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.

Existing energy-efficient approaches to in-network aggregation in sensor networks can be classified into two categories, tree-based and multi-path-based, with each having unique strengths and weaknesses. In this paper, we introduce Tributary-Delta, a novel approach that combines the advantages of the tree and multi-path approaches by running them simultaneously in different regions of the network. We present schemes for adjusting the regions in response to changes in network conditions, and show how many useful aggregates can be readily computed within this new framework. We then show how a difficult aggregate for this context— finding frequent items—can be efficiently computed within the framework. To this end, we devise the first algorithm for frequent items (and for quantiles) that provably minimizes the worst case total communication for non-regular trees. In addition, we give a multi-path algorithm for frequent items that is considerably more accurate than previous approaches. These algorithms form the basis for our efficient Tributary-Delta frequent items algorithm. Through extensive simulation with real-world and synthetic data, we show the significant advantages of our techniques. For example, in computing Count under realistic loss rates, our techniques reduce answer error by up to a factor of 3 compared to any previous technique. 1.

We present the philosophy, design, and initial evaluation of the Trio Testbed, a new outdoor sensor network deployment that consists of 557 solar-powered motes, seven gateway nodes, and a root server. The testbed covers an area of approximately 50,000 square meters and was in continuous operation during the last four months of 2005. This new testbed in one of the largest solar-powered outdoor sensor networks ever constructed and it offers a unique platform on which both systems and application software can be tested safely at scale. The testbed is based on Trio, a new mote platform that provides sustainable operation, enables efficient in situ interaction, and supports fail-safe programming. The motivation behind this testbed was to evaluate robust multi-target tracking algorithms at scale. However, using the testbed has stressed the system software, networking protocols, and management tools in ways that have exposed subtle but serious weaknesses that were never discovered using indoor testbeds or smaller deployments. We have been iteratively improving our support software, with the eventual aim of creating a stable hardware-software platform for sustainable, scalable, and flexible testbed deployments.

In this paper we propose the MicroHash index, which is an efficient external memory structure for Wireless Sensor Devices (WSDs). The most prevalent storage medium for WSDs is flash memory. Our index structure exploits the asymmetric read/write and wear characteristics of flash memory in order to offer high performance indexing and searching capabilities in the presence of a low energy budget which is typical for the devices under discussion. A key idea behind MicroHash is to eliminate expensive random access deletions. We have implemented MicroHash in nesC, the programming language of the TinyOS [7] operating system. Our trace-driven experimentation with several real datasets reveals that our index structure offers excellent search performance at a small cost of constructing and maintaining the index. 1

Sensor networks are fundamentally constrained b y the difficulty and energy expense of delivering information from sensors to sink. Our work has focused on garnerin g additional significant energ y improvements b y d ev isin g computationally-efficient lossless compression algorithms on the source node. These reduce the amount of data that must be passed through the network and to the sink, and thus have energy benefits that are multiplicative with the number of hops the data travels through the network. Currently, if sensor system designers want to compress acquired data, they must either develop application-specific compression algorithms or use off-the-shelf algorithms not designed for resource-constrained sensor nodes. This paper discusses the design issues involved with implementing, adapting, and customizing compression algorithms specifically geared for sensor nodes. While developing Sensor LZW (S-LZW) and some simple, but effective, variations to this algorithm, we show how different amounts of compression can lead to energy savings on both the compressing node and throughout the network and that the savings depends heavily on the radio hardware. To validate and evaluate our work, we apply it to datasets from several different real-world deployments and show that our approaches can reduce energy consumption by up to a factor of 4.5X across the network.

