Advertisers gather data about users and their mobile devices through ads placed within Android and iOS apps. Most of the time, location, device, and app information are linked to the same device using a unique advertising ID (Ad ID). If the Ad ID is not available, advertisers can still use geo-coordinates or IP address to infer links in data gathered from different ad placements.

Inexpensive and highly capable unmanned aerial vehicles (aka drones) have enabled people to contribute high-quality videos at a global scale. However, a key challenge exists for accepting videos from untrusted sources: establishing when a particular video was taken. Once a video has been received or posted publicly, it is evident that the video was created before that time, but there are no current methods for establishing how long it was made before that time.

Mobile systems researchers struggle with conducting experiments with real users: either the scale of the study lacks sufficient scale and diversity, or a great effort must be used to recruit and manage subjects. In this paper, we describe MicroMobile, a system for deploying short data-gathering experiments to an extremely diverse set of users via mobile advertising. We conduct experiments in three mediums: interactive advertisements, mobile browsers, and native applications on both major mobile operating systems.

The most important step in an empirical computer scientist's research is gathering sufficient real-world data to validate a system. Unfortunately, it is also one of the most time-consuming and expensive tasks: placing measurement tools in remote networks or end-clients requires one to marshal resources from different administrative domains, devices, populations, and countries. Often such efforts culminate in a trace that is deficient in multiple ways: a small set of test subjects, a short time frame, missing ground truth for device IDs, networking environments lacking in diversity and geographic spread, or highly biased sampling. We present a method of addressing these challenges by leveraging the most open and globally accessible test and measurement platform: digital advertising. Digital advertising instantly provides a window into 7 billion devices spanning every county for an extremely low cost. We propose Advertising as a Platform (AaaP), an ad-based system to perform massive-scale mobile measurement studies. In contrast with measurements made by large media companies who own platforms, ad networks, and apps, we concentrate on the opportunities and challenges for researchers that are end-users of advertising systems. We evaluate a prototype system, discuss ethical guidelines, and demonstrate its use in four scenarios: IP2Geo databases, bandwidth measurement, energy management, and the identifiability of mobile users. We show the efficacy and ease-of-use of AaaP, and illuminate key challenges and the great promise of using AaaP to study a wide variety of mobile phenomena.

Due to advances in low power sensors, energy harvesting, and disruption tolerant networking, we can now build mobile systems that operate perpetually, sensing and streaming data directly to scientists. However, factors such as energy harvesting variability and unpredictable network connectivity make building robust and perpetual systems difficult. In this paper, we present a system, Tula, that balances sensing with data delivery, to allow perpetual and robust operation across highly dynamic and mobile networks. This balance is especially important in unpredictable environments; sensing more data than can be delivered by the network is not useful, while gathering less underutilizes the system's potential. Tula is decentralized, fair and automatically adapts across different mobility patterns. We evaluate Tula using mobility and energy traces from TurtleNet-a mobile sensor network we deployed to study Gopher tortoises-and publicly available traces from the UMass DieselNet testbed. Our evaluations show that Tula senses and delivers data at up to 85 percent of an optimal, oracular system that perfectly replicates data and has foreknowledge of future energy harvesting. We also demonstrate that Tula can be implemented on a small microcontroller with modest code, memory, and processing requirements.

We present the first in-depth analysis of the performance of attempting concurrent AP connections from highly mobile clients. Previous solutions for concurrent Wi-Fi are limited to stationary wireless clients and do not take into account a myriad of mobile factors. Through an analytical model, optimization framework, and numerous outdoor experiments, we show that connection duration, AP response times, channel scheduling, available and offered bandwidth, node speed, and dhcp joins all affect performance. Building on these results, we design, implement, and evaluate a system, Spider, that establishes and maintains concurrent connections to 802.11 APs in a mobile environment. The system uses multi-AP selection, channel-based scheduling, and opportunistic scanning to maximize throughput while mitigating the overhead of association and dhcp. While Spider can manage multiple channels, we empirically demonstrate that it achieves maximum throughput when using multiple APs on a single channel. Our evaluation shows that Spider provides a 400% improvement in throughput and 54% improvement in connectivity over stock Wi-Fi implementations.

