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.

Sensor networks present a number of novel programming challenges for application developers. Their inherent limitations of computational power, communication bandwidth, and energy demand new approaches to programming that shield the developer from low-level details of resource management, concurrency, and in-network processing. We argue that sensor networks should be programmed at the global level, allowing the compiler to automatically generate nodal behaviors from a high-level specification of the network’s global behavior. This paper presents the design of a functional macroprogramming language for sensor networks, called Regiment. The essential data model in Regiment is based on region streams, which represent spatially distributed, time-varying collections of node state. A region stream might represent the set of sensor values across all nodes in an area or the aggregation of sensor values within that area. Regiment is a purely functional language, which gives the compiler considerable leeway in terms of realizing region stream operations across sensor nodes and exploiting redundancy within the network. We describe the initial design and implementation of Regiment, including a compiler that transforms a macroprogram into an efficient nodal program based on a token machine. We present a progresssion of simple programs that illustrate the power of Regiment to succinctly represent robust, adaptive sensor network applications.

Local storage is required in many sensor network applications, both for archival of detailed event information, as well as to overcome sensor platform memory constraints. While extensive measurement studies have been performed to highlight the trade-off between computation and communication in sensor networks, the role of storage has received little attention. The storage subsystems on currently available sensor platforms have not exploited technology trends, and consequently the energy cost of storage on these platforms is as high as that of communication. Current flash memories, however, offer a low-priced, high-capacity and extremely energy-efficient storage solution. In this paper, we perform a comprehensive evaluation of the active and sleep-mode energy consumption of available flash-based storage options for sensor platforms. Our results demonstrate more than a 100-fold decrease in per-byte energy consumption for surface-mount parallel NAND flash in comparison with the MicaZ on-board serial flash. In addition, this dramatically reduces storage energy costs relative to communication, introducing a new dimension in traditional computation vs communication trade-offs. Our results have significant ramifications on the design of sensor platforms as well as on the energy consumption of sensing applications. We quantify the potential energy gains for two commonly used sensor network services: communication and in-network data aggregation. Our measurements show significant improvements in each service: 50-fold and up to 10-fold reductions in energy for communication and data aggregation respectively.

In-network aggregation has been proposed as one method for reducing energy consumption in sensor networks. In this paper, we explore two ideas related to further reducing energy consumption in the context of in-network aggregation. The first is by influencing the construction of the routing trees for sensor networks with the goal of reducing the size of transmitted data. To this end, we propose a group-aware network configuration method that "clusters" along the same path sensor nodes that belong to the same group. The second idea involves imposing a hierarchy of output filters on the sensor network with the goal of both reducing the size of transmitted data and minimizing the number of transmitted messages. More specifically, we propose a framework to use temporal coherency tolerances in conjunction with in-network aggregation to save energy at the sensor nodes while maintaining specified quality of data. These tolerances are based on user preferences or can be dictated by the network in cases where the network cannot support the current tolerance level. Our framework, called TiNA, works on top of existing in-network aggregation schemes. We evaluate experimentally our proposed schemes in the context of existing in-network aggregation schemes. We present experimental results measuring energy consumption, response time, and quality of data for Group-By queries. Overall, our schemes provide significant energy savings with respect to communication and a negligible drop in quality of data.

Sensor networks have emerged as a fundamentally new tool for monitoring spatial phenomena. This paper describes a theory and methodology for estimating inhomogeneous, two-dimensional fields using wireless sensor networks. Inhomogeneous fields are composed of two or more homogeneous (smoothly varying) regions separated by boundaries. The boundaries, which correspond to abrupt spatial changes in the field, are non-parametric one-dimensional curves. The sensors make noisy measurements of the field, and the goal is to obtain an accurate estimate of the field at some desired destination (typically remote from the sensor network). The presence of boundaries makes this problem especially challenging. There are two key questions: 1. Given n sensors, how accurately can the field be estimated? 2. How much energy will be consumed by the communications required to obtain an accurate estimate at the destination? Theoretical upper and lower bounds on the estimation error and energy consumption are given. A practical strategy for estimation and communication is presented. The strategy, based on a hierarchical data-handling and communication architecture, provides a near-optimal balance of accuracy and energy consumption.

Abstract — There is widespread agreement that a higher level programming model for sensor networks is needed. A variety of models have been developed, but the community is far from consensus. We propose an intermediate language to speed up the exploration of this design space. Our language, called the Token Machine Language (TML) can be targeted by compilers for higher level systems. TML provides a layer of abstraction for a lower-level runtime environment, such as TinyOS. TML is intended to capture coordinated activity in a sensor network. Notable features of TML are its atomic action model of concurrency, and its unification of communication, control, and storage around the concept of a token. Tokens are small objects, typically under a hundred bytes, and can be disseminated across the network. A token causes computation upon its arrival at a site by invoking a token handler. The effect of the computation is to atomically change the token’s own state as well as the state of shared variables at the site. I.

We address the problem of distributed information aggregation and storage in a sensor network, where queries can be injected anywhere in the network. The principle we propose is that a sensor should know a “fraction ” of the information from distant parts of the network, in an exponentially decaying fashion by distance. We show how a sampled scalar field can be stored in this distributed fashion, with only a modest amount of additional storage and network traffic. Our storage scheme makes neighboring sensors have highly correlated world views; this allows smooth information gradients and enables local search algorithms to work well. We study in particular how this principle of fractionally cascaded information can be exploited to answer range queries about the sampled field efficiently. Using local decisions only we are able to route the query to exactly the portions of the field where the sought information is stored. We provide a rigorous theoretical analysis showing that our scheme is close to optimal. Categories and Subject Descriptors H.3.3 [Information Systems]: information storage and retrieval—information search and retrieval; F.2.2 [Theory of Computation]: analysis of algorithms and problem complexity—non-numerical algorithms and problems

The past decade has seen a wealth of research on time series representations, because the manipulation, storage, and indexing of large volumes of raw time series data is impractical. The vast majority of research has concentrated on representations that are calculated in batch mode and represent each value with approximately equal fidelity. However, the increasing deployment of mobile devices and real time sensors has brought home the need for representations that can be incrementally updated, and can approximate the data with fidelity proportional to its age. The latter property allows us to answer queries about the recent past with greater precision, since in many domains recent information is more useful than older information. We call such representations amnesic.

Networking and query processing must be co-designed to allow data self-organization for flexible but efficient in-network storage, access, and processing. Sensor networks have the potential to support applications ranging from habitat and structural monitoring, to home and building automation, to supply chain management. Users are typically interested in continuous streams of information representing the evolving status of systems, combined with periodic statistical reports about specific phenomena. Query processing systems, including Directed Diffusion [3], TinyDB [6], and Cougar [10], provide high-level interfaces that allow users to collect and process such

The emergence of sensor networks as one of the dominant technology trends in the coming decades [1] has posed numerous unique challenges to researchers. These networks are likely to be composed of hundreds, and potentially thousands of tiny sensor nodes, functioning autonomously, and in many cases, without access to renewable energy resources.