Much of the success of the Internet services model can be attributed to the popularity of a class of workloads that we call Online Data-Intensive (OLDI) services. These workloads perform significant computing over massive data sets per user request but, unlike their offline counterparts (such as MapReduce computations), they require responsiveness in the sub-second time scale at high request rates. Large search products, online advertising, and machine translation are examples of workloads in this class. Although the load in OLDI services can vary widely during the day, their energy consumption sees little variance due to the lack of energy proportionality of the underlying machinery. The scale and latency sensitivity of OLDI workloads also make them a challenging target for power management techniques.

Communications in mobile and frequently-disconnected sensor networks are characterized by low-bandwidth radios, unreliable links, and disproportionately high energy costs compared to other system operations. Therefore, we must use as efficiently as possible any periods of connectivity that we have. For this reason, nodes in these networks need mechanisms that organize data to streamline search operations, local computation, and communications.

Sensor networks are fundamentally constrained by the difficulty and energy expense of delivering information from sensors to sink. Our work has focused on garnering additional significant energy improvements by devising 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.

ZebraNet is a mobile, wireless sensor network in which nodes move throughout an environment working to gather and process information about their surroundings[10]. As in many sensor or wireless systems, nodes have critical resource constraints such as processing speed, memory size, and energy supply; they also face special hardware issues such as sensing device sample time, data storage/access restrictions, and wireless transceiver capabilities. This paper discusses and evaluates ZebraNet's system design decisions in the face of a range of real-world constraints.Impala---ZebraNet's middleware layer---serves as a light-weight operating system, but also has been designed to encourage application modularity, simplicity, adaptivity, and repairability. Impala is now implemented on ZebraNet hardware nodes, which include a 16-bit microcontroller, a low-power GPS unit, a 900MHz radio, and 4Mbits of non-volatile FLASH memory. This paper discusses Impala's operation scheduling and event handling model, and explains how system constraints and goals led to the interface designs we chose between the application, middleware, and firmware layers. We also describe Impala's network interface which unifies media access control and transport control into an efficient network protocol. With the minimum overhead in communication, buffering, and processing, it supports a range of message models, all inspired by and tailored to ZebraNet's application needs. By discussing design tradeoffs in the context of a real hardware system and a real sensor network application, this paper's design choices and performance measurements offer some concrete experiences with software systems issues for the mobile sensor design space. More generally, we feel that these experiences can guide design choices in a range of related systems.

The enormous potential for wireless sensor networks to make a positive impact on our society has spawned a great deal of research on the topic, and this research is now producing environment-ready systems. Current technology limits coupled with widely-varying application requirements lead to a diversity of hardware platforms for di#erent portions of the design space. In addition, the unique energy and reliability constraints of a system that must function for months at a time without human intervention mean that demands on sensor network hardware are di#erent from the demands on standard integrated circuits. This paper describes our experiences designing sensor nodes and low level software to control them.