Abstract not found

Advances in microsensor and radio technology will enable small but smart sensors to be deployed for a wide range of environmental monitoring applications. The low per-node cost will allow these wireless networks of sensors and actuators to be densely distributed. The nodes in these dense networks will coordinate to perform the distributed sensing and actuation tasks. Moreover, as described in this paper, the nodes can also coordinate to exploit the redundancy provided by high density so as to extend overall system lifetime. The large number of nodes deployed in these systems will preclude manual configuration, and the environmental dynamics will preclude design-time preconfiguration. Therefore, nodes will have to self-configure to establish a topology that provides communication under stringent energy constraints. ASCENT builds on the notion that, as density increases, only a subset of the nodes are necessary to establish a routing forwarding backbone. In ASCENT, each node assesses its connectivity and adapts its participation in the multihop network topology based on the measured operating region. This paper motivates and describes the ASCENT algorithm and presents analysis, simulation, and experimental measurements. We show that the system achieves linear increase in energy savings as a function of the density and the convergence time required in case of node failures while still providing adequate connectivity.

Habitat and environmental monitoring is a driving application for wireless sensor networks. We present an analysis of data from a second generation sensor networks deployed during the summer and autumn of 2003. During a 4 month deployment, these networks, consisting of 150 devices, produced unique datasets for both systems and biological analysis. This paper focuses on nodal and network performance, with an emphasis on lifetime, reliability, and the the static and dynamic aspects of single and multi-hop networks. We compare the results collected to expectations set during the design phase: we were able to accurately predict lifetime of the single-hop network, but we underestimated the impact of multihop traffic overhearing and the nuances of power source selection. While initial packet loss data was commensurate with lab experiments, over the duration of the deployment, reliability of the backend infrastructure and the transit network had a dominant impact on overall network performance. Finally, we evaluate the physical design of the sensor node based on deployment experience and a post mortem analysis. The results shed light on a number of design issues from network deployment, through selection of power sources to optimizations of routing decisions.

Structural monitoring---the collection and analysis of structural response to ambient or forced excitation--is an important application of networked embedded sensing with significant commercial potential. The first generation of sensor networks for structural monitoring are likely to be data acquisition systems that collect data at a single node for centralized processing. In this paper, we discuss the design and evaluation of a wireless sensor network system (called Wisden) for structural data acquisition. Wisden incorporates two novel mechanisms, reliable data transport using a hybrid of end-to-end and hop-by-hop recovery, and low-overhead data time-stamping that does not require global clock synchronization. We also study the applicability of wavelet-based compression techniques to overcome the bandwidth limitations imposed by lowpower wireless radios. We describe our implementation of these mechanisms on the Mica-2 motes and evaluate the performance of our implementation. We also report experiences from deploying Wisden on a large structure.

Routing protocols for wireless ad hoc networks have traditionally focused on finding paths with minimum hop count. However, such paths can include slow or lossy links, leading to poor throughput. A routing algorithm can select better paths by explicitly taking the quality of the wireless links into account. In this paper, we conduct a detailed, empirical evaluation of the performance of three link-quality metrics— ETX, per-hop RTT, and per-hop packet pair—and compare them against minimum hop count. We study these metrics using a DSR-based routing protocol running in a wireless testbed. We find that the ETX metric has the best performance when all nodes are stationary. We also find that the per-hop RTT and per-hop packet-pair metrics perform poorly due to self-interference. Interestingly, the hop-count metric outperforms all of the link-quality metrics in a scenario where the sender is mobile.

