Shared sensing infrastructures that allow multiple applications to share deployed sensors are emerging and Internet protocol based access for such sensors has already been prototyped and deployed. As a large number of applications start accessing shared sensors, the efficiency of resource usage at the embedded nodes and in the network infrastructure supporting them becomes a concern. To address this, we develop methods that detect when common data and common stream processing is requested by multiple applications, including cases where only some of the data is shared or only intermediate processing steps are common. The communication and processing is then modified to eliminate the redundancies. Specifically, we use an interval-cover graph to minimize communication redundancies and a joint data flow graph optimization to remove computational redundancies. Both methods operate online and allow application requests to be dynamically added or removed. The proposed methods are evaluated using applications on a road traffic sensing infrastructure.

We present empirical measurements of the packet delivery performance of the latest sensor platforms: Micaz and Telos motes. In this article, we present observations that have implications to a set of common assumptions protocol designers make while designing sensornet protocols—specifically—the MAC and network layer protocols. We first distill these common assumptions in to a conceptual model and show how our observations support or dispute these assumptions. We also present case studies of protocols that do not make these assumptions. Understanding the implications of these observations to the conceptual model can improve future protocol designs.

Data centers deploy a variety of middleboxes (e.g., firewalls, load balancers and SSL offloaders) to protect, manage and improve the performance of applications and services they run. Since existing networks provide limited support for middleboxes, administrators typically overload path selection mechanisms to coerce traffic through the desired sequences of middleboxes placed on the network path. These ad-hoc practices result in a data center network that is hard to configure and maintain, wastes middlebox resources, and cannot guarantee middlebox traversal under network churn.

Sensor networks are notoriously difficult to program, given that they encompass the complexities of both distributed and embedded systems. To address this problem, we present the design and implementation of a declarative sensor network platform, DSN: a declarative language, compiler and runtime suitable for programming a broad range of sensornet applications. We demonstrate that our approach is a natural fit for sensor networks by specifying several very different classes of traditional sensor network protocols, services and applications entirely declaratively -- these include tree and geographic routing, link estimation, data collection, event tracking, version coherency, and localization. To our knowledge, this is the first time these disparate sensornet tasks have been addressed by a single high-level programming environment. Moreover, the declarative approach accommodates the desire for architectural flexibility and simple management of limited resources. Our results suggest that the declarative approach is well-suited to sensor networks, and that it can produce concise and flexible code by focusing on what the code is doing, and not on how it is doing it.

We present Flush, a reliable, high goodput bulk data transport protocol for wireless sensor networks. Flush provides end-to-end reliability, reduces transfer time, and adapts to time-varying network conditions. It achieves these properties using end-to-end acknowledgments, implicit snooping of control information, and a rate-control algorithm that operates at each hop along a flow. Using several real network topologies, we show that Flush closely tracks or exceeds the maximum goodput achievable by a hand-tuned but fixed rate for each hop over a wide range of path lengths and varying network conditions. Flush is scalable; its effective bandwidth over a 48-hop wireless network is approximately one-third of the rate achievable over one hop. The design of Flush is simplified by assuming that different flows do not interfere with each other, a reasonable restriction for many sensornet applications that collect bulk data in a coordinated fashion, like structural health monitoring, volcanic activity monitoring, or protocol evaluation. We collected all of the performance data presented in this paper using Flush itself.

We present empirical measurements of the packet delivery performance of the Telos and MicaZ sensor platforms. At a high level, their behavior is similar to that of earlier platforms. They exhibit a reception "grey region," and temporal variations in packet loss. Looking more deeply, however, there are subtle differences, and looking deeper still, the patterns behind these complexities become clear. Environmental noise (802.11b) has high spatial correlation. Packet loss occurs when a receiver operating near its noise floor experiences a small decrease in received signal strength, rather than an increase in environmental noise. These variations cause the reception "grey region." Packet losses are highly correlated over short time periods, but are independent over longer periods. Based on these findings, current practices could be easily changed that would greatly improve efficiency and performance.