Jerry Zhao

Understanding packet delivery performance in dense wireless sensor networks

Wireless sensor networks promise fine-grain monitoring in a wide variety of environments. Many of these environments (e.g., indoor environments or habitats) can be harsh for wireless communication. From a networking perspective, the most basic aspect of wireless communication is the packet delivery performance: the spatio-temporal characteristics of packet loss, and its environmental dependence. These factors will deeply impact the performance of data acquisition from these networks.In this paper, we report on a systematic medium-scale (up to sixty nodes) measurement of packet delivery in three different environments: an indoor office building, a habitat with moderate foliage, and an open parking lot. Our findings have interesting implications for the design and evaluation of routing and medium-access protocols for sensor networks.

Habitat monitoring: application driver for wireless communications technology

As new fabrication and integration technologies reduce the cost and size of micro-sensors and wireless interfaces, it becomes feasible to deploy densely distributed wireless networks of sensors and actuators. These systems promise to revolutionize biological, earth, and environmental monitoring applications, providing data at granularities unrealizable by other means. In addition to the challenges of miniaturization, new system architectures and new network algorithms must be developed to transform the vast quantity of raw sensor data into a manageable stream of high-level data. To address this, we propose a tiered system architecture in which data collected at numerous, inexpensive sensor nodes is filtered by local processing on its way through to larger, more capable and more expensive nodes.We briefly describe Habitat monitoring as our motivating application and introduce initial system building blocks designed to support this application. The remainder of the paper presents details of our experimental platform.

Computing aggregates for monitoring wireless sensor networks

Wireless sensor networks involve very large numbers of small, low-power, wireless devices. Given their unattended nature, and their potential applications in harsh environments, we need a monitoring infrastructure that indicates system failures and resource depletion. We describe an architecture for sensor network monitoring, then focus on one aspect of this architecture: continuously computing aggregates (sum, average, count) of network properties (loss rates, energy-levels etc., packet counts). Our contributions are two-fold. First, we propose a novel tree construction algorithm that enables energy-efficient computation of some classes of aggregates. Second, we show through actual implementation and experiments that wireless communication artifacts in even relatively benign environments can significantly impact the computation of these aggregate properties. In some cases, without careful attention to detail, the relative error in the computed aggregates can be as much as 50%. However, by carefully discarding links with heavy packet loss and asymmetry, we can improve accuracy by an order of magnitude.

Networking issues in wireless sensor networks

The emergence of sensor networks as one of the dominant technology trends in the coming decades (Technol. Rev. (February 2003)) 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. Cost constraints and the need for ubiquitous, invisible deployments will result in small sized, resource-constrained sensor nodes. While the set of challenges in sensor networks are diverse, we focus on fundamental networking challenges in this paper. The key networking challenges in sensor networks that we discuss are: (a) supporting multi-hop communication while limiting radio operation to conserve power, (b) data management, including frameworks that support attribute-based data naming, routing and in-network aggregation, (c) geographic routing challenges in networks where nodes know their locations, and (d) monitoring and maintenance of such dynamic, resource-limited systems. For each of these research areas, we provide an overview of proposed solutions to the problem and discuss in detail one or few representative solutions. Finally, we illustrate how these networking components can be integrated into a complex data storage solution for sensor networks.

Sensor Network Tomography: monitoring wireless sensor networks

Wireless sensor networks have been attracting increasing research interest given the recent advances in miniaturization and low-cost, low-power design. Consisting of a large collection of small wireless, low-power, unattended sensors and/or actuators, wireless sensor network technology poses its unique design challenges. Given their unattended nature and their complexity, it is critical that the users be given continuously updated indications of the sensor network health, i.e., explicit knowledge of the overall state of the sensor network after deployment. We call such indications of network health scans. Such macroscopic view of resources or activities in large sensor networks can provide users early warning of system failure, aid in incremental deployment of sensors, or tuning sensor collaboration algorithms.Monitoring wireless sensor networks leads to different challenges compared to existing diagnosis protocols for the Internet, or monitoring systems in other domains such as telecommunication networks, or power generation systems. The monitoring system should introduce minimal impact on network lifetime, scale with network size, yet preserve the fidelity of the overall picture. We propose Sensor Network Tomography to construct abstracted scans of sensor network health by applying localized algorithms in sensor networks for energy-efficient in-network aggregation of local representations of scans. Rather than collect detailed state information from each individual sensor node and then process centrally, this technique builds a composite scan by combining local scans piecewise on their way towards a collecting point. When local scans are aggregated, detailed information at an individual node may be lost. However, the compactness of such an abstracted representation can reduce the communication and processing cost significantly.