explore the technique of bilinear aggregate signature to extend our main result into a multi-user setting, where TPA can perform multiple auditing tasks simultaneously. Extensive security and performance analysis shows the proposed schemes are provably secure and highly efficient. Read More ›

While the general definition of the Internet of Things (IoT) is almost mature, roughly defining it as an information network connecting virtual and physical objects, there is a consistent lack of consensus around technical and regulatory solutions. There is no doubt, though, that the new paradigm will bring forward a completely new host of issues because of its deep impact on all aspects of human life. In this work, the authors outline the current technological and technical trends and their impacts on the security, privacy, and governance. The work is split into short- and long-term analysis where the former is focused on already or soon available technology, while the latter is based on vision concepts. Also, an overview of the vision of the European Commission on this topic will be provided. Read More ›

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Cloud computing promises to significantly change the way we use computers and access and store our personal and business information. With these new computing and communications paradigms arise new data security challenges. Existing data protection mechanisms such as encryption have failed in preventing data theft attacks, especially those perpetrated by an insider to the cloud provider. We propose a different approach for securing data in the cloud using offensive decoy technology. We monitor data access in the cloud and detect abnormal data access patterns. When unauthorized access is suspected and then verified using challenge questions, we launch a disinformation attack by returning large amounts of decoy information to the attacker. This protects against the misuse of the user's real data. Experiments conducted in a local file setting provide evidence that this approach may provide unprecedented levels of user data security in a Cloud environment. Read More ›

This paper considers current paradigms in computing and outlines the most important aspects concerning their reliability. The Fog computing paradigm as a non-trivial extension of the Cloud is considered and the reliability of the networks of smart devices are discussed. Combining the reliability requirements of grid and cloud paradigms with the reliability requirements of networks of sensor and actuators it follows that designing a reliable Fog computing platform is feasible. Read More ›

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The localization abilities of smartphones have provided a huge boost to the popularity of geosocial applications, which facilitate social interaction between users geographically close to each other. However, today’s geosocial applications raise privacy concerns due to application providers storing large amounts of information about users (e.g., profile information) and locations (e.g., users present at a location). We propose Zerosquare, a privacy-friendly location hub that encourages the development of privacy-preserving geosocial applications. Our primary goal is to store information such that no entity can link a user’s identity to her location. Other goals include decoupling storing data from manipulating data for social networking purposes, designing an architecture flexible enough to support a wide range of use cases, and limiting client-side computation. Zerosquare consists of two separate server components for storing information about users and about locations, respectively, and optional cloud components for supporting applications. We describe the design of the API exposed by the server components and demonstrate how it can be used to build several sample geosocial applications. We provide a proof-of-concept implementation using Python for the server components and the Android platform for the mobile devices and build several realworld geosocial applications on top of Zerosquare. Finally, we present experimental results that demonstrate the practicality of Zerosquare. Read More ›

In this paper, we propose and develop a platform to support data collection for mobile crowdsensing from mobile device sensors that is under-pinned by real-time mobile data stream mining. We experimentally show that mobile data mining provides an efficient and scalable approach for data collection for mobile crowdsensing. Our approach results in reducing the amount of data sent, as well as the energy usage on the mobile phone, while providing comparable levels of accuracy to traditional models of intermittent/continuous sensing and sending. We have implemented our Context-Aware Real-time Open Mobile Miner (CAROMM) to facilitate data collection from mobile users for crowdsensing applications. CAROMM also collects and correlates this real-time sensory information with social media data from both Twitter and Facebook. CAROMM supports delivering real-time information to mobile users for queries that pertain to specific locations of interest. We have evaluated our framework by collecting real-time data over a period of days from mobile users and experimentally demonstrated that mobile data mining is an effective and efficient strategy for mobile crowdsensing Read More ›

Radio Frequency (RF) fingerprinting, based onWiFi or cellular signals, has been a popular approach to indoor localization. However, its adoption in the real world has been stymied by the need for sitespecific calibration, i.e., the creation of a training data set comprising WiFi measurements at known locations in the space of interest. While efforts have been made to reduce this calibration effort using modeling, the need for measurements from known locations still remains a bottleneck. In this paper, we present Zee -- a system that makes the calibration zero-effort, by enabling training data to be crowdsourced without any explicit effort on the part of users. Zee leverages the inertial sensors (e.g., accelerometer, compass, gyroscope) present in the mobile devices such as smartphones carried by users, to track them as they traverse an indoor environment, while simultaneously performing WiFi scans. Zee is designed to run in the background on a device without requiring any explicit user participation. The only site-specific input that Zee depends on is a map showing the pathways (e.g., hallways) and barriers (e.g., walls). A significant challenge that Zee surmounts is to track users without any a priori, user-specific knowledge such as the user's initial location, stride-length, or phone placement. Zee employs a suite of novel techniques to infer location over time: (a) placement-independent step counting and orientation estimation, (b) augmented particle filtering to simultaneously estimate location and user-specific walk characteristics such as the stride length,(c) back propagation to go back and improve the accuracy of ocalization in the past, and (d) WiFi-based particle initialization to enable faster convergence. We present an evaluation of Zee in a large office building. Read More ›

