This paper investigates an application of mobile sensing: detecting and reporting the surface conditions of roads. We describe a system and associated algorithms to monitor this important civil infrastructure using a collection of sensor-equipped vehicles. This system, which we call the Pothole Patrol (P2), uses the inherent mobility of the participating vehicles, opportunistically gathering data from vibration and GPS sensors, and processing the data to assess road surface conditions. We have deployed P2 on 7 taxis running in the Boston area. Using a simple machine-learning approach, we show that we are able to identify potholes and other severe road surface anomalies from accelerometer data. Via careful selection of training data and signal features, we have been able to build a detector that misidentifies good road segments as having potholes less than 0.2% of the time. We evaluate our system on data from thousands of kilometers of taxi drives, and show that it can successfully detect a number of real potholes in and around the Boston area. After clustering to further reduce spurious detections, manual inspection of reported potholes shows that over 90% contain road anomalies in need of repair.

/pdf/the-pothole-patrol-using-a-mobile-sensor-network-for-road-429j4kcazk.pdf

The pothole patrol: using a mobile sensor network for road surface monitoring

As new fabrication and integration technologies reduce the cost and size of micro-sensors and wireless interfaces, it becomes feasible to deploy densely distributed wireless networks of sensors and actuators. These systems promise to revolutionize biological, earth, and environmental monitoring applications, providing data at granularities unrealizable by other means. In addition to the challenges of miniaturization, new system architectures and new network algorithms must be developed to transform the vast quantity of raw sensor data into a manageable stream of high-level data. To address this, we propose a tiered system architecture in which data collected at numerous, inexpensive sensor nodes is filtered by local processing on its way through to larger, more capable and more expensive nodes.We briefly describe Habitat monitoring as our motivating application and introduce initial system building blocks designed to support this application. The remainder of the paper presents details of our experimental platform.

Habitat monitoring: Application driver for wireless communications technology

Mobile phones are set to become the universal interface to online services and cloud computing applications. However, using them for this purpose today is limited to two configurations: applications either run on the phone or run on the server and are remotely accessed by the phone. These two options do not allow for a customized and flexible service interaction, limiting the possibilities for performance optimization as well. In this paper we present a middleware platform that can automatically distribute different layers of an application between the phone and the server, and optimize a variety of objective functions (latency, data transferred, cost, etc.). Our approach builds on existing technology for distributed module management and does not require new infrastructures. In the paper we discuss how to model applications as a consumption graph, and how to process it with a number of novel algorithms to find the optimal distribution of the application modules. The application is then dynamically deployed on the phone in an efficient and transparent manner. We have tested and validated our approach with extensive experiments and with two different applications. The results indicate that the techniques we propose can significantly optimize the performance of cloud applications when used from mobile phones.

/pdf/calling-the-cloud-enabling-mobile-phones-as-interfaces-to-3z62foyj5i.pdf

Calling the cloud: enabling mobile phones as interfaces to cloud applications

Most sensor network research and software design has been guided by an architectural principle that permits multi-node data fusion on small-form-factor, resource-poor nodes, or motes. We argue that this principle leads to fragile and unmanageable systems and explore an alternative. The Tenet architecture is motivated by the observation that future large-scale sensor network deployments will be tiered, consisting of motes in the lower tier and masters, relatively unconstrained 32-bit platform nodes, in the upper tier. Masters provide increased network capacity. Tenet constrains multi-node fusion to the master tier while allowing motes to process locally-generated sensor data. This simplifies application development and allows mote-tier software to be reused. Applications running on masters task motes by composing task descriptions from a novel tasklet library. Our Tenet implementation also contains a robust and scalable networking subsystem for disseminating tasks and reliably delivering responses. We show that a Tenet pursuit-evasion application exhibits performance comparable to a mote-native implementation while being considerably more compact.

/pdf/the-tenet-architecture-for-tiered-sensor-networks-1lyeqw3mof.pdf

The Tenet architecture for tiered sensor networks

We present the design, implementation, and evaluation of the Acoustic Embedded Networked Sensing Box (ENSBox), a platform for prototyping rapid-deployable distributed acoustic sensing systems, particularly distributed source localization. Each ENSBox integrates an ARM processor running Linux and supports key facilities required for source localization: a sensor array, wireless network services, time synchronization, and precise self-calibration of array position and orientation. The ENSBox's integrated, high precision self-calibration facility sets it apart from other platforms. This self-calibration is precise enough to support acoustic source localization applications in complex, realistic environments: e.g., 5 cm average 2D position error and 1.5 degree average orientation error over a partially obstructed 80x50 m outdoor area. Further, our integration of array orientation into the position estimation algorithm is a novel extension of traditional multilateration techniques. We present the result of several different test deployments, measuring the performance of the system in urban settings, as well as forested, hilly environments with obstructing foliage and 20-30 m distances between neighboring nodes.

/pdf/the-design-and-implementation-of-a-self-calibrating-3nxc9xlhaz.pdf

The design and implementation of a self-calibrating distributed acoustic sensing platform

Small-form-factor, low-power wireless sensors-motes-are convenient to deploy, but lack the bandwidth to capture and transmit raw high-frequency data, such as human voices or neural signals, in real time. Local filtering can help, but we show that the right filter settings depend on changing ambient conditions and network effects such as congestion, which makes them dynamic and unpredictable. Mote collection systems for high-frequency data must support iteratively-tuned, deployment-specific filter settings as well as fast samplin.VANGO, our software system for high-frequency data collection, achieves these goals via integrated processing across network tiers. Bandwidth-limited sensor nodes reduce data in network but rely on microservers, which have greater computational capabilities and a wider scope of observation, to plan how. VANGO provides a cross-platform library for data transformation, measurement, and classification; a fast and low-jitter data acquisition system for motes; and a mechanism to control mote and microserver signal processing. With VANGO we have developed new applications: the first acoustic collection system for motes responsive to changing environmental conditions and user interests, and the first neural spike acquisition application capable of supporting a network of nodes.

/pdf/capturing-high-frequency-phenomena-using-a-bandwidth-limited-424buwhct9.pdf

Capturing high-frequency phenomena using a bandwidth-limited sensor network

Embedded systems can already capture data produced at high rates, and embedded CPU and sensor performance are still rapidly improving. Radio technology, however, can not keep pace, and will not in the future due to known physical limits of shared communication channels. This leads to a fundamental gap between the data a sensor network node can collect and the data it can transmit back for analysis. VanGo, our software system for data collection, uses flexible transcoding to narrow this gap. To make effective use of channel bandwidth, data reduction software must run on sensor nodes. However, to calibrate how data reduction software should run, that same software should be capable of running on the back end on real data received from the network. In VanGo, users decide where data processing occurs. To show that transcoding helps, we evaluate two radically different applications: acoustic collection and the measurement of neural activity. Among our findings is that in bandwidth-limited environments, proactive filtering of some of our signal can result in collecting three times the signal energy than we could by removing silent periods alone.

/pdf/collecting-high-rate-data-over-low-rate-sensor-network-1eamwpzcdw.pdf

Collecting High-Rate Data Over Low-Rate Sensor Network Radios

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Capturing high-frequency phenomena using a bandwidth-limited sensor network

Collecting High-Rate Data Over Low-Rate Sensor Network Radios