Mobile sensing systems carried by individuals or machines make it possible to measure position- and time-dependent environmental conditions, such as air quality and radiation. The low-cost, miniature sensors commonly used in these systems are prone to measurement drift, requiring occasional re-calibration to provide accurate data. Requiring end users to periodically do manual calibration work would make many mobile sensing systems impractical. We therefore argue for the use of collaborative, automatic calibration among nearby mobile sensors, and provide solutions to the drift estimation and placement problems posed by such a system.

Rapidly and accurately estimating the impact of design decisions on performance metrics is critical to both the manual and automated design of wireless sensor networks. Estimating system-level performance metrics such as lifetime, data loss rate, and network connectivity is particularly challenging because they depend on many factors, including network design and structure, hardware characteristics, communication protocols, and node reliability. This paper describes a new method for automatically building efficient and accurate predictive models for a wide range of system-level performance metrics. These models can be used to eliminate or reduce the need for simulation during design space exploration. We evaluate our method by building a model for the lifetime of networks containing up to 120 nodes, considering both fault processes and battery energy depletion. With our adaptive sampling technique, only 0.27% of the potential solutions are evaluated via simulation. Notably, one such automatically produced model outperforms the most advanced manually designed analytical model, reducing error by 13% while maintaining very low model evaluation overhead. We also propose a new, more general definition of system lifetime that accurately captures application requirements and decouples the specification of requirements from implementation decisions.

Detecting and reacting to faults is an indispensable capability for many wireless sensor network applications. Unfortunately, implementing fault detection and error correction algorithms is challenging. Programming languages and fault tolerance mechanisms for sensor networks have historically been designed in isolation. This is the first work to combine them. Our goal is to simplify the design of fault-tolerant sensor networks. We describe a system that makes it unnecessary for sensor network application developers and users to understand the intricate implementation details of fault detection and tolerance techniques, while still using their domain knowledge to support fault detection, error correction, and error estimation mechanisms. Our FACTS system translates low-level faults into their consequences for application-level data quality, i.e., consequences domain experts can appreciate and understand. FACTS is an extension of an existing sensor network programming language; its compiler and runtime libraries have been modified to support automatic generation of code for on-line fault detection and tolerance. This code determines the impacts of faults on the accuracies of the results of potentially complex data aggregation and analysis expressions. We evaluate the overhead of the proposed system on code size, memory use, and the accuracy improvements for data analysis expressions using a small experimental testbed and simulations of large-scale networks.

Within-die variation in leakage power consumption is substantial and increasing for chip-level multiprocessors (CMPs) and multiprocessor systems-on-chip. Dealing with this problem via conservative assumptions is sub-optimal. Instead, operating systems may adapt task assignment and power management decisions to the variable characteristics of cores, improving system-wide power consumption and performance. Researchers have proposed such adaptation techniques. However, they rely on knowledge of CMP process variation (PV) maps. These maps are not provided by processor vendors, providing them would impose additional cost during the testing process, and static maps would not permit adaptation to aging effects. Further progress on developing and validating PV aware control techniques for CMPs requires access to PV maps for real processors. We present an online technique to extract the PV maps of CMPs. Potentially automatic temperature measurements with built-in on-die sensors during the execution of characterization workloads are used to determine variation in leakage power consumption. The proposed technique is applied to real CMPs, and the resulting PV maps are used within a PV aware task assignment and scheduling algorithm.

Random access memory (RAM) is tightly constrained in the least expensive, lowest-power embedded systems such as sensor network nodes and portable consumer electronics. The most widely used sensor network nodes have only 4 to 10KB of RAM and do not contain memory management units (MMUs). It is difficult to implement complex applications under such tight memory constraints. Nonetheless, price and power-consumption constraints make it unlikely that increases in RAM in these systems will keep pace with the increasing memory requirements of applications. We propose the use of automated compile-time and runtime techniques to increase the amount of usable memory in MMU-less embedded systems. The proposed techniques do not increase hardware cost, and require few or no changes to existing applications. We have developed runtime library routines and compiler transformations to control and optimize the automatic migration of application data between compressed and uncompressed memory regions, as well as a fast compression algorithm well suited to this application. These techniques were experimentally evaluated on Crossbow TelosB sensor network nodes running a number of data-collection and signal-processing applications. Our results indicate that available memory can be increased by up to 50% with less than 10% performance degradation for most benchmarks.

Embedded system secondary storage size is often constrained, yet storage demands are growing as a result of increasing application complexity and storage of personal data and multimedia flies. Filesystem compression offers a solution. This paper formalizes the problem of automatic filesystem compression using multiple compression algorithms. The average latency of on-line file accesses is optimized under a constraint on filesystem capacity. Our solution is based on predictive control. Predicted latency implications are used to solve the file compression state selection problem using a multiple choice knapsack problem formulation. This approach is evaluated on filesystem traces and compared with other efficient heuristics. Our approach results in 34.1% reduction in file access latency compared to a straight-forward heuristic that decompresses frequently-accessed files and compresses least recently used files with more aggressive compression algorithms. It reduces file access latency by 67.7% compared to uniformly compressing files to the shallowest level required to meet storage capacity constraints.

Random access memory (RAM) is tightly-constrained in many embedded systems. This is especially true for the least expensive, lowest-power embedded systems, such as sensor network nodes and portable consumer electronics. The most widely-used sensor network nodes have only 4-10 KB of RAM and do not contain memory management units (MMUs). It is very difficult to implement increasingly complex applications under such tight memory constraints. Nonetheless, price and power consumption constraints make it unlikely that increases in RAM in these systems will keep pace with the requirements of applications.We propose the use of automated compile-time and run-time techniques to increase the amount of usable memory in MMU-less embedded systems. The proposed techniques do not increase hardware cost, and are designed to require few or no changes to existing applications. We have developed a fast compression algorithm well suited to this application, as well as run-time library routines and compiler transformations to control and optimize the automatic migration of application data between compressed and uncompressed memory regions. These techniques were experimentally evaluated on Crossbow TelosB sensor network nodes running a number of data collection and signal processing applications. The results indicate that available memory can be increased by up to 50% with less than 10% performance degradation for most benchmarks.