Vehicular environments require continuous awareness of the road ahead. It is critical that mobile applications used in such environments (e.g., GPS route planners and location-based search) do not distract drivers from the primary task of operating the vehicle. Fortunately, a large body of research on vehicular interfaces provides best practices that mobile application developers can follow. However, when we studied the most popular vehicular applications in the Android marketplace, no application followed these guidelines. In fact, vehicular applications were not substantially better at meeting best practice guidelines than non-vehicular applications.

Prefetching is a double-edged sword. It can hide the latency of data transfers over poor and intermittently connected wireless networks, but the costs of prefetching in terms of increased energy and cellular data usage are potentially substantial, particularly for data prefetched incorrectly. Weighing the costs and benefits of prefetching is complex, and consequently most mobile applications employ simple but sub-optimal strategies.

The increasing capabilities and decreasing cost of information and communication technologies (ICTs) are enabling new categories of vehicular telematics applications, such as wireless drive-through (WDT) services, in which an external service provider provides services to in-vehicle occupants via a wireless connection. Most telematics systems rely on proprietary protocols for data exchange between an in-vehicle client and its external counterpart in individual applications, making it difficult to share data-exchange methods among applications. Sharing data-exchange methods is important because it allows a rapid return on ICT investments. In this paper, we study the data-modeling issues for data exchange between an in-vehicle client and its service provider for WDT services. We present a novel data model that captures the main characteristics of data exchange for the WDT application family, using a formal mathematical representation and an intuitive graphical representation. We show that the proposed data model provides generality for such data exchange. In addition, we propose several optimization techniques based on the data model to minimize user interaction so that a good usability can be achieved. Our simulations and analysis demonstrate that the proposed data model and relevant optimization techniques are promising in supporting efficient WDT applications.

Mobile devices face a diverse and dynamic set of networking options. Using those options to the fullest requires knowledge of application intent. This paper describes Intentional Networking, a simple but powerful mechanism for handling network diversity. Applications supply a declarative label for network transmissions, and the system matches transmissions to the most appropriate network. The system may also defer and re-order opportunistic transmissions subject to application-supplied mutual exclusion and ordering constraints. We have modified three applications to use Intentional Networking: BlueFS, a distributed file system for pervasive computing, Mozilla's Thunderbird e-mail client, and a vehicular participatory sensing application. We evaluated the performance of these applications using measurements obtained by driving a vehicle through WiFi and cellular 3G network coverage. Compared to an idealized solution that makes optimal use of all aggregated available networks but that lacks knowledge of application intent, Intentional Networking improves the latency of interactive messages from 48% to 13x, while adding no more than 7% throughput overhead.

Emerging Web services, such as email, photo sharing, and web site archives, must preserve large volumes of quickly accessible data indefinitely into the future. The costs of doing so often determine whether the service is economically viable. We make the case that these applications' demands on large scale storage systems over long time horizons require us to reevaluate traditional system designs. We examine threats to long-lived data from an end-to-end perspective, taking into account not just hardware and software faults but also faults due to humans and organizations. We present a simple model of long-term storage failures that helps us reason about various strategies for addressing some of these threats. Using this model we show that the most important strategies for increasing the reliability of long-term storage are detecting latent faults quickly, automating fault repair to make it cheaper and faster, and increasing the independence of data replicas.

The LOCKSS project has developed and deployed in a world-wide test a peer-to-peer system for preserving access to journals and other archival information published on the Web. It consists of a large number of independent, low-cost, persistent Web caches that cooperate to detect and repair damage to their content by voting in “opinion polls.” Based on this experience, we present a design for and simulations of a novel protocol for voting in systems of this kind. It incorporates rate limitation and intrusion detection to ensure that even some very powerful adversaries attacking over many years have only a small probability of causing irrecoverable damage before being detected.

The LOCKSS project has developed and deployed in a world-wide test a peer-to-peer system for preserving access to journals and other archival information published on the Web. It consists of a large number of independent, low-cost, persistent web caches that cooperate to detect and repair damage to their content by voting in "opinion polls." Based on this experience, we present a design for and simulations of a novel protocol for voting in systems of this kind. It incorporates rate limitation and intrusion detection to ensure that even some very powerful adversaries attacking over many years have only a small probability of causing irrecoverable damage before being detected.

Many visions of the future predict a world with pervasive computing, where computing services and resources permeate the environment. In these visions, people will want to execute a service on any available device without worrying about whether the service has been tailored for the device. We believe that it will be difficult to create services that can execute well on the wide variety of devices that are being developed because of problems with diversity and resource constraints. We believe that these problems can be greatly reduced by using an ad-hoc distributed platform to transparently off-load portions of a service from a resource-constrained device to a nearby server. We implemented a preliminary prototype and emulator to study this approach. Our experiments show the beneficial use of nearby resources to relieve both memory and processing constraints, when it is appropriate to do so. We believe that this approach will reduce the burden on developers by masking more device details.

This paper describes two techniques that improve throughput in an ad hoc network in the presence of nodes that agree to forward packets but fail to do so. To mitigate this problem, we propose categorizing nodes based upon their dynamically measured behavior. We use a watchdog that identifies misbehaving nodes and a pathrater that helps routing protocols avoid these nodes. Through simulation we evaluate watchdog and pathrater using packet throughput, percentage of overhead (routing) transmissions, and the accuracy of misbehaving node detection. When used together in a network with moderate mobility, the two techniques increase throughput by 17% in the presence of 40% misbehaving nodes, while increasing the percentage of overhead transmissions from the standard routing protocol's 9% to 17%. During extreme mobility, watchdog and pathrater can increase network throughput by 27%, while increasing the overhead transmissions from the standard routing protocol's 12% to 24%.