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

US water utilities are faced with mounting operational and maintenance costs as a result of aging pipeline infrastructures. Leaks and ruptures in water supply pipelines and blockages and overflow events in sewer collectors cost millions of dollars a year, and monitoring and repairing this underground infrastructure presents a severe challenge. In this paper, we discuss how wireless sensor networks (WSNs) can increase the spatial and temporal resolution of operational data from pipeline infrastructures and thus address the challenge of near real-time monitoring and eventually control. We focus on the use of WSNs for monitoring large diameter bulk-water transmission pipelines. We outline a system, PipeNet, we have been developing for collecting hydraulic and acoustic/vibration data at high sampling rates as well as algorithms for analyzing this data to detect and locate leaks. Challenges include sampling at high data rates, maintaining aggressive duty cycles, and ensuring tightly time- synchronized data collection, all under a strict power budget. We have carried out an extensive field trial with Boston Water and Sewer Commission in order to evaluate some of the critical components of PipeNet. Along with the results of this preliminary trial, we describe the results of extensive laboratory experiments which are used to evaluate our analysis and data processing solutions. Our prototype deployment has led to the development of a reusable, field-reprogrammable software infrastructure for distributed high-rate signal processing in wireless sensor networks, which we also describe.

PIPENET: A Wireless Sensor Network for Pipeline Monitoring

In this paper, we present Spade - the System S declarative stream processing engine. System S is a large-scale, distributed data stream processing middleware under development at IBM T. J. Watson Research Center. As a front-end for rapid application development for System S, Spade provides (1) an intermediate language for flexible composition of parallel and distributed data-flow graphs, (2) a toolkit of type-generic, built-in stream processing operators, that support scalar as well as vectorized processing and can seamlessly inter-operate with user-defined operators, and (3) a rich set of stream adapters to ingest/publish data from/to outside sources. More importantly, Spade automatically brings performance optimization and scalability to System S applications. To that end, Spade employs a code generation framework to create highly-optimized applications that run natively on the Stream Processing Core (SPC), the execution and communication substrate of System S, and take full advantage of other System S services. Spade allows developers to construct their applications with fine granular stream operators without worrying about the performance implications that might exist, even in a distributed system. Spade's optimizing compiler automatically maps applications into appropriately sized execution units in order to minimize communication overhead, while at the same time exploiting available parallelism. By virtue of the scalability of the System S runtime and Spade's effective code generation and optimization, we can scale applications to a large number of nodes. Currently, we can run Spade jobs on ≈ 500 processors within more than 100 physical nodes in a tightly connected cluster environment. Spade has been in use at IBM Research to create real-world streaming applications, ranging from monitoring financial market feeds to radio telescopes to semiconductor fabrication lines.

/pdf/spade-the-system-s-declarative-stream-processing-engine-dlvxacfscs.pdf

SPADE: the system s declarative stream processing engine

Streams of events appear increasingly today in various Web applications such as blogs, feeds, sensor data streams, geospatial information, on-line financial data, etc. Event Processing (EP) is concerned with timely detection of compound events within streams of simple events. State-of-the-art EP provides on-the-fly analysis of event streams, but cannot combine streams with background knowledge and cannot perform reasoning tasks. On the other hand, semantic tools can effectively handle background knowledge and perform reasoning thereon, but cannot deal with rapidly changing data provided by event streams.To bridge the gap, we propose Event Processing SPARQL (EP-SPARQL) as a new language for complex events and Stream Reasoning. We provide syntax and formal semantics of the language and devise an effective execution model for the proposed formalism. The execution model is grounded on logic programming, and features effective event processing and inferencing capabilities over temporal and static knowledge. We provide an open-source prototype implementation and present a set of tests to show the usefulness and effectiveness of our approach.

EP-SPARQL: a unified language for event processing and stream reasoning

Recent advances in computer vision---in the form of deep neural networks---have made it possible to query increasing volumes of video data with high accuracy. However, neural network inference is computationally expensive at scale: applying a state-of-the-art object detector in real time (i.e., 30+ frames per second) to a single video requires a $4000 GPU. In response, we present NoScope, a system for querying videos that can reduce the cost of neural network video analysis by up to three orders of magnitude via inference-optimized model search. Given a target video, object to detect, and reference neural network, NoScope automatically searches for and trains a sequence, or cascade, of models that preserves the accuracy of the reference network but is specialized to the target video and are therefore far less computationally expensive. NoScope cascades two types of models: specialized models that forego the full generality of the reference model but faithfully mimic its behavior for the target video and object; and difference detectors that highlight temporal differences across frames. We show that the optimal cascade architecture differs across videos and objects, so NoScope uses an efficient cost-based optimizer to search across models and cascades. With this approach, NoScope achieves two to three order of magnitude speed-ups (265-15,500x real-time) on binary classification tasks over fixed-angle webcam and surveillance video while maintaining accuracy within 1--5% of state-of-the-art neural networks.