In this paper, we investigate the impact of radio irregularity on the communication performance in wireless sensor networks. Radio irregularity is a common phenomenon which arises from multiple factors, such as variance in RF sending power and different path losses depending on the direction of propagation. From our experiments, we discover that the variance in received signal strength is largely random; however, it exhibits a continuous change with incremental changes in direction. With empirical data obtained from the MICA2 platform, we establish a radio model for simulation, called the Radio Irregularity Model (RIM). This model is the first to bridge the discrepancy between spherical radio models used by simulators and the physical reality of radio signals. With this model, we are able to analyze the impact of radio irregularity on some of the well-known MAC and routing protocols. Our results show that radio irregularity has a significant impact on routing protocols, but a relatively small impact on MAC protocols. Finally, we propose six solutions to deal with radio irregularity. We evaluate two of them in detail. The results obtained from both the simulation and a running testbed demonstrate that our solutions greatly improve communication performance in the presence of radio irregularity.

We present a science-centric evaluation of a 19-day sensor network deployment at Reventador, an active volcano in Ecuador. Each of the 16 sensors continuously sampled seismic and acoustic data at 100 Hz. Nodes used an event-detection algorithm to trigger on interesting volcanic activity and initiate reliable data transfer to the base station. During the deployment, the network recorded 229 earthquakes, eruptions, and other seismoacoustic events. The science requirements of reliable data collection, accurate event detection, and high timing precision drive sensor networks in new directions for geophysical monitoring. The main contribution of this paper is an evaluation of the sensor network as a scientific instrument, holding it to the standards of existing instrumentation in terms of data fidelity (the quality and accuracy of the recorded signals) and yield (the quantity of the captured data). We describe an approach to time rectification of the acquired signals that can recover accurate timing despite failures of the underlying time synchronization protocol. In addition, we perform a detailed study of the sensor network’s data using a direct comparison to a standalone data logger, as well as an investigation of seismic and acoustic wave arrival times across the network. 1

Z-MAC is a hybrid MAC protocol for wireless sensor networks. It combines the strengths of TDMA and CSMA while offsetting their weaknesses. Nodes are assigned time slots using a distributed implementation of RAND. Unlike TDMA where a node is allowed to transmit only during its own assigned slots, a node can transmit in both its own time slots and slots assigned to other nodes. Owners of the current time slot always have priority in accessing the channel over non-owners. Therefore, under low contention where not all owners have data to send, non-owners can “steal ” time slots from owners. This has the effect of switching between CSMA and TDMA depending on contention. Z-MAC is robust to topology changes and clock synchronization errors; in the worst case its performance falls back to that of CSMA. We implemented Z-MAC in TinyOS and evaluated its channel utilization, energy, latency and fairness over single-hop, twohop and multi-hop sensor network topologies constructed using Mica2. The result shows that Z-MAC has remarkably better data throughput than existing sensor MAC protocols while consuming comparable energy (over three times better throughput under high contention).

Abstract — This paper argues for the usefulness of an application-cooperative interactive management system for wireless sensor networks, and presents SNMS, a Sensor Network Management System. SNMS is designed to be simple and have minimal impact on memory and network traffic, while remaining open and flexible. The system is evaluated in light of issues derived from real deployment experiences. I.

Augmenting heavy and power-hungry data collection equipment with lighter, smaller wireless sensor network nodes leads to faster,larger deployments. Arrays comprising dozens of wireless sensor nodes are now possible,allowing scientific studies that aren’t feasible with traditional instrumentation. Designing sensor networks to support volcanic studies requires addressing the high data rates and high data fidelity these studies demand. The authors ’ sensor-network application for volcanic data collection relies on triggered event detection and reliable data retrieval to meet bandwidth and data-quality demands. Wireless sensor networks — in which numerous resource-limited nodes are linked via low-bandwidth wireless radios — have been the focus of intense research during the past few years. Since their conception, they’ve excited a range of scientific communities because of their potential to facilitate data acquisition and scientific studies. Collaborations between computer scientists and other domain scientists have produced networks that can record data at a scale and resolution not previously possible. Taking this progress one step further, wireless sensor networks can potentially advance the pursuit of geophysical studies of volcanic activity. Two years ago, our team of computer scientists at Harvard University began collaborating with volcanologists at the University of North Carolina, the University of New Hampshire, and the Instituto