We present T2, a second generation sensor network operating system written in the nesC language. We describe why the limitations and problems of current OSes necessitate a new design. T2 improves on current systems in three areas: platform support, application construction, and reliability. We argue that existing systems neglected these properties in order to maximize flexibility. In contrast, T2 limits flexibility to that which applications need, and leverages these constraints to improve the rest of the system. We evaluate T2 in comparison to TinyOS, and show how its structure simplifies applications, makes porting to a new platform much easier, and improves system reliability. From these results, we discuss the frictions present in component-based OSes and how T2’s design and structure makes dealing with them more tractable. 1

Energy consumption is a crucial characteristic of sensor networks and their applications as sensor nodes are commonly battery-driven. Although recent research focuses strongly on energy-aware applications and operating systems, energy consumption is still a limiting factor. Once sensor nodes are deployed, it is challenging and sometimes even impossible to change batteries. As a result, erroneous lifetime prediction causes high costs and may render a sensor network useless before its purpose is fulfilled. In this paper, we present AEON (Accurate Prediction of Power Consumption), a novel evaluation tool to quantitatively predict energy consumption of sensor nodes and whole sensor networks. Our energy model, based on measurements of node current draw and the execution of real code, enables accurate prediction of the actual energy consumption of sensor nodes. Consequently, it prevents erroneous assumptions on node and network lifetime. Moreover, our detailed energy model allows to compare different low power and energy aware approaches in terms of energy efficiency. Thus, it enables a highly precise estimation of the overall lifetime of a sensor network.

The development of a reliable large-scale wireless sensor networks (WSNs) is very difficult because of their stringent resource constraints, harsh energy budget, and demanding application requirements. We identify that three OS features – OS protection, virtual memory, and preemptive scheduling – will significantly improve the reliability of WSN systems and facilitate developing complex WSN software. However, due to the limitation of hardware, it is impossible to implement these features with traditional OS design techniques. To solve this problem, we design a new OS kernel, the tkernel, to perform extensive load-time code modification and enhance the system abstraction visible to programmers. After the modification, the application and OS work in a collaborative way supporting the aforementioned features. Having implemented the t-kernel on MICA2 motes with an 8-bit processor and 4KB RAM, we evaluate its performance by measuring the overhead and execution speed. We analyze the CPU utilization in sensor network applications, and verify that, though CPU-bound computation tasks may slow down 0.5–4 times, the performance of applications under typical workloads does not degrade. The t-kernel significantly enhances developers ’ ability to design sophisticated applications and protects WSNs from accidental programming errors. To the authors ’ best knowledge, the t-kernel is unique in the follow ways: it performs efficient binary translation on highly resource constrained sensor nodes with only 4KB RAM, it provides software based virtual memory without repeatedly writable swapping devices, and it protects OS from application error without memory protection or privileged execution hardware. 1

In surveillance and tracking applications, wireless sensor nodes collectively monitor the existence of intruding targets. In this paper, we derive closed form results for predicting surveillance performance attributes, represented by detection probability and average detection delay of intruding targets, based on tunable system parameters, represented by node density and sleep duty cycle. The results apply to both stationary and mobile targets, and shed light on the fundamental connection between aspects of sensing quality and deployment choices. We demonstrate that our results are robust to realistic sensing models, which are proposed based on experimental measurements of passive infrared sensors. We also validate the correctness of our results through extensive simulations. I.

will sooner or later be widely used in wireless sensor networks. Recently, it has been shown that the performance of some publickey algorithms, such as Elliptic Curve Cryptography (ECC), is already close to being practical on sensor nodes. However, the energy consumption of PKC is still expensive, especially compared to symmetric-key algorithms. To maximize the lifetime of batteries, we should minimize the use of PKC whenever possible in sensor networks. This paper investigates how to replace one of the important PKC operations–the public key authentication–with symmetric key operations that are much more efficient. Public key authentication is to verify the authenticity of another party’s public key to make sure that the public key is really owned by the person it is claimed to belong to. In PKC, this operation involves an expensive signature verification on a certificate. We propose an efficient alternative that uses one-way hash function only. Our scheme uses all sensor’s public keys to construct a forest of Merkle trees of different heights. By optimally selecting the height of each tree, we can minimize the computation and communication costs. The performance of our scheme is evaluated in the paper.