With the proliferation of mobile devices and sensors, complex event proceesing (CEP) is becoming increasingly important to scalably detect situations in real time. Current CEP systems are not capable of dealing efficiently with highly dynamic mobile consumers whose interests change with their location. We introduce the distributed mobile CEP (MCEP) system which automatically adapts the processing of events according to a consumer's location. MCEP significantly reduces latency, network utilization, and processing overhead by providing on-demand and opportunistic adaptation algorithms to dynamically assign event streams and computing resources to operators of the MCEP system.

Smart camera networks have recently emerged as a new class of sensor network infrastructure that is capable of supporting high-power in-network signal processing and enabling a wide range of applications. In this article, we provide an exposition of our efforts to build a low-bandwidth wireless camera network platform, called CITRIC, and its applications in smart camera networks. The platform integrates a camera, a microphone, a frequency-scalable (up to 624 MHz) CPU, 16 MB FLASH, and 64 MB RAM onto a single device. The device then connects with a standard sensor network mote to form a wireless camera mote. With reasonably low power consumption and extensive algorithmic libraries running on a decent operating system that is easy to program, CITRIC is ideal for research and applications in distributed image and video processing. Its capabilities of in-network image processing also reduce communication requirements, which has been high in other existing camera networks with centralized processing. Furthermore, the mote easily integrates with other low-bandwidth sensor networks via the IEEE 802.15.4 protocol. To justify the utility of CITRIC, we present several representative applications. In particular, concrete research results will be demonstrated in two areas, namely, distributed coverage hole identification and distributed object recognition.

Sensors of various modalities and capabilities, especially cameras, have become ubiquitous in our environment. Their intended use is wide ranging and encompasses surveillance, transportation, entertainment, education, healthcare, emergency response, disaster recovery, and the like. Technological advances and the low cost of such sensors enable deployment of large-scale camera networks in large metropolises such as London and New York. Multimedia algorithms for analyzing and drawing inferences from video and audio have also matured tremendously in recent times. Despite all these advances, large-scale reliable systems for media-rich sensor-based applications, often classified as situation-awareness applications, are yet to become commonplace. Why is that? There are several forces at work here. First, the system abstractions are just not at the right level for quickly prototyping such applications on a large scale. Second, while Moore's law has held true for predicting the growth of processing power, the volume of data that applications are called upon to handle is growing similarly, if not faster. Enormous amount of sensing data is continually generated for real-time analysis in such applications. Further, due to the very nature of the application domain, there are dynamic and demanding resource requirements for such analyses. The lack of right set of abstractions for programing such applications coupled with their data-intensive nature have hitherto made realizing reliable large-scale situation-awareness applications difficult. Incidentally, situation awareness is a very popular but ill-defined research area that has attracted researchers from many different fields. In this paper, we adopt a strong systems perspective and consider the components that are essential in realizing a fully functional situation-awareness system.

SUMMARY Energy‐efficient implementation techniques for virtual machines (VMs) have received little attention yet: conventional wisdom claims that VMs have a diametrical effect on energy consumption, and VM‐based applications are therefore short‐lived. In this paper, we argue that bytecode interpretation is affordable if we synthesize VMs specifically for energy efficiency. We present TinyVM, an execution infrastructure that seamlessly integrates with C and nesC/TinyOS‐based programming environments. TinyVM achieves high code density through the use of compressed bytecode as the primary program representation. Compressed bytecode allows rapid application deployment with low communication overhead. TinyVM executes compressed bytecode in place, which eliminates the need for a decompression stage and thereby reduces memory consumption on sensor nodes. Our infrastructure automates the creation of energy‐efficient application‐specific VMs. Applications are partitioned in machine code, bytecode, and VM instruction set extensions. Partitioning is manually controlled and/or fully guided by a discrete optimization problem that produces a partitioning with lowest energy consumption for a given program size limit. We provide experimental results for sensor network benchmarks and for selected applications on various CPU architectures including Atmega128‐based motes and the ARM‐based Intel iMote2. TinyVM has been released under the GNU General Public License. Copyright © 2011 John Wiley & Sons, Ltd.

We present TinyVM, a Virtual Machine (VM) for nesC and C applications on sensor motes. TinyVM executes compressed bytecode on-the-fly to conserve memory. To facilitate creation of application-specific VMs, partitioning of applications into bytecode, VM instruction set extensions and machine-code is supported. We provide experimental evidence for the efficiency of TinyVM on Atmega128-based motes and on the Intel iMote2. TinyVM also runs on Windows and Linux, and we are currently porting TinyVM to Telos-based motes.