We investigate attempting concurrent connections to multiple Wi-Fi access points (APs) from highly mobile clients. Previous multi-AP solutions are limited to stationary wireless clients and do not take into account a myriad of mobile factors. We show that connection duration, AP response times, channel scheduling, available and offered bandwidth, node speed, and dhcp joins all affect performance. Building on these results, we present a system, Spider, that establishes and maintains concurrent connections to 802.11 APs in a mobile environment. While Spider can manage multiple channels, we demonstrate that it achieves maximum throughput when using multiple APs on a single channel.

An important challenge in mobile sensor networks is to enable energy-efficient communication over a diversity of distanceYC while being robust to wireless effects caused by node mobility. In this paper, we argue that the pairing of two complementary radios with heterogeneous range characteristics enables greater range and interference diversity at lower energy cost than a single radio. We make three contributions towards the design of such multi-radio mobile sensor systems. First, we present the design of a novel reinforcement learning-based link layer algorithm that continually learns channel characteristics and dynamically decides when to switch between radios. Second, we describe a simple protocol that translates the benefits of the adaptive link layer into practice in an energy-efficient manner. Third, we present the design of Arthropod, a mote-class sensor platform that combines two such heterogneous radios (XE1205 and CC2420) and our implementation of the Q-learning based switching protocol in TinyOS 2.0. Using experiments conducted in a variety of urban and forested environments, we show that our system achieves up to 52% energy gains over a single radio system while handling node mobility. Our results also show that our system can handle short, medium and long-term wireless interference in such environments.

An important challenge in mobile sensor networks is to enable energy-efficient communication over a diversity of distances while being robust to wireless effects caused by node mobility. In this paper, we argue that the pairing of two complementary radios with heterogeneous range characteristics enables greater range diversity at lower energy cost than a single radio. We make three contributions towards the design of such multi-radio mobile sensor systems. First, we present the design of a novel reinforcement learning-based link layer algorithm that continually learns channel characteristics and dynamically decides when to switch between radios. Second, we describe a simple protocol that translates the benefits of the adaptive link layer into practice in an energy-efficient manner. Third, we present the design of Arthropod, a mote-class sensor platform that combines two such heterogeneous radios (XE1205 and CC2420) and our implementation of the Q-learning based switching protocol in TinyOS 2.0. Using experiments conducted in a variety of urban and forested environments, we show that our system achieves up to 52% energy gains over a single radio system.

Crowdsourcing is a new paradigm for utilizing the power of "crowds" of people to facilitate large scale tasks that are costly or time consuming with traditional methods. Crowdsourcing has enormous potential that can be truly unleashed when extended to sensor-rich mobile devices, such as smart phones. In this paper, we demonstrate mCrowd, an iPhone based mobile crowdsourcing platform that enables mobile users to post and work on sensor-related crowdsourcing tasks. mCrowd enables mobile users to fully utilize the rich sensors equipped with iPhone to participate and accomplish crowdsourcing tasks at fingertips, including geolocation-aware image collection, image tagging, road traffic monitoring, and others.

Many data center virtualization solutions, such as VMware ESX, employ content-based page sharing to consolidate the resources of multiple servers. Page sharing identifies virtual machine memory pages with identical content and consolidates them into a single shared page. This technique, implemented at the host level, applies only between VMs placed on a given physical host. In a multi-server data center, opportunities for sharing may be lost because the VMs holding identical pages are resident on different hosts. In order to obtain the full benefit of content-based page sharing it is necessary to place virtual machines such that VMs with similar memory content are located on the same hosts.