Here we present our ubiquitous computing vision, Eywa. Eywa is an open publish-subscribe system that employs crowdsourcing for tasking and social networks & machine learning for identifying relevance. We argue that crowdsourcing (and the social networks and machine learning that enable it) should be a first class citizen in ubiquitous computing. We also observe that cloud computing is a natural platform to host such future ubiquitous computing systems. We discuss about some applications enabled by Eywa, and focus on our CuratedLiving application (which emphasizes “less choice more relevance” approach) as a case study. Read More ›

Crowd-enabled place-centric systems gather and reason over large mobile sensor datasets and target everyday user locations (such as stores, workplaces, and restaurants). Such systems are transforming various consumer services (for example, local search) and data-driven organizations (city planning). As the demand for these systems increases, our understanding of how to design and deploy successful crowdsensing systems must improve. In this paper, we present a systematic study of the coverage and scaling properties of place-centric crowdsensing. During a two-month deployment, we collected smartphone sensor data from 85 participants using a representative crowdsensing system that captures 48,000 different place visits. Our analysis of this dataset examines issues of core interest to place-centric crowdsensing, including place-temporal coverage, the relationship between the user population and coverage, privacy concerns, and the characterization of the collected data. Collectively, our findings provide valuable insights to guide the building of future place-centric crowdsensing systems and applications. Read More ›

Crowd-enabled place-centric systems gather and reason over large mobile sensor datasets and target everyday user locations (such as stores, workplaces, and restaurants). Such systems are transforming various consumer services (for example, local search) and data-driven organizations (city planning). As the demand for these systems increases, our understanding of how to design and deploy successful crowdsensing systems must improve. In this paper, we present a systematic study of the coverage and scaling properties of place-centric crowdsensing. During a two-month deployment, we collected smartphone sensor data from 85 participants using a representative crowdsensing system that captures 48,000 different place visits. Our analysis of this dataset examines issues of core interest to place-centric crowdsensing, including place-temporal coverage, the relationship between the user population and coverage, privacy concerns, and the characterization of the collected data. Collectively, our findings provide valuable insights to guide the building of future place-centric crowdsensing systems and applications. Read More ›

Mobile crowdsensing is becoming a vital technique for environment monitoring, infrastructure management, and social computing. However, deploying mobile crowdsensing applications in large-scale environments is not a trivial task. It creates a tremendous burden on application developers as well as mobile users. In this paper we try to reveal the barriers hampering the scale-up of mobile crowdsensing applications, and to offer our initial thoughts on the potential solutions to lowering the barriers. Read More ›

To minimize battery drain due to background communication in cellular-connected devices such as smartphones, the duration for which the cellular radio is kept active should be minimized. This, in turn, calls for scheduling the background communication so as to maximize the throughput. It has been recognized in prior work that a key determinant of throughput is the wireless link quality. However, as we show here, another key factor is the load in the cell, arising from the communication of other nodes. Unlike link quality, the only way, thus far, for a cellular client to obtain a measure of load has been to perform active probing, which defeats the goal of minimizing the active duration of the radio. In this paper, we address the above dilemma by making the following contributions. First, we show experimentally that to obtain good throughput, considering link quality alone is insufficient, and that cellular load must also be factored in. Second, we present a novel technique called LoadSense for a cellular client to obtain a measure of the cellular load, locally and passively, that allows the client to determine the ideal times for communication when available throughput to the client is likely to be high. Finally, we present the Peek-n-Sneak protocol, which enables a cellular client to 'peek' into the channel and 'sneak' in with its background communication when the conditions are suitable. When multiple clients in a cell perform Peen-n-Sneak, it enables them to coordinate their communications, implicitly and in an entirely distributed manner, akin to CSMA in wireless LANs, helping improve throughput (and reduce energy drain) for all. Our experimental evaluation shows overall device energy savings of 20-60% even when Peek-n-Sneak is deployed incrementally Read More ›

