The Caltech CSN project collects sensor data from thousands of personal devices for realtime response to dangerous earthquakes

The proliferation of smartphones and other powerful sensor-equipped consumer devices enables a new class of Web application: community sense and response (CSR) systems, distinguished from standard Web applications by their use of community-owned commercial sensor hardware. Just as social networks connect and share human-generated content, CSR systems gather, share, and act on sensory data from users' Internet-enabled devices. Here, we discuss the Caltech Community Seismic Network (CSN) as a prototypical CSR system harnessing accelerometers in smartphones and consumer electronics, including the systems and algorithmic challenges of designing, building, and evaluating a scalable network for real-time awareness of dangerous earthquakes

Community Sense and Response Systems: Your Phone as Quake Detector

The proliferation of smartphones and other powerful sensor-equipped consumer devices enables a new class of Web application: community sense and response (CSR) systems, distinguished from standard Web applications by their use of community-owned commercial sensor hardware. Just as social networks connect and share human-generated content, CSR systems gather, share, and act on sensory data from users' Internet-enabled devices. Here, we discuss the Caltech Community Seismic Network (CSN) as a prototypical CSR system harnessing accelerometers in smartphones and consumer electronics, including the systems and algorithmic challenges of designing, building, and evaluating a scalable network for real-time awareness of dangerous earthquakes

Mobility-awareness in complex event processing systems

The proliferation and vast deployment of mobile devices and sensors over the last couple of years enables a huge number of Mobile Situation Awareness (MSA) applications. These applications need to react in near real-time to situations in the environment of mobile objects like vehicles, pedestrians, or cargo. To this end, Complex Event Processing (CEP) is becoming increasingly important as it allows to scalably detect situations “on-the-fly” by continously processing distributed sensor data streams. Furthermore, recent trends in communication networks promise high real-time conformance to CEP systems by processing sensor data streams on distributed computing resources at the edge of the network, where low network latencies can be achieved. Yet, supporting MSA applications with a CEP middleware that utilizes distributed computing resources proves to be challenging due to the dynamics of mobile devices and sensors. In particular, situations need to be efficiently, scalably, and consistently detected with respect to ever-changing sensors in the environment of a mobile object. Moreover, the computing resources that provide low latencies change with the access points of mobile devices and sensors. The goal of this thesis is to provide concepts and algorithms to i) continuously detect situations that recently occurred close to a mobile object, ii) support bandwidth and computational efficient detections of such situations on distributed computing resources, and iii) support consistent, low latency, and high quality detections of such situations. To this end, we introduce the distributed Mobile CEP (MCEP) system which automatically adapts the processing of sensor data streams according to a mobile object’s location. MCEP provides an expressive, location-aware query model for situations that recently occurred at a location close to a mobile object. MCEP significantly reduces latency, bandwidth, and processing overhead by providing on-demand and opportunistic adaptation algorithms to dynamically assign event streams to queries of the MCEP system. Moreover, MCEP incorporates algorithms to adapt the deployment of MCEP queries in a network of computing resources. This way, MCEP supports latency-sensitive, large-scale deployments of MSA applications and ensures a low network utilization while mobile objects change their access points to the system. MCEP also provides methods to increase the scalability in terms of deployed MCEP queries by reusing event streams and computations for detecting common situations for several mobile objects

Towards a Discipline of Geospatial Distributed Event Based Systems

A geospatial system is one in which the state space includes one, two or three-dimensional space and time. A geospatial event is one in which an event impacts points in space over time. Examples of geospatial events include floods, tsunamis, earthquakes, and emission of toxic plumes. This paper discusses aspects of the theory of geospatial distributed event based systems (GDEBS). The paper describes algorithms for rapid detection of geospatial events which can be used on Cloud computing architectures, in which many servers collaborate to detect events by analyzing data streams from large numbers of sensors. Sensor noise and timing errors may result in false detection or missed detection as well as incorrect identification of event attributes such as the location of the event source. The paper presents mathematical analyses and simulations dealing with rapid event detection for geospatial events of varying speeds in the presence of substantial sensor noise and timing error. The paper also describes some of the algorithmic and machine-learning techniques for improving event detection in the Cloud with large numbers of noisy sensors. Experience with GDEBS using a seismic network is described