In this article, we study how a sensor-rich world can be exploited by digital recording devices such as cameras and camcorders to improve a user's ability to search through a large repository of image and video files. We design and implement a digital recording system that records identities and locations of objects (as advertised by their sensors) along with visual images (as recorded by a camera). The process, which we refer to as Sensor-Enhanced Video Annotation (SEVA) , combines a series of correlation, interpolation, and extrapolation techniques. It produces a tagged stream that later can be used to efficiently search for videos or frames containing particular objects or people. We present detailed experiments with a prototype of our system using both stationary and mobile objects as well as GPS and ultrasound. Our experiments show that: (i) SEVA has zero error rates for static objects, except very close to the boundary of the viewable area; (ii) for moving objects or a moving camera, SEVA only misses objects leaving or entering the viewable area by 1--2 frames; (iii) SEVA can scale to 10 fast-moving objects using current sensor technology; and (iv) SEVA runs online using relatively inexpensive hardware.

Networks composed of mobile nodes inherently suffer from intermittent connections and high delays. Performance can be improved by adding supporting infrastructure, including base stations, meshes, and relays, but the cost-performance trade-offs of different designs is poorly understood. To examine these trade-offs, we have deployed a large-scale vehicular network and three infrastructure enhancement alternatives. The results of these deployments demonstrate some of the advantages of each kind of infrastructure; however, these conclusions can be applied only to other networks of similar characteristics, including size, wireless technologies, and mobility patterns. Thus we complement our deployment with a demonstrably accurate analytical model of large-scale networks in the presence of infrastructure. Based on our deployment and analysis, we make several fundamental observations about infrastructure-enhanced mobile networks. First, if the average packet delivery delay in a vehicular deployment can be reduced by a factor of two by adding x base stations, the same reduction requires 2x mesh nodes or 5x relays. Given the high cost of deploying base stations, relays or mesh nodes can be a more cost-effective enhancement. Second, we observe that adding small amount of infrastructure is vastly superior to even a large number of mobile nodes capable of routing to one another, obviating the need for mobile-to-mobile disruption tolerant routing schemes.

Over the course of a day a human interacts with tens or hundreds of individual objects. Many of these articles are nomadic, relying on human memory to manually index, inventory, organize, search, and locate them. However, Radio Frequency Identification (RFID) tags hold great promise for automating these tasks. While originally envisioned for managing supply chains and store inventories, RFID tags support the properties necessary for helping humans to manage their objects. This paper presents Sherlock, a system that leverages RFID tags for human-object interaction. Sherlock combines concepts from sensors, radar technology, and computer graphics to implement a novel localization and visualization system for everyday objects. At the heart of Sherlock is a new RFID localization technique that uses steerable antennas to sweep a room, discovering, localizing and indexing tagged objects. In response to user queries, Sherlock displays the locations of matching objects using images from a video camera. We have implemented a prototype of Sherlock to conduct experiments in a real office environment. Our results demonstrate the effectiveness of Sherlock in localizing to a volume of less than 0.55 cubic meters for 90% of objects.

In this paper, we present Chameleon an application-level power management approach for reducing energy consumption in mobile processors. By using application domain knowledge, as opposed to OS-level or hardware-level inferred knowledge, Chameleon can substantially reduce CPU energy consumption. By exporting the energy management to user-space, designers can design more flexible and easily portable algorithms and systems, and use multiple energy management policies simultaneously. Specifically, we propose a minimal operating system interface that applications use to obtain global knowledge from the kernel in order to make local decisions. We consider three classes of applications soft real-time, interactive and batch and design user level power management strategies for representative applications such as a movie player, a word processor, a web browser, and a batch compiler. Our experiments show that, compared to the traditional system-wide CPU voltage scaling approaches, Chameleon can achieve up to 32-50% energy savings while delivering comparable or better performance to applications. Similarly, Chameleon extracts 9-41% more energy when compared to Grace OS, which uses some application knowledge but operates within the kernel. Further, Chameleon imposes minimal overhead and is effective at scheduling concurrent applications with diverse energy needs.