This paper describes a multitiered architecture for realizing an inference management firewall (IMF) that employs context-aware information masking techniques for systematic management of risk-vs-value trade-off of sensor data. Previously we have demonstrated an initial implementation of the IMF running as messaging services on the Information Fabric, which is a middleware asset developed under the International Technology Alliance (ITA) research program. Furthermore, we have presented an additional asset, recently implemented on a commercially-available mobile device running the Android operating system, which is intended to operate as an information source and first-line inference management capability at the edge of the network. The low-cost and widespread use of Android-based mobile devices offers a popular platform for crowdsourced participatory sensing. The focus of our current work is on the integration of these two technology assets in support of policy-managed, sensor-driven workflows in coalition scenarios. Read More ›

Mobile phones or smartphones are rapidly becoming the central computer and communication device in people's lives. Application delivery channels such as the Apple AppStore are transforming mobile phones into App Phones, capable of downloading a myriad of applications in an instant. Importantly, today's smartphones are programmable and come with a growing set of cheap powerful embedded sensors, such as an accelerometer, digital compass, gyroscope, GPS, microphone, and camera, which are enabling the emergence of personal, group, and communityscale sensing applications. We believe that sensor-equipped mobile phones will revolutionize many sectors of our economy, including business, healthcare, social networks, environmental monitoring, and transportation. In this article we survey existing mobile phone sensing algorithms, applications, and systems. We discuss the emerging sensing paradigms, and formulate an architectural framework for discussing a number of the open issues and challenges emerging in the new area of mobile phone sensing research. Read More ›

We present Darwin, an enabling technology for mobile phone sensing that combines collaborative sensing and classification techniques to reason about human behavior and context on mobile phones. Darwin advances mobile phone sensing through the deployment of efficient but sophisticated machine learning techniques specifically designed to run directly on sensor-enabled mobile phones (i.e., smartphones). Darwin tackles three key sensing and inference challenges that are barriers to mass-scale adoption of mobile phone sensing applications: (i) the human-burden of training classifiers, (ii) the ability to perform reliably in different environments (e.g., indoor, outdoor) and (iii) the ability to scale to a large number of phones without jeopardizing the 'phone experience' (e.g., usability and battery lifetime). Darwin is a collaborative reasoning framework built on three concepts: classifier/model evolution, model pooling, and collaborative inference. To the best of our knowledge Darwin is the first system that applies distributed machine learning techniques and collaborative inference concepts to mobile phones. We implement the Darwin system on the Nokia N97 and Apple iPhone. While Darwin represents a general framework applicable to a wide variety of emerging mobile sensing applications, we implement a speaker recognition application and an augmented reality application to evaluate the benefits of Darwin. We show experimental results from eight individuals carrying Nokia N97s and demonstrate that Darwin improves the reliability and scalability of the proof-of-concept speaker recognition application without additional burden to users. Read More ›

An emerging category of devices at the edge of the Internet are consumer-centric mobile sensing and computing devices, such as smartphones, music players, and in-vehicle sensors. These devices will fuel the evolution of the Internet of Things as they feed sensor data to the Internet at a societal scale. In this article, we examine a category of applications that we term mobile crowdsensing, where individuals with sensing and computing devices collectively share data and extract information to measure and map phenomena of common interest. We present a brief overview of existing mobile crowdsensing applications, explain their unique characteristics, illustrate various research challenges, and discuss possible solutions. Finally, we argue the need for a unified architecture and envision the requirements it must satisfy. Read More ›

A number of novel wireless networked services, ranging from participatory sensing to social networking, leverage the increasing capabilities of mobile devices and the movements of the individuals carrying them. For many of these systems, their effectiveness fundamentally depends on coverage and the particular mobility patterns of the participants. Given the strong spatial and temporal regularity of human mobility, the needed coverage can typically only be attained through a large participant base. In this paper we explore an alternative approach to attain coverage without scale -- (soft) controlling the movement of participants. We present Crowd Soft Control (CSC), an approach to exert limited control over the temporal and spatial movements of mobile users by leveraging the built-in incentives of location-based gaming and social applications. By pairing network services with these location-based apps, CSC allows researchers to use an application's incentives (e.g. game objectives) to control the movement of participating users, increasing the effectiveness and efficiency of the associated network service. After outlining the case for Crowd Soft Control, we present an initial prototype of our ideas and discuss potential benefits and costs in the context of two case studies. Read More ›

