Testbed

About: Testbed is a research topic. Over the lifetime, 10858 publications have been published within this topic receiving 147147 citations. The topic is also known as: test bed.

Wireless link SNR mapping onto an indoor testbed

Abstract: To facilitate a broad range of experimental research on novel protocols and application concepts, we consider an indoor wireless testbed to emulate the performance of real-world networks. A fundamental issue for emulation is the replication of communication links of specified quality. In particular, we need to replicate on the testbed, for every link in the real world, a communication link whose received signal-to-interference-and-noise-ratio (SINR) matches the corresponding link signal-to-noise-ratio (SNR). In this paper, we focus on the downlink SNR mapping associated with a network with a single access point (AP). Four indoor wireless propagation models (commercial buildings with/without line-of-sight path and residential buildings with/without line-of-sight path) and two types of spatial distributions (uniform distribution inside a circular cell and uniform distribution along a line) have been investigated. Based on the characteristics of the indoor testbed, we propose a mapping method with one AP and one interferer, which separates the task into two phases: in the first phase, the best location and transmission power for the interferer node are determined; in the second phase, the topology of receiver nodes is configured by a minimum weight matching algorithm. Through analysis and simulations, we find that when the interferer node is located on the corner across from the AP, we can achieve a mapping range on the order of 57 dB and an average root-mean-square (RMS) mapping error less than 1 dB.

Sensornet Checkpointing: Enabling Repeatability in Testbeds and Realism in Simulations

Abstract: When developing sensor network applications, the shift from simulation to testbed causes application failures, resulting in additional time-consuming iterations between simulation and testbed. We propose transferring sensor network checkpoints between simulation and testbed to reduce the gap between simulation and testbed. Sensornet checkpointing combines the best of both simulation and testbeds: the non-intrusiveness and repeatability of simulation, and the realism of testbeds.

A testbed for data fusion for engine diagnostics and prognostics

Abstract: A key to producing reliable engine diagnostics and prognostics resides in fusion of multisensor data. It is believed that faults will manifest effects in a variety of sensors. By 'integration' (fusion) of information across sensors detections can be made of faults that are undetectable on just a single sensor. Data to support development of prognostic techniques is very rare. The development requires continuous collection of significant amounts of data to capture not only "normal" data but also capture potential fault event data well before the fault is detected by existing techniques, as well as capture data related to rare events. The collected data can be analyzed to develop processing tailored to new events and to continuously update algorithms so as to improve detection and classification performance and reduce false alarms. IAC in collaboration with the Air Force and the Army is developing a testbed to perform data collection and to develop fusion techniques for gas turbine engine health monitoring. The testbed and examples of its operation are presented here.

Differential evolution on the CEC-2013 single-objective continuous optimization testbed

Abstract: Differential evolution (DE) is one of the most powerful continuous optimizers in the field of evolutionary computation. This work systematically benchmarks a classic DE algorithm (DE/rand/1/bin) on the CEC-2013 single-objective continuous optimization testbed. We report, for each test function at different problem dimensionality, the best achieved performance among a wide range of potentially effective parameter settings. It reflects the intrinsic optimization capability of DE/rand/1/bin on this testbed and can serve as a baseline for performance comparison in future research using this testbed. Furthermore, we conduct parameter sensitivity analysis using advanced non-parametric statistical tests to discover statistically significantly superior parameter settings. This analysis provides a statistically reliable rule of thumb for choosing the parameters of DE/rand/1/bin to solve unseen problems. Moreover, we report the performance of DE/rand/1/bin using one superior parameter setting advocated by parameter sensitivity analysis.

Autonomous docking algorithm development and experimentation using the SPHERES testbed

Abstract: The MIT Space Systems Laboratory (SSL) has developed a testbed for the testing of formation flight and autonomous docking algorithms in both 1-g and microgravity environments. The SPHERES testbed consists of multiple micro-satellites, or Spheres, which can autonomously control their position and attitude. The testbed can be operated on an air table in a 1-g laboratory environment, in NASA’s KC-135 reduced gravity research aircraft and inside the International Space Station (ISS). SPHERES launch to the ISS is currently manifested for May 19 2004 on Progress 14P. Various types of docking maneuvers, ranging from doc king with a cooperative target to docking with a tumbling target, have been developed. The ultimate objective of this research is to integrate the different algorithms into one program that can assess the health status of the target vehicle, plan an optimal docking maneuver while accounting for the existing constraints and finally, execute that maneuver even in the presence of simulated failures. In this paper, results obtained to date on the ground based air table using the initial version of the program will be presented, as well as results obtained from microgravity experiments onboard the KC-135. Keywords: autonomous docking, ISS laboratory, microgravity experiment, control systems, autonomy, SPHERES