Disruption Tolerant Networks rely on intermittent contacts between mobile nodes to deliver packets using store-carry-and-forward paradigm. The key to improving performance in DTNs is to engineer a greater number of transfer opportunities. We earlier proposed the use of throwbox nodes, which are stationary, battery powered nodes with storage and processing, to enhance the capacity of DTNs. However, the use of throwboxes without efficient power management is minimally effective. If the nodes are too liberal with their energy consumption, they will fail prematurely. However if they are too conservative, they may miss important transfer opportunities, hence increasing lifetime without improving performance. In this paper, we present a hardware and software architecture for energy efficient throwboxes in DTNs. We propose a hardware platform that uses a multi-tiered, multi-radio, scalable, solar powered platform. The throwbox employs an approximate heuristic for solving the NP-Hard problem of meeting an average power constraint while maximizing the number of bytes forwarded by it. We built and deployed prototype throwboxes in UMassDieselNet -a bus DTN testbed. Through extensive trace-driven simulations and prototype deployment we show that a single throwbox with a 270 cm2solar panel can run perpetually while improving packet delivery by 37% and reducing message delivery latency by at least 10% in the network.

Disruption-Tolerant Networks (DTNs) deliver data in network environments composed of intermittently connected nodes. Just as in traditional networks, malicious nodes within a DTN may attempt to delay or destroy data in transit to its destination. Such attacks include dropping data, flooding the network with extra messages, corrupting routing tables, and counterfeiting network acknowledgments. Many existing methods for securing routing protocols require authentication supported by mechanisms such as a public key infrastructure, which is difficult to deploy and operate in a DTN, where connectivity is sporadic. Furthermore, the complexity of such mechanisms may dissuade node participation so strongly that potential attacker impacts are dwarfed by the loss of contributing participants. In this paper, we use connectivity traces from our UMass DieselNet project and the Haggle project to quantify routing attack effectiveness on a DTN that lacks security. We introduce plausible attackers and attack modalities and provide complexity results for the strongest of attackers. We show that the same routing with packet replication used to provide robustness in the face of unpredictable mobility allows the network to gracefully survive attacks. In the case of the most effective attack, acknowledgment counterfeiting, we show a straightforward defense that uses cryptographic hashes but not a central authority. We conclude that disruption-tolerant networks are extremely robust to attack; in our trace-driven evaluations, an attacker that has compromised 30% of all nodes reduces delivery rates from 70% to 55%, and to 20% with knowledge of future events. By comparison, contemporaneously connected networks are significantly more fragile.

The ease of deployment of battery-powered and mobile systems is pushing the network edge far from powered infrastructures. A primary challenge in building untethered systems is offering powerful aggregation points and gateways between heterogeneous end-points---a role traditionally played by powered servers. Microservers are battery-powered in-network nodes that play a number of roles: processing data fromclients, aggregating data, providing responses to queries, and actingas a network gateway. Providing QoS guarantees for theseservices can be extremely energy intensive. Since increasedenergy consumption translates to a shorter lifetime, there is a need for a new way to provide these QoS guarantees at minimal energy consumption.

Embedded systems can operate perpetually without being connected to a power source by harvesting environmental energy from motion, the sun, wind, or heat differentials. However, programming these perpetual systems is challenging. In response to changing energy levels, programmers can adjust the execution frequency of energy-intensive tasks, or provide higher service levels when energy is plentiful and lower service levels when energy is scarce. However, it is often difficult for programmers to predict the energy consumption resulting from these adjustments. Worse, explicit energy management can tie a program to a particular hardware platform, limiting portability.This paper introduces Eon, a programming language and runtime system designed to support the development of perpetual systems. To our knowledge, Eon is the first energy-aware programming language. Eon is a declarative coordination language that lets programmers compose programs from components written in C or nesC. Paths through the program (flows) may be annotated with different energy states. Eon's automatic energy management then dynamically adapts these states to current and predicted energy levels. It chooses flows to execute and adjusts their rates of execution, maximizing the quality of service under available energy constraints.We demonstrate the utility and portability of Eon by deploying two perpetual applications on widely different hardware platforms: a GPS-based location tracking sensor deployed on a threatened species of turtle and on automobiles, and a solar-powered camera sensor for remote, ad-hoc deployments. We also evaluate the simplicity and effectiveness of Eon with a user study, in which novice Eon programmers produced more efficient efficient energy-adaptive systems in substantially less time than experienced C programmers.