Automated and scalable approaches for understanding the semantics of places are critical to improving both existing and emerging mobile services. In this paper, we present CrowdSense@Place (CSP), a framework that exploits a previously untapped resource -- opportunistically captured images and audio clips from smartphones -- to link place visits with place categories (e.g., store, restaurant). CSP combines signals based on location and user trajectories (using WiFi/GPS) along with various visual and audio place 'hints' mined from opportunistic sensor data. Place hints include words spoken by people, text written on signs or objects recognized in the environment. We evaluate CSP with a seven-week, 36-user experiment involving 1,241 places in five locations around the world. Our results show that CSP can classify places into a variety of categories with an overall accuracy of 69%, outperforming currently available alternative solutions. Read More ›

We are becoming increasingly aware that the effectiveness of mobile crowdsourcing systems critically depends on the whims of their human participants, impacting everything from participant engagement to their compliance with the crowdsourced tasks. In response, a number of such systems have started to incorporate different incentive features aimed at a wide range of goals that span from improving participation levels, to extending the systems' coverage, and enhancing the quality of the collected data. Despite the many related efforts, the inclusion of incentives in crowdsourced systems has so far been mostly ad-hoc, treating incentives as a wild-card response fitted for any occasion and goal. Using data from a large, 2-day experiment with 96 participants at a corporate conference, we present an analysis of the impact of two incentive structures on the recruitment, compliance and user effort of a basic mobile crowdsourced service. We build on these preliminary results to argue for a principled approach for selecting incentive and incentive structures to match the variety of requirements of mobile crowdsourcing applications and discuss key issues in working toward that goal. Read More ›

We present the first study of network access link performance measured directly from home gateway devices. Policymakers, ISPs, and users are increasingly interested in studying the performance of Internet access links. Because of many confounding factors in a home network or on end hosts, however, thoroughly understanding access network performance requires deploying measurement infrastructure in users' homes as gateway devices. In conjunction with the Federal Communication Commission's study of broadband Internet access in the United States, we study the throughput and latency of network access links using longitudinal measurements from nearly 4,000 gateway devices across 8 ISPs from a deployment of over 4,200 devices. We study the performance users achieve and how various factors ranging from the user's choice of modem to the ISP's traffic shaping policies can affect performance. Our study yields many important findings about the characteristics of existing access networks. Our findings also provide insights into the ways that access network performance should be measured and presented to users, which can help inform ongoing broader efforts to benchmark the performance of access networks. Read More ›

A key challenge in client-assisted content delivery is determining how to allocate limited server bandwidth across a large number of files being concurrently served so as to optimize global performance and cost objectives. In this paper, we present a comprehensive experimental evaluation of strategies to control server bandwidth allocation. As part of this effort, we introduce a new model-based control approach that relies on an accurate yet concise “cheat sheet” based on a priori offline measurement to predict swarm performance as a function of the server bandwidth and other swarm parameters. Our evaluation using a prototype system, SwarmServer, instantiating static, dynamic, and model-based controllers shows that static and dynamic controllers can both be suboptimal due to different reasons. In comparison, a model-based approach consistently outperforms both static and dynamic approaches provided it has access to detailed measurements in the regime of interest. Nevertheless, the broad applicability of a model-based approach may be limited in practice because of the overhead of developing and maintaining a comprehensive measurement-based model of swarm performance in each regime of interest. Read More ›

The two largest U.S. wireless ISPs have recently moved towards usage-based pricing to better manage the growing demand on their networks. Yet usage-based pricing still requires ISPs to over-provision capacity for demand at peak times of the day. Time-dependent pricing (TDP) addresses this problem by considering when a user consumes data, in addition to how much is used. We present the architecture, implementation, and a user trial of an end-to-end TDP system called TUBE. TUBE creates a price-based feedback control loop between an ISP and its end users. On the ISP side, it computes TDP prices so as to balance the cost of congestion during peak periods with that of offering lower prices in less congested periods. On mobile devices, it provides a graphical user interface that allows users to respond to the offered prices either by themselves or using an ;'autopilot' mode. We conducted a pilot TUBE trial with 50 iPhone or iPad 3G data users, who were charged according to our TDP algorithms. Our results show that TDP benefits both operators and customers, flattening the temporal fluctuation of demand while allowing users to save money by choosing the time and volume of their usage. Read More ›

