A sensor network data gathering and visualization infrastructure is demonstrated, comprising of global sensor networks (GSN) middleware and Microsoft SensorMap. Users are invited to actively participate in the process of monitoring real-world deployments and can inspect measured data in the form of contour plots overlayed onto a high resolution map and a digital topographic model. Users can go back in time virtually to search for interesting events or simply to visualize the temporal dependencies of the data. The system presented is not only interesting and visually enticing for non-expert users but brings substantial benefits to environmental scientists. The easily installed data acquisition component as well as the powerful data sharing and visualization platform opens up new ground in collaborative data gathering and interpretation in the spirit of Web 2.0 applications.

RACNet is a sensor network for monitoring a data center's environmental conditions. The high spatial and temporal fidelity measurements that RACNet provides can be used to improve the data center's safety and energy efficiency. RACNet overcomes the network's large scale and density and the data center's harsh RF environment to achieve data yields of 99% or higher over a wide range of network sizes and sampling frequencies. It does so through a novel Wireless Reliable Acquisition Protocol (WRAP). WRAP decouples topology control from data collection and implements a token passing mechanism to provide network-wide arbitration. This congestion avoidance philosophy is conceptually different from existing congestion control algorithms that retroactively respond to congestion. Furthermore, WRAP adaptively distributes nodes among multiple frequency channels to balance load and lower data latency. Results from two testbeds and an ongoing production data center deployment indicate that RACNet outperforms previous data collection systems, especially as network load increases.

This article presents the design, implementation, and evaluation of EnviroMic , a low-cost experimental prototype of a novel distributed acoustic monitoring, storage, and trace retrieval system designed for disconnected operation. Our intended use of acoustic monitoring is to study animal populations in the wild. Since a permanent connection to the outside world is not assumed and due to the relatively large size of audio traces, the system must optimally exploit available resources such as energy and network storage capacity. Towards that end, we design, prototype, and evaluate distributed algorithms for coordinating acoustic recording tasks, reducing redundancy of data stored by nearby sensors, filtering out silence, and balancing storage utilization in the network. For experimentation purposes, we implement EnviroMic on a TinyOS-based platform and systematically evaluate its performance through both indoor testbed experiments and an outdoor deployment. Results demonstrate up to a four-fold improvement in effective storage capacity of the network compared to uncoordinated recording.

Energy efficiency is a fundamental issue for outdoor sensor network systems. This article presents the design and implementation of multidimensional power management strategies in VigilNet, a major recent effort to support long-term surveillance using power-constrained sensor devices. A novel tripwire service is integrated with an effective sentry and duty cycle scheduling in order to increase the system lifetime, collaboratively. The tripwire service partitions a network into distinct, nonoverlapping sections and allows each section to be scheduled independently. Sentry scheduling selects a subset of nodes, the sentries, which are turned on while the remaining nodes save energy. Duty cycle scheduling allows the active sentries themselves to be turned on and off, further lowering the average power draw. The multidimensional power management strategies proposed in this article were fully implemented within a real sensor network system using the XSM platform. We evaluate key system parameters using a network of 200 XSM nodes in an outdoor environment, and an analytical probabilistic model. We evaluate network lifetime using a simulation of a 10,000-node network that uses measured XSM power values. These evaluations demonstrate the effectiveness of our integrated approach and identify a set of lessons and guidelines, useful for the future development of energy-efficient sensor systems. One of the key results indicates that the combination of the three presented power management techniques is able to increase the lifetime of a realistic network from 4 days to 200 days.