NoScope: optimizing neural network queries over video at scale

Composite (or Complex) event processing (CEP) systems search sequences of incoming events for occurrences of user-specified event patterns. Recently, they have gained more attention in a variety of areas due to their powerful and expressive query language and performance potential. Sequentiality (temporal ordering) is the primary way in which CEP systems relate events to each other. In this paper, we present a CEP system called ZStream to efficiently process such sequential patterns. Besides simple sequential patterns, ZStream is also able to detect other patterns, including conjunction, disjunction, negation and Kleene closure. Unlike most recently proposed CEP systems, which use non-deterministic finite automata (NFA's) to detect patterns, ZStream uses tree-based query plans for both the logical and physical representation of query patterns. By carefully designing the underlying infrastructure and algorithms, ZStream is able to unify the evaluation of sequence, conjunction, disjunction, negation, and Kleene closure as variants of the join operator. Under this framework, a single pattern in ZStream may have several equivalent physical tree plans, with different evaluation costs. We propose a cost model to estimate the computation costs of a plan. We show that our cost model can accurately capture the actual runtime behavior of a plan, and that choosing the optimal plan can result in a factor of four or more speedup versus an NFA based approach. Based on this cost model and using a simple set of statistics about operator selectivity and data rates, ZStream is able to adaptively and seamlessly adjust the order in which it detects patterns on the fly. Finally, we describe a dynamic programming algorithm used in our cost model to efficiently search for an optimal query plan for a given pattern.

ZStream: a cost-based query processor for adaptively detecting composite events

Sensors capable of sensing phenomena at high data rates on the order of tens to hundreds of thousands of samples per second are now widely deployed in many industrial, civil engineering, scientific, networking, and medical applications. In aggregate, these sensors easily generate several million samples per second that must be processed within milliseconds or seconds. The computation required includes both signal processing and event stream processing. XStream is a stream processing system for such applications. XStream introduces a new data type, the signal segment, which allows applications to manipulate isochronous (regularly spaced in time) collections of sensor samples more conveniently and efficiently than the asynchronous representation used in previous work. XStream includes a memory manager and scheduler optimizations tuned for processing signal segments at high speeds. In benchmark comparisons, we show that XStream outperforms a leading commercial stream processing system by more than three orders of magnitude. On one application, the commercial system processed 72.7 Ksamples/sec, while XStream processed 97.6 Msamples/sec.

/pdf/xstream-a-signal-oriented-data-stream-management-system-4sf33p5yuk.pdf

XStream: a Signal-Oriented Data Stream Management System

Sensors capable of sensing phenomena at high data rates—on the order of tens to hundreds of thousands of samples per second—are useful in many industrial, civil engineering, scientific, networking, and medical applications. In these applications, high-rate streams of data produced by sensors must be processed and analyzed using a combination ofbothevent-stream and signal-processing operations. This paper motivates the need for a data management and continuous query processing architecture that integrates these two different desired classes of functions into a single, unified software system. The key goals of such a system include: the ability to treat a sequence of samples that constitute a “signal segment” as a basic data type; ease of writing arbitrary event-stream and signal-processing functions; the ability to process several million samples per second on conventional PC hardware; and the ability to distribute application code across both PCs and sensor nodes.

/pdf/the-case-for-a-signal-oriented-data-stream-management-system-56kh7lu5hs.pdf

The Case for a Signal-Oriented Data Stream Management System

WaveScope is a data management and continuous sensor data system that integrates relational database and signal processing operations into a single system. WaveScope is motivated by a large number of signal-oriented streaming sensor applications, such as: preventive maintenance of industrial equipment; detection of fractures and ruptures in various structures; in situ animal behavior studies using acoustic sensing; network traffic analysis; and medical applications such as anomaly detection in EKGs. These target applications use a variety of embedded sensors, each sampling at fine resolution and producing data at high rates ranging from hundreds to hundreds of thousands of samples per second. Though there has been some work on applications in the sensor network community that do this kind of signal processing (for example, shooter localization [5], industrial equipment monitoring [4], and urban infrastructure monitoring [2]), these applications are typically custombuilt and do not provide reusable high-level programming framework suitable for easily building new signal processing applications with similar functionality. This poster shows how WaveScope supports these types of application in a single, unified framework, providing both high run-time performance and easy application development.

Yuan Mei

Papers

ZStream: a cost-based query processor for adaptively detecting composite events

XStream: a Signal-Oriented Data Stream Management System

The Case for a Signal-Oriented Data Stream Management System

WaveScope: a signal-oriented data stream management system