In heterogeneous wireless networks, an important task for mobile terminals is to select the best network for various communications at any time anywhere, usually called network selection. In recent years, this topic has been widely studied by using various mathematical theories. The employed theory decides the objective of optimization, complexity and performance, so it is a must to understand the potential mathematical theories and choose the appropriate one for obtaining the best result. Therefore, this paper systematically studies the most important mathematical theories used for modeling the network selection problem in the literature. With a carefully designed unified scenario, we compare the schemes of various mathematical theories and discuss the ways to benefit from combining multiple of them together. Furthermore, an integrated scheme using multiple attribute decision making as the core of the selection procedure is proposed. Read More ›

Mobile users face a tradeoff between cost, throughput, and delay in making their offloading decisions. To navigate this tradeoff, we propose AMUSE (Adaptive bandwidth Management through USer-Empowerment), a practical, costaware WiFi offloading system that takes into account a user's throughput-delay tradeoffs and cellular budget constraint. Based on predicted future usage and WiFi availability, AMUSE decides which applications to offload to what times of the day. To practically enforce the assigned rate of each TCP application, we introduce a receiver-side TCP bandwidth control algorithm that adjusts the rate by controlling the TCP advertisement window from the user side. We implement AMUSE on Windows 7 tablets and evaluate its effectiveness with 3G and WiFi usage data obtained from a trial with 25 mobile users. Our results show that AMUSE improves user utility. Read More ›

Motivated by recent increases in network traffic, we propose a decentralized network edge-based solution to peak-hour broadband congestion that incentivizes users to moderate their bandwidth demands to their actual needs. Our solution is centered on smart home gateways that allocate bandwidth in a two-level hierarchy: first, a gateway purchases guaranteed bandwidth from the Internet Service Provider (ISP) with virtual credits. It then self-limits its bandwidth usage and distributes the bandwidth among its apps and devices according to their relative priorities. To this end, we design a credit allocation and redistribution mechanism for the first level, and implement our gateways on commodity wireless routers for the second level. We demonstrate our system's effectiveness and practicality with theoretical analysis, simulations and experiments on real traffic. Compared to a baseline equal sharing algorithm, our solution significantly improves users' overall satisfaction and yields a fair allocation of bandwidth across users.. Read More ›

We study the dynamics of network selection in heterogeneous wireless networks (HetNets). Users in such networks selfishly select the best radio access technology (RAT) with the objective of maximizing their own throughputs. We propose two general classes of throughput models that capture the basic properties of random access (e.g., Wi-Fi) and scheduled access (e.g., WiMAX, LTE, 3G) networks. Next, we formulate the problem as a non-cooperative game, and study its convergence, efficiency, and practicality. Our results reveal that: (i) Singleclass RAT selection games converge to Nash equilibria, while an improvement path can be repeated infinitely with a mixture of classes. We next introduce a hysteresis mechanism in RAT selection games, and prove that with appropriate hysteresis policies, convergence can still be guaranteed; (ii) We analyze the Pareto-efficiency of the Nash equilibria of these games. We derive the conditions under which Nash equilibria are Paretooptimal, and we quantify the distance of Nash equilibria with respect to the set of Pareto-dominant points when the conditions are not satisfied; (iii) Finally, with extensive measurement-driven simulations we show that RAT selection games converge to Nash equilibria in a small number of steps, and hence are amenable to practical implementation. We also investigate the impact of noisy throughput measurements, and propose solutions to handle them. Read More ›

Many popular, professionally-written smartphone apps today prefetch large amounts of network data to improve performance. However, the typical user may not use all of this network data. When a user is on a limited or pay-per-byte cellular data plan, such as when roaming internationally, this prefetching behavior can cost her in overage fees on her cellular bill. This video demonstrates Procrastinator, which is a system that automatically decides when to fetch each network object that an app requests. This decision is made based on whether the user is on Wi-Fi or cellular, how many bytes are remaining on her data plan, and whether the object is needed at the present time. Procrastinator does not require app developer effort, nor app source code, nor OS changes -- it modifies the app binary to trap specific system calls and inject custom code. Our system can achieve as little as no savings to 4X reduction in total bytes transferred by an app, depending on the user and the app. These savings for the data-poor user come with a 300ms median latency penalty on LTE if the user goes to a part of the app where Procrastinator did not allow data to be prefetched. This video shows how main content on the primary page of apps is unaffected, and the delay that the user will typically experience if she goes to secondary pages in apps when she is running out of cellular data plan bytes.. Read More ›