We present SenseWeb, an open and scalable infrastructure for sharing and geocentric exploration of sensor data streams. SenseWeb allows sensor owners to share data streams across multiple applications and users, thus amortizing sensor deployment costs effectively. It also provides mechanisms to transparently index and cache data, to process spatio-temporal queries on real-time and historic data, and to aggregate and present results on a geocentric web interface. In this paper, we present the architecture of SenseWeb, its techniques to enable global sharing of heterogeneous sensors, and its mapbased front-end for spatio-temporal data exploration. We enable interactive geocentric data exploration in the mapbased front-end using techniques for rapidly changing map overlaid visualizations of numerous data streams. We also demonstrate flexibility and scalability of the architecture by evaluating a deployed prototype of SenseWeb, which has been publicly available since March 2008.

Effective debugging usually involves watching program state to diagnose bugs. When debugging sensor network applications, this approach is often time-consuming and errorprone, not only because of the lack of visibility into system state, but also because of the difficulty to watch the right variables at the right time. In this paper, we present declarative tracepoints, a debugging system that allows the user to insert a group of action-associated checkpoints, or tracepoints, to applications being debugged at runtime. Tracepoints do not require modifying application source code. Instead, they are written in a declarative, SQL-like language called TraceSQL independently. By triggering the associated actions when these checkpoints are reached, this system automates the debugging process by removing the human from the loop. We show that declarative tracepoints are able to express the core functionality of a range of previously isolated debugging techniques, such as EnviroLog, NodeMD, Sympathy, and StackGuard. We describe the design and implementation of the declarative tracepoints system, evaluate its overhead in terms of CPU slowdown, illustrate its expressiveness through the aforementioned debugging techniques, and finally demonstrate that it can be used to detect real bugs using case studies of three bugs based on the development of the LiteOS operating system.

This paper presents EnviroMic, a novel distributed acoustic monitoring, storage, and trace retrieval system. Audio represents one of the least exploited modalities in sensor networks to date. The relatively high frequency and large size of audio traces motivate distributed algorithms for coordinating recording tasks, reducing redundancy of data stored by nearby sensors, filtering out silence, and balancing storage utilization in the network. Applications of acoustic monitoring with EnviroMic range from the study of mating rituals and social behavior of animals in the wild to audio surveillance of military targets. EnviroMic is designed for disconnected operation, where the luxury of having a basestation cannot be assumed. We implement the system on a Tiny OS-based platform and systematically evaluate its performance through both indoor testbed experiments and a preliminary outdoor deployment. Results demonstrate up to a 4-fold improvement in effective storage capacity of the network compared to uncoordinated recording.

This paper presents a new cooperative storage system for sensor networks geared for disconnected operation (where sensor nodes do not have a connected path to a basestation). The goal of the system is to maximize its data storage capacity by appropriately distributing storage utilization and opportunistically offloading data to external devices when possible. The system is motivated by the observation that a large category of sensor network applications, such as environmental data logging, does not require real-time data access. Such networks generally operate in a disconnected mode. Rather than focusing on multihop routing to a basestation, an important concern becomes (i) to maximize the effective storage capacity of the disconnected sensor network such that it accommodates the most data, and (ii) to take the best advantage of data upload opportunities when they become available to relieve network storage. The storage system described in this paper achieves the above goals, leading to significant improvements in the amount of data collected compared to non-cooperative storage. It is implemented in nesC for TinyOS and evaluated in TOSSIM through various application scenarios.

Sensor networks introduce a new application domain and set of challenges in distributed computing including new network-level programming languages, global system abstractions, and general-purpose communication protocols. These challenges are brought about by the tight integration of computation, communication, and distributed real-time interaction with the physical world. With the growing interest in interconnecting different sensor networks across a wide-area communication infrastructure, an overarching challenge becomes one of arriving at an agreed-upon global sensor network architecture that ensures interoperability. Unlike the Internet, where a layered communication stack (namely, the TCP/IP stack) defines the network architecture, a sensor network architecture must unify not only communication interfaces but also programming interfaces, since network communication and computation functions are tightly intertwined. In that sense, the sensor network architecture refers to a layered stack of distributed computing abstractions. This paper presents an architecture and key considerations in designing and interconnecting local and global sensor networks. Candidate protocols and middleware instantiations are described from the authors' ongoing work that meet the discussed considerations