Contributory applications allow users to donate unused resources on their personal computers to a shared pool. Applications such as SETI@home, Folding@home, and Freenet are now in wide use and provide a variety of services, including data processing and content distribution. However, while several research projects have proposed contributory applications that support peer-to-peer storage systems, their adoption has been comparatively limited. We believe that a key barrier to the adoption of contributory storage systems is that contributing a large quantity of local storage interferes with the principal user of the machine. To overcome this barrier, we introduce the Transparent File System (TFS). TFS provides background tasks with large amounts of unreliable storage—all of the currently available space—without impacting the performance of ordinary file access operations. We show that TFS allows a peer-to-peer contributory storage system to provide 40% more storage at twice the performance when compared to a user-space storage mechanism. We analyze the impact of TFS on replication in peer-to-peer storage systems and show that TFS does not appreciably increase the resources needed for file replication.

Disruption tolerant networks (DTNs) are designed to overcome limitations in connectivity due to conditions such as mobility, poor infrastructure, and short range radios. DTNs rely on the inherent mobility in the network to deliver packets around frequent and extended network partitions using a store-carry-and-forward paradigm. However, missed contact opportunities decrease throughput and increase delay in the network. We propose the use of throwboxes in mobile DTNs to create a greater number of contact opportunities, consequently improving the performance of the network. Throwboxes are wireless nodes that act as relays, creating additional contact opportunities in the DTN. We propose algorithms to deploy stationary throwboxes in the network that simultaneously consider routing as well as placement. We also present placement algorithms that use more limited knowledge about the network structure. We perform an extensive evaluation of our algorithms by varying both the underlying routing and mobility models. Our results suggest several findings to guide the design and operation of throwbox-augmented DTNs

Mobile devices are vulnerable to theft and loss due to their small size and the characteristics of their common usage environment. Since they allow users to work while away from their desk, they are most useful in public locations and while traveling. Unfortunately, this is also where they are most at risk. Existing schemes for securing data either do not protect the device after it is stolen or require bothersome reauthentication. Transient Authentication lifts the burden of authentication from the user by use of a wearable token that constantly attests to the user's presence. When the user departs, the token and device lose contact and the device secures itself. We show how to leverage this authentication framework to secure all the memory and storage locations on a device into which secrets may creep. Our evaluation shows this is done without inconveniencing the user, while imposing a minimal performance overhead

In this paper, we study how a sensor-rich world can be exploited by digital recording devices such as cameras and camcorders to improve a user's ability to search through a large repository of image and video files. We design and implement a digital recording system that records identities and locations of objects (as advertised by their sensors) along with visual images (as recorded by a camera). The process, which we refer to as sensor-enhanced video annotation (SEVA), combines a series of correlation, interpolation, and extrapolation techniques. It produces a tagged stream that later can be used to efficiently search for videos or frames containing particular objects or people. We present detailed experiments with a prototype of our system using both stationary and mobile objects as well as GPS and ultrasound. Our experiments show that: (i) SEVA has zero error rates for static objects, except very close to the boundary of the viewable area; (ii) for moving objects or a moving camera, SEVA only misses objects leaving or entering the viewable area by 1-2 frames; (iii) SEVA can scale to 10 fast moving objects using current sensor technology; and (iv) SEVA runs online using relatively inexpensive hardware.

In this paper, we present Chameleon---an application-level power management approach for reducing energy consumption in mobile processors. Our approach exports the entire responsibility of power management decisions to the application level. We propose an operating system interface that can be used by applications to achieve energy savings. We consider three classes of applications---soft real-time, interactive and batch---and design user-level power management strategies for representative applications such as a movie player, a word processor, a web browser, and a batch compiler. We also design a user level power manager based on GraceOS using Chameleon. We implement our approach in the Linux kernel running on a Sony Transmeta laptop. Our experiments show that, compared to the traditional system-wide CPU voltage scaling approaches, Chameleon can achieve up to 32-50% energy savings while delivering comparable or better performance to applications. Further, Chameleon imposes small overheads and is very effective at scheduling concurrent applications with diverse energy needs.