This paper presents a novel framework for managing network congestion from an end-to-end perspective. Our work is motivated by trends in traffic patterns that threaten the long-term stability of the Internet. These trends include the use of multiple independent concurrent flows by Web applications and the increasing use of transport protocols and applications that do not adapt to congestion. We present an end-system architecture centered around a Congestion Manager (CM) that ensures proper congestion behavior and allows applications to easily adapt to network congestion. Our framework integrates congestion management across all applications and transport protocols. The CM maintains congestion parameters and exposes an API to enable applications to learn about network characteristics, pass information to the CM, and schedule data transmissions. Internally, it uses a window-based control algorithm, a scheduler to regulate transmissions, and a lightweight protocol to elicit feedback from receivers.We describe how TCP and an adaptive real-time streaming audio application can be implemented using the CM. Our simulation results show that an ensemble of concurrent TCP connections can effectively share bandwidth and obtain consistent performance, without adversely affecting other network flows. Our results also show that the CM enables audio applications to adapt to congestion conditions without having to perform congestion control or bandwidth probing on their own. We conclude that the CM provides a useful and pragmatic framework for building adaptive Internet applications. Read More ›

A new generation of content delivery networks for live streaming, video on demand, and software updates takes advantage of a peer-to-peer architecture to reduce their operating cost. In contrast with previous uncoordinated peer-to-peer schemes, users opt-in to dedicate part of the resources they own to help the content delivery, in exchange for receiving the same service at a reduced price. Such incentive mechanisms are appealing, as they simplify coordination and accounting. However, they also increase a user's expectation that she will receive a fair price for the resources she provides. Addressing this issue carefully is critical in ensuring that all interested parties--including the provider--are willing to participate in such a system, thereby guaranteeing its stability. In this paper, we take a cooperative game theory approach to identify the ideal incentive structure that follows the axioms formulated by Lloyd Shapley. This ensures that each player, be it the provider or a peer, receives an amount proportional to its contribution and bargaining power when entering the game. In general, the drawback of this ideal incentive structure is its computational complexity. However, we prove that as the number of peers receiving the service becomes large, the Shapley value received by each player approaches a fluid limit. This limit follows a simple closed form expression and can be computed in several scenarios of interest: by applying our technique, we show that several peer-assisted services, deployed on both wired and wireless networks, can benefit from important cost and energy savings with a proper incentive structure that follows simple compensation rules. Read More ›

As networking moves into the home, home users are increasingly being faced with complex network management chores. Previous research, however, has demonstrated the difficulty many users have in managing their networks. This difficulty is compounded by the fact that advanced network management tools - such as those developed for the enterprise - are generally too complex for home users, do not support the common tasks they face, and are not a good fit for the technical peculiarities of the home. This paper presents Eden, an interactive, direct manipulation home network management system aimed at end users. Eden supports a range of common tasks, and provides a simple conceptual model that can help users understand key aspects of networking better. The system leverages a novel home network router that acts as a 'dropin' replacement for users' current router. We demonstrate that Eden not only improves the user experience of networking, but also aids users in forming workable conceptual models of how the network works. Read More ›

There are two emerging trends in the mobile data world. First, mobile data is exploding at a rapid rate with analysts predicting 25-50X growth by the year 2015. The second trend is that users are demanding greater degree of flexibility in selecting their operators at fine timescales. Across Asia, dual-SIM phones have become popular, while Apple is rumored to be designing a Universal SIM that will allow iPhone users to toggle between different operators. This latter trend points towards an impending disruption in wireless service models which could also be the need of the hour from the spectrum shortage perspective. This points towards a new service model where users can choose an operator based on application needs. However, if users make this choice greedily without network assistance, it can exacerbate spectrum scarcity and degrade user experience. In this work, we consider user devices with multiple network interfaces (3G, LTE etc.) that can be simultaneously active and each running multiple applications. We propose the MOTA service model to enable users to associate each interface with the operator of choice at fine time scales. Under the MOTA service model, through concise signalling information, operators provide information about their own network, so that each user can (i) choose a suitable operator for each interface, and (ii) choose an interface for each active application. We make the following contributions in this paper. First, we propose concise network signalling that assists users to make informed choices even under mobility. Second, we develop user-choice algorithms that maximize a suitable notion of user satisfaction while using spectrum resources efficiently. Third, we perform extensive evaluation over actual base station deployment in a city coupled with real signal propagation maps. Our results with two operators show that, MOTA service model provides capacity gain in the range 2.5-4X over the current existing service model. Finally, we argue that our solution is practically implementable by combining appropriate IEEE standards and IETF proposals. Read More ›