Abstract — Energy efficiency is a fundamental issue for out-door sensor network systems. This paper presents the design and implementation of multi-dimensional power management strategies in VigilNet, a major recent effort to support long-term surveillance using power-constrained sensor devices. We integrate a novel tripwire service with an effective sentry and duty cycle scheduling in order to increase the system lifetime, collaboratively. Through extensive system implementation, we demonstrate the feasibility to achieve high surveillance perfor-mance and energy efficiency, simultaneously. We invest a fair amount of effort to evaluate our architecture with a network of 200 XSM motes in an outdoor environment, an extensive simulation with 10,000 nodes, as well as an analytical probabilistic model. These evaluations demonstrate the effectiveness of our integrated approach and identify many interesting lessons and guidelines, useful for the future development of energy-efficient sensor systems. I.

Abstract — Sensing events from dynamic environments are normally asynchronous and non-repeatable. This lack of repeatability makes it particularly difficult to statistically evaluate the performance of sensor network applications. Hence, it is essential to have the capability to capture and replay sensing events, providing a basis not only for system evaluation, but also for realistic protocol comparison and parameter tuning. To achieve that, we design and implement EnviroLog, a distributed service that improves repeatability of experimental testing of sensor networks via asynchronous event recording and replay. To use EnviroLog, an application programmer needs only to specify two types of simple annotations to the source code. Automatically, the preprocessor embeds EnviroLog into any desired level of an event-driven architecture. It records all events generated by lower layers and can replay them later to upper layers on demand. We validate the accuracy and performance of recording and replay through a set of microbenchmarks, using the latest XSM platforms. We further demonstrate the strength of EnviroLog in system tuning and performance evaluation for sensor network applications in an outdoor environment with 37 XSMs. I.

Since sensor nodes normally have limited resources in terms of energy, bandwidth and computation capability, efficiency is a key design goal in sensor network research. As one of techniques to achieve efficiency, data aggregation has been extensively investigated in recent literature. Previous research on data aggregation has demonstrated its effectiveness in reducing traffic, easing congestion and decreasing the energy consumption. However few are actually designed for a real-world application and implemented in a running system. This paper describes our design and implementation of a physical tracking system, using an aggressive data aggregation architecture as one of building blocks. This architecture can be generally applied to other sensor systems, where communication efficiency is a paramount concern and networking resources are limited.

Sensor networks open a new frontier for embedded-distributed computing. Paradigms for sensor network programming-in-the-large have been identified as a significant challenge toward developing large-scale applications. Classical programming languages are too low-level. This paper presents the design, implementation, and evaluation of EnviroSuite , a programming framework that introduces a new paradigm, called environmentally immersive programming , to abstract distributed interactions with the environment. Environmentally immersive programming refers to an object-based programming model in which individual objects represent physical elements in the external environment. It allows the programmer to think directly in terms of environmental abstractions. EnviroSuite provides language primitives for environmentally immersive programming that map transparently into a support library of distributed algorithms for tracking and environmental monitoring. We show how nesC code of realistic applications is significantly simplified using EnviroSuite and demonstrate the resulting system performance on Mica2 and XSM platforms.

This article describes one of the major efforts in the sensor network community to build an integrated sensor network system for surveillance missions. The focus of this effort is to acquire and verify information about enemy capabilities and positions of hostile targets. Such missions often involve a high element of risk for human personnel and require a high degree of stealthiness. Hence, the ability to deploy unmanned surveillance missions, by using wireless sensor networks, is of great practical importance for the military. Because of the energy constraints of sensor devices, such systems necessitate an energy-aware design to ensure the longevity of surveillance missions. Solutions proposed recently for this type of system show promising results through simulations. However, the simplified assumptions they make about the system in the simulator often do not hold well in practice, and energy consumption is narrowly accounted for within a single protocol. In this article, we describe the design and implementation of a complete running system, called VigilNet, for energy-efficient surveillance. The VigilNet allows a group of cooperating sensor devices to detect and track the positions of moving vehicles in an energy-efficient and stealthy manner. We evaluate VigilNet middleware components and integrated system extensively on a network of 70 MICA2 motes. Our results show that our surveillance strategy is adaptable and achieves a significant extension of network lifetime. Finally, we share lessons learned in building such an integrated sensor system.