Maintaining optimal consistency in a distributed system requires that nodes be always-on to synchronize information. Unfortunately, mobile devices such as laptops do not have adequate battery capacity for constant processing and communication. Even by powering off unnecessary components, such as the screen and disk, current laptops only have a lifetime of a few hours. Although PDAs and sensors are similarly limited in lifetime, a PDA's power requirement is an order-of-magnitude smaller than a laptop's, and a sensor's is an order-of-magnitude smaller than a PDA's. By combining these diverse platforms into a single integrated laptop, we can reduce the power cost of always-on operation. This paper presents the design, implementation, and evaluation of Turducken, a Hierarchical Power Management architecture for mobile systems. We focus on a particular instantiation of HPM, which provides high levels of consistency in a laptop by integrating two additional low power processors. We demonstrate that a Turducken system can provide battery lifetimes of up to ten times that of a standard laptop for always-on operation and three times for a system that periodically sleeps.

How does a machine know who is using it? Current systems authenticate their users infrequently, and assume the user's identity does not change. Such persistent authentication is inappropriate for mobile and ubiquitous systems, where associations between people and devices are fluid and unpredictable. We solve this problem with Transient Authentication, in which a small hardware token continuously authenticates the user's presence over a short-range, wireless link. We present the four principles underlying Transient Authentication, and describe two techniques for securing applications. Applications can be protected transparently by encrypting in-memory state when the user departs and decrypting this state when the user returns. This technique is effective, requiring just under 10 seconds to protect and restore an entire machine, but indiscriminate. Instead, applications can utilize an API for Transient Authentication, protecting only sensitive state. We describe our ports of three applications---PGP, SSH, and Mozilla---to this API. Mozilla, the most complicated application we have ported, suffers less than 4% overhead in page loads in the worst case, and in typical use can be protected in less than 250 milliseconds.

Laptops are vulnerable to theft, greatly increasing the likelihood of exposing sensitive files. Unfortunately, storing data in a cryptographic file system does not fully address this problem. Such systems ask the user to imbue them with long-term authority for decryption, but that authority can be used by anyone who physically possesses the machine. Forcing the user to frequently reestablish his identity is intrusive, encouraging him to disable encryption.Our solution to this problem is Zero-Interaction Authentication, or ZIA. In ZIA, a user wears a small authentication token that communicates with a laptop over a short-range, wireless link. Whenever the laptop needs decryption authority, it acquires it from the token; authority is retained only as long as necessary. With careful key management, ZIA imposes an overhead of only 9.3% for representative workloads. The largest file cache on our hardware can be re-encrypted within five seconds of the user's departure, and restored in just over six seconds after detecting the user's return. This secures the machine before an attacker can gain physical access, but recovers full performance before a returning user resumes work.

In order to support quality-of-service (QoS) for real-time data communications such as voice, video and interactive services, multiaccess networks must provide an effective priority mechanism. The context of this work is the IEEE 802.14 standard for hybrid fiber coaxial (HFC) networks which has a shared upstream channel for transmissions from stations to the headend. This work presents a multilevel priority collision resolution scheme, which separates and resolves collisions between stations in a priority order, thereby, achieving the capability for preemptive priorities. We present a set of simulation scenarios which show the robustness and efficiency of the scheme, such as its ability to isolate higher priority traffic from lower priorities and to provide quick access to high-priority requests. In March 1998, a framework for handling priorities in the collision resolution process, which adopts a semantics similar to the semantics of our scheme, was included in the 802.14 standard.

In order to provide quality of service (QoS) to users with real-time data such as voice, video and interactive services, the evolving IEEE 802.14 standard for hybrid fiber coaxial (HFC) networks must include an effective priority scheme. In this paper we investigate the ability of the current IEEE 802.14 specification to provide priority service and show that a preemptive scheduler is not a sufficient solution. We propose to augment the scheduler with a novel scheme for implementing priority access in an HFC random access environment. The proposed mechanism integrates a multilevel priority collision resolution system into the proposed IEEE 802.14 MAC. The scheme separates and resolves collisions between stations in a priority order. A set of simulation scenarios is presented that shows the robustness and efficiency of the protocol, such as its ability to isolate higher priorities from lower ones and provide quick access to high priority requests.