Search

Technological advancements in communications and embedded systems have led to the proliferation of Wireless Sensor Networks (WSNs) in a wide variety of application domains. These application domains include but are not limited to mission-critical (e.g., security, defense, space, satellite) or safety-related (e.g., health care, active volcano monitoring) systems. One commonality across all WSN application domains is the need to meet application requirements (e.g., lifetime, reliability). Many application domains require that sensor nodes be deployed in harsh environments, such as on the ocean floor or in an active volcano, making these nodes more prone to failures. Sensor node failures can be catastrophic for critical or safety related systems. This article models and analyzes fault detection and fault tolerance in WSNs. To determine the effectiveness and accuracy of fault detection algorithms, we simulate these algorithms using ns-2. We investigate the synergy between fault detection and fault tolerance and use the fault detection algorithms' accuracies in our modeling of Fault-Tolerant (FT) WSNs. We develop Markov models for characterizing WSN reliability and Mean Time to Failure (MTTF) to facilitate WSN application-specific design. Results obtained from our FT modeling reveal that an FT WSN composed of duplex sensor nodes can result in as high as a 100% MTTF increase and approximately a 350% improvement in reliability over a Non-Fault-Tolerant (NFT) WSN. The article also highlights future research directions for the design and deployment of reliable and trustworthy WSNs. [ABSTRACT FROM AUTHOR]

The commercialization of sensor-based platforms is facilitating the realization of numerous sensor network applications with diverse application requirements. However, sensor network platforms are becoming increasingly complex to design and optimize due to the multitude of interdependent parameters that must be considered. To further complicate matters, application experts often times are not trained engineers, but rather biologists, teachers, or agriculturists who wish to utilize the sensor-based platforms for various domain-specific tasks. To assist both platform developers and application experts, we present a centralized dynamic profiling and optimization platform for sensor-based systems that enables application experts to rapidly optimize a sensor network for a particular application without requiring extensive knowledge of, and experience with, the underlying physical hardware platform. In this article, we present an optimization framework that allows developers to characterize application requirements through high-level design metrics and fuzzy-logic-based optimization. We further analyze the benefits of utilizing dynamic profiling information to eliminate the guesswork of creating a "good" benchmark, present several reoptimization evaluation algorithms used to detect if re-optimization is necessary, and highlight the benefits of the proposed dynamic optimization framework compared to static optimization alternatives. [ABSTRACT FROM AUTHOR]

Low power and/or energy consumption is a requirement not only in embedded systems that run on batteries or have limited cooling capabilities, but also in desktop and mainframes where chips require costly cooling techniques. Since the cache subsystem is typically the most power/energy-consuming subsystem, caches are good candidates for power/energy optimizations, and therefore, cache tuning techniques are widely researched. This survey focuses on state-of-the-art offline static and online dynamic cache tuning techniques and summarizes the techniques' attributes, major challenges, and potential research trends to inspire novel ideas and future research avenues. [ABSTRACT FROM AUTHOR]

Loop caches provide an effective method for decreasing memory hierarchy energy consumption by storing frequently executed code (critical regions) in a more energy efficient structure than the level one cache. However, due to code structure restrictions or costly design time pre-analysis efforts, previous loop cache designs are not suitable for all applications and system scenarios. We present an adaptive loop cache that is amenable to a wider range of system scenarios, which can provide an additional 20% average instruction cache energy savings (with individual benchmark energy savings as high as 69%) compared to the next best loop cache, the preloaded loop cache. [ABSTRACT FROM AUTHOR]

The instruction cache is a popular optimization target due to the cache's high impact on system performance and power and because of the cache's predictable temporal and spatial locality. This article is an in depth study on the interaction of code reordering (a long-known technique) and cache configuration (a relatively new technique). Experimental results show that code reordering coupled with cache configuration reveals additional energy savings as high as 10-15% for several benchmarks with reduced cache area as high as 48%. To exploit these additional benefits, we architect and evaluate several design exploration heuristics for combining these two methods. [ABSTRACT FROM AUTHOR]

In recent years, efficient dynamic reconfiguration techniques have been widely employed for system optimization. Dynamic cache reconfiguration is a promising approach for reducing energy consumption as well as for improving overall system performance. It is a major challenge to introduce cache reconfiguration into real-time multitasking systems, since dynamic analysis may adversely affect tasks with timing constraints. This article presents a novel approach for implementing cache reconfiguration in soft real-time systems by efficiently leveraging static analysis during runtime to minimize energy while maintaining the same service level. To the best of our knowledge, this is the first attempt to integrate dynamic cache reconfiguration in real-time scheduling techniques. Our experimental results using a wide variety of applications have demonstrated that our approach can significantly reduce the cache energy consumption in soft real-time systems (up to 74%). [ABSTRACT FROM AUTHOR]