Battlefield surveillance often involves a high element of risk for military operators. Hence, it is very important for the military to execute unmanned surveillance by using large-scale wireless sensor systems. This invited paper summarizes the architecture of the VigilNet system - a long-term real-time networked sensor system for military surveillance. Specifically, we review the design of several major subsystems within VigilNet including sensing and classification, localization, tracking, networking, power management, reconfiguration, graphic user interface, and the debugging subsystem. High-level programming abstractions are also presented. This is a balanced design to achieve realtime response, high confidence detection, accurate tracking and energy efficiency simultaneously.

A wide variety of sensors have been incorporated into a spectrum of wireless sensor network (WSN) platforms, providing flexible sensing capability over a large number of low-power and inexpensive nodes. Traditional signal processing algorithms, however, often prove too complex for energy-and-cost-effective WSN nodes. This study explores how to design efficient sensing and classification algorithms that achieve reliable sensing performance on energy-and-cost effective hardware without special powerful nodes in a continuously changing physical environment. We present the detection and classification system in a cutting-edge surveillance sensor network, which classifies vehicles, persons, and persons carrying ferrous objects, and tracks these targets with a maximum error in velocity of 15%. Considering the demanding requirements and strict resource constraints, we design a hierarchical classification architecture that naturally distributes sensing and computation tasks at different levels of the system. Such a distribution allows multiple sensors to collaborate on a sensor node, and the detection and classification results to be continuously refined at different levels of the WSN. This design enables reliable detection and classification without involving high-complexity computation, reduces network traffic, and emphasizes resilience and adaptation to the realistic environment. We evaluate the system with performance data collected from outdoor experiments and field assessments. Based on the experience acquired and lessons learned when developing this system, we abstract common issues and introduce several guidelines which can direct future development of detection and classification solutions based on WSNs.

Distributed sensor networks are quickly gaining recognition as viable embedded computing platforms. Current techniques for programming sensor networks are cumbersome, inflexible, and low-level. We introduce EnviroTrack, an object-based distributed middleware system that raises the level of programming abstraction by providing a convenient and powerful interface to the application developer geared towards tracking the physical environment. EnviroTrack is novel in its seamless integration of objects that live in physical time and space into the computational environment of the application. Performance results demonstrate the ability of the middleware to track realistic targets.

The focus of surveillance missions is to acquire and verify information about enemy capabilities and positions of hostile targets. Such missions often involve a high element of risk for human personnel and require a high degree of stealthiness. Hence, the ability to deploy unmanned surveillance missions, by using wireless sensor networks, is of great practical importance for the military. Because of the energy constraints of sensor devices, such systems necessitate an energy-aware design to ensure the longevity of surveillance missions. Solutions proposed recently for this type of system show promising results through simulations. However, the simplified assumptions they make about the system in the simulator often do not hold well in practice and energy consumption is narrowly accounted for within a single protocol. In this paper, we describe the design and implementation of a running system for energy-efficient surveillance. The system allows a group of cooperating sensor devices to detect and track the positions of moving vehicles in an energy-efficient and stealthy manner. We can trade off energy-awareness and surveillance performance by adaptively adjusting the sensitivity of the system. We evaluate the performance on a network of 70 MICA2 motes equipped with dual-axis magnetometers. Our results show that our surveillance strategy is adaptable and achieves a significant extension of network lifetime. Finally, we share lessons learned in building such a complete running system.

No abstract available.